|
- # [No.1] construct_wrapper.0 @ctx.addr=0xaaaae87a9c70
- #
- funcgraph fg_0(
- %para1 : Tensor(F64)[16, 3, 32, 32] # lr
- , %para2 : Tensor(F64)[16, 3, 128, 128] # hr
- , %para3 : Tensor(F32)[16, 1] # fake_labels
- , %para4 : Tensor(F32)[16, 1] # real_labels
- , %para5 : Ref[Tensor(F32)][64, 3, 3, 3] # network.network.G.conv_first.weight
- , %para6 : Ref[Tensor(F32)][64] # network.network.G.conv_first.bias
- , %para7 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.0.RDB1.conv1.weight
- , %para8 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.0.RDB1.conv1.bias
- , %para9 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.0.RDB1.conv2.weight
- , %para10 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.0.RDB1.conv2.bias
- , %para11 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.0.RDB1.conv3.weight
- , %para12 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.0.RDB1.conv3.bias
- , %para13 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.0.RDB1.conv4.weight
- , %para14 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.0.RDB1.conv4.bias
- , %para15 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.0.RDB1.conv5.weight
- , %para16 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.0.RDB1.conv5.bias
- , %para17 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.0.RDB2.conv1.weight
- , %para18 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.0.RDB2.conv1.bias
- , %para19 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.0.RDB2.conv2.weight
- , %para20 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.0.RDB2.conv2.bias
- , %para21 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.0.RDB2.conv3.weight
- , %para22 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.0.RDB2.conv3.bias
- , %para23 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.0.RDB2.conv4.weight
- , %para24 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.0.RDB2.conv4.bias
- , %para25 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.0.RDB2.conv5.weight
- , %para26 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.0.RDB2.conv5.bias
- , %para27 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- , %para28 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para29 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- , %para30 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para31 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- , %para32 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para33 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- , %para34 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para35 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- , %para36 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para37 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.1.RDB1.conv1.weight
- , %para38 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.1.RDB1.conv1.bias
- , %para39 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.1.RDB1.conv2.weight
- , %para40 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.1.RDB1.conv2.bias
- , %para41 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.1.RDB1.conv3.weight
- , %para42 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.1.RDB1.conv3.bias
- , %para43 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.1.RDB1.conv4.weight
- , %para44 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.1.RDB1.conv4.bias
- , %para45 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.1.RDB1.conv5.weight
- , %para46 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.1.RDB1.conv5.bias
- , %para47 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.1.RDB2.conv1.weight
- , %para48 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.1.RDB2.conv1.bias
- , %para49 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.1.RDB2.conv2.weight
- , %para50 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.1.RDB2.conv2.bias
- , %para51 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.1.RDB2.conv3.weight
- , %para52 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.1.RDB2.conv3.bias
- , %para53 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.1.RDB2.conv4.weight
- , %para54 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.1.RDB2.conv4.bias
- , %para55 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.1.RDB2.conv5.weight
- , %para56 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.1.RDB2.conv5.bias
- , %para57 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.1.RDB3.conv1.weight
- , %para58 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.1.RDB3.conv1.bias
- , %para59 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.1.RDB3.conv2.weight
- , %para60 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.1.RDB3.conv2.bias
- , %para61 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.1.RDB3.conv3.weight
- , %para62 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.1.RDB3.conv3.bias
- , %para63 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.1.RDB3.conv4.weight
- , %para64 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.1.RDB3.conv4.bias
- , %para65 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.1.RDB3.conv5.weight
- , %para66 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.1.RDB3.conv5.bias
- , %para67 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.2.RDB1.conv1.weight
- , %para68 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.2.RDB1.conv1.bias
- , %para69 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.2.RDB1.conv2.weight
- , %para70 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.2.RDB1.conv2.bias
- , %para71 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.2.RDB1.conv3.weight
- , %para72 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.2.RDB1.conv3.bias
- , %para73 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.2.RDB1.conv4.weight
- , %para74 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.2.RDB1.conv4.bias
- , %para75 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.2.RDB1.conv5.weight
- , %para76 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.2.RDB1.conv5.bias
- , %para77 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.2.RDB2.conv1.weight
- , %para78 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.2.RDB2.conv1.bias
- , %para79 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.2.RDB2.conv2.weight
- , %para80 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.2.RDB2.conv2.bias
- , %para81 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.2.RDB2.conv3.weight
- , %para82 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.2.RDB2.conv3.bias
- , %para83 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.2.RDB2.conv4.weight
- , %para84 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.2.RDB2.conv4.bias
- , %para85 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.2.RDB2.conv5.weight
- , %para86 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.2.RDB2.conv5.bias
- , %para87 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.2.RDB3.conv1.weight
- , %para88 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.2.RDB3.conv1.bias
- , %para89 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.2.RDB3.conv2.weight
- , %para90 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.2.RDB3.conv2.bias
- , %para91 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.2.RDB3.conv3.weight
- , %para92 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.2.RDB3.conv3.bias
- , %para93 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.2.RDB3.conv4.weight
- , %para94 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.2.RDB3.conv4.bias
- , %para95 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.2.RDB3.conv5.weight
- , %para96 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.2.RDB3.conv5.bias
- , %para97 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.3.RDB1.conv1.weight
- , %para98 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.3.RDB1.conv1.bias
- , %para99 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.3.RDB1.conv2.weight
- , %para100 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.3.RDB1.conv2.bias
- , %para101 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.3.RDB1.conv3.weight
- , %para102 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.3.RDB1.conv3.bias
- , %para103 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.3.RDB1.conv4.weight
- , %para104 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.3.RDB1.conv4.bias
- , %para105 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.3.RDB1.conv5.weight
- , %para106 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.3.RDB1.conv5.bias
- , %para107 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.3.RDB2.conv1.weight
- , %para108 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.3.RDB2.conv1.bias
- , %para109 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.3.RDB2.conv2.weight
- , %para110 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.3.RDB2.conv2.bias
- , %para111 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.3.RDB2.conv3.weight
- , %para112 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.3.RDB2.conv3.bias
- , %para113 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.3.RDB2.conv4.weight
- , %para114 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.3.RDB2.conv4.bias
- , %para115 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.3.RDB2.conv5.weight
- , %para116 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.3.RDB2.conv5.bias
- , %para117 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.3.RDB3.conv1.weight
- , %para118 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.3.RDB3.conv1.bias
- , %para119 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.3.RDB3.conv2.weight
- , %para120 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.3.RDB3.conv2.bias
- , %para121 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.3.RDB3.conv3.weight
- , %para122 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.3.RDB3.conv3.bias
- , %para123 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.3.RDB3.conv4.weight
- , %para124 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.3.RDB3.conv4.bias
- , %para125 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.3.RDB3.conv5.weight
- , %para126 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.3.RDB3.conv5.bias
- , %para127 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.4.RDB1.conv1.weight
- , %para128 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.4.RDB1.conv1.bias
- , %para129 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.4.RDB1.conv2.weight
- , %para130 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.4.RDB1.conv2.bias
- , %para131 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.4.RDB1.conv3.weight
- , %para132 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.4.RDB1.conv3.bias
- , %para133 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.4.RDB1.conv4.weight
- , %para134 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.4.RDB1.conv4.bias
- , %para135 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.4.RDB1.conv5.weight
- , %para136 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.4.RDB1.conv5.bias
- , %para137 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.4.RDB2.conv1.weight
- , %para138 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.4.RDB2.conv1.bias
- , %para139 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.4.RDB2.conv2.weight
- , %para140 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.4.RDB2.conv2.bias
- , %para141 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.4.RDB2.conv3.weight
- , %para142 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.4.RDB2.conv3.bias
- , %para143 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.4.RDB2.conv4.weight
- , %para144 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.4.RDB2.conv4.bias
- , %para145 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.4.RDB2.conv5.weight
- , %para146 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.4.RDB2.conv5.bias
- , %para147 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.4.RDB3.conv1.weight
- , %para148 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.4.RDB3.conv1.bias
- , %para149 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.4.RDB3.conv2.weight
- , %para150 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.4.RDB3.conv2.bias
- , %para151 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.4.RDB3.conv3.weight
- , %para152 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.4.RDB3.conv3.bias
- , %para153 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.4.RDB3.conv4.weight
- , %para154 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.4.RDB3.conv4.bias
- , %para155 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.4.RDB3.conv5.weight
- , %para156 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.4.RDB3.conv5.bias
- , %para157 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.5.RDB1.conv1.weight
- , %para158 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.5.RDB1.conv1.bias
- , %para159 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.5.RDB1.conv2.weight
- , %para160 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.5.RDB1.conv2.bias
- , %para161 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.5.RDB1.conv3.weight
- , %para162 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.5.RDB1.conv3.bias
- , %para163 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.5.RDB1.conv4.weight
- , %para164 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.5.RDB1.conv4.bias
- , %para165 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.5.RDB1.conv5.weight
- , %para166 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.5.RDB1.conv5.bias
- , %para167 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.5.RDB2.conv1.weight
- , %para168 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.5.RDB2.conv1.bias
- , %para169 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.5.RDB2.conv2.weight
- , %para170 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.5.RDB2.conv2.bias
- , %para171 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.5.RDB2.conv3.weight
- , %para172 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.5.RDB2.conv3.bias
- , %para173 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.5.RDB2.conv4.weight
- , %para174 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.5.RDB2.conv4.bias
- , %para175 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.5.RDB2.conv5.weight
- , %para176 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.5.RDB2.conv5.bias
- , %para177 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.5.RDB3.conv1.weight
- , %para178 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.5.RDB3.conv1.bias
- , %para179 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.5.RDB3.conv2.weight
- , %para180 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.5.RDB3.conv2.bias
- , %para181 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.5.RDB3.conv3.weight
- , %para182 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.5.RDB3.conv3.bias
- , %para183 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.5.RDB3.conv4.weight
- , %para184 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.5.RDB3.conv4.bias
- , %para185 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.5.RDB3.conv5.weight
- , %para186 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.5.RDB3.conv5.bias
- , %para187 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.6.RDB1.conv1.weight
- , %para188 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.6.RDB1.conv1.bias
- , %para189 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.6.RDB1.conv2.weight
- , %para190 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.6.RDB1.conv2.bias
- , %para191 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.6.RDB1.conv3.weight
- , %para192 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.6.RDB1.conv3.bias
- , %para193 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.6.RDB1.conv4.weight
- , %para194 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.6.RDB1.conv4.bias
- , %para195 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.6.RDB1.conv5.weight
- , %para196 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.6.RDB1.conv5.bias
- , %para197 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.6.RDB2.conv1.weight
- , %para198 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.6.RDB2.conv1.bias
- , %para199 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.6.RDB2.conv2.weight
- , %para200 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.6.RDB2.conv2.bias
- , %para201 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.6.RDB2.conv3.weight
- , %para202 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.6.RDB2.conv3.bias
- , %para203 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.6.RDB2.conv4.weight
- , %para204 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.6.RDB2.conv4.bias
- , %para205 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.6.RDB2.conv5.weight
- , %para206 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.6.RDB2.conv5.bias
- , %para207 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.6.RDB3.conv1.weight
- , %para208 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.6.RDB3.conv1.bias
- , %para209 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.6.RDB3.conv2.weight
- , %para210 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.6.RDB3.conv2.bias
- , %para211 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.6.RDB3.conv3.weight
- , %para212 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.6.RDB3.conv3.bias
- , %para213 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.6.RDB3.conv4.weight
- , %para214 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.6.RDB3.conv4.bias
- , %para215 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.6.RDB3.conv5.weight
- , %para216 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.6.RDB3.conv5.bias
- , %para217 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.7.RDB1.conv1.weight
- , %para218 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.7.RDB1.conv1.bias
- , %para219 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.7.RDB1.conv2.weight
- , %para220 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.7.RDB1.conv2.bias
- , %para221 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.7.RDB1.conv3.weight
- , %para222 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.7.RDB1.conv3.bias
- , %para223 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.7.RDB1.conv4.weight
- , %para224 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.7.RDB1.conv4.bias
- , %para225 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.7.RDB1.conv5.weight
- , %para226 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.7.RDB1.conv5.bias
- , %para227 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.7.RDB2.conv1.weight
- , %para228 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.7.RDB2.conv1.bias
- , %para229 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.7.RDB2.conv2.weight
- , %para230 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.7.RDB2.conv2.bias
- , %para231 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.7.RDB2.conv3.weight
- , %para232 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.7.RDB2.conv3.bias
- , %para233 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.7.RDB2.conv4.weight
- , %para234 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.7.RDB2.conv4.bias
- , %para235 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.7.RDB2.conv5.weight
- , %para236 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.7.RDB2.conv5.bias
- , %para237 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.7.RDB3.conv1.weight
- , %para238 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.7.RDB3.conv1.bias
- , %para239 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.7.RDB3.conv2.weight
- , %para240 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.7.RDB3.conv2.bias
- , %para241 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.7.RDB3.conv3.weight
- , %para242 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.7.RDB3.conv3.bias
- , %para243 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.7.RDB3.conv4.weight
- , %para244 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.7.RDB3.conv4.bias
- , %para245 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.7.RDB3.conv5.weight
- , %para246 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.7.RDB3.conv5.bias
- , %para247 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.8.RDB1.conv1.weight
- , %para248 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.8.RDB1.conv1.bias
- , %para249 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.8.RDB1.conv2.weight
- , %para250 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.8.RDB1.conv2.bias
- , %para251 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.8.RDB1.conv3.weight
- , %para252 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.8.RDB1.conv3.bias
- , %para253 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.8.RDB1.conv4.weight
- , %para254 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.8.RDB1.conv4.bias
- , %para255 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.8.RDB1.conv5.weight
- , %para256 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.8.RDB1.conv5.bias
- , %para257 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.8.RDB2.conv1.weight
- , %para258 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.8.RDB2.conv1.bias
- , %para259 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.8.RDB2.conv2.weight
- , %para260 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.8.RDB2.conv2.bias
- , %para261 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.8.RDB2.conv3.weight
- , %para262 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.8.RDB2.conv3.bias
- , %para263 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.8.RDB2.conv4.weight
- , %para264 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.8.RDB2.conv4.bias
- , %para265 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.8.RDB2.conv5.weight
- , %para266 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.8.RDB2.conv5.bias
- , %para267 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.8.RDB3.conv1.weight
- , %para268 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.8.RDB3.conv1.bias
- , %para269 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.8.RDB3.conv2.weight
- , %para270 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.8.RDB3.conv2.bias
- , %para271 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.8.RDB3.conv3.weight
- , %para272 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.8.RDB3.conv3.bias
- , %para273 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.8.RDB3.conv4.weight
- , %para274 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.8.RDB3.conv4.bias
- , %para275 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.8.RDB3.conv5.weight
- , %para276 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.8.RDB3.conv5.bias
- , %para277 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.9.RDB1.conv1.weight
- , %para278 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.9.RDB1.conv1.bias
- , %para279 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.9.RDB1.conv2.weight
- , %para280 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.9.RDB1.conv2.bias
- , %para281 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.9.RDB1.conv3.weight
- , %para282 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.9.RDB1.conv3.bias
- , %para283 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.9.RDB1.conv4.weight
- , %para284 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.9.RDB1.conv4.bias
- , %para285 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.9.RDB1.conv5.weight
- , %para286 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.9.RDB1.conv5.bias
- , %para287 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.9.RDB2.conv1.weight
- , %para288 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.9.RDB2.conv1.bias
- , %para289 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.9.RDB2.conv2.weight
- , %para290 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.9.RDB2.conv2.bias
- , %para291 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.9.RDB2.conv3.weight
- , %para292 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.9.RDB2.conv3.bias
- , %para293 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.9.RDB2.conv4.weight
- , %para294 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.9.RDB2.conv4.bias
- , %para295 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.9.RDB2.conv5.weight
- , %para296 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.9.RDB2.conv5.bias
- , %para297 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.9.RDB3.conv1.weight
- , %para298 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.9.RDB3.conv1.bias
- , %para299 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.9.RDB3.conv2.weight
- , %para300 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.9.RDB3.conv2.bias
- , %para301 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.9.RDB3.conv3.weight
- , %para302 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.9.RDB3.conv3.bias
- , %para303 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.9.RDB3.conv4.weight
- , %para304 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.9.RDB3.conv4.bias
- , %para305 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.9.RDB3.conv5.weight
- , %para306 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.9.RDB3.conv5.bias
- , %para307 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.10.RDB1.conv1.weight
- , %para308 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.10.RDB1.conv1.bias
- , %para309 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.10.RDB1.conv2.weight
- , %para310 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.10.RDB1.conv2.bias
- , %para311 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.10.RDB1.conv3.weight
- , %para312 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.10.RDB1.conv3.bias
- , %para313 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.10.RDB1.conv4.weight
- , %para314 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.10.RDB1.conv4.bias
- , %para315 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.10.RDB1.conv5.weight
- , %para316 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.10.RDB1.conv5.bias
- , %para317 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.10.RDB2.conv1.weight
- , %para318 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.10.RDB2.conv1.bias
- , %para319 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.10.RDB2.conv2.weight
- , %para320 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.10.RDB2.conv2.bias
- , %para321 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.10.RDB2.conv3.weight
- , %para322 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.10.RDB2.conv3.bias
- , %para323 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.10.RDB2.conv4.weight
- , %para324 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.10.RDB2.conv4.bias
- , %para325 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.10.RDB2.conv5.weight
- , %para326 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.10.RDB2.conv5.bias
- , %para327 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.10.RDB3.conv1.weight
- , %para328 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.10.RDB3.conv1.bias
- , %para329 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.10.RDB3.conv2.weight
- , %para330 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.10.RDB3.conv2.bias
- , %para331 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.10.RDB3.conv3.weight
- , %para332 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.10.RDB3.conv3.bias
- , %para333 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.10.RDB3.conv4.weight
- , %para334 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.10.RDB3.conv4.bias
- , %para335 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.10.RDB3.conv5.weight
- , %para336 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.10.RDB3.conv5.bias
- , %para337 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.11.RDB1.conv1.weight
- , %para338 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.11.RDB1.conv1.bias
- , %para339 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.11.RDB1.conv2.weight
- , %para340 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.11.RDB1.conv2.bias
- , %para341 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.11.RDB1.conv3.weight
- , %para342 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.11.RDB1.conv3.bias
- , %para343 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.11.RDB1.conv4.weight
- , %para344 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.11.RDB1.conv4.bias
- , %para345 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.11.RDB1.conv5.weight
- , %para346 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.11.RDB1.conv5.bias
- , %para347 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.11.RDB2.conv1.weight
- , %para348 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.11.RDB2.conv1.bias
- , %para349 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.11.RDB2.conv2.weight
- , %para350 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.11.RDB2.conv2.bias
- , %para351 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.11.RDB2.conv3.weight
- , %para352 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.11.RDB2.conv3.bias
- , %para353 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.11.RDB2.conv4.weight
- , %para354 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.11.RDB2.conv4.bias
- , %para355 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.11.RDB2.conv5.weight
- , %para356 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.11.RDB2.conv5.bias
- , %para357 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.11.RDB3.conv1.weight
- , %para358 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.11.RDB3.conv1.bias
- , %para359 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.11.RDB3.conv2.weight
- , %para360 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.11.RDB3.conv2.bias
- , %para361 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.11.RDB3.conv3.weight
- , %para362 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.11.RDB3.conv3.bias
- , %para363 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.11.RDB3.conv4.weight
- , %para364 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.11.RDB3.conv4.bias
- , %para365 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.11.RDB3.conv5.weight
- , %para366 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.11.RDB3.conv5.bias
- , %para367 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.12.RDB1.conv1.weight
- , %para368 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.12.RDB1.conv1.bias
- , %para369 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.12.RDB1.conv2.weight
- , %para370 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.12.RDB1.conv2.bias
- , %para371 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.12.RDB1.conv3.weight
- , %para372 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.12.RDB1.conv3.bias
- , %para373 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.12.RDB1.conv4.weight
- , %para374 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.12.RDB1.conv4.bias
- , %para375 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.12.RDB1.conv5.weight
- , %para376 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.12.RDB1.conv5.bias
- , %para377 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.12.RDB2.conv1.weight
- , %para378 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.12.RDB2.conv1.bias
- , %para379 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.12.RDB2.conv2.weight
- , %para380 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.12.RDB2.conv2.bias
- , %para381 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.12.RDB2.conv3.weight
- , %para382 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.12.RDB2.conv3.bias
- , %para383 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.12.RDB2.conv4.weight
- , %para384 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.12.RDB2.conv4.bias
- , %para385 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.12.RDB2.conv5.weight
- , %para386 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.12.RDB2.conv5.bias
- , %para387 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.12.RDB3.conv1.weight
- , %para388 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.12.RDB3.conv1.bias
- , %para389 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.12.RDB3.conv2.weight
- , %para390 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.12.RDB3.conv2.bias
- , %para391 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.12.RDB3.conv3.weight
- , %para392 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.12.RDB3.conv3.bias
- , %para393 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.12.RDB3.conv4.weight
- , %para394 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.12.RDB3.conv4.bias
- , %para395 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.12.RDB3.conv5.weight
- , %para396 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.12.RDB3.conv5.bias
- , %para397 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.13.RDB1.conv1.weight
- , %para398 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.13.RDB1.conv1.bias
- , %para399 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.13.RDB1.conv2.weight
- , %para400 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.13.RDB1.conv2.bias
- , %para401 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.13.RDB1.conv3.weight
- , %para402 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.13.RDB1.conv3.bias
- , %para403 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.13.RDB1.conv4.weight
- , %para404 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.13.RDB1.conv4.bias
- , %para405 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.13.RDB1.conv5.weight
- , %para406 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.13.RDB1.conv5.bias
- , %para407 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.13.RDB2.conv1.weight
- , %para408 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.13.RDB2.conv1.bias
- , %para409 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.13.RDB2.conv2.weight
- , %para410 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.13.RDB2.conv2.bias
- , %para411 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.13.RDB2.conv3.weight
- , %para412 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.13.RDB2.conv3.bias
- , %para413 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.13.RDB2.conv4.weight
- , %para414 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.13.RDB2.conv4.bias
- , %para415 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.13.RDB2.conv5.weight
- , %para416 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.13.RDB2.conv5.bias
- , %para417 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.13.RDB3.conv1.weight
- , %para418 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.13.RDB3.conv1.bias
- , %para419 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.13.RDB3.conv2.weight
- , %para420 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.13.RDB3.conv2.bias
- , %para421 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.13.RDB3.conv3.weight
- , %para422 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.13.RDB3.conv3.bias
- , %para423 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.13.RDB3.conv4.weight
- , %para424 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.13.RDB3.conv4.bias
- , %para425 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.13.RDB3.conv5.weight
- , %para426 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.13.RDB3.conv5.bias
- , %para427 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.14.RDB1.conv1.weight
- , %para428 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.14.RDB1.conv1.bias
- , %para429 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.14.RDB1.conv2.weight
- , %para430 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.14.RDB1.conv2.bias
- , %para431 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.14.RDB1.conv3.weight
- , %para432 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.14.RDB1.conv3.bias
- , %para433 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.14.RDB1.conv4.weight
- , %para434 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.14.RDB1.conv4.bias
- , %para435 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.14.RDB1.conv5.weight
- , %para436 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.14.RDB1.conv5.bias
- , %para437 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.14.RDB2.conv1.weight
- , %para438 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.14.RDB2.conv1.bias
- , %para439 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.14.RDB2.conv2.weight
- , %para440 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.14.RDB2.conv2.bias
- , %para441 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.14.RDB2.conv3.weight
- , %para442 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.14.RDB2.conv3.bias
- , %para443 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.14.RDB2.conv4.weight
- , %para444 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.14.RDB2.conv4.bias
- , %para445 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.14.RDB2.conv5.weight
- , %para446 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.14.RDB2.conv5.bias
- , %para447 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.14.RDB3.conv1.weight
- , %para448 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.14.RDB3.conv1.bias
- , %para449 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.14.RDB3.conv2.weight
- , %para450 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.14.RDB3.conv2.bias
- , %para451 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.14.RDB3.conv3.weight
- , %para452 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.14.RDB3.conv3.bias
- , %para453 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.14.RDB3.conv4.weight
- , %para454 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.14.RDB3.conv4.bias
- , %para455 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.14.RDB3.conv5.weight
- , %para456 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.14.RDB3.conv5.bias
- , %para457 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.15.RDB1.conv1.weight
- , %para458 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.15.RDB1.conv1.bias
- , %para459 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.15.RDB1.conv2.weight
- , %para460 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.15.RDB1.conv2.bias
- , %para461 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.15.RDB1.conv3.weight
- , %para462 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.15.RDB1.conv3.bias
- , %para463 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.15.RDB1.conv4.weight
- , %para464 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.15.RDB1.conv4.bias
- , %para465 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.15.RDB1.conv5.weight
- , %para466 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.15.RDB1.conv5.bias
- , %para467 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.15.RDB2.conv1.weight
- , %para468 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.15.RDB2.conv1.bias
- , %para469 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.15.RDB2.conv2.weight
- , %para470 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.15.RDB2.conv2.bias
- , %para471 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.15.RDB2.conv3.weight
- , %para472 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.15.RDB2.conv3.bias
- , %para473 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.15.RDB2.conv4.weight
- , %para474 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.15.RDB2.conv4.bias
- , %para475 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.15.RDB2.conv5.weight
- , %para476 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.15.RDB2.conv5.bias
- , %para477 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.15.RDB3.conv1.weight
- , %para478 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.15.RDB3.conv1.bias
- , %para479 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.15.RDB3.conv2.weight
- , %para480 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.15.RDB3.conv2.bias
- , %para481 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.15.RDB3.conv3.weight
- , %para482 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.15.RDB3.conv3.bias
- , %para483 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.15.RDB3.conv4.weight
- , %para484 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.15.RDB3.conv4.bias
- , %para485 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.15.RDB3.conv5.weight
- , %para486 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.15.RDB3.conv5.bias
- , %para487 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.16.RDB1.conv1.weight
- , %para488 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.16.RDB1.conv1.bias
- , %para489 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.16.RDB1.conv2.weight
- , %para490 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.16.RDB1.conv2.bias
- , %para491 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.16.RDB1.conv3.weight
- , %para492 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.16.RDB1.conv3.bias
- , %para493 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.16.RDB1.conv4.weight
- , %para494 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.16.RDB1.conv4.bias
- , %para495 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.16.RDB1.conv5.weight
- , %para496 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.16.RDB1.conv5.bias
- , %para497 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.16.RDB2.conv1.weight
- , %para498 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.16.RDB2.conv1.bias
- , %para499 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.16.RDB2.conv2.weight
- , %para500 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.16.RDB2.conv2.bias
- , %para501 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.16.RDB2.conv3.weight
- , %para502 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.16.RDB2.conv3.bias
- , %para503 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.16.RDB2.conv4.weight
- , %para504 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.16.RDB2.conv4.bias
- , %para505 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.16.RDB2.conv5.weight
- , %para506 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.16.RDB2.conv5.bias
- , %para507 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.16.RDB3.conv1.weight
- , %para508 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.16.RDB3.conv1.bias
- , %para509 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.16.RDB3.conv2.weight
- , %para510 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.16.RDB3.conv2.bias
- , %para511 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.16.RDB3.conv3.weight
- , %para512 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.16.RDB3.conv3.bias
- , %para513 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.16.RDB3.conv4.weight
- , %para514 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.16.RDB3.conv4.bias
- , %para515 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.16.RDB3.conv5.weight
- , %para516 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.16.RDB3.conv5.bias
- , %para517 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.17.RDB1.conv1.weight
- , %para518 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.17.RDB1.conv1.bias
- , %para519 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.17.RDB1.conv2.weight
- , %para520 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.17.RDB1.conv2.bias
- , %para521 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.17.RDB1.conv3.weight
- , %para522 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.17.RDB1.conv3.bias
- , %para523 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.17.RDB1.conv4.weight
- , %para524 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.17.RDB1.conv4.bias
- , %para525 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.17.RDB1.conv5.weight
- , %para526 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.17.RDB1.conv5.bias
- , %para527 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.17.RDB2.conv1.weight
- , %para528 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.17.RDB2.conv1.bias
- , %para529 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.17.RDB2.conv2.weight
- , %para530 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.17.RDB2.conv2.bias
- , %para531 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.17.RDB2.conv3.weight
- , %para532 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.17.RDB2.conv3.bias
- , %para533 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.17.RDB2.conv4.weight
- , %para534 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.17.RDB2.conv4.bias
- , %para535 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.17.RDB2.conv5.weight
- , %para536 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.17.RDB2.conv5.bias
- , %para537 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.17.RDB3.conv1.weight
- , %para538 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.17.RDB3.conv1.bias
- , %para539 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.17.RDB3.conv2.weight
- , %para540 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.17.RDB3.conv2.bias
- , %para541 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.17.RDB3.conv3.weight
- , %para542 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.17.RDB3.conv3.bias
- , %para543 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.17.RDB3.conv4.weight
- , %para544 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.17.RDB3.conv4.bias
- , %para545 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.17.RDB3.conv5.weight
- , %para546 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.17.RDB3.conv5.bias
- , %para547 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.18.RDB1.conv1.weight
- , %para548 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.18.RDB1.conv1.bias
- , %para549 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.18.RDB1.conv2.weight
- , %para550 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.18.RDB1.conv2.bias
- , %para551 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.18.RDB1.conv3.weight
- , %para552 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.18.RDB1.conv3.bias
- , %para553 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.18.RDB1.conv4.weight
- , %para554 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.18.RDB1.conv4.bias
- , %para555 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.18.RDB1.conv5.weight
- , %para556 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.18.RDB1.conv5.bias
- , %para557 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.18.RDB2.conv1.weight
- , %para558 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.18.RDB2.conv1.bias
- , %para559 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.18.RDB2.conv2.weight
- , %para560 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.18.RDB2.conv2.bias
- , %para561 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.18.RDB2.conv3.weight
- , %para562 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.18.RDB2.conv3.bias
- , %para563 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.18.RDB2.conv4.weight
- , %para564 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.18.RDB2.conv4.bias
- , %para565 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.18.RDB2.conv5.weight
- , %para566 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.18.RDB2.conv5.bias
- , %para567 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.18.RDB3.conv1.weight
- , %para568 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.18.RDB3.conv1.bias
- , %para569 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.18.RDB3.conv2.weight
- , %para570 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.18.RDB3.conv2.bias
- , %para571 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.18.RDB3.conv3.weight
- , %para572 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.18.RDB3.conv3.bias
- , %para573 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.18.RDB3.conv4.weight
- , %para574 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.18.RDB3.conv4.bias
- , %para575 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.18.RDB3.conv5.weight
- , %para576 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.18.RDB3.conv5.bias
- , %para577 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.19.RDB1.conv1.weight
- , %para578 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.19.RDB1.conv1.bias
- , %para579 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.19.RDB1.conv2.weight
- , %para580 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.19.RDB1.conv2.bias
- , %para581 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.19.RDB1.conv3.weight
- , %para582 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.19.RDB1.conv3.bias
- , %para583 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.19.RDB1.conv4.weight
- , %para584 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.19.RDB1.conv4.bias
- , %para585 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.19.RDB1.conv5.weight
- , %para586 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.19.RDB1.conv5.bias
- , %para587 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.19.RDB2.conv1.weight
- , %para588 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.19.RDB2.conv1.bias
- , %para589 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.19.RDB2.conv2.weight
- , %para590 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.19.RDB2.conv2.bias
- , %para591 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.19.RDB2.conv3.weight
- , %para592 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.19.RDB2.conv3.bias
- , %para593 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.19.RDB2.conv4.weight
- , %para594 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.19.RDB2.conv4.bias
- , %para595 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.19.RDB2.conv5.weight
- , %para596 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.19.RDB2.conv5.bias
- , %para597 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.19.RDB3.conv1.weight
- , %para598 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.19.RDB3.conv1.bias
- , %para599 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.19.RDB3.conv2.weight
- , %para600 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.19.RDB3.conv2.bias
- , %para601 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.19.RDB3.conv3.weight
- , %para602 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.19.RDB3.conv3.bias
- , %para603 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.19.RDB3.conv4.weight
- , %para604 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.19.RDB3.conv4.bias
- , %para605 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.19.RDB3.conv5.weight
- , %para606 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.19.RDB3.conv5.bias
- , %para607 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.20.RDB1.conv1.weight
- , %para608 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.20.RDB1.conv1.bias
- , %para609 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.20.RDB1.conv2.weight
- , %para610 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.20.RDB1.conv2.bias
- , %para611 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.20.RDB1.conv3.weight
- , %para612 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.20.RDB1.conv3.bias
- , %para613 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.20.RDB1.conv4.weight
- , %para614 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.20.RDB1.conv4.bias
- , %para615 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.20.RDB1.conv5.weight
- , %para616 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.20.RDB1.conv5.bias
- , %para617 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.20.RDB2.conv1.weight
- , %para618 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.20.RDB2.conv1.bias
- , %para619 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.20.RDB2.conv2.weight
- , %para620 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.20.RDB2.conv2.bias
- , %para621 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.20.RDB2.conv3.weight
- , %para622 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.20.RDB2.conv3.bias
- , %para623 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.20.RDB2.conv4.weight
- , %para624 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.20.RDB2.conv4.bias
- , %para625 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.20.RDB2.conv5.weight
- , %para626 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.20.RDB2.conv5.bias
- , %para627 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.20.RDB3.conv1.weight
- , %para628 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.20.RDB3.conv1.bias
- , %para629 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.20.RDB3.conv2.weight
- , %para630 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.20.RDB3.conv2.bias
- , %para631 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.20.RDB3.conv3.weight
- , %para632 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.20.RDB3.conv3.bias
- , %para633 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.20.RDB3.conv4.weight
- , %para634 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.20.RDB3.conv4.bias
- , %para635 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.20.RDB3.conv5.weight
- , %para636 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.20.RDB3.conv5.bias
- , %para637 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.21.RDB1.conv1.weight
- , %para638 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.21.RDB1.conv1.bias
- , %para639 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.21.RDB1.conv2.weight
- , %para640 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.21.RDB1.conv2.bias
- , %para641 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.21.RDB1.conv3.weight
- , %para642 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.21.RDB1.conv3.bias
- , %para643 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.21.RDB1.conv4.weight
- , %para644 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.21.RDB1.conv4.bias
- , %para645 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.21.RDB1.conv5.weight
- , %para646 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.21.RDB1.conv5.bias
- , %para647 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.21.RDB2.conv1.weight
- , %para648 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.21.RDB2.conv1.bias
- , %para649 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.21.RDB2.conv2.weight
- , %para650 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.21.RDB2.conv2.bias
- , %para651 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.21.RDB2.conv3.weight
- , %para652 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.21.RDB2.conv3.bias
- , %para653 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.21.RDB2.conv4.weight
- , %para654 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.21.RDB2.conv4.bias
- , %para655 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.21.RDB2.conv5.weight
- , %para656 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.21.RDB2.conv5.bias
- , %para657 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.21.RDB3.conv1.weight
- , %para658 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.21.RDB3.conv1.bias
- , %para659 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.21.RDB3.conv2.weight
- , %para660 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.21.RDB3.conv2.bias
- , %para661 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.21.RDB3.conv3.weight
- , %para662 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.21.RDB3.conv3.bias
- , %para663 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.21.RDB3.conv4.weight
- , %para664 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.21.RDB3.conv4.bias
- , %para665 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.21.RDB3.conv5.weight
- , %para666 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.21.RDB3.conv5.bias
- , %para667 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.22.RDB1.conv1.weight
- , %para668 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.22.RDB1.conv1.bias
- , %para669 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.22.RDB1.conv2.weight
- , %para670 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.22.RDB1.conv2.bias
- , %para671 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.22.RDB1.conv3.weight
- , %para672 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.22.RDB1.conv3.bias
- , %para673 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.22.RDB1.conv4.weight
- , %para674 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.22.RDB1.conv4.bias
- , %para675 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.22.RDB1.conv5.weight
- , %para676 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.22.RDB1.conv5.bias
- , %para677 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.22.RDB2.conv1.weight
- , %para678 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.22.RDB2.conv1.bias
- , %para679 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.22.RDB2.conv2.weight
- , %para680 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.22.RDB2.conv2.bias
- , %para681 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.22.RDB2.conv3.weight
- , %para682 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.22.RDB2.conv3.bias
- , %para683 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.22.RDB2.conv4.weight
- , %para684 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.22.RDB2.conv4.bias
- , %para685 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.22.RDB2.conv5.weight
- , %para686 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.22.RDB2.conv5.bias
- , %para687 : Ref[Tensor(F32)][32, 64, 3, 3] # network.network.G.RRDB_trunk.22.RDB3.conv1.weight
- , %para688 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.22.RDB3.conv1.bias
- , %para689 : Ref[Tensor(F32)][32, 96, 3, 3] # network.network.G.RRDB_trunk.22.RDB3.conv2.weight
- , %para690 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.22.RDB3.conv2.bias
- , %para691 : Ref[Tensor(F32)][32, 128, 3, 3] # network.network.G.RRDB_trunk.22.RDB3.conv3.weight
- , %para692 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.22.RDB3.conv3.bias
- , %para693 : Ref[Tensor(F32)][32, 160, 3, 3] # network.network.G.RRDB_trunk.22.RDB3.conv4.weight
- , %para694 : Ref[Tensor(F32)][32] # network.network.G.RRDB_trunk.22.RDB3.conv4.bias
- , %para695 : Ref[Tensor(F32)][64, 192, 3, 3] # network.network.G.RRDB_trunk.22.RDB3.conv5.weight
- , %para696 : Ref[Tensor(F32)][64] # network.network.G.RRDB_trunk.22.RDB3.conv5.bias
- , %para697 : Ref[Tensor(F32)][64, 64, 3, 3] # network.network.G.trunk_conv.weight
- , %para698 : Ref[Tensor(F32)][64] # network.network.G.trunk_conv.bias
- , %para699 : Ref[Tensor(F32)][64, 64, 3, 3] # network.network.G.upconv1.weight
- , %para700 : Ref[Tensor(F32)][64] # network.network.G.upconv1.bias
- , %para701 : Ref[Tensor(F32)][64, 64, 3, 3] # network.network.G.upconv2.weight
- , %para702 : Ref[Tensor(F32)][64] # network.network.G.upconv2.bias
- , %para703 : Ref[Tensor(F32)][64, 64, 3, 3] # network.network.G.HRconv.weight
- , %para704 : Ref[Tensor(F32)][64] # network.network.G.HRconv.bias
- , %para705 : Ref[Tensor(F32)][3, 64, 3, 3] # network.network.G.conv_last.weight
- , %para706 : Ref[Tensor(F32)][3] # network.network.G.conv_last.bias
- , %para707 : Ref[Tensor(F32)][1] # optimizer.beta1_power
- , %para708 : Ref[Tensor(F32)][1] # optimizer.beta2_power
- , %para709 : Ref[Tensor(F32)][64, 3, 3, 3] # optimizer.moment1.G.conv_first.weight
- , %para710 : Ref[Tensor(F32)][64] # optimizer.moment1.G.conv_first.bias
- , %para711 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.0.RDB1.conv1.weight
- , %para712 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.0.RDB1.conv1.bias
- , %para713 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.0.RDB1.conv2.weight
- , %para714 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.0.RDB1.conv2.bias
- , %para715 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.0.RDB1.conv3.weight
- , %para716 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.0.RDB1.conv3.bias
- , %para717 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.0.RDB1.conv4.weight
- , %para718 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.0.RDB1.conv4.bias
- , %para719 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.0.RDB1.conv5.weight
- , %para720 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.0.RDB1.conv5.bias
- , %para721 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.0.RDB2.conv1.weight
- , %para722 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.0.RDB2.conv1.bias
- , %para723 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.0.RDB2.conv2.weight
- , %para724 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.0.RDB2.conv2.bias
- , %para725 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.0.RDB2.conv3.weight
- , %para726 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.0.RDB2.conv3.bias
- , %para727 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.0.RDB2.conv4.weight
- , %para728 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.0.RDB2.conv4.bias
- , %para729 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.0.RDB2.conv5.weight
- , %para730 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.0.RDB2.conv5.bias
- , %para731 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.0.RDB3.conv1.weight
- , %para732 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para733 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.0.RDB3.conv2.weight
- , %para734 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para735 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.0.RDB3.conv3.weight
- , %para736 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para737 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.0.RDB3.conv4.weight
- , %para738 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para739 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.0.RDB3.conv5.weight
- , %para740 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para741 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.1.RDB1.conv1.weight
- , %para742 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.1.RDB1.conv1.bias
- , %para743 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.1.RDB1.conv2.weight
- , %para744 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.1.RDB1.conv2.bias
- , %para745 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.1.RDB1.conv3.weight
- , %para746 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.1.RDB1.conv3.bias
- , %para747 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.1.RDB1.conv4.weight
- , %para748 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.1.RDB1.conv4.bias
- , %para749 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.1.RDB1.conv5.weight
- , %para750 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.1.RDB1.conv5.bias
- , %para751 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.1.RDB2.conv1.weight
- , %para752 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.1.RDB2.conv1.bias
- , %para753 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.1.RDB2.conv2.weight
- , %para754 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.1.RDB2.conv2.bias
- , %para755 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.1.RDB2.conv3.weight
- , %para756 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.1.RDB2.conv3.bias
- , %para757 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.1.RDB2.conv4.weight
- , %para758 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.1.RDB2.conv4.bias
- , %para759 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.1.RDB2.conv5.weight
- , %para760 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.1.RDB2.conv5.bias
- , %para761 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.1.RDB3.conv1.weight
- , %para762 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.1.RDB3.conv1.bias
- , %para763 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.1.RDB3.conv2.weight
- , %para764 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.1.RDB3.conv2.bias
- , %para765 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.1.RDB3.conv3.weight
- , %para766 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.1.RDB3.conv3.bias
- , %para767 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.1.RDB3.conv4.weight
- , %para768 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.1.RDB3.conv4.bias
- , %para769 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.1.RDB3.conv5.weight
- , %para770 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.1.RDB3.conv5.bias
- , %para771 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.2.RDB1.conv1.weight
- , %para772 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.2.RDB1.conv1.bias
- , %para773 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.2.RDB1.conv2.weight
- , %para774 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.2.RDB1.conv2.bias
- , %para775 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.2.RDB1.conv3.weight
- , %para776 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.2.RDB1.conv3.bias
- , %para777 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.2.RDB1.conv4.weight
- , %para778 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.2.RDB1.conv4.bias
- , %para779 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.2.RDB1.conv5.weight
- , %para780 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.2.RDB1.conv5.bias
- , %para781 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.2.RDB2.conv1.weight
- , %para782 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.2.RDB2.conv1.bias
- , %para783 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.2.RDB2.conv2.weight
- , %para784 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.2.RDB2.conv2.bias
- , %para785 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.2.RDB2.conv3.weight
- , %para786 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.2.RDB2.conv3.bias
- , %para787 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.2.RDB2.conv4.weight
- , %para788 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.2.RDB2.conv4.bias
- , %para789 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.2.RDB2.conv5.weight
- , %para790 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.2.RDB2.conv5.bias
- , %para791 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.2.RDB3.conv1.weight
- , %para792 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.2.RDB3.conv1.bias
- , %para793 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.2.RDB3.conv2.weight
- , %para794 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.2.RDB3.conv2.bias
- , %para795 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.2.RDB3.conv3.weight
- , %para796 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.2.RDB3.conv3.bias
- , %para797 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.2.RDB3.conv4.weight
- , %para798 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.2.RDB3.conv4.bias
- , %para799 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.2.RDB3.conv5.weight
- , %para800 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.2.RDB3.conv5.bias
- , %para801 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.3.RDB1.conv1.weight
- , %para802 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.3.RDB1.conv1.bias
- , %para803 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.3.RDB1.conv2.weight
- , %para804 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.3.RDB1.conv2.bias
- , %para805 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.3.RDB1.conv3.weight
- , %para806 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.3.RDB1.conv3.bias
- , %para807 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.3.RDB1.conv4.weight
- , %para808 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.3.RDB1.conv4.bias
- , %para809 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.3.RDB1.conv5.weight
- , %para810 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.3.RDB1.conv5.bias
- , %para811 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.3.RDB2.conv1.weight
- , %para812 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.3.RDB2.conv1.bias
- , %para813 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.3.RDB2.conv2.weight
- , %para814 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.3.RDB2.conv2.bias
- , %para815 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.3.RDB2.conv3.weight
- , %para816 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.3.RDB2.conv3.bias
- , %para817 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.3.RDB2.conv4.weight
- , %para818 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.3.RDB2.conv4.bias
- , %para819 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.3.RDB2.conv5.weight
- , %para820 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.3.RDB2.conv5.bias
- , %para821 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.3.RDB3.conv1.weight
- , %para822 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.3.RDB3.conv1.bias
- , %para823 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.3.RDB3.conv2.weight
- , %para824 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.3.RDB3.conv2.bias
- , %para825 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.3.RDB3.conv3.weight
- , %para826 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.3.RDB3.conv3.bias
- , %para827 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.3.RDB3.conv4.weight
- , %para828 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.3.RDB3.conv4.bias
- , %para829 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.3.RDB3.conv5.weight
- , %para830 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.3.RDB3.conv5.bias
- , %para831 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.4.RDB1.conv1.weight
- , %para832 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.4.RDB1.conv1.bias
- , %para833 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.4.RDB1.conv2.weight
- , %para834 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.4.RDB1.conv2.bias
- , %para835 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.4.RDB1.conv3.weight
- , %para836 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.4.RDB1.conv3.bias
- , %para837 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.4.RDB1.conv4.weight
- , %para838 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.4.RDB1.conv4.bias
- , %para839 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.4.RDB1.conv5.weight
- , %para840 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.4.RDB1.conv5.bias
- , %para841 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.4.RDB2.conv1.weight
- , %para842 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.4.RDB2.conv1.bias
- , %para843 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.4.RDB2.conv2.weight
- , %para844 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.4.RDB2.conv2.bias
- , %para845 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.4.RDB2.conv3.weight
- , %para846 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.4.RDB2.conv3.bias
- , %para847 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.4.RDB2.conv4.weight
- , %para848 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.4.RDB2.conv4.bias
- , %para849 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.4.RDB2.conv5.weight
- , %para850 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.4.RDB2.conv5.bias
- , %para851 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.4.RDB3.conv1.weight
- , %para852 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.4.RDB3.conv1.bias
- , %para853 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.4.RDB3.conv2.weight
- , %para854 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.4.RDB3.conv2.bias
- , %para855 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.4.RDB3.conv3.weight
- , %para856 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.4.RDB3.conv3.bias
- , %para857 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.4.RDB3.conv4.weight
- , %para858 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.4.RDB3.conv4.bias
- , %para859 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.4.RDB3.conv5.weight
- , %para860 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.4.RDB3.conv5.bias
- , %para861 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.5.RDB1.conv1.weight
- , %para862 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.5.RDB1.conv1.bias
- , %para863 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.5.RDB1.conv2.weight
- , %para864 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.5.RDB1.conv2.bias
- , %para865 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.5.RDB1.conv3.weight
- , %para866 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.5.RDB1.conv3.bias
- , %para867 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.5.RDB1.conv4.weight
- , %para868 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.5.RDB1.conv4.bias
- , %para869 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.5.RDB1.conv5.weight
- , %para870 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.5.RDB1.conv5.bias
- , %para871 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.5.RDB2.conv1.weight
- , %para872 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.5.RDB2.conv1.bias
- , %para873 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.5.RDB2.conv2.weight
- , %para874 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.5.RDB2.conv2.bias
- , %para875 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.5.RDB2.conv3.weight
- , %para876 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.5.RDB2.conv3.bias
- , %para877 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.5.RDB2.conv4.weight
- , %para878 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.5.RDB2.conv4.bias
- , %para879 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.5.RDB2.conv5.weight
- , %para880 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.5.RDB2.conv5.bias
- , %para881 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.5.RDB3.conv1.weight
- , %para882 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.5.RDB3.conv1.bias
- , %para883 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.5.RDB3.conv2.weight
- , %para884 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.5.RDB3.conv2.bias
- , %para885 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.5.RDB3.conv3.weight
- , %para886 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.5.RDB3.conv3.bias
- , %para887 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.5.RDB3.conv4.weight
- , %para888 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.5.RDB3.conv4.bias
- , %para889 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.5.RDB3.conv5.weight
- , %para890 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.5.RDB3.conv5.bias
- , %para891 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.6.RDB1.conv1.weight
- , %para892 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.6.RDB1.conv1.bias
- , %para893 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.6.RDB1.conv2.weight
- , %para894 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.6.RDB1.conv2.bias
- , %para895 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.6.RDB1.conv3.weight
- , %para896 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.6.RDB1.conv3.bias
- , %para897 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.6.RDB1.conv4.weight
- , %para898 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.6.RDB1.conv4.bias
- , %para899 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.6.RDB1.conv5.weight
- , %para900 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.6.RDB1.conv5.bias
- , %para901 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.6.RDB2.conv1.weight
- , %para902 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.6.RDB2.conv1.bias
- , %para903 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.6.RDB2.conv2.weight
- , %para904 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.6.RDB2.conv2.bias
- , %para905 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.6.RDB2.conv3.weight
- , %para906 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.6.RDB2.conv3.bias
- , %para907 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.6.RDB2.conv4.weight
- , %para908 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.6.RDB2.conv4.bias
- , %para909 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.6.RDB2.conv5.weight
- , %para910 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.6.RDB2.conv5.bias
- , %para911 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.6.RDB3.conv1.weight
- , %para912 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.6.RDB3.conv1.bias
- , %para913 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.6.RDB3.conv2.weight
- , %para914 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.6.RDB3.conv2.bias
- , %para915 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.6.RDB3.conv3.weight
- , %para916 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.6.RDB3.conv3.bias
- , %para917 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.6.RDB3.conv4.weight
- , %para918 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.6.RDB3.conv4.bias
- , %para919 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.6.RDB3.conv5.weight
- , %para920 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.6.RDB3.conv5.bias
- , %para921 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.7.RDB1.conv1.weight
- , %para922 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.7.RDB1.conv1.bias
- , %para923 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.7.RDB1.conv2.weight
- , %para924 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.7.RDB1.conv2.bias
- , %para925 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.7.RDB1.conv3.weight
- , %para926 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.7.RDB1.conv3.bias
- , %para927 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.7.RDB1.conv4.weight
- , %para928 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.7.RDB1.conv4.bias
- , %para929 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.7.RDB1.conv5.weight
- , %para930 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.7.RDB1.conv5.bias
- , %para931 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.7.RDB2.conv1.weight
- , %para932 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.7.RDB2.conv1.bias
- , %para933 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.7.RDB2.conv2.weight
- , %para934 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.7.RDB2.conv2.bias
- , %para935 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.7.RDB2.conv3.weight
- , %para936 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.7.RDB2.conv3.bias
- , %para937 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.7.RDB2.conv4.weight
- , %para938 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.7.RDB2.conv4.bias
- , %para939 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.7.RDB2.conv5.weight
- , %para940 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.7.RDB2.conv5.bias
- , %para941 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.7.RDB3.conv1.weight
- , %para942 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.7.RDB3.conv1.bias
- , %para943 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.7.RDB3.conv2.weight
- , %para944 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.7.RDB3.conv2.bias
- , %para945 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.7.RDB3.conv3.weight
- , %para946 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.7.RDB3.conv3.bias
- , %para947 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.7.RDB3.conv4.weight
- , %para948 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.7.RDB3.conv4.bias
- , %para949 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.7.RDB3.conv5.weight
- , %para950 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.7.RDB3.conv5.bias
- , %para951 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.8.RDB1.conv1.weight
- , %para952 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.8.RDB1.conv1.bias
- , %para953 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.8.RDB1.conv2.weight
- , %para954 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.8.RDB1.conv2.bias
- , %para955 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.8.RDB1.conv3.weight
- , %para956 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.8.RDB1.conv3.bias
- , %para957 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.8.RDB1.conv4.weight
- , %para958 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.8.RDB1.conv4.bias
- , %para959 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.8.RDB1.conv5.weight
- , %para960 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.8.RDB1.conv5.bias
- , %para961 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.8.RDB2.conv1.weight
- , %para962 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.8.RDB2.conv1.bias
- , %para963 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.8.RDB2.conv2.weight
- , %para964 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.8.RDB2.conv2.bias
- , %para965 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.8.RDB2.conv3.weight
- , %para966 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.8.RDB2.conv3.bias
- , %para967 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.8.RDB2.conv4.weight
- , %para968 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.8.RDB2.conv4.bias
- , %para969 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.8.RDB2.conv5.weight
- , %para970 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.8.RDB2.conv5.bias
- , %para971 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.8.RDB3.conv1.weight
- , %para972 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.8.RDB3.conv1.bias
- , %para973 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.8.RDB3.conv2.weight
- , %para974 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.8.RDB3.conv2.bias
- , %para975 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.8.RDB3.conv3.weight
- , %para976 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.8.RDB3.conv3.bias
- , %para977 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.8.RDB3.conv4.weight
- , %para978 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.8.RDB3.conv4.bias
- , %para979 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.8.RDB3.conv5.weight
- , %para980 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.8.RDB3.conv5.bias
- , %para981 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.9.RDB1.conv1.weight
- , %para982 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.9.RDB1.conv1.bias
- , %para983 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.9.RDB1.conv2.weight
- , %para984 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.9.RDB1.conv2.bias
- , %para985 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.9.RDB1.conv3.weight
- , %para986 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.9.RDB1.conv3.bias
- , %para987 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.9.RDB1.conv4.weight
- , %para988 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.9.RDB1.conv4.bias
- , %para989 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.9.RDB1.conv5.weight
- , %para990 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.9.RDB1.conv5.bias
- , %para991 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.9.RDB2.conv1.weight
- , %para992 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.9.RDB2.conv1.bias
- , %para993 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.9.RDB2.conv2.weight
- , %para994 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.9.RDB2.conv2.bias
- , %para995 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.9.RDB2.conv3.weight
- , %para996 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.9.RDB2.conv3.bias
- , %para997 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.9.RDB2.conv4.weight
- , %para998 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.9.RDB2.conv4.bias
- , %para999 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.9.RDB2.conv5.weight
- , %para1000 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.9.RDB2.conv5.bias
- , %para1001 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.9.RDB3.conv1.weight
- , %para1002 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.9.RDB3.conv1.bias
- , %para1003 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.9.RDB3.conv2.weight
- , %para1004 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.9.RDB3.conv2.bias
- , %para1005 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.9.RDB3.conv3.weight
- , %para1006 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.9.RDB3.conv3.bias
- , %para1007 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.9.RDB3.conv4.weight
- , %para1008 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.9.RDB3.conv4.bias
- , %para1009 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.9.RDB3.conv5.weight
- , %para1010 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.9.RDB3.conv5.bias
- , %para1011 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.10.RDB1.conv1.weight
- , %para1012 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.10.RDB1.conv1.bias
- , %para1013 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.10.RDB1.conv2.weight
- , %para1014 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.10.RDB1.conv2.bias
- , %para1015 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.10.RDB1.conv3.weight
- , %para1016 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.10.RDB1.conv3.bias
- , %para1017 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.10.RDB1.conv4.weight
- , %para1018 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.10.RDB1.conv4.bias
- , %para1019 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.10.RDB1.conv5.weight
- , %para1020 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.10.RDB1.conv5.bias
- , %para1021 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.10.RDB2.conv1.weight
- , %para1022 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.10.RDB2.conv1.bias
- , %para1023 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.10.RDB2.conv2.weight
- , %para1024 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.10.RDB2.conv2.bias
- , %para1025 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.10.RDB2.conv3.weight
- , %para1026 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.10.RDB2.conv3.bias
- , %para1027 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.10.RDB2.conv4.weight
- , %para1028 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.10.RDB2.conv4.bias
- , %para1029 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.10.RDB2.conv5.weight
- , %para1030 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.10.RDB2.conv5.bias
- , %para1031 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.10.RDB3.conv1.weight
- , %para1032 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.10.RDB3.conv1.bias
- , %para1033 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.10.RDB3.conv2.weight
- , %para1034 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.10.RDB3.conv2.bias
- , %para1035 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.10.RDB3.conv3.weight
- , %para1036 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.10.RDB3.conv3.bias
- , %para1037 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.10.RDB3.conv4.weight
- , %para1038 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.10.RDB3.conv4.bias
- , %para1039 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.10.RDB3.conv5.weight
- , %para1040 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.10.RDB3.conv5.bias
- , %para1041 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.11.RDB1.conv1.weight
- , %para1042 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.11.RDB1.conv1.bias
- , %para1043 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.11.RDB1.conv2.weight
- , %para1044 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.11.RDB1.conv2.bias
- , %para1045 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.11.RDB1.conv3.weight
- , %para1046 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.11.RDB1.conv3.bias
- , %para1047 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.11.RDB1.conv4.weight
- , %para1048 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.11.RDB1.conv4.bias
- , %para1049 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.11.RDB1.conv5.weight
- , %para1050 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.11.RDB1.conv5.bias
- , %para1051 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.11.RDB2.conv1.weight
- , %para1052 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.11.RDB2.conv1.bias
- , %para1053 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.11.RDB2.conv2.weight
- , %para1054 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.11.RDB2.conv2.bias
- , %para1055 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.11.RDB2.conv3.weight
- , %para1056 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.11.RDB2.conv3.bias
- , %para1057 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.11.RDB2.conv4.weight
- , %para1058 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.11.RDB2.conv4.bias
- , %para1059 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.11.RDB2.conv5.weight
- , %para1060 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.11.RDB2.conv5.bias
- , %para1061 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.11.RDB3.conv1.weight
- , %para1062 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.11.RDB3.conv1.bias
- , %para1063 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.11.RDB3.conv2.weight
- , %para1064 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.11.RDB3.conv2.bias
- , %para1065 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.11.RDB3.conv3.weight
- , %para1066 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.11.RDB3.conv3.bias
- , %para1067 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.11.RDB3.conv4.weight
- , %para1068 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.11.RDB3.conv4.bias
- , %para1069 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.11.RDB3.conv5.weight
- , %para1070 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.11.RDB3.conv5.bias
- , %para1071 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.12.RDB1.conv1.weight
- , %para1072 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.12.RDB1.conv1.bias
- , %para1073 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.12.RDB1.conv2.weight
- , %para1074 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.12.RDB1.conv2.bias
- , %para1075 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.12.RDB1.conv3.weight
- , %para1076 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.12.RDB1.conv3.bias
- , %para1077 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.12.RDB1.conv4.weight
- , %para1078 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.12.RDB1.conv4.bias
- , %para1079 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.12.RDB1.conv5.weight
- , %para1080 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.12.RDB1.conv5.bias
- , %para1081 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.12.RDB2.conv1.weight
- , %para1082 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.12.RDB2.conv1.bias
- , %para1083 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.12.RDB2.conv2.weight
- , %para1084 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.12.RDB2.conv2.bias
- , %para1085 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.12.RDB2.conv3.weight
- , %para1086 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.12.RDB2.conv3.bias
- , %para1087 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.12.RDB2.conv4.weight
- , %para1088 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.12.RDB2.conv4.bias
- , %para1089 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.12.RDB2.conv5.weight
- , %para1090 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.12.RDB2.conv5.bias
- , %para1091 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.12.RDB3.conv1.weight
- , %para1092 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.12.RDB3.conv1.bias
- , %para1093 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.12.RDB3.conv2.weight
- , %para1094 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.12.RDB3.conv2.bias
- , %para1095 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.12.RDB3.conv3.weight
- , %para1096 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.12.RDB3.conv3.bias
- , %para1097 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.12.RDB3.conv4.weight
- , %para1098 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.12.RDB3.conv4.bias
- , %para1099 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.12.RDB3.conv5.weight
- , %para1100 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.12.RDB3.conv5.bias
- , %para1101 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.13.RDB1.conv1.weight
- , %para1102 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.13.RDB1.conv1.bias
- , %para1103 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.13.RDB1.conv2.weight
- , %para1104 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.13.RDB1.conv2.bias
- , %para1105 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.13.RDB1.conv3.weight
- , %para1106 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.13.RDB1.conv3.bias
- , %para1107 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.13.RDB1.conv4.weight
- , %para1108 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.13.RDB1.conv4.bias
- , %para1109 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.13.RDB1.conv5.weight
- , %para1110 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.13.RDB1.conv5.bias
- , %para1111 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.13.RDB2.conv1.weight
- , %para1112 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.13.RDB2.conv1.bias
- , %para1113 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.13.RDB2.conv2.weight
- , %para1114 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.13.RDB2.conv2.bias
- , %para1115 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.13.RDB2.conv3.weight
- , %para1116 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.13.RDB2.conv3.bias
- , %para1117 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.13.RDB2.conv4.weight
- , %para1118 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.13.RDB2.conv4.bias
- , %para1119 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.13.RDB2.conv5.weight
- , %para1120 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.13.RDB2.conv5.bias
- , %para1121 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.13.RDB3.conv1.weight
- , %para1122 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.13.RDB3.conv1.bias
- , %para1123 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.13.RDB3.conv2.weight
- , %para1124 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.13.RDB3.conv2.bias
- , %para1125 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.13.RDB3.conv3.weight
- , %para1126 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.13.RDB3.conv3.bias
- , %para1127 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.13.RDB3.conv4.weight
- , %para1128 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.13.RDB3.conv4.bias
- , %para1129 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.13.RDB3.conv5.weight
- , %para1130 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.13.RDB3.conv5.bias
- , %para1131 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.14.RDB1.conv1.weight
- , %para1132 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.14.RDB1.conv1.bias
- , %para1133 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.14.RDB1.conv2.weight
- , %para1134 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.14.RDB1.conv2.bias
- , %para1135 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.14.RDB1.conv3.weight
- , %para1136 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.14.RDB1.conv3.bias
- , %para1137 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.14.RDB1.conv4.weight
- , %para1138 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.14.RDB1.conv4.bias
- , %para1139 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.14.RDB1.conv5.weight
- , %para1140 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.14.RDB1.conv5.bias
- , %para1141 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.14.RDB2.conv1.weight
- , %para1142 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.14.RDB2.conv1.bias
- , %para1143 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.14.RDB2.conv2.weight
- , %para1144 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.14.RDB2.conv2.bias
- , %para1145 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.14.RDB2.conv3.weight
- , %para1146 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.14.RDB2.conv3.bias
- , %para1147 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.14.RDB2.conv4.weight
- , %para1148 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.14.RDB2.conv4.bias
- , %para1149 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.14.RDB2.conv5.weight
- , %para1150 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.14.RDB2.conv5.bias
- , %para1151 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.14.RDB3.conv1.weight
- , %para1152 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.14.RDB3.conv1.bias
- , %para1153 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.14.RDB3.conv2.weight
- , %para1154 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.14.RDB3.conv2.bias
- , %para1155 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.14.RDB3.conv3.weight
- , %para1156 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.14.RDB3.conv3.bias
- , %para1157 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.14.RDB3.conv4.weight
- , %para1158 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.14.RDB3.conv4.bias
- , %para1159 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.14.RDB3.conv5.weight
- , %para1160 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.14.RDB3.conv5.bias
- , %para1161 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.15.RDB1.conv1.weight
- , %para1162 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.15.RDB1.conv1.bias
- , %para1163 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.15.RDB1.conv2.weight
- , %para1164 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.15.RDB1.conv2.bias
- , %para1165 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.15.RDB1.conv3.weight
- , %para1166 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.15.RDB1.conv3.bias
- , %para1167 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.15.RDB1.conv4.weight
- , %para1168 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.15.RDB1.conv4.bias
- , %para1169 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.15.RDB1.conv5.weight
- , %para1170 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.15.RDB1.conv5.bias
- , %para1171 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.15.RDB2.conv1.weight
- , %para1172 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.15.RDB2.conv1.bias
- , %para1173 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.15.RDB2.conv2.weight
- , %para1174 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.15.RDB2.conv2.bias
- , %para1175 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.15.RDB2.conv3.weight
- , %para1176 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.15.RDB2.conv3.bias
- , %para1177 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.15.RDB2.conv4.weight
- , %para1178 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.15.RDB2.conv4.bias
- , %para1179 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.15.RDB2.conv5.weight
- , %para1180 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.15.RDB2.conv5.bias
- , %para1181 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.15.RDB3.conv1.weight
- , %para1182 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.15.RDB3.conv1.bias
- , %para1183 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.15.RDB3.conv2.weight
- , %para1184 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.15.RDB3.conv2.bias
- , %para1185 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.15.RDB3.conv3.weight
- , %para1186 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.15.RDB3.conv3.bias
- , %para1187 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.15.RDB3.conv4.weight
- , %para1188 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.15.RDB3.conv4.bias
- , %para1189 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.15.RDB3.conv5.weight
- , %para1190 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.15.RDB3.conv5.bias
- , %para1191 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.16.RDB1.conv1.weight
- , %para1192 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.16.RDB1.conv1.bias
- , %para1193 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.16.RDB1.conv2.weight
- , %para1194 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.16.RDB1.conv2.bias
- , %para1195 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.16.RDB1.conv3.weight
- , %para1196 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.16.RDB1.conv3.bias
- , %para1197 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.16.RDB1.conv4.weight
- , %para1198 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.16.RDB1.conv4.bias
- , %para1199 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.16.RDB1.conv5.weight
- , %para1200 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.16.RDB1.conv5.bias
- , %para1201 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.16.RDB2.conv1.weight
- , %para1202 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.16.RDB2.conv1.bias
- , %para1203 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.16.RDB2.conv2.weight
- , %para1204 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.16.RDB2.conv2.bias
- , %para1205 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.16.RDB2.conv3.weight
- , %para1206 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.16.RDB2.conv3.bias
- , %para1207 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.16.RDB2.conv4.weight
- , %para1208 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.16.RDB2.conv4.bias
- , %para1209 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.16.RDB2.conv5.weight
- , %para1210 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.16.RDB2.conv5.bias
- , %para1211 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.16.RDB3.conv1.weight
- , %para1212 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.16.RDB3.conv1.bias
- , %para1213 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.16.RDB3.conv2.weight
- , %para1214 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.16.RDB3.conv2.bias
- , %para1215 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.16.RDB3.conv3.weight
- , %para1216 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.16.RDB3.conv3.bias
- , %para1217 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.16.RDB3.conv4.weight
- , %para1218 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.16.RDB3.conv4.bias
- , %para1219 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.16.RDB3.conv5.weight
- , %para1220 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.16.RDB3.conv5.bias
- , %para1221 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.17.RDB1.conv1.weight
- , %para1222 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.17.RDB1.conv1.bias
- , %para1223 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.17.RDB1.conv2.weight
- , %para1224 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.17.RDB1.conv2.bias
- , %para1225 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.17.RDB1.conv3.weight
- , %para1226 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.17.RDB1.conv3.bias
- , %para1227 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.17.RDB1.conv4.weight
- , %para1228 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.17.RDB1.conv4.bias
- , %para1229 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.17.RDB1.conv5.weight
- , %para1230 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.17.RDB1.conv5.bias
- , %para1231 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.17.RDB2.conv1.weight
- , %para1232 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.17.RDB2.conv1.bias
- , %para1233 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.17.RDB2.conv2.weight
- , %para1234 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.17.RDB2.conv2.bias
- , %para1235 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.17.RDB2.conv3.weight
- , %para1236 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.17.RDB2.conv3.bias
- , %para1237 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.17.RDB2.conv4.weight
- , %para1238 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.17.RDB2.conv4.bias
- , %para1239 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.17.RDB2.conv5.weight
- , %para1240 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.17.RDB2.conv5.bias
- , %para1241 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.17.RDB3.conv1.weight
- , %para1242 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.17.RDB3.conv1.bias
- , %para1243 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.17.RDB3.conv2.weight
- , %para1244 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.17.RDB3.conv2.bias
- , %para1245 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.17.RDB3.conv3.weight
- , %para1246 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.17.RDB3.conv3.bias
- , %para1247 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.17.RDB3.conv4.weight
- , %para1248 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.17.RDB3.conv4.bias
- , %para1249 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.17.RDB3.conv5.weight
- , %para1250 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.17.RDB3.conv5.bias
- , %para1251 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.18.RDB1.conv1.weight
- , %para1252 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.18.RDB1.conv1.bias
- , %para1253 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.18.RDB1.conv2.weight
- , %para1254 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.18.RDB1.conv2.bias
- , %para1255 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.18.RDB1.conv3.weight
- , %para1256 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.18.RDB1.conv3.bias
- , %para1257 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.18.RDB1.conv4.weight
- , %para1258 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.18.RDB1.conv4.bias
- , %para1259 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.18.RDB1.conv5.weight
- , %para1260 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.18.RDB1.conv5.bias
- , %para1261 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.18.RDB2.conv1.weight
- , %para1262 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.18.RDB2.conv1.bias
- , %para1263 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.18.RDB2.conv2.weight
- , %para1264 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.18.RDB2.conv2.bias
- , %para1265 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.18.RDB2.conv3.weight
- , %para1266 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.18.RDB2.conv3.bias
- , %para1267 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.18.RDB2.conv4.weight
- , %para1268 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.18.RDB2.conv4.bias
- , %para1269 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.18.RDB2.conv5.weight
- , %para1270 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.18.RDB2.conv5.bias
- , %para1271 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.18.RDB3.conv1.weight
- , %para1272 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.18.RDB3.conv1.bias
- , %para1273 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.18.RDB3.conv2.weight
- , %para1274 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.18.RDB3.conv2.bias
- , %para1275 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.18.RDB3.conv3.weight
- , %para1276 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.18.RDB3.conv3.bias
- , %para1277 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.18.RDB3.conv4.weight
- , %para1278 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.18.RDB3.conv4.bias
- , %para1279 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.18.RDB3.conv5.weight
- , %para1280 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.18.RDB3.conv5.bias
- , %para1281 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.19.RDB1.conv1.weight
- , %para1282 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.19.RDB1.conv1.bias
- , %para1283 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.19.RDB1.conv2.weight
- , %para1284 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.19.RDB1.conv2.bias
- , %para1285 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.19.RDB1.conv3.weight
- , %para1286 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.19.RDB1.conv3.bias
- , %para1287 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.19.RDB1.conv4.weight
- , %para1288 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.19.RDB1.conv4.bias
- , %para1289 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.19.RDB1.conv5.weight
- , %para1290 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.19.RDB1.conv5.bias
- , %para1291 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.19.RDB2.conv1.weight
- , %para1292 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.19.RDB2.conv1.bias
- , %para1293 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.19.RDB2.conv2.weight
- , %para1294 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.19.RDB2.conv2.bias
- , %para1295 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.19.RDB2.conv3.weight
- , %para1296 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.19.RDB2.conv3.bias
- , %para1297 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.19.RDB2.conv4.weight
- , %para1298 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.19.RDB2.conv4.bias
- , %para1299 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.19.RDB2.conv5.weight
- , %para1300 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.19.RDB2.conv5.bias
- , %para1301 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.19.RDB3.conv1.weight
- , %para1302 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.19.RDB3.conv1.bias
- , %para1303 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.19.RDB3.conv2.weight
- , %para1304 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.19.RDB3.conv2.bias
- , %para1305 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.19.RDB3.conv3.weight
- , %para1306 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.19.RDB3.conv3.bias
- , %para1307 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.19.RDB3.conv4.weight
- , %para1308 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.19.RDB3.conv4.bias
- , %para1309 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.19.RDB3.conv5.weight
- , %para1310 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.19.RDB3.conv5.bias
- , %para1311 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.20.RDB1.conv1.weight
- , %para1312 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.20.RDB1.conv1.bias
- , %para1313 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.20.RDB1.conv2.weight
- , %para1314 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.20.RDB1.conv2.bias
- , %para1315 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.20.RDB1.conv3.weight
- , %para1316 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.20.RDB1.conv3.bias
- , %para1317 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.20.RDB1.conv4.weight
- , %para1318 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.20.RDB1.conv4.bias
- , %para1319 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.20.RDB1.conv5.weight
- , %para1320 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.20.RDB1.conv5.bias
- , %para1321 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.20.RDB2.conv1.weight
- , %para1322 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.20.RDB2.conv1.bias
- , %para1323 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.20.RDB2.conv2.weight
- , %para1324 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.20.RDB2.conv2.bias
- , %para1325 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.20.RDB2.conv3.weight
- , %para1326 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.20.RDB2.conv3.bias
- , %para1327 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.20.RDB2.conv4.weight
- , %para1328 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.20.RDB2.conv4.bias
- , %para1329 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.20.RDB2.conv5.weight
- , %para1330 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.20.RDB2.conv5.bias
- , %para1331 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.20.RDB3.conv1.weight
- , %para1332 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.20.RDB3.conv1.bias
- , %para1333 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.20.RDB3.conv2.weight
- , %para1334 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.20.RDB3.conv2.bias
- , %para1335 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.20.RDB3.conv3.weight
- , %para1336 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.20.RDB3.conv3.bias
- , %para1337 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.20.RDB3.conv4.weight
- , %para1338 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.20.RDB3.conv4.bias
- , %para1339 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.20.RDB3.conv5.weight
- , %para1340 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.20.RDB3.conv5.bias
- , %para1341 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.21.RDB1.conv1.weight
- , %para1342 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.21.RDB1.conv1.bias
- , %para1343 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.21.RDB1.conv2.weight
- , %para1344 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.21.RDB1.conv2.bias
- , %para1345 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.21.RDB1.conv3.weight
- , %para1346 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.21.RDB1.conv3.bias
- , %para1347 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.21.RDB1.conv4.weight
- , %para1348 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.21.RDB1.conv4.bias
- , %para1349 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.21.RDB1.conv5.weight
- , %para1350 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.21.RDB1.conv5.bias
- , %para1351 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.21.RDB2.conv1.weight
- , %para1352 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.21.RDB2.conv1.bias
- , %para1353 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.21.RDB2.conv2.weight
- , %para1354 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.21.RDB2.conv2.bias
- , %para1355 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.21.RDB2.conv3.weight
- , %para1356 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.21.RDB2.conv3.bias
- , %para1357 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.21.RDB2.conv4.weight
- , %para1358 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.21.RDB2.conv4.bias
- , %para1359 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.21.RDB2.conv5.weight
- , %para1360 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.21.RDB2.conv5.bias
- , %para1361 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.21.RDB3.conv1.weight
- , %para1362 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.21.RDB3.conv1.bias
- , %para1363 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.21.RDB3.conv2.weight
- , %para1364 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.21.RDB3.conv2.bias
- , %para1365 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.21.RDB3.conv3.weight
- , %para1366 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.21.RDB3.conv3.bias
- , %para1367 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.21.RDB3.conv4.weight
- , %para1368 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.21.RDB3.conv4.bias
- , %para1369 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.21.RDB3.conv5.weight
- , %para1370 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.21.RDB3.conv5.bias
- , %para1371 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.22.RDB1.conv1.weight
- , %para1372 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.22.RDB1.conv1.bias
- , %para1373 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.22.RDB1.conv2.weight
- , %para1374 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.22.RDB1.conv2.bias
- , %para1375 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.22.RDB1.conv3.weight
- , %para1376 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.22.RDB1.conv3.bias
- , %para1377 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.22.RDB1.conv4.weight
- , %para1378 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.22.RDB1.conv4.bias
- , %para1379 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.22.RDB1.conv5.weight
- , %para1380 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.22.RDB1.conv5.bias
- , %para1381 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.22.RDB2.conv1.weight
- , %para1382 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.22.RDB2.conv1.bias
- , %para1383 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.22.RDB2.conv2.weight
- , %para1384 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.22.RDB2.conv2.bias
- , %para1385 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.22.RDB2.conv3.weight
- , %para1386 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.22.RDB2.conv3.bias
- , %para1387 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.22.RDB2.conv4.weight
- , %para1388 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.22.RDB2.conv4.bias
- , %para1389 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.22.RDB2.conv5.weight
- , %para1390 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.22.RDB2.conv5.bias
- , %para1391 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment1.G.RRDB_trunk.22.RDB3.conv1.weight
- , %para1392 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.22.RDB3.conv1.bias
- , %para1393 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment1.G.RRDB_trunk.22.RDB3.conv2.weight
- , %para1394 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.22.RDB3.conv2.bias
- , %para1395 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment1.G.RRDB_trunk.22.RDB3.conv3.weight
- , %para1396 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.22.RDB3.conv3.bias
- , %para1397 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment1.G.RRDB_trunk.22.RDB3.conv4.weight
- , %para1398 : Ref[Tensor(F32)][32] # optimizer.moment1.G.RRDB_trunk.22.RDB3.conv4.bias
- , %para1399 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment1.G.RRDB_trunk.22.RDB3.conv5.weight
- , %para1400 : Ref[Tensor(F32)][64] # optimizer.moment1.G.RRDB_trunk.22.RDB3.conv5.bias
- , %para1401 : Ref[Tensor(F32)][64, 64, 3, 3] # optimizer.moment1.G.trunk_conv.weight
- , %para1402 : Ref[Tensor(F32)][64] # optimizer.moment1.G.trunk_conv.bias
- , %para1403 : Ref[Tensor(F32)][64, 64, 3, 3] # optimizer.moment1.G.upconv1.weight
- , %para1404 : Ref[Tensor(F32)][64] # optimizer.moment1.G.upconv1.bias
- , %para1405 : Ref[Tensor(F32)][64, 64, 3, 3] # optimizer.moment1.G.upconv2.weight
- , %para1406 : Ref[Tensor(F32)][64] # optimizer.moment1.G.upconv2.bias
- , %para1407 : Ref[Tensor(F32)][64, 64, 3, 3] # optimizer.moment1.G.HRconv.weight
- , %para1408 : Ref[Tensor(F32)][64] # optimizer.moment1.G.HRconv.bias
- , %para1409 : Ref[Tensor(F32)][3, 64, 3, 3] # optimizer.moment1.G.conv_last.weight
- , %para1410 : Ref[Tensor(F32)][3] # optimizer.moment1.G.conv_last.bias
- , %para1411 : Ref[Tensor(F32)][64, 3, 3, 3] # optimizer.moment2.G.conv_first.weight
- , %para1412 : Ref[Tensor(F32)][64] # optimizer.moment2.G.conv_first.bias
- , %para1413 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.0.RDB1.conv1.weight
- , %para1414 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.0.RDB1.conv1.bias
- , %para1415 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.0.RDB1.conv2.weight
- , %para1416 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.0.RDB1.conv2.bias
- , %para1417 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.0.RDB1.conv3.weight
- , %para1418 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.0.RDB1.conv3.bias
- , %para1419 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.0.RDB1.conv4.weight
- , %para1420 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.0.RDB1.conv4.bias
- , %para1421 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.0.RDB1.conv5.weight
- , %para1422 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.0.RDB1.conv5.bias
- , %para1423 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.0.RDB2.conv1.weight
- , %para1424 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.0.RDB2.conv1.bias
- , %para1425 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.0.RDB2.conv2.weight
- , %para1426 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.0.RDB2.conv2.bias
- , %para1427 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.0.RDB2.conv3.weight
- , %para1428 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.0.RDB2.conv3.bias
- , %para1429 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.0.RDB2.conv4.weight
- , %para1430 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.0.RDB2.conv4.bias
- , %para1431 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.0.RDB2.conv5.weight
- , %para1432 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.0.RDB2.conv5.bias
- , %para1433 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.0.RDB3.conv1.weight
- , %para1434 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para1435 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.0.RDB3.conv2.weight
- , %para1436 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para1437 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.0.RDB3.conv3.weight
- , %para1438 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para1439 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.0.RDB3.conv4.weight
- , %para1440 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para1441 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.0.RDB3.conv5.weight
- , %para1442 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para1443 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.1.RDB1.conv1.weight
- , %para1444 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.1.RDB1.conv1.bias
- , %para1445 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.1.RDB1.conv2.weight
- , %para1446 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.1.RDB1.conv2.bias
- , %para1447 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.1.RDB1.conv3.weight
- , %para1448 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.1.RDB1.conv3.bias
- , %para1449 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.1.RDB1.conv4.weight
- , %para1450 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.1.RDB1.conv4.bias
- , %para1451 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.1.RDB1.conv5.weight
- , %para1452 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.1.RDB1.conv5.bias
- , %para1453 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.1.RDB2.conv1.weight
- , %para1454 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.1.RDB2.conv1.bias
- , %para1455 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.1.RDB2.conv2.weight
- , %para1456 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.1.RDB2.conv2.bias
- , %para1457 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.1.RDB2.conv3.weight
- , %para1458 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.1.RDB2.conv3.bias
- , %para1459 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.1.RDB2.conv4.weight
- , %para1460 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.1.RDB2.conv4.bias
- , %para1461 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.1.RDB2.conv5.weight
- , %para1462 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.1.RDB2.conv5.bias
- , %para1463 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.1.RDB3.conv1.weight
- , %para1464 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.1.RDB3.conv1.bias
- , %para1465 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.1.RDB3.conv2.weight
- , %para1466 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.1.RDB3.conv2.bias
- , %para1467 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.1.RDB3.conv3.weight
- , %para1468 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.1.RDB3.conv3.bias
- , %para1469 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.1.RDB3.conv4.weight
- , %para1470 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.1.RDB3.conv4.bias
- , %para1471 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.1.RDB3.conv5.weight
- , %para1472 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.1.RDB3.conv5.bias
- , %para1473 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.2.RDB1.conv1.weight
- , %para1474 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.2.RDB1.conv1.bias
- , %para1475 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.2.RDB1.conv2.weight
- , %para1476 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.2.RDB1.conv2.bias
- , %para1477 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.2.RDB1.conv3.weight
- , %para1478 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.2.RDB1.conv3.bias
- , %para1479 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.2.RDB1.conv4.weight
- , %para1480 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.2.RDB1.conv4.bias
- , %para1481 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.2.RDB1.conv5.weight
- , %para1482 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.2.RDB1.conv5.bias
- , %para1483 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.2.RDB2.conv1.weight
- , %para1484 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.2.RDB2.conv1.bias
- , %para1485 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.2.RDB2.conv2.weight
- , %para1486 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.2.RDB2.conv2.bias
- , %para1487 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.2.RDB2.conv3.weight
- , %para1488 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.2.RDB2.conv3.bias
- , %para1489 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.2.RDB2.conv4.weight
- , %para1490 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.2.RDB2.conv4.bias
- , %para1491 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.2.RDB2.conv5.weight
- , %para1492 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.2.RDB2.conv5.bias
- , %para1493 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.2.RDB3.conv1.weight
- , %para1494 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.2.RDB3.conv1.bias
- , %para1495 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.2.RDB3.conv2.weight
- , %para1496 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.2.RDB3.conv2.bias
- , %para1497 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.2.RDB3.conv3.weight
- , %para1498 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.2.RDB3.conv3.bias
- , %para1499 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.2.RDB3.conv4.weight
- , %para1500 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.2.RDB3.conv4.bias
- , %para1501 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.2.RDB3.conv5.weight
- , %para1502 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.2.RDB3.conv5.bias
- , %para1503 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.3.RDB1.conv1.weight
- , %para1504 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.3.RDB1.conv1.bias
- , %para1505 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.3.RDB1.conv2.weight
- , %para1506 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.3.RDB1.conv2.bias
- , %para1507 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.3.RDB1.conv3.weight
- , %para1508 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.3.RDB1.conv3.bias
- , %para1509 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.3.RDB1.conv4.weight
- , %para1510 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.3.RDB1.conv4.bias
- , %para1511 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.3.RDB1.conv5.weight
- , %para1512 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.3.RDB1.conv5.bias
- , %para1513 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.3.RDB2.conv1.weight
- , %para1514 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.3.RDB2.conv1.bias
- , %para1515 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.3.RDB2.conv2.weight
- , %para1516 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.3.RDB2.conv2.bias
- , %para1517 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.3.RDB2.conv3.weight
- , %para1518 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.3.RDB2.conv3.bias
- , %para1519 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.3.RDB2.conv4.weight
- , %para1520 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.3.RDB2.conv4.bias
- , %para1521 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.3.RDB2.conv5.weight
- , %para1522 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.3.RDB2.conv5.bias
- , %para1523 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.3.RDB3.conv1.weight
- , %para1524 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.3.RDB3.conv1.bias
- , %para1525 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.3.RDB3.conv2.weight
- , %para1526 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.3.RDB3.conv2.bias
- , %para1527 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.3.RDB3.conv3.weight
- , %para1528 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.3.RDB3.conv3.bias
- , %para1529 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.3.RDB3.conv4.weight
- , %para1530 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.3.RDB3.conv4.bias
- , %para1531 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.3.RDB3.conv5.weight
- , %para1532 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.3.RDB3.conv5.bias
- , %para1533 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.4.RDB1.conv1.weight
- , %para1534 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.4.RDB1.conv1.bias
- , %para1535 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.4.RDB1.conv2.weight
- , %para1536 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.4.RDB1.conv2.bias
- , %para1537 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.4.RDB1.conv3.weight
- , %para1538 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.4.RDB1.conv3.bias
- , %para1539 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.4.RDB1.conv4.weight
- , %para1540 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.4.RDB1.conv4.bias
- , %para1541 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.4.RDB1.conv5.weight
- , %para1542 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.4.RDB1.conv5.bias
- , %para1543 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.4.RDB2.conv1.weight
- , %para1544 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.4.RDB2.conv1.bias
- , %para1545 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.4.RDB2.conv2.weight
- , %para1546 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.4.RDB2.conv2.bias
- , %para1547 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.4.RDB2.conv3.weight
- , %para1548 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.4.RDB2.conv3.bias
- , %para1549 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.4.RDB2.conv4.weight
- , %para1550 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.4.RDB2.conv4.bias
- , %para1551 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.4.RDB2.conv5.weight
- , %para1552 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.4.RDB2.conv5.bias
- , %para1553 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.4.RDB3.conv1.weight
- , %para1554 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.4.RDB3.conv1.bias
- , %para1555 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.4.RDB3.conv2.weight
- , %para1556 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.4.RDB3.conv2.bias
- , %para1557 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.4.RDB3.conv3.weight
- , %para1558 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.4.RDB3.conv3.bias
- , %para1559 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.4.RDB3.conv4.weight
- , %para1560 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.4.RDB3.conv4.bias
- , %para1561 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.4.RDB3.conv5.weight
- , %para1562 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.4.RDB3.conv5.bias
- , %para1563 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.5.RDB1.conv1.weight
- , %para1564 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.5.RDB1.conv1.bias
- , %para1565 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.5.RDB1.conv2.weight
- , %para1566 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.5.RDB1.conv2.bias
- , %para1567 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.5.RDB1.conv3.weight
- , %para1568 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.5.RDB1.conv3.bias
- , %para1569 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.5.RDB1.conv4.weight
- , %para1570 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.5.RDB1.conv4.bias
- , %para1571 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.5.RDB1.conv5.weight
- , %para1572 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.5.RDB1.conv5.bias
- , %para1573 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.5.RDB2.conv1.weight
- , %para1574 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.5.RDB2.conv1.bias
- , %para1575 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.5.RDB2.conv2.weight
- , %para1576 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.5.RDB2.conv2.bias
- , %para1577 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.5.RDB2.conv3.weight
- , %para1578 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.5.RDB2.conv3.bias
- , %para1579 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.5.RDB2.conv4.weight
- , %para1580 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.5.RDB2.conv4.bias
- , %para1581 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.5.RDB2.conv5.weight
- , %para1582 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.5.RDB2.conv5.bias
- , %para1583 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.5.RDB3.conv1.weight
- , %para1584 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.5.RDB3.conv1.bias
- , %para1585 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.5.RDB3.conv2.weight
- , %para1586 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.5.RDB3.conv2.bias
- , %para1587 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.5.RDB3.conv3.weight
- , %para1588 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.5.RDB3.conv3.bias
- , %para1589 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.5.RDB3.conv4.weight
- , %para1590 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.5.RDB3.conv4.bias
- , %para1591 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.5.RDB3.conv5.weight
- , %para1592 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.5.RDB3.conv5.bias
- , %para1593 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.6.RDB1.conv1.weight
- , %para1594 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.6.RDB1.conv1.bias
- , %para1595 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.6.RDB1.conv2.weight
- , %para1596 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.6.RDB1.conv2.bias
- , %para1597 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.6.RDB1.conv3.weight
- , %para1598 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.6.RDB1.conv3.bias
- , %para1599 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.6.RDB1.conv4.weight
- , %para1600 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.6.RDB1.conv4.bias
- , %para1601 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.6.RDB1.conv5.weight
- , %para1602 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.6.RDB1.conv5.bias
- , %para1603 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.6.RDB2.conv1.weight
- , %para1604 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.6.RDB2.conv1.bias
- , %para1605 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.6.RDB2.conv2.weight
- , %para1606 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.6.RDB2.conv2.bias
- , %para1607 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.6.RDB2.conv3.weight
- , %para1608 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.6.RDB2.conv3.bias
- , %para1609 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.6.RDB2.conv4.weight
- , %para1610 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.6.RDB2.conv4.bias
- , %para1611 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.6.RDB2.conv5.weight
- , %para1612 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.6.RDB2.conv5.bias
- , %para1613 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.6.RDB3.conv1.weight
- , %para1614 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.6.RDB3.conv1.bias
- , %para1615 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.6.RDB3.conv2.weight
- , %para1616 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.6.RDB3.conv2.bias
- , %para1617 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.6.RDB3.conv3.weight
- , %para1618 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.6.RDB3.conv3.bias
- , %para1619 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.6.RDB3.conv4.weight
- , %para1620 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.6.RDB3.conv4.bias
- , %para1621 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.6.RDB3.conv5.weight
- , %para1622 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.6.RDB3.conv5.bias
- , %para1623 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.7.RDB1.conv1.weight
- , %para1624 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.7.RDB1.conv1.bias
- , %para1625 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.7.RDB1.conv2.weight
- , %para1626 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.7.RDB1.conv2.bias
- , %para1627 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.7.RDB1.conv3.weight
- , %para1628 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.7.RDB1.conv3.bias
- , %para1629 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.7.RDB1.conv4.weight
- , %para1630 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.7.RDB1.conv4.bias
- , %para1631 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.7.RDB1.conv5.weight
- , %para1632 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.7.RDB1.conv5.bias
- , %para1633 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.7.RDB2.conv1.weight
- , %para1634 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.7.RDB2.conv1.bias
- , %para1635 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.7.RDB2.conv2.weight
- , %para1636 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.7.RDB2.conv2.bias
- , %para1637 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.7.RDB2.conv3.weight
- , %para1638 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.7.RDB2.conv3.bias
- , %para1639 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.7.RDB2.conv4.weight
- , %para1640 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.7.RDB2.conv4.bias
- , %para1641 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.7.RDB2.conv5.weight
- , %para1642 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.7.RDB2.conv5.bias
- , %para1643 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.7.RDB3.conv1.weight
- , %para1644 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.7.RDB3.conv1.bias
- , %para1645 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.7.RDB3.conv2.weight
- , %para1646 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.7.RDB3.conv2.bias
- , %para1647 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.7.RDB3.conv3.weight
- , %para1648 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.7.RDB3.conv3.bias
- , %para1649 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.7.RDB3.conv4.weight
- , %para1650 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.7.RDB3.conv4.bias
- , %para1651 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.7.RDB3.conv5.weight
- , %para1652 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.7.RDB3.conv5.bias
- , %para1653 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.8.RDB1.conv1.weight
- , %para1654 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.8.RDB1.conv1.bias
- , %para1655 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.8.RDB1.conv2.weight
- , %para1656 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.8.RDB1.conv2.bias
- , %para1657 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.8.RDB1.conv3.weight
- , %para1658 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.8.RDB1.conv3.bias
- , %para1659 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.8.RDB1.conv4.weight
- , %para1660 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.8.RDB1.conv4.bias
- , %para1661 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.8.RDB1.conv5.weight
- , %para1662 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.8.RDB1.conv5.bias
- , %para1663 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.8.RDB2.conv1.weight
- , %para1664 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.8.RDB2.conv1.bias
- , %para1665 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.8.RDB2.conv2.weight
- , %para1666 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.8.RDB2.conv2.bias
- , %para1667 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.8.RDB2.conv3.weight
- , %para1668 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.8.RDB2.conv3.bias
- , %para1669 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.8.RDB2.conv4.weight
- , %para1670 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.8.RDB2.conv4.bias
- , %para1671 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.8.RDB2.conv5.weight
- , %para1672 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.8.RDB2.conv5.bias
- , %para1673 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.8.RDB3.conv1.weight
- , %para1674 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.8.RDB3.conv1.bias
- , %para1675 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.8.RDB3.conv2.weight
- , %para1676 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.8.RDB3.conv2.bias
- , %para1677 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.8.RDB3.conv3.weight
- , %para1678 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.8.RDB3.conv3.bias
- , %para1679 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.8.RDB3.conv4.weight
- , %para1680 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.8.RDB3.conv4.bias
- , %para1681 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.8.RDB3.conv5.weight
- , %para1682 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.8.RDB3.conv5.bias
- , %para1683 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.9.RDB1.conv1.weight
- , %para1684 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.9.RDB1.conv1.bias
- , %para1685 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.9.RDB1.conv2.weight
- , %para1686 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.9.RDB1.conv2.bias
- , %para1687 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.9.RDB1.conv3.weight
- , %para1688 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.9.RDB1.conv3.bias
- , %para1689 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.9.RDB1.conv4.weight
- , %para1690 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.9.RDB1.conv4.bias
- , %para1691 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.9.RDB1.conv5.weight
- , %para1692 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.9.RDB1.conv5.bias
- , %para1693 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.9.RDB2.conv1.weight
- , %para1694 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.9.RDB2.conv1.bias
- , %para1695 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.9.RDB2.conv2.weight
- , %para1696 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.9.RDB2.conv2.bias
- , %para1697 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.9.RDB2.conv3.weight
- , %para1698 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.9.RDB2.conv3.bias
- , %para1699 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.9.RDB2.conv4.weight
- , %para1700 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.9.RDB2.conv4.bias
- , %para1701 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.9.RDB2.conv5.weight
- , %para1702 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.9.RDB2.conv5.bias
- , %para1703 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.9.RDB3.conv1.weight
- , %para1704 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.9.RDB3.conv1.bias
- , %para1705 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.9.RDB3.conv2.weight
- , %para1706 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.9.RDB3.conv2.bias
- , %para1707 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.9.RDB3.conv3.weight
- , %para1708 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.9.RDB3.conv3.bias
- , %para1709 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.9.RDB3.conv4.weight
- , %para1710 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.9.RDB3.conv4.bias
- , %para1711 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.9.RDB3.conv5.weight
- , %para1712 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.9.RDB3.conv5.bias
- , %para1713 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.10.RDB1.conv1.weight
- , %para1714 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.10.RDB1.conv1.bias
- , %para1715 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.10.RDB1.conv2.weight
- , %para1716 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.10.RDB1.conv2.bias
- , %para1717 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.10.RDB1.conv3.weight
- , %para1718 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.10.RDB1.conv3.bias
- , %para1719 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.10.RDB1.conv4.weight
- , %para1720 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.10.RDB1.conv4.bias
- , %para1721 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.10.RDB1.conv5.weight
- , %para1722 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.10.RDB1.conv5.bias
- , %para1723 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.10.RDB2.conv1.weight
- , %para1724 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.10.RDB2.conv1.bias
- , %para1725 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.10.RDB2.conv2.weight
- , %para1726 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.10.RDB2.conv2.bias
- , %para1727 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.10.RDB2.conv3.weight
- , %para1728 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.10.RDB2.conv3.bias
- , %para1729 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.10.RDB2.conv4.weight
- , %para1730 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.10.RDB2.conv4.bias
- , %para1731 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.10.RDB2.conv5.weight
- , %para1732 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.10.RDB2.conv5.bias
- , %para1733 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.10.RDB3.conv1.weight
- , %para1734 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.10.RDB3.conv1.bias
- , %para1735 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.10.RDB3.conv2.weight
- , %para1736 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.10.RDB3.conv2.bias
- , %para1737 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.10.RDB3.conv3.weight
- , %para1738 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.10.RDB3.conv3.bias
- , %para1739 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.10.RDB3.conv4.weight
- , %para1740 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.10.RDB3.conv4.bias
- , %para1741 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.10.RDB3.conv5.weight
- , %para1742 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.10.RDB3.conv5.bias
- , %para1743 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.11.RDB1.conv1.weight
- , %para1744 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.11.RDB1.conv1.bias
- , %para1745 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.11.RDB1.conv2.weight
- , %para1746 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.11.RDB1.conv2.bias
- , %para1747 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.11.RDB1.conv3.weight
- , %para1748 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.11.RDB1.conv3.bias
- , %para1749 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.11.RDB1.conv4.weight
- , %para1750 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.11.RDB1.conv4.bias
- , %para1751 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.11.RDB1.conv5.weight
- , %para1752 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.11.RDB1.conv5.bias
- , %para1753 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.11.RDB2.conv1.weight
- , %para1754 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.11.RDB2.conv1.bias
- , %para1755 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.11.RDB2.conv2.weight
- , %para1756 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.11.RDB2.conv2.bias
- , %para1757 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.11.RDB2.conv3.weight
- , %para1758 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.11.RDB2.conv3.bias
- , %para1759 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.11.RDB2.conv4.weight
- , %para1760 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.11.RDB2.conv4.bias
- , %para1761 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.11.RDB2.conv5.weight
- , %para1762 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.11.RDB2.conv5.bias
- , %para1763 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.11.RDB3.conv1.weight
- , %para1764 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.11.RDB3.conv1.bias
- , %para1765 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.11.RDB3.conv2.weight
- , %para1766 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.11.RDB3.conv2.bias
- , %para1767 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.11.RDB3.conv3.weight
- , %para1768 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.11.RDB3.conv3.bias
- , %para1769 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.11.RDB3.conv4.weight
- , %para1770 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.11.RDB3.conv4.bias
- , %para1771 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.11.RDB3.conv5.weight
- , %para1772 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.11.RDB3.conv5.bias
- , %para1773 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.12.RDB1.conv1.weight
- , %para1774 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.12.RDB1.conv1.bias
- , %para1775 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.12.RDB1.conv2.weight
- , %para1776 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.12.RDB1.conv2.bias
- , %para1777 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.12.RDB1.conv3.weight
- , %para1778 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.12.RDB1.conv3.bias
- , %para1779 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.12.RDB1.conv4.weight
- , %para1780 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.12.RDB1.conv4.bias
- , %para1781 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.12.RDB1.conv5.weight
- , %para1782 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.12.RDB1.conv5.bias
- , %para1783 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.12.RDB2.conv1.weight
- , %para1784 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.12.RDB2.conv1.bias
- , %para1785 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.12.RDB2.conv2.weight
- , %para1786 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.12.RDB2.conv2.bias
- , %para1787 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.12.RDB2.conv3.weight
- , %para1788 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.12.RDB2.conv3.bias
- , %para1789 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.12.RDB2.conv4.weight
- , %para1790 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.12.RDB2.conv4.bias
- , %para1791 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.12.RDB2.conv5.weight
- , %para1792 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.12.RDB2.conv5.bias
- , %para1793 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.12.RDB3.conv1.weight
- , %para1794 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.12.RDB3.conv1.bias
- , %para1795 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.12.RDB3.conv2.weight
- , %para1796 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.12.RDB3.conv2.bias
- , %para1797 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.12.RDB3.conv3.weight
- , %para1798 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.12.RDB3.conv3.bias
- , %para1799 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.12.RDB3.conv4.weight
- , %para1800 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.12.RDB3.conv4.bias
- , %para1801 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.12.RDB3.conv5.weight
- , %para1802 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.12.RDB3.conv5.bias
- , %para1803 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.13.RDB1.conv1.weight
- , %para1804 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.13.RDB1.conv1.bias
- , %para1805 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.13.RDB1.conv2.weight
- , %para1806 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.13.RDB1.conv2.bias
- , %para1807 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.13.RDB1.conv3.weight
- , %para1808 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.13.RDB1.conv3.bias
- , %para1809 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.13.RDB1.conv4.weight
- , %para1810 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.13.RDB1.conv4.bias
- , %para1811 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.13.RDB1.conv5.weight
- , %para1812 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.13.RDB1.conv5.bias
- , %para1813 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.13.RDB2.conv1.weight
- , %para1814 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.13.RDB2.conv1.bias
- , %para1815 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.13.RDB2.conv2.weight
- , %para1816 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.13.RDB2.conv2.bias
- , %para1817 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.13.RDB2.conv3.weight
- , %para1818 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.13.RDB2.conv3.bias
- , %para1819 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.13.RDB2.conv4.weight
- , %para1820 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.13.RDB2.conv4.bias
- , %para1821 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.13.RDB2.conv5.weight
- , %para1822 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.13.RDB2.conv5.bias
- , %para1823 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.13.RDB3.conv1.weight
- , %para1824 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.13.RDB3.conv1.bias
- , %para1825 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.13.RDB3.conv2.weight
- , %para1826 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.13.RDB3.conv2.bias
- , %para1827 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.13.RDB3.conv3.weight
- , %para1828 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.13.RDB3.conv3.bias
- , %para1829 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.13.RDB3.conv4.weight
- , %para1830 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.13.RDB3.conv4.bias
- , %para1831 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.13.RDB3.conv5.weight
- , %para1832 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.13.RDB3.conv5.bias
- , %para1833 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.14.RDB1.conv1.weight
- , %para1834 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.14.RDB1.conv1.bias
- , %para1835 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.14.RDB1.conv2.weight
- , %para1836 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.14.RDB1.conv2.bias
- , %para1837 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.14.RDB1.conv3.weight
- , %para1838 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.14.RDB1.conv3.bias
- , %para1839 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.14.RDB1.conv4.weight
- , %para1840 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.14.RDB1.conv4.bias
- , %para1841 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.14.RDB1.conv5.weight
- , %para1842 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.14.RDB1.conv5.bias
- , %para1843 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.14.RDB2.conv1.weight
- , %para1844 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.14.RDB2.conv1.bias
- , %para1845 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.14.RDB2.conv2.weight
- , %para1846 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.14.RDB2.conv2.bias
- , %para1847 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.14.RDB2.conv3.weight
- , %para1848 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.14.RDB2.conv3.bias
- , %para1849 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.14.RDB2.conv4.weight
- , %para1850 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.14.RDB2.conv4.bias
- , %para1851 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.14.RDB2.conv5.weight
- , %para1852 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.14.RDB2.conv5.bias
- , %para1853 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.14.RDB3.conv1.weight
- , %para1854 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.14.RDB3.conv1.bias
- , %para1855 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.14.RDB3.conv2.weight
- , %para1856 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.14.RDB3.conv2.bias
- , %para1857 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.14.RDB3.conv3.weight
- , %para1858 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.14.RDB3.conv3.bias
- , %para1859 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.14.RDB3.conv4.weight
- , %para1860 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.14.RDB3.conv4.bias
- , %para1861 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.14.RDB3.conv5.weight
- , %para1862 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.14.RDB3.conv5.bias
- , %para1863 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.15.RDB1.conv1.weight
- , %para1864 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.15.RDB1.conv1.bias
- , %para1865 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.15.RDB1.conv2.weight
- , %para1866 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.15.RDB1.conv2.bias
- , %para1867 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.15.RDB1.conv3.weight
- , %para1868 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.15.RDB1.conv3.bias
- , %para1869 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.15.RDB1.conv4.weight
- , %para1870 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.15.RDB1.conv4.bias
- , %para1871 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.15.RDB1.conv5.weight
- , %para1872 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.15.RDB1.conv5.bias
- , %para1873 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.15.RDB2.conv1.weight
- , %para1874 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.15.RDB2.conv1.bias
- , %para1875 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.15.RDB2.conv2.weight
- , %para1876 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.15.RDB2.conv2.bias
- , %para1877 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.15.RDB2.conv3.weight
- , %para1878 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.15.RDB2.conv3.bias
- , %para1879 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.15.RDB2.conv4.weight
- , %para1880 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.15.RDB2.conv4.bias
- , %para1881 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.15.RDB2.conv5.weight
- , %para1882 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.15.RDB2.conv5.bias
- , %para1883 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.15.RDB3.conv1.weight
- , %para1884 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.15.RDB3.conv1.bias
- , %para1885 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.15.RDB3.conv2.weight
- , %para1886 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.15.RDB3.conv2.bias
- , %para1887 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.15.RDB3.conv3.weight
- , %para1888 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.15.RDB3.conv3.bias
- , %para1889 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.15.RDB3.conv4.weight
- , %para1890 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.15.RDB3.conv4.bias
- , %para1891 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.15.RDB3.conv5.weight
- , %para1892 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.15.RDB3.conv5.bias
- , %para1893 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.16.RDB1.conv1.weight
- , %para1894 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.16.RDB1.conv1.bias
- , %para1895 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.16.RDB1.conv2.weight
- , %para1896 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.16.RDB1.conv2.bias
- , %para1897 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.16.RDB1.conv3.weight
- , %para1898 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.16.RDB1.conv3.bias
- , %para1899 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.16.RDB1.conv4.weight
- , %para1900 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.16.RDB1.conv4.bias
- , %para1901 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.16.RDB1.conv5.weight
- , %para1902 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.16.RDB1.conv5.bias
- , %para1903 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.16.RDB2.conv1.weight
- , %para1904 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.16.RDB2.conv1.bias
- , %para1905 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.16.RDB2.conv2.weight
- , %para1906 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.16.RDB2.conv2.bias
- , %para1907 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.16.RDB2.conv3.weight
- , %para1908 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.16.RDB2.conv3.bias
- , %para1909 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.16.RDB2.conv4.weight
- , %para1910 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.16.RDB2.conv4.bias
- , %para1911 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.16.RDB2.conv5.weight
- , %para1912 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.16.RDB2.conv5.bias
- , %para1913 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.16.RDB3.conv1.weight
- , %para1914 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.16.RDB3.conv1.bias
- , %para1915 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.16.RDB3.conv2.weight
- , %para1916 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.16.RDB3.conv2.bias
- , %para1917 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.16.RDB3.conv3.weight
- , %para1918 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.16.RDB3.conv3.bias
- , %para1919 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.16.RDB3.conv4.weight
- , %para1920 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.16.RDB3.conv4.bias
- , %para1921 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.16.RDB3.conv5.weight
- , %para1922 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.16.RDB3.conv5.bias
- , %para1923 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.17.RDB1.conv1.weight
- , %para1924 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.17.RDB1.conv1.bias
- , %para1925 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.17.RDB1.conv2.weight
- , %para1926 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.17.RDB1.conv2.bias
- , %para1927 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.17.RDB1.conv3.weight
- , %para1928 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.17.RDB1.conv3.bias
- , %para1929 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.17.RDB1.conv4.weight
- , %para1930 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.17.RDB1.conv4.bias
- , %para1931 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.17.RDB1.conv5.weight
- , %para1932 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.17.RDB1.conv5.bias
- , %para1933 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.17.RDB2.conv1.weight
- , %para1934 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.17.RDB2.conv1.bias
- , %para1935 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.17.RDB2.conv2.weight
- , %para1936 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.17.RDB2.conv2.bias
- , %para1937 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.17.RDB2.conv3.weight
- , %para1938 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.17.RDB2.conv3.bias
- , %para1939 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.17.RDB2.conv4.weight
- , %para1940 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.17.RDB2.conv4.bias
- , %para1941 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.17.RDB2.conv5.weight
- , %para1942 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.17.RDB2.conv5.bias
- , %para1943 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.17.RDB3.conv1.weight
- , %para1944 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.17.RDB3.conv1.bias
- , %para1945 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.17.RDB3.conv2.weight
- , %para1946 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.17.RDB3.conv2.bias
- , %para1947 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.17.RDB3.conv3.weight
- , %para1948 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.17.RDB3.conv3.bias
- , %para1949 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.17.RDB3.conv4.weight
- , %para1950 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.17.RDB3.conv4.bias
- , %para1951 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.17.RDB3.conv5.weight
- , %para1952 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.17.RDB3.conv5.bias
- , %para1953 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.18.RDB1.conv1.weight
- , %para1954 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.18.RDB1.conv1.bias
- , %para1955 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.18.RDB1.conv2.weight
- , %para1956 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.18.RDB1.conv2.bias
- , %para1957 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.18.RDB1.conv3.weight
- , %para1958 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.18.RDB1.conv3.bias
- , %para1959 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.18.RDB1.conv4.weight
- , %para1960 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.18.RDB1.conv4.bias
- , %para1961 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.18.RDB1.conv5.weight
- , %para1962 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.18.RDB1.conv5.bias
- , %para1963 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.18.RDB2.conv1.weight
- , %para1964 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.18.RDB2.conv1.bias
- , %para1965 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.18.RDB2.conv2.weight
- , %para1966 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.18.RDB2.conv2.bias
- , %para1967 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.18.RDB2.conv3.weight
- , %para1968 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.18.RDB2.conv3.bias
- , %para1969 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.18.RDB2.conv4.weight
- , %para1970 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.18.RDB2.conv4.bias
- , %para1971 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.18.RDB2.conv5.weight
- , %para1972 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.18.RDB2.conv5.bias
- , %para1973 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.18.RDB3.conv1.weight
- , %para1974 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.18.RDB3.conv1.bias
- , %para1975 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.18.RDB3.conv2.weight
- , %para1976 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.18.RDB3.conv2.bias
- , %para1977 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.18.RDB3.conv3.weight
- , %para1978 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.18.RDB3.conv3.bias
- , %para1979 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.18.RDB3.conv4.weight
- , %para1980 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.18.RDB3.conv4.bias
- , %para1981 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.18.RDB3.conv5.weight
- , %para1982 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.18.RDB3.conv5.bias
- , %para1983 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.19.RDB1.conv1.weight
- , %para1984 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.19.RDB1.conv1.bias
- , %para1985 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.19.RDB1.conv2.weight
- , %para1986 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.19.RDB1.conv2.bias
- , %para1987 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.19.RDB1.conv3.weight
- , %para1988 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.19.RDB1.conv3.bias
- , %para1989 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.19.RDB1.conv4.weight
- , %para1990 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.19.RDB1.conv4.bias
- , %para1991 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.19.RDB1.conv5.weight
- , %para1992 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.19.RDB1.conv5.bias
- , %para1993 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.19.RDB2.conv1.weight
- , %para1994 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.19.RDB2.conv1.bias
- , %para1995 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.19.RDB2.conv2.weight
- , %para1996 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.19.RDB2.conv2.bias
- , %para1997 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.19.RDB2.conv3.weight
- , %para1998 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.19.RDB2.conv3.bias
- , %para1999 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.19.RDB2.conv4.weight
- , %para2000 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.19.RDB2.conv4.bias
- , %para2001 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.19.RDB2.conv5.weight
- , %para2002 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.19.RDB2.conv5.bias
- , %para2003 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.19.RDB3.conv1.weight
- , %para2004 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.19.RDB3.conv1.bias
- , %para2005 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.19.RDB3.conv2.weight
- , %para2006 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.19.RDB3.conv2.bias
- , %para2007 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.19.RDB3.conv3.weight
- , %para2008 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.19.RDB3.conv3.bias
- , %para2009 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.19.RDB3.conv4.weight
- , %para2010 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.19.RDB3.conv4.bias
- , %para2011 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.19.RDB3.conv5.weight
- , %para2012 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.19.RDB3.conv5.bias
- , %para2013 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.20.RDB1.conv1.weight
- , %para2014 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.20.RDB1.conv1.bias
- , %para2015 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.20.RDB1.conv2.weight
- , %para2016 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.20.RDB1.conv2.bias
- , %para2017 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.20.RDB1.conv3.weight
- , %para2018 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.20.RDB1.conv3.bias
- , %para2019 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.20.RDB1.conv4.weight
- , %para2020 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.20.RDB1.conv4.bias
- , %para2021 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.20.RDB1.conv5.weight
- , %para2022 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.20.RDB1.conv5.bias
- , %para2023 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.20.RDB2.conv1.weight
- , %para2024 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.20.RDB2.conv1.bias
- , %para2025 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.20.RDB2.conv2.weight
- , %para2026 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.20.RDB2.conv2.bias
- , %para2027 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.20.RDB2.conv3.weight
- , %para2028 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.20.RDB2.conv3.bias
- , %para2029 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.20.RDB2.conv4.weight
- , %para2030 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.20.RDB2.conv4.bias
- , %para2031 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.20.RDB2.conv5.weight
- , %para2032 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.20.RDB2.conv5.bias
- , %para2033 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.20.RDB3.conv1.weight
- , %para2034 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.20.RDB3.conv1.bias
- , %para2035 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.20.RDB3.conv2.weight
- , %para2036 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.20.RDB3.conv2.bias
- , %para2037 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.20.RDB3.conv3.weight
- , %para2038 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.20.RDB3.conv3.bias
- , %para2039 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.20.RDB3.conv4.weight
- , %para2040 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.20.RDB3.conv4.bias
- , %para2041 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.20.RDB3.conv5.weight
- , %para2042 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.20.RDB3.conv5.bias
- , %para2043 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.21.RDB1.conv1.weight
- , %para2044 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.21.RDB1.conv1.bias
- , %para2045 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.21.RDB1.conv2.weight
- , %para2046 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.21.RDB1.conv2.bias
- , %para2047 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.21.RDB1.conv3.weight
- , %para2048 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.21.RDB1.conv3.bias
- , %para2049 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.21.RDB1.conv4.weight
- , %para2050 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.21.RDB1.conv4.bias
- , %para2051 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.21.RDB1.conv5.weight
- , %para2052 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.21.RDB1.conv5.bias
- , %para2053 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.21.RDB2.conv1.weight
- , %para2054 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.21.RDB2.conv1.bias
- , %para2055 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.21.RDB2.conv2.weight
- , %para2056 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.21.RDB2.conv2.bias
- , %para2057 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.21.RDB2.conv3.weight
- , %para2058 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.21.RDB2.conv3.bias
- , %para2059 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.21.RDB2.conv4.weight
- , %para2060 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.21.RDB2.conv4.bias
- , %para2061 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.21.RDB2.conv5.weight
- , %para2062 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.21.RDB2.conv5.bias
- , %para2063 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.21.RDB3.conv1.weight
- , %para2064 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.21.RDB3.conv1.bias
- , %para2065 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.21.RDB3.conv2.weight
- , %para2066 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.21.RDB3.conv2.bias
- , %para2067 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.21.RDB3.conv3.weight
- , %para2068 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.21.RDB3.conv3.bias
- , %para2069 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.21.RDB3.conv4.weight
- , %para2070 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.21.RDB3.conv4.bias
- , %para2071 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.21.RDB3.conv5.weight
- , %para2072 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.21.RDB3.conv5.bias
- , %para2073 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.22.RDB1.conv1.weight
- , %para2074 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.22.RDB1.conv1.bias
- , %para2075 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.22.RDB1.conv2.weight
- , %para2076 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.22.RDB1.conv2.bias
- , %para2077 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.22.RDB1.conv3.weight
- , %para2078 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.22.RDB1.conv3.bias
- , %para2079 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.22.RDB1.conv4.weight
- , %para2080 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.22.RDB1.conv4.bias
- , %para2081 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.22.RDB1.conv5.weight
- , %para2082 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.22.RDB1.conv5.bias
- , %para2083 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.22.RDB2.conv1.weight
- , %para2084 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.22.RDB2.conv1.bias
- , %para2085 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.22.RDB2.conv2.weight
- , %para2086 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.22.RDB2.conv2.bias
- , %para2087 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.22.RDB2.conv3.weight
- , %para2088 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.22.RDB2.conv3.bias
- , %para2089 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.22.RDB2.conv4.weight
- , %para2090 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.22.RDB2.conv4.bias
- , %para2091 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.22.RDB2.conv5.weight
- , %para2092 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.22.RDB2.conv5.bias
- , %para2093 : Ref[Tensor(F32)][32, 64, 3, 3] # optimizer.moment2.G.RRDB_trunk.22.RDB3.conv1.weight
- , %para2094 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.22.RDB3.conv1.bias
- , %para2095 : Ref[Tensor(F32)][32, 96, 3, 3] # optimizer.moment2.G.RRDB_trunk.22.RDB3.conv2.weight
- , %para2096 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.22.RDB3.conv2.bias
- , %para2097 : Ref[Tensor(F32)][32, 128, 3, 3] # optimizer.moment2.G.RRDB_trunk.22.RDB3.conv3.weight
- , %para2098 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.22.RDB3.conv3.bias
- , %para2099 : Ref[Tensor(F32)][32, 160, 3, 3] # optimizer.moment2.G.RRDB_trunk.22.RDB3.conv4.weight
- , %para2100 : Ref[Tensor(F32)][32] # optimizer.moment2.G.RRDB_trunk.22.RDB3.conv4.bias
- , %para2101 : Ref[Tensor(F32)][64, 192, 3, 3] # optimizer.moment2.G.RRDB_trunk.22.RDB3.conv5.weight
- , %para2102 : Ref[Tensor(F32)][64] # optimizer.moment2.G.RRDB_trunk.22.RDB3.conv5.bias
- , %para2103 : Ref[Tensor(F32)][64, 64, 3, 3] # optimizer.moment2.G.trunk_conv.weight
- , %para2104 : Ref[Tensor(F32)][64] # optimizer.moment2.G.trunk_conv.bias
- , %para2105 : Ref[Tensor(F32)][64, 64, 3, 3] # optimizer.moment2.G.upconv1.weight
- , %para2106 : Ref[Tensor(F32)][64] # optimizer.moment2.G.upconv1.bias
- , %para2107 : Ref[Tensor(F32)][64, 64, 3, 3] # optimizer.moment2.G.upconv2.weight
- , %para2108 : Ref[Tensor(F32)][64] # optimizer.moment2.G.upconv2.bias
- , %para2109 : Ref[Tensor(F32)][64, 64, 3, 3] # optimizer.moment2.G.HRconv.weight
- , %para2110 : Ref[Tensor(F32)][64] # optimizer.moment2.G.HRconv.bias
- , %para2111 : Ref[Tensor(F32)][3, 64, 3, 3] # optimizer.moment2.G.conv_last.weight
- , %para2112 : Ref[Tensor(F32)][3] # optimizer.moment2.G.conv_last.bias
- , %para2113 : Ref[Tensor(F32)][300000] # optimizer.learning_rate.learning_rate
- , %para2114 : Ref[Tensor(I32)][1] # optimizer.global_step
- , %para2115 : Ref[Tensor(F32)][64, 3, 3, 3] # network.network.perception_criterion.loss_network.0.weight
- , %para2116 : Ref[Tensor(F32)][64, 64, 3, 3] # network.network.perception_criterion.loss_network.2.weight
- , %para2117 : Ref[Tensor(F32)][128, 64, 3, 3] # network.network.perception_criterion.loss_network.5.weight
- , %para2118 : Ref[Tensor(F32)][128, 128, 3, 3] # network.network.perception_criterion.loss_network.7.weight
- , %para2119 : Ref[Tensor(F32)][256, 128, 3, 3] # network.network.perception_criterion.loss_network.10.weight
- , %para2120 : Ref[Tensor(F32)][256, 256, 3, 3] # network.network.perception_criterion.loss_network.12.weight
- , %para2121 : Ref[Tensor(F32)][256, 256, 3, 3] # network.network.perception_criterion.loss_network.14.weight
- , %para2122 : Ref[Tensor(F32)][256, 256, 3, 3] # network.network.perception_criterion.loss_network.16.weight
- , %para2123 : Ref[Tensor(F32)][512, 256, 3, 3] # network.network.perception_criterion.loss_network.19.weight
- , %para2124 : Ref[Tensor(F32)][512, 512, 3, 3] # network.network.perception_criterion.loss_network.21.weight
- , %para2125 : Ref[Tensor(F32)][512, 512, 3, 3] # network.network.perception_criterion.loss_network.23.weight
- , %para2126 : Ref[Tensor(F32)][512, 512, 3, 3] # network.network.perception_criterion.loss_network.25.weight
- , %para2127 : Ref[Tensor(F32)][512, 512, 3, 3] # network.network.perception_criterion.loss_network.28.weight
- , %para2128 : Ref[Tensor(F32)][512, 512, 3, 3] # network.network.perception_criterion.loss_network.30.weight
- , %para2129 : Ref[Tensor(F32)][512, 512, 3, 3] # network.network.perception_criterion.loss_network.32.weight
- , %para2130 : Ref[Tensor(F32)][512, 512, 3, 3] # network.network.perception_criterion.loss_network.34.weight
- , %para2131 : Ref[Tensor(F32)][1] # network.network.D.linear2.bias
- , %para2132 : Ref[Tensor(F32)][1, 100] # network.network.D.linear2.weight
- , %para2133 : Ref[Tensor(F32)][512, 512, 4, 4] # network.network.D.conv3_1.weight
- , %para2134 : Ref[Tensor(F32)][100] # network.network.D.linear1.bias
- , %para2135 : Ref[Tensor(F32)][100, 32768] # network.network.D.linear1.weight
- , %para2136 : Ref[Tensor(F32)][512, 256, 3, 3] # network.network.D.conv3_0.weight
- , %para2137 : Ref[Tensor(F32)][512] # network.network.D.bn3_1.gamma
- , %para2138 : Ref[Tensor(F32)][512] # network.network.D.bn3_1.beta
- , %para2139 : Ref[Tensor(F32)][512] # network.network.D.bn3_1.moving_mean
- , %para2140 : Ref[Tensor(F32)][512] # network.network.D.bn3_1.moving_variance
- , %para2141 : Ref[Tensor(F32)][256, 256, 4, 4] # network.network.D.conv2_1.weight
- , %para2142 : Ref[Tensor(F32)][512] # network.network.D.bn3_0.gamma
- , %para2143 : Ref[Tensor(F32)][512] # network.network.D.bn3_0.beta
- , %para2144 : Ref[Tensor(F32)][512] # network.network.D.bn3_0.moving_mean
- , %para2145 : Ref[Tensor(F32)][512] # network.network.D.bn3_0.moving_variance
- , %para2146 : Ref[Tensor(F32)][256, 128, 3, 3] # network.network.D.conv2_0.weight
- , %para2147 : Ref[Tensor(F32)][256] # network.network.D.bn2_1.gamma
- , %para2148 : Ref[Tensor(F32)][256] # network.network.D.bn2_1.beta
- , %para2149 : Ref[Tensor(F32)][256] # network.network.D.bn2_1.moving_mean
- , %para2150 : Ref[Tensor(F32)][256] # network.network.D.bn2_1.moving_variance
- , %para2151 : Ref[Tensor(F32)][128, 128, 4, 4] # network.network.D.conv1_1.weight
- , %para2152 : Ref[Tensor(F32)][256] # network.network.D.bn2_0.gamma
- , %para2153 : Ref[Tensor(F32)][256] # network.network.D.bn2_0.beta
- , %para2154 : Ref[Tensor(F32)][256] # network.network.D.bn2_0.moving_mean
- , %para2155 : Ref[Tensor(F32)][256] # network.network.D.bn2_0.moving_variance
- , %para2156 : Ref[Tensor(F32)][128, 64, 3, 3] # network.network.D.conv1_0.weight
- , %para2157 : Ref[Tensor(F32)][128] # network.network.D.bn1_1.gamma
- , %para2158 : Ref[Tensor(F32)][128] # network.network.D.bn1_1.beta
- , %para2159 : Ref[Tensor(F32)][128] # network.network.D.bn1_1.moving_mean
- , %para2160 : Ref[Tensor(F32)][128] # network.network.D.bn1_1.moving_variance
- , %para2161 : Ref[Tensor(F32)][64, 64, 4, 4] # network.network.D.conv0_1.weight
- , %para2162 : Ref[Tensor(F32)][128] # network.network.D.bn1_0.gamma
- , %para2163 : Ref[Tensor(F32)][128] # network.network.D.bn1_0.beta
- , %para2164 : Ref[Tensor(F32)][128] # network.network.D.bn1_0.moving_mean
- , %para2165 : Ref[Tensor(F32)][128] # network.network.D.bn1_0.moving_variance
- , %para2166 : Ref[Tensor(F32)][64] # network.network.D.conv0_0.bias
- , %para2167 : Ref[Tensor(F32)][64, 3, 3, 3] # network.network.D.conv0_0.weight
- , %para2168 : Ref[Tensor(F32)][64] # network.network.D.bn0_1.gamma
- , %para2169 : Ref[Tensor(F32)][64] # network.network.D.bn0_1.beta
- , %para2170 : Ref[Tensor(F32)][64] # network.network.D.bn0_1.moving_mean
- , %para2171 : Ref[Tensor(F32)][64] # network.network.D.bn0_1.moving_variance
- ) {
-
- #------------------------> 0
- %1 = FuncGraph::fg_1(%para1, %para2, %para3, %para4) #(Tensor(F64)[16, 3, 32, 32], Tensor(F64)[16, 3, 128, 128], Tensor(F32)[16, 1], Tensor(F32)[16, 1]) # fg_1=construct.1(@ctx.addr=0xaaaaf07302f0) #scope: Default @ctx.addr=0xaaaaf07302f0
- #
- Primitive::Return{prim_type=1}(%1) #(Undefined) #scope: Default
- #
- }
-
-
- # [No.2] construct.1 @ctx.addr=0xaaaaf07302f0
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(119)/ def construct(self, lr, hr, fake_labels, real_labels):/
- funcgraph fg_1[fg_0](
- %para2172 : Tensor(F64)[16, 3, 32, 32] # lr
- , %para2173 : Tensor(F64)[16, 3, 128, 128] # hr
- , %para2174 : Tensor(F32)[16, 1] # fake_labels
- , %para2175 : Tensor(F32)[16, 1] # real_labels
- ) {
-
- #------------------------> 1
- %1 = FuncGraph::fg_2(%para2172, %para2173, %para2174, %para2175) #(Tensor(F64)[16, 3, 32, 32], Tensor(F64)[16, 3, 128, 128], Tensor(F32)[16, 1], Tensor(F32)[16, 1]) # fg_2=construct.2(@ctx.addr=0xaaaaf426c130) #scope: Default @ctx.addr=0xaaaaf426c130
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(121)/ hr_fake,generator_loss,content_loss,perception_loss,adversarial_loss = self.G(lr, hr, fake_labels, real_labels)/
- %2 = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(4)) #(Undefined, Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(121)/ hr_fake,generator_loss,content_loss,perception_loss,adversarial_loss = self.G(lr, hr, fake_labels, real_labels)/
- %3 = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(2)) #(Undefined, Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(121)/ hr_fake,generator_loss,content_loss,perception_loss,adversarial_loss = self.G(lr, hr, fake_labels, real_labels)/
- %4 = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(3)) #(Undefined, Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(121)/ hr_fake,generator_loss,content_loss,perception_loss,adversarial_loss = self.G(lr, hr, fake_labels, real_labels)/
- %5 = Primitive::MakeTuple{prim_type=1}(%2, %3, %4) #(Undefined, Undefined, Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(119)/ def construct(self, lr, hr, fake_labels, real_labels):/
- %6 = Primitive::stop_gradient{prim_type=1}(%5) #(Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(119)/ def construct(self, lr, hr, fake_labels, real_labels):/
- %7 = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Undefined, Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(121)/ hr_fake,generator_loss,content_loss,perception_loss,adversarial_loss = self.G(lr, hr, fake_labels, real_labels)/
- %8 = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Undefined, Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(121)/ hr_fake,generator_loss,content_loss,perception_loss,adversarial_loss = self.G(lr, hr, fake_labels, real_labels)/
- %9 = ClassType() #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(122)/ sens = P.Fill()(P.DType()(generator_loss), P.Shape()(generator_loss), self.sens)/
- %10 = ClassType() #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(122)/ sens = P.Fill()(P.DType()(generator_loss), P.Shape()(generator_loss), self.sens)/
- %11 = %10(%8) #(Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(122)/ sens = P.Fill()(P.DType()(generator_loss), P.Shape()(generator_loss), self.sens)/
- %12 = ClassType() #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(122)/ sens = P.Fill()(P.DType()(generator_loss), P.Shape()(generator_loss), self.sens)/
- %13 = %12(%8) #(Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(122)/ sens = P.Fill()(P.DType()(generator_loss), P.Shape()(generator_loss), self.sens)/
- %14 = %9(%11, %13, F32(1)) #(Undefined, Undefined, Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(122)/ sens = P.Fill()(P.DType()(generator_loss), P.Shape()(generator_loss), self.sens)/
- %15 = UnpackGraphPrimitive::UnpackGraph{prim_type=1}(FuncGraph::fg_3, %para2172, %para2173, %para2174, %para2175, %14) #(Undefined, Tensor(F64)[16, 3, 32, 32], Tensor(F64)[16, 3, 128, 128], Tensor(F32)[16, 1], Tensor(F32)[16, 1], Undefined) # fg_3=construct.3 #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(123)/ grads = self.grad(self.network, weights)(lr, hr, fake_labels, real_labels, sens)/
- %16 = Primitive::MakeTuple{prim_type=1}(%para5, %para6, %para7, %para8, %para9, %para10, %para11, %para12, %para13, %para14, %para15, %para16, %para17, %para18, %para19, %para20, %para21, %para22, %para23, %para24, %para25, %para26, %para27, %para28, %para29, %para30, %para31, %para32, %para33, %para34, %para35, %para36, %para37, %para38, %para39, %para40, %para41, %para42, %para43, %para44, %para45, %para46, %para47, %para48, %para49, %para50, %para51, %para52, %para53, %para54, %para55, %para56, %para57, %para58, %para59, %para60, %para61, %para62, %para63, %para64, %para65, %para66, %para67, %para68, %para69, %para70, %para71, %para72, %para73, %para74, %para75, %para76, %para77, %para78, %para79, %para80, %para81, %para82, %para83, %para84, %para85, %para86, %para87, %para88, %para89, %para90, %para91, %para92, %para93, %para94, %para95, %para96, %para97, %para98, %para99, %para100, %para101, %para102, %para103, %para104, %para105, %para106, %para107, %para108, %para109, %para110, %para111, %para112, %para113, %para114, %para115, %para116, %para117, %para118, %para119, %para120, %para121, %para122, %para123, %para124, %para125, %para126, %para127, %para128, %para129, %para130, %para131, %para132, %para133, %para134, %para135, %para136, %para137, %para138, %para139, %para140, %para141, %para142, %para143, %para144, %para145, %para146, %para147, %para148, %para149, %para150, %para151, %para152, %para153, %para154, %para155, %para156, %para157, %para158, %para159, %para160, %para161, %para162, %para163, %para164, %para165, %para166, %para167, %para168, %para169, %para170, %para171, %para172, %para173, %para174, %para175, %para176, %para177, %para178, %para179, %para180, %para181, %para182, %para183, %para184, %para185, %para186, %para187, %para188, %para189, %para190, %para191, %para192, %para193, %para194, %para195, %para196, %para197, %para198, %para199, %para200, %para201, %para202, %para203, %para204, %para205, %para206, %para207, %para208, %para209, %para210, %para211, %para212, %para213, %para214, %para215, %para216, %para217, %para218, %para219, %para220, %para221, %para222, %para223, %para224, %para225, %para226, %para227, %para228, %para229, %para230, %para231, %para232, %para233, %para234, %para235, %para236, %para237, %para238, %para239, %para240, %para241, %para242, %para243, %para244, %para245, %para246, %para247, %para248, %para249, %para250, %para251, %para252, %para253, %para254, %para255, %para256, %para257, %para258, %para259, %para260, %para261, %para262, %para263, %para264, %para265, %para266, %para267, %para268, %para269, %para270, %para271, %para272, %para273, %para274, %para275, %para276, %para277, %para278, %para279, %para280, %para281, %para282, %para283, %para284, %para285, %para286, %para287, %para288, %para289, %para290, %para291, %para292, %para293, %para294, %para295, %para296, %para297, %para298, %para299, %para300, %para301, %para302, %para303, %para304, %para305, %para306, %para307, %para308, %para309, %para310, %para311, %para312, %para313, %para314, %para315, %para316, %para317, %para318, %para319, %para320, %para321, %para322, %para323, %para324, %para325, %para326, %para327, %para328, %para329, %para330, %para331, %para332, %para333, %para334, %para335, %para336, %para337, %para338, %para339, %para340, %para341, %para342, %para343, %para344, %para345, %para346, %para347, %para348, %para349, %para350, %para351, %para352, %para353, %para354, %para355, %para356, %para357, %para358, %para359, %para360, %para361, %para362, %para363, %para364, %para365, %para366, %para367, %para368, %para369, %para370, %para371, %para372, %para373, %para374, %para375, %para376, %para377, %para378, %para379, %para380, %para381, %para382, %para383, %para384, %para385, %para386, %para387, %para388, %para389, %para390, %para391, %para392, %para393, %para394, %para395, %para396, %para397, %para398, %para399, %para400, %para401, %para402, %para403, %para404, %para405, %para406, %para407, %para408, %para409, %para410, %para411, %para412, %para413, %para414, %para415, %para416, %para417, %para418, %para419, %para420, %para421, %para422, %para423, %para424, %para425, %para426, %para427, %para428, %para429, %para430, %para431, %para432, %para433, %para434, %para435, %para436, %para437, %para438, %para439, %para440, %para441, %para442, %para443, %para444, %para445, %para446, %para447, %para448, %para449, %para450, %para451, %para452, %para453, %para454, %para455, %para456, %para457, %para458, %para459, %para460, %para461, %para462, %para463, %para464, %para465, %para466, %para467, %para468, %para469, %para470, %para471, %para472, %para473, %para474, %para475, %para476, %para477, %para478, %para479, %para480, %para481, %para482, %para483, %para484, %para485, %para486, %para487, %para488, %para489, %para490, %para491, %para492, %para493, %para494, %para495, %para496, %para497, %para498, %para499, %para500, %para501, %para502, %para503, %para504, %para505, %para506, %para507, %para508, %para509, %para510, %para511, %para512, %para513, %para514, %para515, %para516, %para517, %para518, %para519, %para520, %para521, %para522, %para523, %para524, %para525, %para526, %para527, %para528, %para529, %para530, %para531, %para532, %para533, %para534, %para535, %para536, %para537, %para538, %para539, %para540, %para541, %para542, %para543, %para544, %para545, %para546, %para547, %para548, %para549, %para550, %para551, %para552, %para553, %para554, %para555, %para556, %para557, %para558, %para559, %para560, %para561, %para562, %para563, %para564, %para565, %para566, %para567, %para568, %para569, %para570, %para571, %para572, %para573, %para574, %para575, %para576, %para577, %para578, %para579, %para580, %para581, %para582, %para583, %para584, %para585, %para586, %para587, %para588, %para589, %para590, %para591, %para592, %para593, %para594, %para595, %para596, %para597, %para598, %para599, %para600, %para601, %para602, %para603, %para604, %para605, %para606, %para607, %para608, %para609, %para610, %para611, %para612, %para613, %para614, %para615, %para616, %para617, %para618, %para619, %para620, %para621, %para622, %para623, %para624, %para625, %para626, %para627, %para628, %para629, %para630, %para631, %para632, %para633, %para634, %para635, %para636, %para637, %para638, %para639, %para640, %para641, %para642, %para643, %para644, %para645, %para646, %para647, %para648, %para649, %para650, %para651, %para652, %para653, %para654, %para655, %para656, %para657, %para658, %para659, %para660, %para661, %para662, %para663, %para664, %para665, %para666, %para667, %para668, %para669, %para670, %para671, %para672, %para673, %para674, %para675, %para676, %para677, %para678, %para679, %para680, %para681, %para682, %para683, %para684, %para685, %para686, %para687, %para688, %para689, %para690, %para691, %para692, %para693, %para694, %para695, %para696, %para697, %para698, %para699, %para700, %para701, %para702, %para703, %para704, %para705, %para706) #(Ref[Tensor(F32)][64, 3, 3, 3], Ref[Tensor(F32)][64], Ref[Tensor(F32)][32, 64, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][64, 192, 3, 3], Ref[Tensor(F32)][64], Ref[Tensor(F32)][32, 64, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][64, 192, 3, 3], Ref[Tensor(F32)][64], Ref[Tensor(F32)][32, 64, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][64, 192, 3, 3], Ref[Tensor(F32)][64], Ref[Tensor(F32)][32, 64, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3], 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Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][64, 192, 3, 3], Ref[Tensor(F32)][64], Ref[Tensor(F32)][32, 64, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][64, 192, 3, 3], Ref[Tensor(F32)][64], Ref[Tensor(F32)][32, 64, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][64, 192, 3, 3], Ref[Tensor(F32)][64], Ref[Tensor(F32)][32, 64, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][64, 192, 3, 3], Ref[Tensor(F32)][64], Ref[Tensor(F32)][32, 64, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][64, 192, 3, 3], Ref[Tensor(F32)][64], Ref[Tensor(F32)][32, 64, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][64, 192, 3, 3], Ref[Tensor(F32)][64], Ref[Tensor(F32)][32, 64, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][64, 192, 3, 3], Ref[Tensor(F32)][64], Ref[Tensor(F32)][32, 64, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3], Ref[Tensor(F32)][32], Ref[Tensor(F32)][64, 192, 3, 3], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 64, 3, 3], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 64, 3, 3], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 64, 3, 3], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 64, 3, 3], Ref[Tensor(F32)][64], Ref[Tensor(F32)][3, 64, 3, 3], Ref[Tensor(F32)][3]) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(120)/ weights = self.weights/
- %17 = DoSignaturePrimitive::S-Prim-grad{prim_type=1}(%15, %16) #(Undefined, Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(123)/ grads = self.grad(self.network, weights)(lr, hr, fake_labels, real_labels, sens)/
- %18 = %17(%para2172, %para2173, %para2174, %para2175, %14) #(Tensor(F64)[16, 3, 32, 32], Tensor(F64)[16, 3, 128, 128], Tensor(F32)[16, 1], Tensor(F32)[16, 1], Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(123)/ grads = self.grad(self.network, weights)(lr, hr, fake_labels, real_labels, sens)/
- %19 = DoSignaturePrimitive::S-Prim-identity{prim_type=1}[side_effect_propagate=I64(1)](%18) #(Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(124)/ grads = self.grad_reducer(grads)/
- %20 = FuncGraph::fg_4(%19) #(Undefined) # fg_4=construct.4 #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(126)/ return hr_fake,F.depend(generator_loss, self.optimizer(grads))/
- %21 = DoSignaturePrimitive::S-Prim-Depend{prim_type=1}[side_effect_propagate=I64(1)](%8, %20) #(Undefined, Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(126)/ return hr_fake,F.depend(generator_loss, self.optimizer(grads))/
- %22 = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%7, %21) #(Undefined, Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(126)/ return hr_fake,F.depend(generator_loss, self.optimizer(grads))/
- %23 = Primitive::Depend{prim_type=1}[side_effect_propagate=I64(1)](%22, %6) #(Undefined, Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(119)/ def construct(self, lr, hr, fake_labels, real_labels):/
- Primitive::Return{prim_type=1}(%23) #(Undefined) #scope: Default
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(119)/ def construct(self, lr, hr, fake_labels, real_labels):/
- }
-
-
- # [No.3] construct.2 @ctx.addr=0xaaaaf426c130
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(54)/ def construct(self, lr, hr, fake_labels, real_labels):/
- funcgraph fg_2[fg_0](
- %para2176 : Tensor(F64)[16, 3, 32, 32] # lr
- , %para2177 : Tensor(F64)[16, 3, 128, 128] # hr
- , %para2178 : Tensor(F32)[16, 1] # fake_labels
- , %para2179 : Tensor(F32)[16, 1] # real_labels
- ) {
- %1 : Tensor(F32)[16, 3, 128, 128] = FuncGraph::fg_5(%para2176) #(Tensor(F64)[16, 3, 32, 32]) # fg_5=construct.5(@ctx.addr=0xaaaaf43b35d0) #scope: Default/G-GeneratorLossCell @ctx.addr=0xaaaaf43b35d0
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(55)/ fake_hr = self.G(lr)/
- %2 : Tensor(F32)[16, 1] = FuncGraph::fg_6(%1) #(Tensor(F32)[16, 3, 128, 128]) # fg_6=construct.6(@ctx.addr=0xaaaae3a21b90) #scope: Default/G-GeneratorLossCell @ctx.addr=0xaaaae3a21b90
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(57)/ score_fake = self.D(fake_hr)/
-
- #------------------------> 2
- %3 = FuncGraph::fg_6(%para2177) #(Tensor(F64)[16, 3, 128, 128]) # fg_6=construct.6(@ctx.addr=0xaaaae3bd8820) #scope: Default/G-GeneratorLossCell @ctx.addr=0xaaaae3bd8820
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(56)/ score_real = self.D(hr)/
- %4 = DoSignaturePrimitive::S-Prim-ReduceMean{prim_type=1}[keep_dims=Bool(1), input_names=["input_x", "axis"], output_names=["y"]](%3) #(Undefined) #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(59)/ discriminator_fr = score_fake - self.mean(score_real)/
- %5 = DoSignaturePrimitive::S-Prim-sub{prim_type=1}(%2, %4) #(Tensor(F32)[16, 1], Undefined) #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(59)/ discriminator_fr = score_fake - self.mean(score_real)/
- %6 = FuncGraph::fg_7(%5, %para2179) #(Undefined, Tensor(F32)[16, 1]) # fg_7=construct.7 #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(62)/ adversarial_loss_fr = self.adversarial_criterion(/
- %7 = DoSignaturePrimitive::S-Prim-ReduceMean{prim_type=1}[keep_dims=Bool(1), input_names=["input_x", "axis"], output_names=["y"]](%2) #(Tensor(F32)[16, 1]) #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(58)/ discriminator_rf = score_real - self.mean(score_fake)/
- %8 = DoSignaturePrimitive::S-Prim-sub{prim_type=1}(%3, %7) #(Undefined, Undefined) #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(58)/ discriminator_rf = score_real - self.mean(score_fake)/
- %9 = FuncGraph::fg_7(%8, %para2178) #(Undefined, Tensor(F32)[16, 1]) # fg_7=construct.7 #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(60)/ adversarial_loss_rf = self.adversarial_criterion(/
- %10 = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%6, %9) #(Undefined, Undefined) #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(64)/ adversarial_loss = (adversarial_loss_fr + adversarial_loss_rf) / 2/
- %11 = DoSignaturePrimitive::S-Prim-div{prim_type=1}(%10, I64(2)) #(Undefined, Undefined) #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(64)/ adversarial_loss = (adversarial_loss_fr + adversarial_loss_rf) / 2/
- %12 = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(F32(0.005), %11) #(Undefined, Undefined) #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(68)/ 5e-3 * adversarial_loss/
- %13 = FuncGraph::fg_8(%para2177, %1) #(Tensor(F64)[16, 3, 128, 128], Tensor(F32)[16, 3, 128, 128]) # fg_8=construct.8 #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(65)/ perceptual_loss = self.perception_criterion(hr, fake_hr)/
- %14 = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(F32(1), %13) #(Undefined, Undefined) #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(69)/ + 1.0 * perceptual_loss/
- %15 = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%12, %14) #(Undefined, Undefined) #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(68)/ 5e-3 * adversarial_loss/
- %16 = FuncGraph::fg_9(%para2177, %1) #(Tensor(F64)[16, 3, 128, 128], Tensor(F32)[16, 3, 128, 128]) # fg_9=construct.9 #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(66)/ content_loss = self.content_criterion(hr, fake_hr)/
- %17 = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(F32(0.1), %16) #(Undefined, Undefined) #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(70)/ + 1e-1 * content_loss/
- %18 = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%15, %17) #(Undefined, Undefined) #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(68)/ 5e-3 * adversarial_loss/
- %19 = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%1, %18, %16, %13, %11) #(Tensor(F32)[16, 3, 128, 128], Undefined, Undefined, Undefined, Undefined) #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(72)/ return (fake_hr, generator_loss, content_loss, perceptual_loss, adversarial_loss)/
- Primitive::Return{prim_type=1}(%19) #(Undefined) #scope: Default/G-GeneratorLossCell
- # In file /home/HEU_535/ESRGAN/src/model/cell.py(72)/ return (fake_hr, generator_loss, content_loss, perceptual_loss, adversarial_loss)/
- }
-
-
- # [No.4] construct.6 @ctx.addr=0xaaaae3bd8820
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(57)/ def construct(self, x)://
- funcgraph fg_6[fg_0](
- %para2180 : Tensor(F64)[16, 3, 128, 128] # x
- ) {
-
- #------------------------> 3
- %1 = FuncGraph::fg_10(%para2180) #(Tensor(F64)[16, 3, 128, 128]) # fg_10=construct.10(@ctx.addr=0xaaaae3bddd20) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3bddd20
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(59)/ feat = self.lrelu(self.conv0_0(x))//
- %2 = FuncGraph::fg_11(%1) #(Undefined) # fg_11=construct.11(@ctx.addr=0xaaaae3a29100) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a29100
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(59)/ feat = self.lrelu(self.conv0_0(x))//
- %3 = FuncGraph::fg_12(%2) #(Undefined) # fg_12=construct.12(@ctx.addr=0xaaaae3a39050) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a39050
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(61)/ self.bn0_1(self.conv0_1(feat))//
- %4 = FuncGraph::fg_13(%3) #(Undefined) # fg_13=construct.13(@ctx.addr=0xaaaae3a3a610) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a3a610
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(61)/ self.bn0_1(self.conv0_1(feat))//
- %5 = FuncGraph::fg_11(%4) #(Undefined) # fg_11=construct.11(@ctx.addr=0xaaaae3a57780) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a57780
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(60)/ feat = self.lrelu(//
- %6 = FuncGraph::fg_14(%5) #(Undefined) # fg_14=construct.14(@ctx.addr=0xaaaae3a67860) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a67860
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(64)/ feat = self.lrelu(self.bn1_0(self.conv1_0(feat)))//
- %7 = FuncGraph::fg_15(%6) #(Undefined) # fg_15=construct.15(@ctx.addr=0xaaaae3a68f00) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a68f00
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(64)/ feat = self.lrelu(self.bn1_0(self.conv1_0(feat)))//
- %8 = FuncGraph::fg_11(%7) #(Undefined) # fg_11=construct.11(@ctx.addr=0xaaaae3a82010) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a82010
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(64)/ feat = self.lrelu(self.bn1_0(self.conv1_0(feat)))//
- %9 = FuncGraph::fg_16(%8) #(Undefined) # fg_16=construct.16(@ctx.addr=0xaaaae3a91c90) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a91c90
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(66)/ self.bn1_1(self.conv1_1(feat))//
- %10 = FuncGraph::fg_17(%9) #(Undefined) # fg_17=construct.17(@ctx.addr=0xaaaae3a932f0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a932f0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(66)/ self.bn1_1(self.conv1_1(feat))//
- %11 = FuncGraph::fg_11(%10) #(Undefined) # fg_11=construct.11(@ctx.addr=0xaaaae3aa5df0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3aa5df0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(65)/ feat = self.lrelu(//
- %12 = FuncGraph::fg_18(%11) #(Undefined) # fg_18=construct.18(@ctx.addr=0xaaaae3ab44e0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3ab44e0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(69)/ feat = self.lrelu(self.bn2_0(self.conv2_0(feat)))//
- %13 = FuncGraph::fg_19(%12) #(Undefined) # fg_19=construct.19(@ctx.addr=0xaaaae3ab5b20) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3ab5b20
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(69)/ feat = self.lrelu(self.bn2_0(self.conv2_0(feat)))//
- %14 = FuncGraph::fg_11(%13) #(Undefined) # fg_11=construct.11(@ctx.addr=0xaaaae3aceca0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3aceca0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(69)/ feat = self.lrelu(self.bn2_0(self.conv2_0(feat)))//
- %15 = FuncGraph::fg_20(%14) #(Undefined) # fg_20=construct.20(@ctx.addr=0xaaaae3ade8b0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3ade8b0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(71)/ self.bn2_1(self.conv2_1(feat))//
- %16 = FuncGraph::fg_21(%15) #(Undefined) # fg_21=construct.21(@ctx.addr=0xaaaae3adff50) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3adff50
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(71)/ self.bn2_1(self.conv2_1(feat))//
- %17 = FuncGraph::fg_11(%16) #(Undefined) # fg_11=construct.11(@ctx.addr=0xaaaae3af29e0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3af29e0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(70)/ feat = self.lrelu(//
- %18 = FuncGraph::fg_22(%17) #(Undefined) # fg_22=construct.22(@ctx.addr=0xaaaae3b01060) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3b01060
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(74)/ feat = self.lrelu(self.bn3_0(self.conv3_0(feat)))//
- %19 = FuncGraph::fg_23(%18) #(Undefined) # fg_23=construct.23(@ctx.addr=0xaaaae3b02720) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3b02720
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(74)/ feat = self.lrelu(self.bn3_0(self.conv3_0(feat)))//
- %20 = FuncGraph::fg_11(%19) #(Undefined) # fg_11=construct.11(@ctx.addr=0xaaaae3b1ba50) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3b1ba50
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(74)/ feat = self.lrelu(self.bn3_0(self.conv3_0(feat)))//
- %21 = FuncGraph::fg_24(%20) #(Undefined) # fg_24=construct.24(@ctx.addr=0xaaaae266d320) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae266d320
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(75)/ feat = self.lrelu(self.bn3_1(self.conv3_1(feat))) # output spatial size: (8, 8)//
- %22 = FuncGraph::fg_25(%21) #(Undefined) # fg_25=construct.25(@ctx.addr=0xaaaae266e9e0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae266e9e0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(75)/ feat = self.lrelu(self.bn3_1(self.conv3_1(feat))) # output spatial size: (8, 8)//
- %23 = FuncGraph::fg_11(%22) #(Undefined) # fg_11=construct.11(@ctx.addr=0xaaaae3b46eb0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3b46eb0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(75)/ feat = self.lrelu(self.bn3_1(self.conv3_1(feat))) # output spatial size: (8, 8)//
- %24 = FuncGraph::fg_26(%23) #(Undefined) # fg_26=construct.26(@ctx.addr=0xaaaae3b554c0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3b554c0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(78)/ feat = self.flatten(feat)//
- %25 = FuncGraph::fg_27(%24) #(Undefined) # fg_27=construct.27(@ctx.addr=0xaaaae3b5f060) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3b5f060
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(79)/ feat = self.lrelu(self.linear1(feat))//
- %26 = FuncGraph::fg_11(%25) #(Undefined) # fg_11=construct.11(@ctx.addr=0xaaaae3b9cce0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3b9cce0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(79)/ feat = self.lrelu(self.linear1(feat))//
- %27 = FuncGraph::fg_28(%26) #(Undefined) # fg_28=construct.28(@ctx.addr=0xaaaae3bb75d0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3bb75d0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(81)/ out = self.linear2(feat)//
- Primitive::Return{prim_type=1}(%27) #(Undefined) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(82)/ return out//
- }
-
-
- # [No.5] construct.10 @ctx.addr=0xaaaae3bddd20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_10[fg_0](
- %para2181 : Tensor(F64)[16, 3, 128, 128] # x
- ) {
- %1 : Bool = FuncGraph::fg_29(Bool(1)) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_0-Conv2d @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_30, FuncGraph::fg_31) #(Bool, Func, Func) # fg_30=✓construct.30(@ctx.addr=0xaaaae3bddf70), fg_31=✗construct.31 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
-
- #------------------------> 4
- %3 = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_0-Conv2d @ctx.addr=0xaaaae3bddf70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Undefined) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.6] ✓construct.30 @ctx.addr=0xaaaae3bddf70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_30[fg_10](
- ) {
- %1 = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para2181, %para2167) #(Tensor(F64)[16, 3, 128, 128], Ref[Tensor(F32)][64, 3, 3, 3]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
-
- #------------------------> 5
- %2 = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para2166) #(Undefined, Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 = FuncGraph::fg_32(%2) #(Undefined) # fg_32=↓construct.32(@ctx.addr=0xaaaae3a27d10) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_0-Conv2d @ctx.addr=0xaaaae3a27d10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Undefined) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- #===============================================================================
-
-
- # [No.7] construct.5 @ctx.addr=0xaaaaf43b35d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(92)/ def construct(self, x)://
- funcgraph fg_5[fg_0](
- %para2182 : Tensor(F64)[16, 3, 32, 32] # x
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para2182, "astype") #(Tensor(F64)[16, 3, 32, 32], String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(93)/ x = x.astype(mindspore.float32)//
- %2 : Tensor(F32)[16, 3, 32, 32] = %1(F32) #(TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(93)/ x = x.astype(mindspore.float32)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_33(%2) #(Tensor(F32)[16, 3, 32, 32]) # fg_33=construct.33(@ctx.addr=0xaaaad925c810) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaad925c810
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(94)/ fea = self.conv_first(x)//
- %4 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_34(%3) #(Tensor(F32)[16, 64, 32, 32]) # fg_34=construct.34(@ctx.addr=0xaaaaf709b3c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaaf709b3c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(95)/ trunk = self.trunk_conv(self.RRDB_trunk(fea))//
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_35(%4) #(Tensor(F32)[16, 64, 32, 32]) # fg_35=construct.35(@ctx.addr=0xaaaae3980260) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae3980260
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(95)/ trunk = self.trunk_conv(self.RRDB_trunk(fea))//
- %6 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%3, %5) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae399d700
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(96)/ fea = fea + trunk//
- %7 : Tuple[I64*4] = DoSignaturePrimitive::S-Prim-Shape{prim_type=1}(%6) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(98)/ fea_size = self.shape(fea)//
- %8 : I64 = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%7, I64(2)) #(Tuple[I64*4], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae399eb80
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(102)/ (fea_size[2] * 2, fea_size[3] * 2), True)(fea)//
- %9 : I64 = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%8, I64(2)) #(I64, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae39b4c40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(102)/ (fea_size[2] * 2, fea_size[3] * 2), True)(fea)//
- %10 : I64 = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%7, I64(3)) #(Tuple[I64*4], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae39b55a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(102)/ (fea_size[2] * 2, fea_size[3] * 2), True)(fea)//
- %11 : I64 = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%10, I64(2)) #(I64, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae39bb220
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(102)/ (fea_size[2] * 2, fea_size[3] * 2), True)(fea)//
- %12 : Tuple[I64*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%9, %11) #(I64, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(102)/ (fea_size[2] * 2, fea_size[3] * 2), True)(fea)//
- %13 : Func = ClassType(%12, Bool(1)) #(Tuple[I64*2], Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(101)/ ops.ResizeNearestNeighbor(//
- %14 : Tensor(F32)[16, 64, 64, 64] = %13(%6) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(101)/ ops.ResizeNearestNeighbor(//
- %15 : Tensor(F32)[16, 64, 64, 64] = FuncGraph::fg_36(%14) #(Tensor(F32)[16, 64, 64, 64]) # fg_36=construct.36(@ctx.addr=0xaaaae39bc530) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae39bc530
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(100)/ self.upconv1(//
- %16 : Tensor(F32)[16, 64, 64, 64] = FuncGraph::fg_37(%15) #(Tensor(F32)[16, 64, 64, 64]) # fg_37=construct.37(@ctx.addr=0xaaaae39c22d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae39c22d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(99)/ fea = self.lrelu(//
- %17 : Tuple[I64*4] = DoSignaturePrimitive::S-Prim-Shape{prim_type=1}(%16) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(105)/ fea_size = self.shape(fea)//
- %18 : I64 = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%17, I64(2)) #(Tuple[I64*4], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae39df010
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(109)/ (fea_size[2] * 2, fea_size[3] * 2), True)(fea)//
- %19 : I64 = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%18, I64(2)) #(I64, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae39e7c70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(109)/ (fea_size[2] * 2, fea_size[3] * 2), True)(fea)//
- %20 : I64 = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%17, I64(3)) #(Tuple[I64*4], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae39edbd0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(109)/ (fea_size[2] * 2, fea_size[3] * 2), True)(fea)//
- %21 : I64 = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%20, I64(2)) #(I64, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae39f3b30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(109)/ (fea_size[2] * 2, fea_size[3] * 2), True)(fea)//
- %22 : Tuple[I64*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%19, %21) #(I64, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(109)/ (fea_size[2] * 2, fea_size[3] * 2), True)(fea)//
- %23 : Func = ClassType(%22, Bool(1)) #(Tuple[I64*2], Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(108)/ ops.ResizeNearestNeighbor(//
- %24 : Tensor(F32)[16, 64, 128, 128] = %23(%16) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(108)/ ops.ResizeNearestNeighbor(//
- %25 : Tensor(F32)[16, 64, 128, 128] = FuncGraph::fg_38(%24) #(Tensor(F32)[16, 64, 128, 128]) # fg_38=construct.38(@ctx.addr=0xaaaae39f4d20) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae39f4d20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(107)/ self.upconv2(//
- %26 : Tensor(F32)[16, 64, 128, 128] = FuncGraph::fg_37(%25) #(Tensor(F32)[16, 64, 128, 128]) # fg_37=construct.37(@ctx.addr=0xaaaae39faa50) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae39faa50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(106)/ fea = self.lrelu(//
- %27 : Tensor(F32)[16, 64, 128, 128] = FuncGraph::fg_39(%26) #(Tensor(F32)[16, 64, 128, 128]) # fg_39=construct.39(@ctx.addr=0xaaaae3a0fbf0) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae3a0fbf0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(112)/ out = self.conv_last(self.lrelu(self.HRconv(fea)))//
- %28 : Tensor(F32)[16, 64, 128, 128] = FuncGraph::fg_37(%27) #(Tensor(F32)[16, 64, 128, 128]) # fg_37=construct.37(@ctx.addr=0xaaaae39faa50) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae39faa50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(112)/ out = self.conv_last(self.lrelu(self.HRconv(fea)))//
- %29 : Tensor(F32)[16, 3, 128, 128] = FuncGraph::fg_40(%28) #(Tensor(F32)[16, 64, 128, 128]) # fg_40=construct.40(@ctx.addr=0xaaaae3a11250) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae3a11250
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(112)/ out = self.conv_last(self.lrelu(self.HRconv(fea)))//
- Primitive::Return{prim_type=1}(%29) #(Tensor(F32)[16, 3, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(114)/ return out//
- }
-
-
- # [No.8] construct.6 @ctx.addr=0xaaaae3a21b90
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(57)/ def construct(self, x)://
- funcgraph fg_6[fg_0](
- %para2183 : Tensor(F32)[16, 3, 128, 128] # x
- ) {
- %1 : Tensor(F32)[16, 64, 128, 128] = FuncGraph::fg_10(%para2180) #(Tensor(F32)[16, 3, 128, 128]) # fg_10=construct.10(@ctx.addr=0xaaaae3bddd20) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3bddd20
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(59)/ feat = self.lrelu(self.conv0_0(x))//
- %2 : Tensor(F32)[16, 64, 128, 128] = FuncGraph::fg_11(%1) #(Tensor(F32)[16, 64, 128, 128]) # fg_11=construct.11(@ctx.addr=0xaaaae3a29100) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a29100
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(59)/ feat = self.lrelu(self.conv0_0(x))//
- %3 : Tensor(F32)[16, 64, 64, 64] = FuncGraph::fg_12(%2) #(Tensor(F32)[16, 64, 128, 128]) # fg_12=construct.12(@ctx.addr=0xaaaae3a39050) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a39050
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(61)/ self.bn0_1(self.conv0_1(feat))//
- %4 : Tensor(F32)[16, 64, 64, 64] = FuncGraph::fg_13(%3) #(Tensor(F32)[16, 64, 64, 64]) # fg_13=construct.13(@ctx.addr=0xaaaae3a3a610) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a3a610
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(61)/ self.bn0_1(self.conv0_1(feat))//
- %5 : Tensor(F32)[16, 64, 64, 64] = FuncGraph::fg_11(%4) #(Tensor(F32)[16, 64, 64, 64]) # fg_11=construct.11(@ctx.addr=0xaaaae3a57780) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a57780
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(60)/ feat = self.lrelu(//
- %6 : Tensor(F32)[16, 128, 64, 64] = FuncGraph::fg_14(%5) #(Tensor(F32)[16, 64, 64, 64]) # fg_14=construct.14(@ctx.addr=0xaaaae3a67860) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a67860
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(64)/ feat = self.lrelu(self.bn1_0(self.conv1_0(feat)))//
- %7 : Tensor(F32)[16, 128, 64, 64] = FuncGraph::fg_15(%6) #(Tensor(F32)[16, 128, 64, 64]) # fg_15=construct.15(@ctx.addr=0xaaaae3a68f00) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a68f00
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(64)/ feat = self.lrelu(self.bn1_0(self.conv1_0(feat)))//
- %8 : Tensor(F32)[16, 128, 64, 64] = FuncGraph::fg_11(%7) #(Tensor(F32)[16, 128, 64, 64]) # fg_11=construct.11(@ctx.addr=0xaaaae3a82010) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a82010
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(64)/ feat = self.lrelu(self.bn1_0(self.conv1_0(feat)))//
- %9 : Tensor(F32)[16, 128, 32, 32] = FuncGraph::fg_16(%8) #(Tensor(F32)[16, 128, 64, 64]) # fg_16=construct.16(@ctx.addr=0xaaaae3a91c90) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a91c90
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(66)/ self.bn1_1(self.conv1_1(feat))//
- %10 : Tensor(F32)[16, 128, 32, 32] = FuncGraph::fg_17(%9) #(Tensor(F32)[16, 128, 32, 32]) # fg_17=construct.17(@ctx.addr=0xaaaae3a932f0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3a932f0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(66)/ self.bn1_1(self.conv1_1(feat))//
- %11 : Tensor(F32)[16, 128, 32, 32] = FuncGraph::fg_11(%10) #(Tensor(F32)[16, 128, 32, 32]) # fg_11=construct.11(@ctx.addr=0xaaaae3aa5df0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3aa5df0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(65)/ feat = self.lrelu(//
- %12 : Tensor(F32)[16, 256, 32, 32] = FuncGraph::fg_18(%11) #(Tensor(F32)[16, 128, 32, 32]) # fg_18=construct.18(@ctx.addr=0xaaaae3ab44e0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3ab44e0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(69)/ feat = self.lrelu(self.bn2_0(self.conv2_0(feat)))//
- %13 : Tensor(F32)[16, 256, 32, 32] = FuncGraph::fg_19(%12) #(Tensor(F32)[16, 256, 32, 32]) # fg_19=construct.19(@ctx.addr=0xaaaae3ab5b20) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3ab5b20
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(69)/ feat = self.lrelu(self.bn2_0(self.conv2_0(feat)))//
- %14 : Tensor(F32)[16, 256, 32, 32] = FuncGraph::fg_11(%13) #(Tensor(F32)[16, 256, 32, 32]) # fg_11=construct.11(@ctx.addr=0xaaaae3aceca0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3aceca0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(69)/ feat = self.lrelu(self.bn2_0(self.conv2_0(feat)))//
- %15 : Tensor(F32)[16, 256, 16, 16] = FuncGraph::fg_20(%14) #(Tensor(F32)[16, 256, 32, 32]) # fg_20=construct.20(@ctx.addr=0xaaaae3ade8b0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3ade8b0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(71)/ self.bn2_1(self.conv2_1(feat))//
- %16 : Tensor(F32)[16, 256, 16, 16] = FuncGraph::fg_21(%15) #(Tensor(F32)[16, 256, 16, 16]) # fg_21=construct.21(@ctx.addr=0xaaaae3adff50) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3adff50
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(71)/ self.bn2_1(self.conv2_1(feat))//
- %17 : Tensor(F32)[16, 256, 16, 16] = FuncGraph::fg_11(%16) #(Tensor(F32)[16, 256, 16, 16]) # fg_11=construct.11(@ctx.addr=0xaaaae3af29e0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3af29e0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(70)/ feat = self.lrelu(//
- %18 : Tensor(F32)[16, 512, 16, 16] = FuncGraph::fg_22(%17) #(Tensor(F32)[16, 256, 16, 16]) # fg_22=construct.22(@ctx.addr=0xaaaae3b01060) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3b01060
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(74)/ feat = self.lrelu(self.bn3_0(self.conv3_0(feat)))//
- %19 : Tensor(F32)[16, 512, 16, 16] = FuncGraph::fg_23(%18) #(Tensor(F32)[16, 512, 16, 16]) # fg_23=construct.23(@ctx.addr=0xaaaae3b02720) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3b02720
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(74)/ feat = self.lrelu(self.bn3_0(self.conv3_0(feat)))//
- %20 : Tensor(F32)[16, 512, 16, 16] = FuncGraph::fg_11(%19) #(Tensor(F32)[16, 512, 16, 16]) # fg_11=construct.11(@ctx.addr=0xaaaae3b1ba50) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3b1ba50
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(74)/ feat = self.lrelu(self.bn3_0(self.conv3_0(feat)))//
- %21 : Tensor(F32)[16, 512, 8, 8] = FuncGraph::fg_24(%20) #(Tensor(F32)[16, 512, 16, 16]) # fg_24=construct.24(@ctx.addr=0xaaaae266d320) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae266d320
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(75)/ feat = self.lrelu(self.bn3_1(self.conv3_1(feat))) # output spatial size: (8, 8)//
- %22 : Tensor(F32)[16, 512, 8, 8] = FuncGraph::fg_25(%21) #(Tensor(F32)[16, 512, 8, 8]) # fg_25=construct.25(@ctx.addr=0xaaaae266e9e0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae266e9e0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(75)/ feat = self.lrelu(self.bn3_1(self.conv3_1(feat))) # output spatial size: (8, 8)//
- %23 : Tensor(F32)[16, 512, 8, 8] = FuncGraph::fg_11(%22) #(Tensor(F32)[16, 512, 8, 8]) # fg_11=construct.11(@ctx.addr=0xaaaae3b46eb0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3b46eb0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(75)/ feat = self.lrelu(self.bn3_1(self.conv3_1(feat))) # output spatial size: (8, 8)//
- %24 : Tensor(F32)[16, 32768] = FuncGraph::fg_26(%23) #(Tensor(F32)[16, 512, 8, 8]) # fg_26=construct.26(@ctx.addr=0xaaaae3b554c0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3b554c0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(78)/ feat = self.flatten(feat)//
- %25 : Tensor(F32)[16, 100] = FuncGraph::fg_27(%24) #(Tensor(F32)[16, 32768]) # fg_27=construct.27(@ctx.addr=0xaaaae3b5f060) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3b5f060
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(79)/ feat = self.lrelu(self.linear1(feat))//
- %26 : Tensor(F32)[16, 100] = FuncGraph::fg_11(%25) #(Tensor(F32)[16, 100]) # fg_11=construct.11(@ctx.addr=0xaaaae3b9cce0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3b9cce0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(79)/ feat = self.lrelu(self.linear1(feat))//
- %27 : Tensor(F32)[16, 1] = FuncGraph::fg_28(%26) #(Tensor(F32)[16, 100]) # fg_28=construct.28(@ctx.addr=0xaaaae3bb75d0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128 @ctx.addr=0xaaaae3bb75d0
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(81)/ out = self.linear2(feat)//
- Primitive::Return{prim_type=1}(%27) #(Tensor(F32)[16, 1]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128
- # In file /home/HEU_535/ESRGAN/src/model/discriminator_net.py(82)/ return out//
- }
-
-
- # [No.8] construct.11 @ctx.addr=0xaaaae3a29100
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_11(
- %para2184 : Tensor(F32)[16, 64, 128, 128] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3a2ac30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_41, FuncGraph::fg_42) #(Bool, Func, Func) # fg_41=✓construct.41(@ctx.addr=0xaaaae3a2ad80), fg_42=✗construct.42 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 64, 128, 128] = %3() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3a2ad80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.9] construct.12 @ctx.addr=0xaaaae3a39050
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_12[fg_0](
- %para2185 : Tensor(F32)[16, 64, 128, 128] # x
- ) {
- %1 : Bool = FuncGraph::fg_29(Bool(0)) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaae0cad1c0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_1-Conv2d @ctx.addr=0xaaaae0cad1c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_43, FuncGraph::fg_44) #(Bool, Func, Func) # fg_43=✓construct.43, fg_44=✗construct.44(@ctx.addr=0xaaaae3a39150) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 64, 64] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_1-Conv2d @ctx.addr=0xaaaae3a39150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.10] construct.13 @ctx.addr=0xaaaae3a3a610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- funcgraph fg_13[fg_0](
- %para2186 : Tensor(F32)[16, 64, 64, 64] # Φx
- ) {
- %1 : Tuple[I64*4] = DoSignaturePrimitive::S-Prim-Shape{prim_type=1}(%para2186) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(184)/ _shape_check_bn(self.shape(x), self.input_dims)/
- %2 : NoneType = DoSignaturePrimitive::S-Prim-_shape_check_bn{prim_type=1}(%1, "2d") #(Tuple[I64*4], String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(184)/ _shape_check_bn(self.shape(x), self.input_dims)/
- %3 : NoneType = Primitive::stop_gradient{prim_type=1}(%2) #(NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- %4 : Bool = DoSignaturePrimitive::S-Prim-is_{prim_type=1}(None, None) #(NoneType, NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %5 : Bool = FuncGraph::fg_29(%4) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %6 : Func = Primitive::Switch{prim_type=1}(%5, FuncGraph::fg_45, FuncGraph::fg_46) #(Bool, Func, Func) # fg_45=✓construct.45(@ctx.addr=0xaaaae3a39bc0), fg_46=✗construct.46 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %7 : Tensor(F32)[16, 64, 64, 64] = %6() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d @ctx.addr=0xaaaae3a39bc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %8 : Tensor(F32)[16, 64, 64, 64] = Primitive::Depend{prim_type=1}[side_effect_propagate=I64(1)](%7, %3) #(Tensor(F32)[16, 64, 64, 64], NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- Primitive::Return{prim_type=1}(%8) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- }
-
-
- # [No.11] construct.11 @ctx.addr=0xaaaae3a57780
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_11(
- %para2187 : Tensor(F32)[16, 64, 64, 64] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3a59af0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_41, FuncGraph::fg_42) #(Bool, Func, Func) # fg_41=✓construct.41(@ctx.addr=0xaaaae3a574c0), fg_42=✗construct.42 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 64, 64, 64] = %3() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3a574c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.11] construct.14 @ctx.addr=0xaaaae3a67860
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_14[fg_0](
- %para2188 : Tensor(F32)[16, 64, 64, 64] # x
- ) {
- %1 : Bool = FuncGraph::fg_29(Bool(0)) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaae0cad1c0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv1_0-Conv2d @ctx.addr=0xaaaae0cad1c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_47, FuncGraph::fg_48) #(Bool, Func, Func) # fg_47=✓construct.47, fg_48=✗construct.48(@ctx.addr=0xaaaae3a67960) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv1_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 128, 64, 64] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv1_0-Conv2d @ctx.addr=0xaaaae3a67960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv1_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.12] construct.15 @ctx.addr=0xaaaae3a68f00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- funcgraph fg_15[fg_0](
- %para2189 : Tensor(F32)[16, 128, 64, 64] # Φx
- ) {
- %1 : Tensor(F32)[16, 128, 64, 64] = FuncGraph::fg_49(%para2189, %para2162, %para2163, %para2164, %para2165) #(Tensor(F32)[16, 128, 64, 64], Ref[Tensor(F32)][128], Ref[Tensor(F32)][128], Ref[Tensor(F32)][128], Ref[Tensor(F32)][128]) # fg_49=L-construct.49(@ctx.addr=0xaaaae3a684b0) #scope: Default @ctx.addr=0xaaaae3a684b0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_0-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- }
-
-
- # [No.13] construct.11 @ctx.addr=0xaaaae3a82010
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_11(
- %para2190 : Tensor(F32)[16, 128, 64, 64] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3a83760
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_41, FuncGraph::fg_42) #(Bool, Func, Func) # fg_41=✓construct.41(@ctx.addr=0xaaaae3a82d10), fg_42=✗construct.42 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 128, 64, 64] = %3() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3a82d10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.13] construct.16 @ctx.addr=0xaaaae3a91c90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_16[fg_0](
- %para2191 : Tensor(F32)[16, 128, 64, 64] # x
- ) {
- %1 : Bool = FuncGraph::fg_29(Bool(0)) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaae0cad1c0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv1_1-Conv2d @ctx.addr=0xaaaae0cad1c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_50, FuncGraph::fg_51) #(Bool, Func, Func) # fg_50=✓construct.50, fg_51=✗construct.51(@ctx.addr=0xaaaae3a91d90) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv1_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 128, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv1_1-Conv2d @ctx.addr=0xaaaae3a91d90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv1_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.14] construct.17 @ctx.addr=0xaaaae3a932f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- funcgraph fg_17[fg_0](
- %para2192 : Tensor(F32)[16, 128, 32, 32] # Φx
- ) {
- %1 : Tensor(F32)[16, 128, 32, 32] = FuncGraph::fg_49(%para2192, %para2157, %para2158, %para2159, %para2160) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][128], Ref[Tensor(F32)][128], Ref[Tensor(F32)][128], Ref[Tensor(F32)][128]) # fg_49=L-construct.49(@ctx.addr=0xaaaae3a928a0) #scope: Default @ctx.addr=0xaaaae3a928a0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- }
-
-
- # [No.15] construct.11 @ctx.addr=0xaaaae3aa5df0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_11(
- %para2193 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3aa7540
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_41, FuncGraph::fg_42) #(Bool, Func, Func) # fg_41=✓construct.41(@ctx.addr=0xaaaae3aa6af0), fg_42=✗construct.42 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 128, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3aa6af0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.15] construct.18 @ctx.addr=0xaaaae3ab44e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_18[fg_0](
- %para2194 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Bool = FuncGraph::fg_29(Bool(0)) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaae0cad1c0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv2_0-Conv2d @ctx.addr=0xaaaae0cad1c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_52, FuncGraph::fg_53) #(Bool, Func, Func) # fg_52=✓construct.52, fg_53=✗construct.53(@ctx.addr=0xaaaae3ab45e0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv2_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 256, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv2_0-Conv2d @ctx.addr=0xaaaae3ab45e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv2_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.16] construct.19 @ctx.addr=0xaaaae3ab5b20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- funcgraph fg_19[fg_0](
- %para2195 : Tensor(F32)[16, 256, 32, 32] # Φx
- ) {
- %1 : Tensor(F32)[16, 256, 32, 32] = FuncGraph::fg_54(%para2195, %para2152, %para2153, %para2154, %para2155) #(Tensor(F32)[16, 256, 32, 32], Ref[Tensor(F32)][256], Ref[Tensor(F32)][256], Ref[Tensor(F32)][256], Ref[Tensor(F32)][256]) # fg_54=L-construct.54(@ctx.addr=0xaaaae3ab50d0) #scope: Default @ctx.addr=0xaaaae3ab50d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_0-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- }
-
-
- # [No.17] construct.11 @ctx.addr=0xaaaae3aceca0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_11(
- %para2196 : Tensor(F32)[16, 256, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3ad03f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_41, FuncGraph::fg_42) #(Bool, Func, Func) # fg_41=✓construct.41(@ctx.addr=0xaaaae3acf9a0), fg_42=✗construct.42 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 256, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3acf9a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.17] construct.20 @ctx.addr=0xaaaae3ade8b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_20[fg_0](
- %para2197 : Tensor(F32)[16, 256, 32, 32] # x
- ) {
- %1 : Bool = FuncGraph::fg_29(Bool(0)) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaae0cad1c0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv2_1-Conv2d @ctx.addr=0xaaaae0cad1c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_55, FuncGraph::fg_56) #(Bool, Func, Func) # fg_55=✓construct.55, fg_56=✗construct.56(@ctx.addr=0xaaaae3ade9b0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv2_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 256, 16, 16] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv2_1-Conv2d @ctx.addr=0xaaaae3ade9b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv2_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.18] construct.21 @ctx.addr=0xaaaae3adff50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- funcgraph fg_21[fg_0](
- %para2198 : Tensor(F32)[16, 256, 16, 16] # Φx
- ) {
- %1 : Tensor(F32)[16, 256, 16, 16] = FuncGraph::fg_54(%para2198, %para2147, %para2148, %para2149, %para2150) #(Tensor(F32)[16, 256, 16, 16], Ref[Tensor(F32)][256], Ref[Tensor(F32)][256], Ref[Tensor(F32)][256], Ref[Tensor(F32)][256]) # fg_54=L-construct.54(@ctx.addr=0xaaaae3adf500) #scope: Default @ctx.addr=0xaaaae3adf500
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- }
-
-
- # [No.19] construct.11 @ctx.addr=0xaaaae3af29e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_11(
- %para2199 : Tensor(F32)[16, 256, 16, 16] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3af4130
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_41, FuncGraph::fg_42) #(Bool, Func, Func) # fg_41=✓construct.41(@ctx.addr=0xaaaae3af36e0), fg_42=✗construct.42 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 256, 16, 16] = %3() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3af36e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.19] construct.22 @ctx.addr=0xaaaae3b01060
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_22[fg_0](
- %para2200 : Tensor(F32)[16, 256, 16, 16] # x
- ) {
- %1 : Bool = FuncGraph::fg_29(Bool(0)) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaae0cad1c0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv3_0-Conv2d @ctx.addr=0xaaaae0cad1c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_57, FuncGraph::fg_58) #(Bool, Func, Func) # fg_57=✓construct.57, fg_58=✗construct.58(@ctx.addr=0xaaaae3b01180) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv3_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 512, 16, 16] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv3_0-Conv2d @ctx.addr=0xaaaae3b01180
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv3_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.20] construct.23 @ctx.addr=0xaaaae3b02720
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- funcgraph fg_23[fg_0](
- %para2201 : Tensor(F32)[16, 512, 16, 16] # Φx
- ) {
- %1 : Tensor(F32)[16, 512, 16, 16] = FuncGraph::fg_59(%para2201, %para2142, %para2143, %para2144, %para2145) #(Tensor(F32)[16, 512, 16, 16], Ref[Tensor(F32)][512], Ref[Tensor(F32)][512], Ref[Tensor(F32)][512], Ref[Tensor(F32)][512]) # fg_59=L-construct.59(@ctx.addr=0xaaaae3b01cd0) #scope: Default @ctx.addr=0xaaaae3b01cd0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_0-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- }
-
-
- # [No.21] construct.11 @ctx.addr=0xaaaae3b1ba50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_11(
- %para2202 : Tensor(F32)[16, 512, 16, 16] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3b1d1a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_41, FuncGraph::fg_42) #(Bool, Func, Func) # fg_41=✓construct.41(@ctx.addr=0xaaaae3b1c750), fg_42=✗construct.42 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 512, 16, 16] = %3() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3b1c750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.21] construct.24 @ctx.addr=0xaaaae266d320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_24[fg_0](
- %para2203 : Tensor(F32)[16, 512, 16, 16] # x
- ) {
- %1 : Bool = FuncGraph::fg_29(Bool(0)) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaae0cad1c0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv3_1-Conv2d @ctx.addr=0xaaaae0cad1c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_60, FuncGraph::fg_61) #(Bool, Func, Func) # fg_60=✓construct.60, fg_61=✗construct.61(@ctx.addr=0xaaaae266d440) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv3_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 512, 8, 8] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv3_1-Conv2d @ctx.addr=0xaaaae266d440
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv3_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.22] construct.25 @ctx.addr=0xaaaae266e9e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- funcgraph fg_25[fg_0](
- %para2204 : Tensor(F32)[16, 512, 8, 8] # Φx
- ) {
- %1 : Tensor(F32)[16, 512, 8, 8] = FuncGraph::fg_59(%para2204, %para2137, %para2138, %para2139, %para2140) #(Tensor(F32)[16, 512, 8, 8], Ref[Tensor(F32)][512], Ref[Tensor(F32)][512], Ref[Tensor(F32)][512], Ref[Tensor(F32)][512]) # fg_59=L-construct.59(@ctx.addr=0xaaaae266df90) #scope: Default @ctx.addr=0xaaaae266df90
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- }
-
-
- # [No.23] construct.11 @ctx.addr=0xaaaae3b46eb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_11(
- %para2205 : Tensor(F32)[16, 512, 8, 8] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3b48600
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_41, FuncGraph::fg_42) #(Bool, Func, Func) # fg_41=✓construct.41(@ctx.addr=0xaaaae3b47bb0), fg_42=✗construct.42 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 512, 8, 8] = %3() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3b47bb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.23] construct.26 @ctx.addr=0xaaaae3b554c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(202)/ def construct(self, x):/
- funcgraph fg_26(
- %para2206 : Tensor(F32)[16, 512, 8, 8] # x
- ) {
- %1 : Tuple[I64*4] = DoSignaturePrimitive::S-Prim-Shape{prim_type=1}(%para2206) #(Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/flatten-Flatten
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(203)/ return F.reshape(x, (F.shape(x)[0], -1))/
- %2 : I64 = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[I64*4], I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/flatten-Flatten @ctx.addr=0xaaaae3b56110
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(203)/ return F.reshape(x, (F.shape(x)[0], -1))/
- %3 : I64 = DoSignaturePrimitive::S-Prim-negative{prim_type=1}(I64(1)) #(I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/flatten-Flatten @ctx.addr=0xaaaae3b5ef40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(203)/ return F.reshape(x, (F.shape(x)[0], -1))/
- %4 : Tuple[I64*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%2, %3) #(I64, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/flatten-Flatten
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(203)/ return F.reshape(x, (F.shape(x)[0], -1))/
- %5 : Tensor(F32)[16, 32768] = DoSignaturePrimitive::S-Prim-Reshape{prim_type=1}[input_names=["tensor", "shape"], output_names=["output"]](%para2206, %4) #(Tensor(F32)[16, 512, 8, 8], Tuple[I64*2]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/flatten-Flatten
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(203)/ return F.reshape(x, (F.shape(x)[0], -1))/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 32768]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/flatten-Flatten
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(203)/ return F.reshape(x, (F.shape(x)[0], -1))/
- }
-
-
- # [No.24] construct.27 @ctx.addr=0xaaaae3b5f060
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(298)/ def construct(self, x):/
- funcgraph fg_27[fg_0](
- %para2207 : Tensor(F32)[16, 32768] # x
- ) {
- %1 : Tensor(F32)[16, 100] = FuncGraph::fg_62(%para2207, %para2134, %para2135) #(Tensor(F32)[16, 32768], Ref[Tensor(F32)][100], Ref[Tensor(F32)][100, 32768]) # fg_62=L-construct.62(@ctx.addr=0xaaaae3b5f1e0) #scope: Default @ctx.addr=0xaaaae3b5f1e0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear1-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(298)/ def construct(self, x):/
- }
-
-
- # [No.25] construct.11 @ctx.addr=0xaaaae3b9cce0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_11(
- %para2208 : Tensor(F32)[16, 100] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3baeef0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_41, FuncGraph::fg_42) #(Bool, Func, Func) # fg_41=✓construct.41(@ctx.addr=0xaaaae3baf040), fg_42=✗construct.42 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 100] = %3() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3baf040
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.25] construct.28 @ctx.addr=0xaaaae3bb75d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(298)/ def construct(self, x):/
- funcgraph fg_28[fg_0](
- %para2209 : Tensor(F32)[16, 100] # x
- ) {
- %1 : Tensor(F32)[16, 1] = FuncGraph::fg_62(%para2209, %para2131, %para2132) #(Tensor(F32)[16, 100], Ref[Tensor(F32)][1], Ref[Tensor(F32)][1, 100]) # fg_62=L-construct.62(@ctx.addr=0xaaaae3bb76d0) #scope: Default @ctx.addr=0xaaaae3bb76d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 1]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(298)/ def construct(self, x):/
- }
-
-
- # [No.26] bool_.29 @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(530)/def bool_(x):/
- funcgraph fg_29(
- %para2210 : Bool # x
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para2210, "__bool__") #(Bool, String) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- %2 : Bool = %1() #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- Primitive::Return{prim_type=1}(%2) #(Bool) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- }
-
-
- # [No.27] ↓construct.32 @ctx.addr=0xaaaae3a27d10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_32(
- %para2211 : Tensor(F32)[16, 64, 128, 128] # Φoutput
- ) {
- Primitive::Return{prim_type=1}(%para2211) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(261)/ return output/
- }
-
-
- # [No.28] astype.63 @ctx.addr=0xaaaaf42ec0b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(150)/def astype(x, dtype, copy=True):/
- funcgraph fg_63(
- %para2212 : Tensor(F64)[16, 3, 32, 32] # x
- , %para2213 : TypeType # dtype
- ) {
- %1 : Func = Primitive::resolve{prim_type=1}(NameSpace::CommonOPS, bool_) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- %2 : Func = Primitive::resolve{prim_type=1}(NameSpace::CommonOPS, bool_) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- %3 : Func = Primitive::resolve{prim_type=1}(NameSpace::Ast, not_) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(0)/
- %4 : Bool = %3(Bool(1)) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae0db6df0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- %5 : Bool = %2(%4) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae0cad1c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- %6 : Func = Primitive::Switch{prim_type=1}(%5, FuncGraph::fg_64, FuncGraph::fg_65) #(Bool, Func, Func) # fg_64=↰astype.64, fg_65=↱astype.65(@ctx.addr=0xaaaadd9809f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- %7 : Bool = %6() #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaadd9809f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- %8 : Bool = %1(%7) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae0cad1c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- %9 : Func = Primitive::Switch{prim_type=1}(%8, FuncGraph::fg_66, FuncGraph::fg_67) #(Bool, Func, Func) # fg_66=✓astype.66, fg_67=✗astype.67(@ctx.addr=0xaaaadcc7fc70) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- %10 : Tensor(F32)[16, 3, 32, 32] = %9() #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaadcc7fc70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- Primitive::Return{prim_type=1}(%10) #(Tensor(F32)[16, 3, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- }
-
-
- # [No.29] construct.33 @ctx.addr=0xaaaad925c810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_33[fg_0](
- %para2214 : Tensor(F32)[16, 3, 32, 32] # x
- ) {
- %1 : Bool = FuncGraph::fg_29(Bool(1)) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_first-Conv2d @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_68, FuncGraph::fg_69) #(Bool, Func, Func) # fg_68=✓construct.68(@ctx.addr=0xaaaad7f5ab50), fg_69=✗construct.69 #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_first-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_first-Conv2d @ctx.addr=0xaaaad7f5ab50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_first-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.30] construct.34 @ctx.addr=0xaaaaf709b3c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(233)/ def construct(self, input_data):/
- funcgraph fg_34[fg_0](
- %para2215 : Tensor(F32)[16, 64, 32, 32] # input_data
- ) {
- %1 : Tuple[Func*23] = Primitive::MakeTuple{prim_type=1}(FuncGraph::fg_70, FuncGraph::fg_71, FuncGraph::fg_72, FuncGraph::fg_73, FuncGraph::fg_74, FuncGraph::fg_75, FuncGraph::fg_76, FuncGraph::fg_77, FuncGraph::fg_78, FuncGraph::fg_79, FuncGraph::fg_80, FuncGraph::fg_81, FuncGraph::fg_82, FuncGraph::fg_83, FuncGraph::fg_84, FuncGraph::fg_85, FuncGraph::fg_86, FuncGraph::fg_87, FuncGraph::fg_88, FuncGraph::fg_89, FuncGraph::fg_90, FuncGraph::fg_91, FuncGraph::fg_92) #(Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func) # fg_70=construct.70(@ctx.addr=0xaaaaebd8cc70), fg_71=construct.71(@ctx.addr=0xaaaaf1779fc0), fg_72=construct.72(@ctx.addr=0xaaaae1f68670), fg_73=construct.73(@ctx.addr=0xaaaadd25dfa0), fg_74=construct.74(@ctx.addr=0xaaaadec3a420), fg_75=construct.75(@ctx.addr=0xaaaaed7bde00), fg_76=construct.76(@ctx.addr=0xaaaaee562fc0), fg_77=construct.77(@ctx.addr=0xaaaaefa4f520), fg_78=construct.78(@ctx.addr=0xaaaaed73b7b0), fg_79=construct.79(@ctx.addr=0xaaaae8380080), fg_80=construct.80(@ctx.addr=0xaaaae250d460), fg_81=construct.81(@ctx.addr=0xaaaae2573890), fg_82=construct.82(@ctx.addr=0xaaaad9b96ae0), fg_83=construct.83(@ctx.addr=0xaaaaf4dde750), fg_84=construct.84(@ctx.addr=0xaaaaedd66cb0), fg_85=construct.85(@ctx.addr=0xaaaad6995330), fg_86=construct.86(@ctx.addr=0xaaaaeb60ddc0), fg_87=construct.87(@ctx.addr=0xaaaaf4a76eb0), fg_88=construct.88(@ctx.addr=0xaaaade3e0ac0), fg_89=construct.89(@ctx.addr=0xaaaae27c7b30), fg_90=construct.90(@ctx.addr=0xaaaae28de3d0), fg_91=construct.91(@ctx.addr=0xaaaae3782510), fg_92=construct.92(@ctx.addr=0xaaaae3896870) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : I64 = FuncGraph::fg_93(%1) #(Tuple[Func*23]) # fg_93=ms_len.93(@ctx.addr=0xaaaadf8a87b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaadf8a87b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Bool = Primitive::scalar_lt{prim_type=1}(%2, I64(9223372036854775807)) #(I64, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Func = Primitive::Switch{prim_type=1}(%3, FuncGraph::fg_94, FuncGraph::fg_95) #(Bool, Func, Func) # fg_94=✓construct.94(@ctx.addr=0xaaaaf6f2efe0), fg_95=✗construct.95 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %5 : Tensor(F32)[16, 64, 32, 32] = %4() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf6f2efe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.31] construct.35 @ctx.addr=0xaaaae3980260
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_35[fg_0](
- %para2216 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_96(%para2216, %para698, %para697) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 64, 3, 3]) # fg_96=L-construct.96(@ctx.addr=0xaaaae397f810) #scope: Default @ctx.addr=0xaaaae397f810
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/trunk_conv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.32] _tensor_add_tensor.97 @ctx.addr=0xaaaae399d700
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_97(
- %para2217 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2218 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2217, %para2218) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.33] _tuple_getitem_by_number.98 @ctx.addr=0xaaaae399eb80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_98(
- %para2219 : Tuple[I64*4] # data
- , %para2220 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : I64 = %2(%para2219, %para2220) #(Tuple[I64*4], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.34] _mul_scalar.99 @ctx.addr=0xaaaae39b4c40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(32)/def _mul_scalar(x, y):/
- funcgraph fg_99(
- %para2221 : I64 # x
- , %para2222 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(39)/ return F.scalar_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(39)/ return F.scalar_mul(x, y)/
- %3 : I64 = %2(%para2221, %para2222) #(I64, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(39)/ return F.scalar_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(39)/ return F.scalar_mul(x, y)/
- }
-
-
- # [No.35] _tuple_getitem_by_number.100 @ctx.addr=0xaaaae39b55a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_100(
- %para2223 : Tuple[I64*4] # data
- , %para2224 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : I64 = %2(%para2223, %para2224) #(Tuple[I64*4], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.36] _mul_scalar.101 @ctx.addr=0xaaaae39bb220
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(32)/def _mul_scalar(x, y):/
- funcgraph fg_101(
- %para2225 : I64 # x
- , %para2226 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(39)/ return F.scalar_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(39)/ return F.scalar_mul(x, y)/
- %3 : I64 = %2(%para2225, %para2226) #(I64, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(39)/ return F.scalar_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(39)/ return F.scalar_mul(x, y)/
- }
-
-
- # [No.37] construct.36 @ctx.addr=0xaaaae39bc530
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_36[fg_0](
- %para2227 : Tensor(F32)[16, 64, 64, 64] # x
- ) {
- %1 : Tensor(F32)[16, 64, 64, 64] = FuncGraph::fg_96(%para2227, %para700, %para699) #(Tensor(F32)[16, 64, 64, 64], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 64, 3, 3]) # fg_96=L-construct.96(@ctx.addr=0xaaaae39bbae0) #scope: Default @ctx.addr=0xaaaae39bbae0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/upconv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.38] construct.37 @ctx.addr=0xaaaae39c22d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_37(
- %para2228 : Tensor(F32)[16, 64, 64, 64] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU @ctx.addr=0xaaaae39cf760
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_102, FuncGraph::fg_103) #(Bool, Func, Func) # fg_102=✓construct.102(@ctx.addr=0xaaaae39cf8b0), fg_103=✗construct.103 #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 64, 64, 64] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU @ctx.addr=0xaaaae39cf8b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.39] _tuple_getitem_by_number.104 @ctx.addr=0xaaaae39df010
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_104(
- %para2229 : Tuple[I64*4] # data
- , %para2230 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : I64 = %2(%para2229, %para2230) #(Tuple[I64*4], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.40] _mul_scalar.105 @ctx.addr=0xaaaae39e7c70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(32)/def _mul_scalar(x, y):/
- funcgraph fg_105(
- %para2231 : I64 # x
- , %para2232 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(39)/ return F.scalar_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(39)/ return F.scalar_mul(x, y)/
- %3 : I64 = %2(%para2231, %para2232) #(I64, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(39)/ return F.scalar_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(39)/ return F.scalar_mul(x, y)/
- }
-
-
- # [No.41] _tuple_getitem_by_number.106 @ctx.addr=0xaaaae39edbd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_106(
- %para2233 : Tuple[I64*4] # data
- , %para2234 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : I64 = %2(%para2233, %para2234) #(Tuple[I64*4], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.42] _mul_scalar.107 @ctx.addr=0xaaaae39f3b30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(32)/def _mul_scalar(x, y):/
- funcgraph fg_107(
- %para2235 : I64 # x
- , %para2236 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(39)/ return F.scalar_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(39)/ return F.scalar_mul(x, y)/
- %3 : I64 = %2(%para2235, %para2236) #(I64, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(39)/ return F.scalar_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(39)/ return F.scalar_mul(x, y)/
- }
-
-
- # [No.43] construct.38 @ctx.addr=0xaaaae39f4d20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_38[fg_0](
- %para2237 : Tensor(F32)[16, 64, 128, 128] # x
- ) {
- %1 : Tensor(F32)[16, 64, 128, 128] = FuncGraph::fg_96(%para2237, %para702, %para701) #(Tensor(F32)[16, 64, 128, 128], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 64, 3, 3]) # fg_96=L-construct.96(@ctx.addr=0xaaaae39f42d0) #scope: Default @ctx.addr=0xaaaae39f42d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/upconv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.44] construct.37 @ctx.addr=0xaaaae39faa50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_37(
- %para2238 : Tensor(F32)[16, 64, 128, 128] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU @ctx.addr=0xaaaae3a074c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_102, FuncGraph::fg_103) #(Bool, Func, Func) # fg_102=✓construct.102(@ctx.addr=0xaaaae3a07610), fg_103=✗construct.103 #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 64, 128, 128] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU @ctx.addr=0xaaaae3a07610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.44] construct.39 @ctx.addr=0xaaaae3a0fbf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_39[fg_0](
- %para2239 : Tensor(F32)[16, 64, 128, 128] # x
- ) {
- %1 : Tensor(F32)[16, 64, 128, 128] = FuncGraph::fg_96(%para2239, %para704, %para703) #(Tensor(F32)[16, 64, 128, 128], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 64, 3, 3]) # fg_96=L-construct.96(@ctx.addr=0xaaaae3a0fcf0) #scope: Default @ctx.addr=0xaaaae3a0fcf0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.45] construct.40 @ctx.addr=0xaaaae3a11250
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_40[fg_0](
- %para2240 : Tensor(F32)[16, 64, 128, 128] # x
- ) {
- %1 : Bool = FuncGraph::fg_29(Bool(1)) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_last-Conv2d @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_108, FuncGraph::fg_109) #(Bool, Func, Func) # fg_108=✓construct.108(@ctx.addr=0xaaaae3a10800), fg_109=✗construct.109 #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_last-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 3, 128, 128] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_last-Conv2d @ctx.addr=0xaaaae3a10800
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 3, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_last-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.46] _less_equal_scala.110 @ctx.addr=0xaaaae3a2ac30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_110(
- %para2241 : F32 # x
- , %para2242 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para2241, %para2242) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.47] ✓construct.41 @ctx.addr=0xaaaae3a2ad80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_41[fg_11](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2184) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 64, 128, 128] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2184) #(Tensor(F32)[], Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3a2c650
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 64, 128, 128] = %1(%7, %para2184) #(Tensor(F32)[16, 64, 128, 128], Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 64, 128, 128] = FuncGraph::fg_111(%8) #(Tensor(F32)[16, 64, 128, 128]) # fg_111=↓construct.111(@ctx.addr=0xaaaae3bafec0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3bafec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.48] bool_.29 @ctx.addr=0xaaaae0cad1c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(530)/def bool_(x):/
- funcgraph fg_29(
- %para2243 : Bool # x
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para2210, "__bool__") #(Bool, String) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- %2 : Bool = %1() #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- Primitive::Return{prim_type=1}(%2) #(Bool) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- }
-
-
- # [No.48] ✗construct.44 @ctx.addr=0xaaaae3a39150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_44[fg_12](
- ) {
- %1 : Tensor(F32)[16, 64, 64, 64] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(4), I64(4)), stride=(I64(1), I64(1), I64(2), I64(2)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para2185, %para2161) #(Tensor(F32)[16, 64, 128, 128], Ref[Tensor(F32)][64, 64, 4, 4]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 64, 64] = FuncGraph::fg_112(%1) #(Tensor(F32)[16, 64, 64, 64]) # fg_112=↓construct.112(@ctx.addr=0xaaaae3a393a0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_1-Conv2d @ctx.addr=0xaaaae3a393a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%2) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.49] ✓construct.45 @ctx.addr=0xaaaae3a39bc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- funcgraph fg_45[fg_13](
- ) {
- %1 : Tensor(F32)[16, 64, 64, 64] = FuncGraph::fg_113(Bool(1)) #(Bool) # fg_113=↓construct.113(@ctx.addr=0xaaaae3a4d930) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d @ctx.addr=0xaaaae3a4d930
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- }
-
-
- # [No.50] _less_equal_scala.114 @ctx.addr=0xaaaae3a59af0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_114(
- %para2244 : F32 # x
- , %para2245 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para2244, %para2245) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.51] ✓construct.41 @ctx.addr=0xaaaae3a574c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_41[fg_11](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2184) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 64, 64, 64] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2184) #(Tensor(F32)[], Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3a60ba0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 64, 64, 64] = %1(%7, %para2184) #(Tensor(F32)[16, 64, 64, 64], Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 64, 64, 64] = FuncGraph::fg_111(%8) #(Tensor(F32)[16, 64, 64, 64]) # fg_111=↓construct.111(@ctx.addr=0xaaaae3bafec0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3bafec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.51] ✗construct.48 @ctx.addr=0xaaaae3a67960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_48[fg_14](
- ) {
- %1 : Tensor(F32)[16, 128, 64, 64] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(128), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para2188, %para2156) #(Tensor(F32)[16, 64, 64, 64], Ref[Tensor(F32)][128, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv1_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 128, 64, 64] = FuncGraph::fg_115(%1) #(Tensor(F32)[16, 128, 64, 64]) # fg_115=↓construct.115(@ctx.addr=0xaaaae3a67bf0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv1_0-Conv2d @ctx.addr=0xaaaae3a67bf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%2) #(Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv1_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.52] L-construct.49 @ctx.addr=0xaaaae3a684b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- funcgraph fg_49(
- %para2246 : Tensor(F32)[16, 128, 64, 64] # Φx
- , %para2247 : Ref[Tensor(F32)][128] # L-network.network.D.bn1_1.gamma
- , %para2248 : Ref[Tensor(F32)][128] # L-network.network.D.bn1_1.beta
- , %para2249 : Ref[Tensor(F32)][128] # L-network.network.D.bn1_1.moving_mean
- , %para2250 : Ref[Tensor(F32)][128] # L-network.network.D.bn1_1.moving_variance
- ) {
- %1 : Tuple[I64*4] = DoSignaturePrimitive::S-Prim-Shape{prim_type=1}(%para2246) #(Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(184)/ _shape_check_bn(self.shape(x), self.input_dims)/
- %2 : NoneType = DoSignaturePrimitive::S-Prim-_shape_check_bn{prim_type=1}(%1, "2d") #(Tuple[I64*4], String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(184)/ _shape_check_bn(self.shape(x), self.input_dims)/
- %3 : NoneType = Primitive::stop_gradient{prim_type=1}(%2) #(NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- %4 : Bool = DoSignaturePrimitive::S-Prim-is_{prim_type=1}(None, None) #(NoneType, NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %5 : Bool = FuncGraph::fg_116(%4) #(Bool) # fg_116=L-bool_.116(@ctx.addr=0xaaaae3a79710) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d @ctx.addr=0xaaaae3a79710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %6 : Func = Primitive::Switch{prim_type=1}(%5, FuncGraph::fg_117, FuncGraph::fg_118) #(Bool, Func, Func) # fg_117=L-✓construct.117(@ctx.addr=0xaaaae3a79cf0), fg_118=L-✗construct.118 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %7 : Tensor(F32)[16, 128, 64, 64] = %6() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d @ctx.addr=0xaaaae3a79cf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %8 : Tensor(F32)[16, 128, 64, 64] = Primitive::Depend{prim_type=1}[side_effect_propagate=I64(1)](%7, %3) #(Tensor(F32)[16, 128, 64, 64], NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- Primitive::Return{prim_type=1}(%8) #(Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- }
-
-
- # [No.53] _less_equal_scala.119 @ctx.addr=0xaaaae3a83760
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_119(
- %para2251 : F32 # x
- , %para2252 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para2251, %para2252) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.54] ✓construct.41 @ctx.addr=0xaaaae3a82d10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_41[fg_11](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2184) #(Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 128, 64, 64] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2184) #(Tensor(F32)[], Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3a8af70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 128, 64, 64] = %1(%7, %para2184) #(Tensor(F32)[16, 128, 64, 64], Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 128, 64, 64] = FuncGraph::fg_111(%8) #(Tensor(F32)[16, 128, 64, 64]) # fg_111=↓construct.111(@ctx.addr=0xaaaae3bafec0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3bafec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.54] ✗construct.51 @ctx.addr=0xaaaae3a91d90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_51[fg_16](
- ) {
- %1 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(128), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(4), I64(4)), stride=(I64(1), I64(1), I64(2), I64(2)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para2191, %para2151) #(Tensor(F32)[16, 128, 64, 64], Ref[Tensor(F32)][128, 128, 4, 4]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv1_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 128, 32, 32] = FuncGraph::fg_120(%1) #(Tensor(F32)[16, 128, 32, 32]) # fg_120=↓construct.120(@ctx.addr=0xaaaae3a91fe0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv1_1-Conv2d @ctx.addr=0xaaaae3a91fe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%2) #(Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv1_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.55] L-construct.49 @ctx.addr=0xaaaae3a928a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- funcgraph fg_49(
- %para2253 : Tensor(F32)[16, 128, 32, 32] # Φx
- , %para2254 : Ref[Tensor(F32)][128] # L-network.network.D.bn1_1.gamma
- , %para2255 : Ref[Tensor(F32)][128] # L-network.network.D.bn1_1.beta
- , %para2256 : Ref[Tensor(F32)][128] # L-network.network.D.bn1_1.moving_mean
- , %para2257 : Ref[Tensor(F32)][128] # L-network.network.D.bn1_1.moving_variance
- ) {
- %1 : Tuple[I64*4] = DoSignaturePrimitive::S-Prim-Shape{prim_type=1}(%para2246) #(Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(184)/ _shape_check_bn(self.shape(x), self.input_dims)/
- %2 : NoneType = DoSignaturePrimitive::S-Prim-_shape_check_bn{prim_type=1}(%1, "2d") #(Tuple[I64*4], String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(184)/ _shape_check_bn(self.shape(x), self.input_dims)/
- %3 : NoneType = Primitive::stop_gradient{prim_type=1}(%2) #(NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- %4 : Bool = DoSignaturePrimitive::S-Prim-is_{prim_type=1}(None, None) #(NoneType, NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %5 : Bool = FuncGraph::fg_116(%4) #(Bool) # fg_116=L-bool_.116(@ctx.addr=0xaaaae3a79710) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d @ctx.addr=0xaaaae3a79710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %6 : Func = Primitive::Switch{prim_type=1}(%5, FuncGraph::fg_117, FuncGraph::fg_118) #(Bool, Func, Func) # fg_117=L-✓construct.117(@ctx.addr=0xaaaae3aa1f10), fg_118=L-✗construct.118 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %7 : Tensor(F32)[16, 128, 32, 32] = %6() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d @ctx.addr=0xaaaae3aa1f10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %8 : Tensor(F32)[16, 128, 32, 32] = Primitive::Depend{prim_type=1}[side_effect_propagate=I64(1)](%7, %3) #(Tensor(F32)[16, 128, 32, 32], NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- Primitive::Return{prim_type=1}(%8) #(Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- }
-
-
- # [No.55] _less_equal_scala.121 @ctx.addr=0xaaaae3aa7540
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_121(
- %para2258 : F32 # x
- , %para2259 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para2258, %para2259) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.56] ✓construct.41 @ctx.addr=0xaaaae3aa6af0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_41[fg_11](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2184) #(Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2184) #(Tensor(F32)[], Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3aad950
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 128, 32, 32] = %1(%7, %para2184) #(Tensor(F32)[16, 128, 32, 32], Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 128, 32, 32] = FuncGraph::fg_111(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_111=↓construct.111(@ctx.addr=0xaaaae3bafec0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3bafec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.56] ✗construct.53 @ctx.addr=0xaaaae3ab45e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_53[fg_18](
- ) {
- %1 : Tensor(F32)[16, 256, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(256), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para2194, %para2146) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][256, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv2_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 256, 32, 32] = FuncGraph::fg_122(%1) #(Tensor(F32)[16, 256, 32, 32]) # fg_122=↓construct.122(@ctx.addr=0xaaaae3ab4830) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv2_0-Conv2d @ctx.addr=0xaaaae3ab4830
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%2) #(Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv2_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.57] L-construct.54 @ctx.addr=0xaaaae3ab50d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- funcgraph fg_54(
- %para2260 : Tensor(F32)[16, 256, 32, 32] # Φx
- , %para2261 : Ref[Tensor(F32)][256] # L-network.network.D.bn2_1.gamma
- , %para2262 : Ref[Tensor(F32)][256] # L-network.network.D.bn2_1.beta
- , %para2263 : Ref[Tensor(F32)][256] # L-network.network.D.bn2_1.moving_mean
- , %para2264 : Ref[Tensor(F32)][256] # L-network.network.D.bn2_1.moving_variance
- ) {
- %1 : Tuple[I64*4] = DoSignaturePrimitive::S-Prim-Shape{prim_type=1}(%para2260) #(Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(184)/ _shape_check_bn(self.shape(x), self.input_dims)/
- %2 : NoneType = DoSignaturePrimitive::S-Prim-_shape_check_bn{prim_type=1}(%1, "2d") #(Tuple[I64*4], String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(184)/ _shape_check_bn(self.shape(x), self.input_dims)/
- %3 : NoneType = Primitive::stop_gradient{prim_type=1}(%2) #(NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- %4 : Bool = DoSignaturePrimitive::S-Prim-is_{prim_type=1}(None, None) #(NoneType, NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %5 : Bool = FuncGraph::fg_123(%4) #(Bool) # fg_123=L-bool_.123(@ctx.addr=0xaaaae3ac63a0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d @ctx.addr=0xaaaae3ac63a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %6 : Func = Primitive::Switch{prim_type=1}(%5, FuncGraph::fg_124, FuncGraph::fg_125) #(Bool, Func, Func) # fg_124=L-✓construct.124(@ctx.addr=0xaaaae3ac6980), fg_125=L-✗construct.125 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %7 : Tensor(F32)[16, 256, 32, 32] = %6() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d @ctx.addr=0xaaaae3ac6980
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %8 : Tensor(F32)[16, 256, 32, 32] = Primitive::Depend{prim_type=1}[side_effect_propagate=I64(1)](%7, %3) #(Tensor(F32)[16, 256, 32, 32], NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- Primitive::Return{prim_type=1}(%8) #(Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- }
-
-
- # [No.58] _less_equal_scala.126 @ctx.addr=0xaaaae3ad03f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_126(
- %para2265 : F32 # x
- , %para2266 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para2265, %para2266) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.59] ✓construct.41 @ctx.addr=0xaaaae3acf9a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_41[fg_11](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2184) #(Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 256, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2184) #(Tensor(F32)[], Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3ad7bf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 256, 32, 32] = %1(%7, %para2184) #(Tensor(F32)[16, 256, 32, 32], Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 256, 32, 32] = FuncGraph::fg_111(%8) #(Tensor(F32)[16, 256, 32, 32]) # fg_111=↓construct.111(@ctx.addr=0xaaaae3bafec0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3bafec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.59] ✗construct.56 @ctx.addr=0xaaaae3ade9b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_56[fg_20](
- ) {
- %1 : Tensor(F32)[16, 256, 16, 16] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(256), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(4), I64(4)), stride=(I64(1), I64(1), I64(2), I64(2)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para2197, %para2141) #(Tensor(F32)[16, 256, 32, 32], Ref[Tensor(F32)][256, 256, 4, 4]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv2_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 256, 16, 16] = FuncGraph::fg_127(%1) #(Tensor(F32)[16, 256, 16, 16]) # fg_127=↓construct.127(@ctx.addr=0xaaaae3adec40) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv2_1-Conv2d @ctx.addr=0xaaaae3adec40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%2) #(Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv2_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.60] L-construct.54 @ctx.addr=0xaaaae3adf500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- funcgraph fg_54(
- %para2267 : Tensor(F32)[16, 256, 16, 16] # Φx
- , %para2268 : Ref[Tensor(F32)][256] # L-network.network.D.bn2_1.gamma
- , %para2269 : Ref[Tensor(F32)][256] # L-network.network.D.bn2_1.beta
- , %para2270 : Ref[Tensor(F32)][256] # L-network.network.D.bn2_1.moving_mean
- , %para2271 : Ref[Tensor(F32)][256] # L-network.network.D.bn2_1.moving_variance
- ) {
- %1 : Tuple[I64*4] = DoSignaturePrimitive::S-Prim-Shape{prim_type=1}(%para2260) #(Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(184)/ _shape_check_bn(self.shape(x), self.input_dims)/
- %2 : NoneType = DoSignaturePrimitive::S-Prim-_shape_check_bn{prim_type=1}(%1, "2d") #(Tuple[I64*4], String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(184)/ _shape_check_bn(self.shape(x), self.input_dims)/
- %3 : NoneType = Primitive::stop_gradient{prim_type=1}(%2) #(NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- %4 : Bool = DoSignaturePrimitive::S-Prim-is_{prim_type=1}(None, None) #(NoneType, NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %5 : Bool = FuncGraph::fg_123(%4) #(Bool) # fg_123=L-bool_.123(@ctx.addr=0xaaaae3ac63a0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d @ctx.addr=0xaaaae3ac63a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %6 : Func = Primitive::Switch{prim_type=1}(%5, FuncGraph::fg_124, FuncGraph::fg_125) #(Bool, Func, Func) # fg_124=L-✓construct.124(@ctx.addr=0xaaaae3aeeae0), fg_125=L-✗construct.125 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %7 : Tensor(F32)[16, 256, 16, 16] = %6() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d @ctx.addr=0xaaaae3aeeae0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %8 : Tensor(F32)[16, 256, 16, 16] = Primitive::Depend{prim_type=1}[side_effect_propagate=I64(1)](%7, %3) #(Tensor(F32)[16, 256, 16, 16], NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- Primitive::Return{prim_type=1}(%8) #(Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- }
-
-
- # [No.60] _less_equal_scala.128 @ctx.addr=0xaaaae3af4130
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_128(
- %para2272 : F32 # x
- , %para2273 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para2272, %para2273) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.61] ✓construct.41 @ctx.addr=0xaaaae3af36e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_41[fg_11](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2184) #(Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 256, 16, 16] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2184) #(Tensor(F32)[], Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3afa500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 256, 16, 16] = %1(%7, %para2184) #(Tensor(F32)[16, 256, 16, 16], Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 256, 16, 16] = FuncGraph::fg_111(%8) #(Tensor(F32)[16, 256, 16, 16]) # fg_111=↓construct.111(@ctx.addr=0xaaaae3bafec0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3bafec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.61] ✗construct.58 @ctx.addr=0xaaaae3b01180
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_58[fg_22](
- ) {
- %1 : Tensor(F32)[16, 512, 16, 16] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(512), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para2200, %para2136) #(Tensor(F32)[16, 256, 16, 16], Ref[Tensor(F32)][512, 256, 3, 3]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv3_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 512, 16, 16] = FuncGraph::fg_129(%1) #(Tensor(F32)[16, 512, 16, 16]) # fg_129=↓construct.129(@ctx.addr=0xaaaae3b01410) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv3_0-Conv2d @ctx.addr=0xaaaae3b01410
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%2) #(Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv3_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.62] L-construct.59 @ctx.addr=0xaaaae3b01cd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- funcgraph fg_59(
- %para2274 : Tensor(F32)[16, 512, 16, 16] # Φx
- , %para2275 : Ref[Tensor(F32)][512] # L-network.network.D.bn3_1.gamma
- , %para2276 : Ref[Tensor(F32)][512] # L-network.network.D.bn3_1.beta
- , %para2277 : Ref[Tensor(F32)][512] # L-network.network.D.bn3_1.moving_mean
- , %para2278 : Ref[Tensor(F32)][512] # L-network.network.D.bn3_1.moving_variance
- ) {
- %1 : Tuple[I64*4] = DoSignaturePrimitive::S-Prim-Shape{prim_type=1}(%para2274) #(Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(184)/ _shape_check_bn(self.shape(x), self.input_dims)/
- %2 : NoneType = DoSignaturePrimitive::S-Prim-_shape_check_bn{prim_type=1}(%1, "2d") #(Tuple[I64*4], String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(184)/ _shape_check_bn(self.shape(x), self.input_dims)/
- %3 : NoneType = Primitive::stop_gradient{prim_type=1}(%2) #(NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- %4 : Bool = DoSignaturePrimitive::S-Prim-is_{prim_type=1}(None, None) #(NoneType, NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %5 : Bool = FuncGraph::fg_130(%4) #(Bool) # fg_130=L-bool_.130(@ctx.addr=0xaaaae3b13150) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d @ctx.addr=0xaaaae3b13150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %6 : Func = Primitive::Switch{prim_type=1}(%5, FuncGraph::fg_131, FuncGraph::fg_132) #(Bool, Func, Func) # fg_131=L-✓construct.131(@ctx.addr=0xaaaae3b13730), fg_132=L-✗construct.132 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %7 : Tensor(F32)[16, 512, 16, 16] = %6() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d @ctx.addr=0xaaaae3b13730
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %8 : Tensor(F32)[16, 512, 16, 16] = Primitive::Depend{prim_type=1}[side_effect_propagate=I64(1)](%7, %3) #(Tensor(F32)[16, 512, 16, 16], NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- Primitive::Return{prim_type=1}(%8) #(Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- }
-
-
- # [No.63] _less_equal_scala.133 @ctx.addr=0xaaaae3b1d1a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_133(
- %para2279 : F32 # x
- , %para2280 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para2279, %para2280) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.64] ✓construct.41 @ctx.addr=0xaaaae3b1c750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_41[fg_11](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2184) #(Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 512, 16, 16] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2184) #(Tensor(F32)[], Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3b24ae0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 512, 16, 16] = %1(%7, %para2184) #(Tensor(F32)[16, 512, 16, 16], Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 512, 16, 16] = FuncGraph::fg_111(%8) #(Tensor(F32)[16, 512, 16, 16]) # fg_111=↓construct.111(@ctx.addr=0xaaaae3bafec0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3bafec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.64] ✗construct.61 @ctx.addr=0xaaaae266d440
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_61[fg_24](
- ) {
- %1 : Tensor(F32)[16, 512, 8, 8] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(512), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(4), I64(4)), stride=(I64(1), I64(1), I64(2), I64(2)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para2203, %para2133) #(Tensor(F32)[16, 512, 16, 16], Ref[Tensor(F32)][512, 512, 4, 4]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv3_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 512, 8, 8] = FuncGraph::fg_134(%1) #(Tensor(F32)[16, 512, 8, 8]) # fg_134=↓construct.134(@ctx.addr=0xaaaae266d6d0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv3_1-Conv2d @ctx.addr=0xaaaae266d6d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%2) #(Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv3_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.65] L-construct.59 @ctx.addr=0xaaaae266df90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- funcgraph fg_59(
- %para2281 : Tensor(F32)[16, 512, 8, 8] # Φx
- , %para2282 : Ref[Tensor(F32)][512] # L-network.network.D.bn3_1.gamma
- , %para2283 : Ref[Tensor(F32)][512] # L-network.network.D.bn3_1.beta
- , %para2284 : Ref[Tensor(F32)][512] # L-network.network.D.bn3_1.moving_mean
- , %para2285 : Ref[Tensor(F32)][512] # L-network.network.D.bn3_1.moving_variance
- ) {
- %1 : Tuple[I64*4] = DoSignaturePrimitive::S-Prim-Shape{prim_type=1}(%para2274) #(Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(184)/ _shape_check_bn(self.shape(x), self.input_dims)/
- %2 : NoneType = DoSignaturePrimitive::S-Prim-_shape_check_bn{prim_type=1}(%1, "2d") #(Tuple[I64*4], String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(184)/ _shape_check_bn(self.shape(x), self.input_dims)/
- %3 : NoneType = Primitive::stop_gradient{prim_type=1}(%2) #(NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- %4 : Bool = DoSignaturePrimitive::S-Prim-is_{prim_type=1}(None, None) #(NoneType, NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %5 : Bool = FuncGraph::fg_130(%4) #(Bool) # fg_130=L-bool_.130(@ctx.addr=0xaaaae3b13150) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d @ctx.addr=0xaaaae3b13150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %6 : Func = Primitive::Switch{prim_type=1}(%5, FuncGraph::fg_131, FuncGraph::fg_132) #(Bool, Func, Func) # fg_131=L-✓construct.131(@ctx.addr=0xaaaae3b42fb0), fg_132=L-✗construct.132 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %7 : Tensor(F32)[16, 512, 8, 8] = %6() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d @ctx.addr=0xaaaae3b42fb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- %8 : Tensor(F32)[16, 512, 8, 8] = Primitive::Depend{prim_type=1}[side_effect_propagate=I64(1)](%7, %3) #(Tensor(F32)[16, 512, 8, 8], NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- Primitive::Return{prim_type=1}(%8) #(Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(183)/ def construct(self, x):/
- }
-
-
- # [No.65] _less_equal_scala.135 @ctx.addr=0xaaaae3b48600
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_135(
- %para2286 : F32 # x
- , %para2287 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para2286, %para2287) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.66] ✓construct.41 @ctx.addr=0xaaaae3b47bb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_41[fg_11](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2184) #(Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 512, 8, 8] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2184) #(Tensor(F32)[], Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3b4e9a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 512, 8, 8] = %1(%7, %para2184) #(Tensor(F32)[16, 512, 8, 8], Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 512, 8, 8] = FuncGraph::fg_111(%8) #(Tensor(F32)[16, 512, 8, 8]) # fg_111=↓construct.111(@ctx.addr=0xaaaae3bafec0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3bafec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.66] _tuple_getitem_by_number.136 @ctx.addr=0xaaaae3b56110
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_136(
- %para2288 : Tuple[I64*4] # data
- , %para2289 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : I64 = %2(%para2288, %para2289) #(Tuple[I64*4], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.67] _neg_scalar.137 @ctx.addr=0xaaaae3b5ef40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/negative_impl.py(30)/def _neg_scalar(x):/
- funcgraph fg_137(
- %para2290 : I64 # x
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/flatten-Flatten
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/negative_impl.py(37)/ return F.scalar_usub(x)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_usub") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/flatten-Flatten
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/negative_impl.py(37)/ return F.scalar_usub(x)/
- %3 : I64 = %2(%para2290) #(I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/flatten-Flatten
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/negative_impl.py(37)/ return F.scalar_usub(x)/
- Primitive::Return{prim_type=1}(%3) #(I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/flatten-Flatten
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/negative_impl.py(37)/ return F.scalar_usub(x)/
- }
-
-
- # [No.68] L-construct.62 @ctx.addr=0xaaaae3b5f1e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(298)/ def construct(self, x):/
- funcgraph fg_62(
- %para2291 : Tensor(F32)[16, 32768] # x
- , %para2292 : Ref[Tensor(F32)][100] # L-network.network.D.linear2.bias
- , %para2293 : Ref[Tensor(F32)][100, 32768] # L-network.network.D.linear2.weight
- ) {
- %1 : Tuple[I64*2] = DoSignaturePrimitive::S-Prim-Shape{prim_type=1}(%para2291) #(Tensor(F32)[16, 32768]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- %2 : NoneType = DoSignaturePrimitive::S-Prim-check_dense_input_shape{prim_type=1}(%1) #(Tuple[I64*2]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(300)/ check_dense_input_shape(x_shape)/
- %3 : NoneType = Primitive::stop_gradient{prim_type=1}(%2) #(NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(298)/ def construct(self, x):/
- %4 : I64 = FuncGraph::fg_138(%1) #(Tuple[I64*2]) # fg_138=L-ms_len.138(@ctx.addr=0xaaaae3bb7960) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bb7960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- %5 : Bool = DoSignaturePrimitive::S-Prim-not_equal{prim_type=1}(%4, I64(2)) #(I64, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b7e3d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- %6 : Bool = FuncGraph::fg_139(%5) #(Bool) # fg_139=L-bool_.139(@ctx.addr=0xaaaae3b7e750) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b7e750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- %7 : Func = Primitive::Switch{prim_type=1}(%6, FuncGraph::fg_140, FuncGraph::fg_141) #(Bool, Func, Func) # fg_140=L-✓construct.140, fg_141=L-✗construct.141(@ctx.addr=0xaaaae3b7fd40) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- %8 : Tensor(F32)[16, 100] = %7() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b7fd40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- %9 : Tensor(F32)[16, 100] = Primitive::Depend{prim_type=1}[side_effect_propagate=I64(1)](%8, %3) #(Tensor(F32)[16, 100], NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(298)/ def construct(self, x):/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(298)/ def construct(self, x):/
- }
-
-
- # [No.69] _less_equal_scala.142 @ctx.addr=0xaaaae3baeef0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_142(
- %para2294 : F32 # x
- , %para2295 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para2294, %para2295) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.70] ✓construct.41 @ctx.addr=0xaaaae3baf040
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_41[fg_11](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2184) #(Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 100] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2184) #(Tensor(F32)[], Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3bb0910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 100] = %1(%7, %para2184) #(Tensor(F32)[16, 100], Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 100] = FuncGraph::fg_111(%8) #(Tensor(F32)[16, 100]) # fg_111=↓construct.111(@ctx.addr=0xaaaae3bafec0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU @ctx.addr=0xaaaae3bafec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.70] L-construct.62 @ctx.addr=0xaaaae3bb76d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(298)/ def construct(self, x):/
- funcgraph fg_62(
- %para2296 : Tensor(F32)[16, 100] # x
- , %para2297 : Ref[Tensor(F32)][1] # L-network.network.D.linear2.bias
- , %para2298 : Ref[Tensor(F32)][1, 100] # L-network.network.D.linear2.weight
- ) {
- %1 : Tuple[I64*2] = DoSignaturePrimitive::S-Prim-Shape{prim_type=1}(%para2291) #(Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- %2 : NoneType = DoSignaturePrimitive::S-Prim-check_dense_input_shape{prim_type=1}(%1) #(Tuple[I64*2]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(300)/ check_dense_input_shape(x_shape)/
- %3 : NoneType = Primitive::stop_gradient{prim_type=1}(%2) #(NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(298)/ def construct(self, x):/
- %4 : I64 = FuncGraph::fg_138(%1) #(Tuple[I64*2]) # fg_138=L-ms_len.138(@ctx.addr=0xaaaae3bb7960) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bb7960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- %5 : Bool = DoSignaturePrimitive::S-Prim-not_equal{prim_type=1}(%4, I64(2)) #(I64, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bb8220
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- %6 : Bool = FuncGraph::fg_139(%5) #(Bool) # fg_139=L-bool_.139(@ctx.addr=0xaaaae3b7e750) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b7e750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- %7 : Func = Primitive::Switch{prim_type=1}(%6, FuncGraph::fg_140, FuncGraph::fg_141) #(Bool, Func, Func) # fg_140=L-✓construct.140, fg_141=L-✗construct.141(@ctx.addr=0xaaaae3bc33d0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- %8 : Tensor(F32)[16, 1] = %7() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bc33d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- %9 : Tensor(F32)[16, 1] = Primitive::Depend{prim_type=1}[side_effect_propagate=I64(1)](%8, %3) #(Tensor(F32)[16, 1], NoneType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(298)/ def construct(self, x):/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 1]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(298)/ def construct(self, x):/
- }
-
-
- # [No.70] _logical_not_scala.143 @ctx.addr=0xaaaae0db6df0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(26)/def _logical_not_scala(x):/
- funcgraph fg_143(
- %para2299 : Bool # x
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "bool_not") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %3 : Func = Primitive::getattr{prim_type=1}(%para2299, "__bool__") #(Bool, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %4 : Bool = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %5 : Bool = %2(%4) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- Primitive::Return{prim_type=1}(%5) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- }
-
-
- # [No.71] ↱astype.65 @ctx.addr=0xaaaadd9809f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- funcgraph fg_65[fg_63](
- ) {
- %1 : Func = Primitive::resolve{prim_type=1}(NameSpace::Ast, not_) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(0)/
- %2 : Bool = %1(Bool(1)) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae0db6df0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- Primitive::Return{prim_type=1}(%2) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- }
-
-
- # [No.72] ✗astype.67 @ctx.addr=0xaaaadcc7fc70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- funcgraph fg_67[fg_63](
- ) {
- %1 : Tensor(F32)[16, 3, 32, 32] = FuncGraph::fg_144() # fg_144=↓astype.144(@ctx.addr=0xaaaae34e6e10) #scope: Default/G-GeneratorLossCell/G-RRDBNet @ctx.addr=0xaaaae34e6e10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 3, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- }
-
-
- # [No.73] ✓construct.68 @ctx.addr=0xaaaad7f5ab50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_68[fg_33](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para2214, %para5) #(Tensor(F32)[16, 3, 32, 32], Ref[Tensor(F32)][64, 3, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_first-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para6) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_first-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_145(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_145=↓construct.145(@ctx.addr=0xaaaadc8992f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_first-Conv2d @ctx.addr=0xaaaadc8992f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_first-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.74] construct.70 @ctx.addr=0xaaaaebd8cc70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_70[fg_0](
- %para2300 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_146(%para2300) #(Tensor(F32)[16, 64, 32, 32]) # fg_146=construct.146(@ctx.addr=0xaaaae23b4b20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB @ctx.addr=0xaaaae23b4b20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_147(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_147=construct.147(@ctx.addr=0xaaaad61b20e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB @ctx.addr=0xaaaad61b20e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_148(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_148=construct.148(@ctx.addr=0xaaaae8491460) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB @ctx.addr=0xaaaae8491460
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB @ctx.addr=0xaaaadc46c8e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2300) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB @ctx.addr=0xaaaaddb31820
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.75] construct.71 @ctx.addr=0xaaaaf1779fc0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_71[fg_0](
- %para2301 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_149(%para2301) #(Tensor(F32)[16, 64, 32, 32]) # fg_149=construct.149(@ctx.addr=0xaaaaf733f5e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB @ctx.addr=0xaaaaf733f5e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_150(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_150=construct.150(@ctx.addr=0xaaaaf4a6f090) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB @ctx.addr=0xaaaaf4a6f090
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_151(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_151=construct.151(@ctx.addr=0xaaaae84beba0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB @ctx.addr=0xaaaae84beba0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB @ctx.addr=0xaaaae238c9a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2301) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB @ctx.addr=0xaaaafa6eb140
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.76] construct.72 @ctx.addr=0xaaaae1f68670
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_72[fg_0](
- %para2302 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_152(%para2302) #(Tensor(F32)[16, 64, 32, 32]) # fg_152=construct.152(@ctx.addr=0xaaaaf21c8410) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB @ctx.addr=0xaaaaf21c8410
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_153(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_153=construct.153(@ctx.addr=0xaaaaf2aa14f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB @ctx.addr=0xaaaaf2aa14f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_154(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_154=construct.154(@ctx.addr=0xaaaafae72b20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB @ctx.addr=0xaaaafae72b20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB @ctx.addr=0xaaaae24fb8e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2302) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB @ctx.addr=0xaaaae1e4a170
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.77] construct.73 @ctx.addr=0xaaaadd25dfa0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_73[fg_0](
- %para2303 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_155(%para2303) #(Tensor(F32)[16, 64, 32, 32]) # fg_155=construct.155(@ctx.addr=0xaaaae11c08e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB @ctx.addr=0xaaaae11c08e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_156(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_156=construct.156(@ctx.addr=0xaaaae5649660) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB @ctx.addr=0xaaaae5649660
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_157(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_157=construct.157(@ctx.addr=0xaaaaefd0da50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB @ctx.addr=0xaaaaefd0da50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB @ctx.addr=0xaaaad5c94230
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2303) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB @ctx.addr=0xaaaae98a6890
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.78] construct.74 @ctx.addr=0xaaaadec3a420
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_74[fg_0](
- %para2304 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_158(%para2304) #(Tensor(F32)[16, 64, 32, 32]) # fg_158=construct.158(@ctx.addr=0xaaaafaee10d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB @ctx.addr=0xaaaafaee10d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_159(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_159=construct.159(@ctx.addr=0xaaaaf0c790a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB @ctx.addr=0xaaaaf0c790a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_160(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_160=construct.160(@ctx.addr=0xaaaae2472280) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB @ctx.addr=0xaaaae2472280
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB @ctx.addr=0xaaaaf2cc8c60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2304) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB @ctx.addr=0xaaaae23bec70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.79] construct.75 @ctx.addr=0xaaaaed7bde00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_75[fg_0](
- %para2305 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_161(%para2305) #(Tensor(F32)[16, 64, 32, 32]) # fg_161=construct.161(@ctx.addr=0xaaaae84611f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB @ctx.addr=0xaaaae84611f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_162(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_162=construct.162(@ctx.addr=0xaaaae6f7ed40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB @ctx.addr=0xaaaae6f7ed40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_163(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_163=construct.163(@ctx.addr=0xaaaad813bdf0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB @ctx.addr=0xaaaad813bdf0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB @ctx.addr=0xaaaae8228000
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2305) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB @ctx.addr=0xaaaae21ed900
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.80] construct.76 @ctx.addr=0xaaaaee562fc0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_76[fg_0](
- %para2306 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_164(%para2306) #(Tensor(F32)[16, 64, 32, 32]) # fg_164=construct.164(@ctx.addr=0xaaaadce2cb40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB @ctx.addr=0xaaaadce2cb40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_165(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_165=construct.165(@ctx.addr=0xaaaae81d7750) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB @ctx.addr=0xaaaae81d7750
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_166(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_166=construct.166(@ctx.addr=0xaaaae210af70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB @ctx.addr=0xaaaae210af70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB @ctx.addr=0xaaaae851fad0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2306) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB @ctx.addr=0xaaaadec0dd70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.81] construct.77 @ctx.addr=0xaaaaefa4f520
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_77[fg_0](
- %para2307 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_167(%para2307) #(Tensor(F32)[16, 64, 32, 32]) # fg_167=construct.167(@ctx.addr=0xaaaaf48c6c60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB @ctx.addr=0xaaaaf48c6c60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_168(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_168=construct.168(@ctx.addr=0xaaaaf1f58560) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB @ctx.addr=0xaaaaf1f58560
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_169(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_169=construct.169(@ctx.addr=0xaaaae6994220) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB @ctx.addr=0xaaaae6994220
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB @ctx.addr=0xaaaaea14e370
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2307) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB @ctx.addr=0xaaaae1f4a4d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.82] construct.78 @ctx.addr=0xaaaaed73b7b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_78[fg_0](
- %para2308 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_170(%para2308) #(Tensor(F32)[16, 64, 32, 32]) # fg_170=construct.170(@ctx.addr=0xaaaaf2492ef0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB @ctx.addr=0xaaaaf2492ef0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_171(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_171=construct.171(@ctx.addr=0xaaaaf10b8d50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB @ctx.addr=0xaaaaf10b8d50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_172(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_172=construct.172(@ctx.addr=0xaaaad6a035e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB @ctx.addr=0xaaaad6a035e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB @ctx.addr=0xaaaae217ab20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2308) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB @ctx.addr=0xaaaafaed8280
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.83] construct.79 @ctx.addr=0xaaaae8380080
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_79[fg_0](
- %para2309 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_173(%para2309) #(Tensor(F32)[16, 64, 32, 32]) # fg_173=construct.173(@ctx.addr=0xaaaae2485d70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB @ctx.addr=0xaaaae2485d70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_174(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_174=construct.174(@ctx.addr=0xaaaaec606420) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB @ctx.addr=0xaaaaec606420
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_175(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_175=construct.175(@ctx.addr=0xaaaae21d8f10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB @ctx.addr=0xaaaae21d8f10
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB @ctx.addr=0xaaaad8cb1630
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2309) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB @ctx.addr=0xaaaaebfaba40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.84] construct.80 @ctx.addr=0xaaaae250d460
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_80[fg_0](
- %para2310 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_176(%para2310) #(Tensor(F32)[16, 64, 32, 32]) # fg_176=construct.176(@ctx.addr=0xaaaae21e55b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB @ctx.addr=0xaaaae21e55b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_177(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_177=construct.177(@ctx.addr=0xaaaafadb51b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB @ctx.addr=0xaaaafadb51b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_178(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_178=construct.178(@ctx.addr=0xaaaaf43fe5a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB @ctx.addr=0xaaaaf43fe5a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB @ctx.addr=0xaaaad90aa330
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2310) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB @ctx.addr=0xaaaae0aa8ca0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.85] construct.81 @ctx.addr=0xaaaae2573890
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_81[fg_0](
- %para2311 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_179(%para2311) #(Tensor(F32)[16, 64, 32, 32]) # fg_179=construct.179(@ctx.addr=0xaaaae1f0b8d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB @ctx.addr=0xaaaae1f0b8d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_180(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_180=construct.180(@ctx.addr=0xaaaae55363f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB @ctx.addr=0xaaaae55363f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_181(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_181=construct.181(@ctx.addr=0xaaaae6be96c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB @ctx.addr=0xaaaae6be96c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB @ctx.addr=0xaaaaf3ab1260
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2311) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB @ctx.addr=0xaaaae101a820
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.86] construct.82 @ctx.addr=0xaaaad9b96ae0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_82[fg_0](
- %para2312 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_182(%para2312) #(Tensor(F32)[16, 64, 32, 32]) # fg_182=construct.182(@ctx.addr=0xaaaadc689be0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB @ctx.addr=0xaaaadc689be0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_183(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_183=construct.183(@ctx.addr=0xaaaae9874e40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB @ctx.addr=0xaaaae9874e40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_184(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_184=construct.184(@ctx.addr=0xaaaaf3ee0fa0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB @ctx.addr=0xaaaaf3ee0fa0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB @ctx.addr=0xaaaad74242d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2312) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB @ctx.addr=0xaaaad6e44500
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.87] construct.83 @ctx.addr=0xaaaaf4dde750
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_83[fg_0](
- %para2313 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_185(%para2313) #(Tensor(F32)[16, 64, 32, 32]) # fg_185=construct.185(@ctx.addr=0xaaaae0eee8e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB @ctx.addr=0xaaaae0eee8e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_186(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_186=construct.186(@ctx.addr=0xaaaaf709d480) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB @ctx.addr=0xaaaaf709d480
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_187(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_187=construct.187(@ctx.addr=0xaaaafae4dec0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB @ctx.addr=0xaaaafae4dec0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB @ctx.addr=0xaaaae23a29e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2313) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB @ctx.addr=0xaaaaf0dea030
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.88] construct.84 @ctx.addr=0xaaaaedd66cb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_84[fg_0](
- %para2314 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_188(%para2314) #(Tensor(F32)[16, 64, 32, 32]) # fg_188=construct.188(@ctx.addr=0xaaaae2286150) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB @ctx.addr=0xaaaae2286150
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_189(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_189=construct.189(@ctx.addr=0xaaaae205a960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB @ctx.addr=0xaaaae205a960
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_190(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_190=construct.190(@ctx.addr=0xaaaae85f96c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB @ctx.addr=0xaaaae85f96c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB @ctx.addr=0xaaaae246cb60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2314) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB @ctx.addr=0xaaaae22a1c40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.89] construct.85 @ctx.addr=0xaaaad6995330
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_85[fg_0](
- %para2315 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_191(%para2315) #(Tensor(F32)[16, 64, 32, 32]) # fg_191=construct.191(@ctx.addr=0xaaaad942ff50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB @ctx.addr=0xaaaad942ff50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_192(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_192=construct.192(@ctx.addr=0xaaaaeede3b50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB @ctx.addr=0xaaaaeede3b50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_193(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_193=construct.193(@ctx.addr=0xaaaae248ceb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB @ctx.addr=0xaaaae248ceb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB @ctx.addr=0xaaaae225ca60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2315) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB @ctx.addr=0xaaaae2096180
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.90] construct.86 @ctx.addr=0xaaaaeb60ddc0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_86[fg_0](
- %para2316 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_194(%para2316) #(Tensor(F32)[16, 64, 32, 32]) # fg_194=construct.194(@ctx.addr=0xaaaae22d0570) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB @ctx.addr=0xaaaae22d0570
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_195(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_195=construct.195(@ctx.addr=0xaaaae2317950) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB @ctx.addr=0xaaaae2317950
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_196(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_196=construct.196(@ctx.addr=0xaaaae2522f00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB @ctx.addr=0xaaaae2522f00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB @ctx.addr=0xaaaae205e0a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2316) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB @ctx.addr=0xaaaaeedd46d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.91] construct.87 @ctx.addr=0xaaaaf4a76eb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_87[fg_0](
- %para2317 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_197(%para2317) #(Tensor(F32)[16, 64, 32, 32]) # fg_197=construct.197(@ctx.addr=0xaaaae233f2e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB @ctx.addr=0xaaaae233f2e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_198(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_198=construct.198(@ctx.addr=0xaaaae2443740) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB @ctx.addr=0xaaaae2443740
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_199(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_199=construct.199(@ctx.addr=0xaaaae85c93d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB @ctx.addr=0xaaaae85c93d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB @ctx.addr=0xaaaae2395290
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2317) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB @ctx.addr=0xaaaafae22740
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.92] construct.88 @ctx.addr=0xaaaade3e0ac0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_88[fg_0](
- %para2318 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_200(%para2318) #(Tensor(F32)[16, 64, 32, 32]) # fg_200=construct.200(@ctx.addr=0xaaaaf17caae0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB @ctx.addr=0xaaaaf17caae0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_201(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_201=construct.201(@ctx.addr=0xaaaae267c010) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB @ctx.addr=0xaaaae267c010
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_202(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_202=construct.202(@ctx.addr=0xaaaae273a300) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB @ctx.addr=0xaaaae273a300
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB @ctx.addr=0xaaaae2789640
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2318) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB @ctx.addr=0xaaaae278f4b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.93] construct.89 @ctx.addr=0xaaaae27c7b30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_89[fg_0](
- %para2319 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_203(%para2319) #(Tensor(F32)[16, 64, 32, 32]) # fg_203=construct.203(@ctx.addr=0xaaaae27a3e30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB @ctx.addr=0xaaaae27a3e30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_204(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_204=construct.204(@ctx.addr=0xaaaae280af50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB @ctx.addr=0xaaaae280af50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_205(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_205=construct.205(@ctx.addr=0xaaaae2852300) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB @ctx.addr=0xaaaae2852300
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB @ctx.addr=0xaaaae28a1610
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2319) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB @ctx.addr=0xaaaae28a7290
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.94] construct.90 @ctx.addr=0xaaaae28de3d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_90[fg_0](
- %para2320 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_206(%para2320) #(Tensor(F32)[16, 64, 32, 32]) # fg_206=construct.206(@ctx.addr=0xaaaae28cb590) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB @ctx.addr=0xaaaae28cb590
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_207(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_207=construct.207(@ctx.addr=0xaaaae29215b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB @ctx.addr=0xaaaae29215b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_208(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_208=construct.208(@ctx.addr=0xaaaae36f8a20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB @ctx.addr=0xaaaae36f8a20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB @ctx.addr=0xaaaae3746f00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2320) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB @ctx.addr=0xaaaae374cd70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.95] construct.91 @ctx.addr=0xaaaae3782510
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_91[fg_0](
- %para2321 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_209(%para2321) #(Tensor(F32)[16, 64, 32, 32]) # fg_209=construct.209(@ctx.addr=0xaaaae3770dd0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB @ctx.addr=0xaaaae3770dd0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_210(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_210=construct.210(@ctx.addr=0xaaaae37c63d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB @ctx.addr=0xaaaae37c63d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_211(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_211=construct.211(@ctx.addr=0xaaaae380d780) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB @ctx.addr=0xaaaae380d780
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB @ctx.addr=0xaaaae385ca70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2321) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB @ctx.addr=0xaaaae38628e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.96] construct.92 @ctx.addr=0xaaaae3896870
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(55)/ def construct(self, x)://
- funcgraph fg_92[fg_0](
- %para2322 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_212(%para2322) #(Tensor(F32)[16, 64, 32, 32]) # fg_212=construct.212(@ctx.addr=0xaaaae38868b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB @ctx.addr=0xaaaae38868b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(56)/ out = self.RDB1(x)//
- %2 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_213(%1) #(Tensor(F32)[16, 64, 32, 32]) # fg_213=construct.213(@ctx.addr=0xaaaae38d9a40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB @ctx.addr=0xaaaae38d9a40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(57)/ out = self.RDB2(out)//
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_214(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_214=construct.214(@ctx.addr=0xaaaae3920de0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB @ctx.addr=0xaaaae3920de0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(58)/ out = self.RDB3(out)//
- %4 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%3, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB @ctx.addr=0xaaaae3970070
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- %5 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%4, %para2322) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB @ctx.addr=0xaaaae3975ee0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(59)/ return out * 0.2 + x//
- }
-
-
- # [No.97] ms_len.93 @ctx.addr=0xaaaadf8a87b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(495)/def ms_len(data):/
- funcgraph fg_93(
- %para2323 : Tuple[Func*23] # data
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para2323, "__len__") #(Tuple[Func*23], String) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(497)/ return data.__len__()/
- %2 : I64 = %1() #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(497)/ return data.__len__()/
- Primitive::Return{prim_type=1}(%2) #(I64) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(497)/ return data.__len__()/
- }
-
-
- # [No.98] ✓construct.94 @ctx.addr=0xaaaaf6f2efe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_94[fg_34](
- ) {
- %1 : Tuple[Func*23] = Primitive::MakeTuple{prim_type=1}(FuncGraph::fg_70, FuncGraph::fg_71, FuncGraph::fg_72, FuncGraph::fg_73, FuncGraph::fg_74, FuncGraph::fg_75, FuncGraph::fg_76, FuncGraph::fg_77, FuncGraph::fg_78, FuncGraph::fg_79, FuncGraph::fg_80, FuncGraph::fg_81, FuncGraph::fg_82, FuncGraph::fg_83, FuncGraph::fg_84, FuncGraph::fg_85, FuncGraph::fg_86, FuncGraph::fg_87, FuncGraph::fg_88, FuncGraph::fg_89, FuncGraph::fg_90, FuncGraph::fg_91, FuncGraph::fg_92) #(Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func, Func) # fg_70=construct.70(@ctx.addr=0xaaaaebd8cc70), fg_71=construct.71(@ctx.addr=0xaaaaf1779fc0), fg_72=construct.72(@ctx.addr=0xaaaae1f68670), fg_73=construct.73(@ctx.addr=0xaaaadd25dfa0), fg_74=construct.74(@ctx.addr=0xaaaadec3a420), fg_75=construct.75(@ctx.addr=0xaaaaed7bde00), fg_76=construct.76(@ctx.addr=0xaaaaee562fc0), fg_77=construct.77(@ctx.addr=0xaaaaefa4f520), fg_78=construct.78(@ctx.addr=0xaaaaed73b7b0), fg_79=construct.79(@ctx.addr=0xaaaae8380080), fg_80=construct.80(@ctx.addr=0xaaaae250d460), fg_81=construct.81(@ctx.addr=0xaaaae2573890), fg_82=construct.82(@ctx.addr=0xaaaad9b96ae0), fg_83=construct.83(@ctx.addr=0xaaaaf4dde750), fg_84=construct.84(@ctx.addr=0xaaaaedd66cb0), fg_85=construct.85(@ctx.addr=0xaaaad6995330), fg_86=construct.86(@ctx.addr=0xaaaaeb60ddc0), fg_87=construct.87(@ctx.addr=0xaaaaf4a76eb0), fg_88=construct.88(@ctx.addr=0xaaaade3e0ac0), fg_89=construct.89(@ctx.addr=0xaaaae27c7b30), fg_90=construct.90(@ctx.addr=0xaaaae28de3d0), fg_91=construct.91(@ctx.addr=0xaaaae3782510), fg_92=construct.92(@ctx.addr=0xaaaae3896870) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*23] = FuncGraph::fg_215(%1) #(Tuple[Func*23]) # fg_215=ms_iter.215(@ctx.addr=0xaaaad95f2670) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaad95f2670
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %para2215) #(Tuple[Func*23], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaaf3089d40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf3089d40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.99] L-construct.96 @ctx.addr=0xaaaae397f810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_96(
- %para2324 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2325 : Ref[Tensor(F32)][64] # L-network.network.G.HRconv.bias
- , %para2326 : Ref[Tensor(F32)][64, 64, 3, 3] # L-network.network.G.HRconv.weight
- ) {
- %1 : Bool = FuncGraph::fg_217(Bool(1)) #(Bool) # fg_217=L-bool_.217(@ctx.addr=0xaaaae3989a00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d @ctx.addr=0xaaaae3989a00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_218, FuncGraph::fg_219) #(Bool, Func, Func) # fg_218=L-✓construct.218(@ctx.addr=0xaaaae398ad70), fg_219=L-✗construct.219 #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d @ctx.addr=0xaaaae398ad70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.100] L-construct.96 @ctx.addr=0xaaaae39bbae0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_96(
- %para2327 : Tensor(F32)[16, 64, 64, 64] # x
- , %para2328 : Ref[Tensor(F32)][64] # L-network.network.G.HRconv.bias
- , %para2329 : Ref[Tensor(F32)][64, 64, 3, 3] # L-network.network.G.HRconv.weight
- ) {
- %1 : Bool = FuncGraph::fg_217(Bool(1)) #(Bool) # fg_217=L-bool_.217(@ctx.addr=0xaaaae3989a00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d @ctx.addr=0xaaaae3989a00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_218, FuncGraph::fg_219) #(Bool, Func, Func) # fg_218=L-✓construct.218(@ctx.addr=0xaaaae39bb7b0), fg_219=L-✗construct.219 #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 64, 64] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d @ctx.addr=0xaaaae39bb7b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.100] _less_equal_scala.220 @ctx.addr=0xaaaae39cf760
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_220(
- %para2330 : F32 # x
- , %para2331 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para2330, %para2331) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.101] ✓construct.102 @ctx.addr=0xaaaae39cf8b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_102[fg_37](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2228) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 64, 64, 64] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2228) #(Tensor(F32)[], Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU @ctx.addr=0xaaaae39d1180
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 64, 64, 64] = %1(%7, %para2228) #(Tensor(F32)[16, 64, 64, 64], Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 64, 64, 64] = FuncGraph::fg_221(%8) #(Tensor(F32)[16, 64, 64, 64]) # fg_221=↓construct.221(@ctx.addr=0xaaaae3a08490) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU @ctx.addr=0xaaaae3a08490
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.102] L-construct.96 @ctx.addr=0xaaaae39f42d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_96(
- %para2332 : Tensor(F32)[16, 64, 128, 128] # x
- , %para2333 : Ref[Tensor(F32)][64] # L-network.network.G.HRconv.bias
- , %para2334 : Ref[Tensor(F32)][64, 64, 3, 3] # L-network.network.G.HRconv.weight
- ) {
- %1 : Bool = FuncGraph::fg_217(Bool(1)) #(Bool) # fg_217=L-bool_.217(@ctx.addr=0xaaaae3989a00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d @ctx.addr=0xaaaae3989a00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_218, FuncGraph::fg_219) #(Bool, Func, Func) # fg_218=L-✓construct.218(@ctx.addr=0xaaaae39f9640), fg_219=L-✗construct.219 #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 128, 128] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d @ctx.addr=0xaaaae39f9640
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.102] _less_equal_scala.222 @ctx.addr=0xaaaae3a074c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_222(
- %para2335 : F32 # x
- , %para2336 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para2335, %para2336) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.103] ✓construct.102 @ctx.addr=0xaaaae3a07610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_102[fg_37](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2228) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 64, 128, 128] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2228) #(Tensor(F32)[], Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU @ctx.addr=0xaaaae3a08ee0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 64, 128, 128] = %1(%7, %para2228) #(Tensor(F32)[16, 64, 128, 128], Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 64, 128, 128] = FuncGraph::fg_221(%8) #(Tensor(F32)[16, 64, 128, 128]) # fg_221=↓construct.221(@ctx.addr=0xaaaae3a08490) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU @ctx.addr=0xaaaae3a08490
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.103] L-construct.96 @ctx.addr=0xaaaae3a0fcf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_96(
- %para2337 : Tensor(F32)[16, 64, 128, 128] # x
- , %para2338 : Ref[Tensor(F32)][64] # L-network.network.G.HRconv.bias
- , %para2339 : Ref[Tensor(F32)][64, 64, 3, 3] # L-network.network.G.HRconv.weight
- ) {
- %1 : Bool = FuncGraph::fg_217(Bool(1)) #(Bool) # fg_217=L-bool_.217(@ctx.addr=0xaaaae3989a00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d @ctx.addr=0xaaaae3989a00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_218, FuncGraph::fg_219) #(Bool, Func, Func) # fg_218=L-✓construct.218(@ctx.addr=0xaaaae3a0ff40), fg_219=L-✗construct.219 #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 128, 128] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d @ctx.addr=0xaaaae3a0ff40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.103] ✓construct.108 @ctx.addr=0xaaaae3a10800
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_108[fg_40](
- ) {
- %1 : Tensor(F32)[16, 3, 128, 128] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(3), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para2240, %para705) #(Tensor(F32)[16, 64, 128, 128], Ref[Tensor(F32)][3, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_last-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 3, 128, 128] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para706) #(Tensor(F32)[16, 3, 128, 128], Ref[Tensor(F32)][3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_last-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 3, 128, 128] = FuncGraph::fg_223(%2) #(Tensor(F32)[16, 3, 128, 128]) # fg_223=↓construct.223(@ctx.addr=0xaaaae3a1feb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_last-Conv2d @ctx.addr=0xaaaae3a1feb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 3, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_last-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.104] _mul_tensor.224 @ctx.addr=0xaaaae3a2c650
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_224(
- %para2340 : Tensor(F32)[] # x
- , %para2341 : Tensor(F32)[16, 64, 128, 128] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 128, 128] = %2(%para2340, %para2341) #(Tensor(F32)[], Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.105] ↓construct.111 @ctx.addr=0xaaaae3bafec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_111(
- %para2342 : Tensor(F32)[16, 100] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para2342) #(Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.106] ↓construct.112 @ctx.addr=0xaaaae3a393a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_112(
- %para2343 : Tensor(F32)[16, 64, 64, 64] # Φoutput
- ) {
- Primitive::Return{prim_type=1}(%para2343) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv0_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(261)/ return output/
- }
-
-
- # [No.107] ↓construct.113 @ctx.addr=0xaaaae3a4d930
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- funcgraph fg_113[fg_13](
- %para2344 : Bool # Φflag
- ) {
- %1 : Bool = FuncGraph::fg_29(%para2344) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_225, FuncGraph::fg_226) #(Bool, Func, Func) # fg_225=✓↓construct.225(@ctx.addr=0xaaaae3a4f520), fg_226=✗↓construct.226 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- %3 : Tensor(F32)[16, 64, 64, 64] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d @ctx.addr=0xaaaae3a4f520
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- }
-
-
- # [No.108] _mul_tensor.227 @ctx.addr=0xaaaae3a60ba0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_227(
- %para2345 : Tensor(F32)[] # x
- , %para2346 : Tensor(F32)[16, 64, 64, 64] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 64, 64] = %2(%para2345, %para2346) #(Tensor(F32)[], Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.109] ↓construct.115 @ctx.addr=0xaaaae3a67bf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_115(
- %para2347 : Tensor(F32)[16, 128, 64, 64] # Φoutput
- ) {
- Primitive::Return{prim_type=1}(%para2347) #(Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv1_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(261)/ return output/
- }
-
-
- # [No.110] L-bool_.116 @ctx.addr=0xaaaae3a79710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(530)/def bool_(x):/
- funcgraph fg_116(
- %para2348 : Bool # x
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para2348, "__bool__") #(Bool, String) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- %2 : Bool = %1() #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- Primitive::Return{prim_type=1}(%2) #(Bool) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- }
-
-
- # [No.111] L-✓construct.117 @ctx.addr=0xaaaae3a79cf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- funcgraph fg_117[fg_49](
- ) {
- %1 : Tensor(F32)[16, 128, 64, 64] = FuncGraph::fg_228(Bool(1)) #(Bool) # fg_228=L-↓construct.228(@ctx.addr=0xaaaae3a7ce10) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d @ctx.addr=0xaaaae3a7ce10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- }
-
-
- # [No.112] _mul_tensor.229 @ctx.addr=0xaaaae3a8af70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_229(
- %para2349 : Tensor(F32)[] # x
- , %para2350 : Tensor(F32)[16, 128, 64, 64] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 128, 64, 64] = %2(%para2349, %para2350) #(Tensor(F32)[], Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.113] ↓construct.120 @ctx.addr=0xaaaae3a91fe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_120(
- %para2351 : Tensor(F32)[16, 128, 32, 32] # Φoutput
- ) {
- Primitive::Return{prim_type=1}(%para2351) #(Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv1_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(261)/ return output/
- }
-
-
- # [No.114] L-✓construct.117 @ctx.addr=0xaaaae3aa1f10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- funcgraph fg_117[fg_49](
- ) {
- %1 : Tensor(F32)[16, 128, 32, 32] = FuncGraph::fg_228(Bool(1)) #(Bool) # fg_228=L-↓construct.228(@ctx.addr=0xaaaae3aa2660) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d @ctx.addr=0xaaaae3aa2660
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- }
-
-
- # [No.114] _mul_tensor.230 @ctx.addr=0xaaaae3aad950
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_230(
- %para2352 : Tensor(F32)[] # x
- , %para2353 : Tensor(F32)[16, 128, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 128, 32, 32] = %2(%para2352, %para2353) #(Tensor(F32)[], Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.115] ↓construct.122 @ctx.addr=0xaaaae3ab4830
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_122(
- %para2354 : Tensor(F32)[16, 256, 32, 32] # Φoutput
- ) {
- Primitive::Return{prim_type=1}(%para2354) #(Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv2_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(261)/ return output/
- }
-
-
- # [No.116] L-bool_.123 @ctx.addr=0xaaaae3ac63a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(530)/def bool_(x):/
- funcgraph fg_123(
- %para2355 : Bool # x
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para2355, "__bool__") #(Bool, String) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- %2 : Bool = %1() #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- Primitive::Return{prim_type=1}(%2) #(Bool) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- }
-
-
- # [No.117] L-✓construct.124 @ctx.addr=0xaaaae3ac6980
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- funcgraph fg_124[fg_54](
- ) {
- %1 : Tensor(F32)[16, 256, 32, 32] = FuncGraph::fg_231(Bool(1)) #(Bool) # fg_231=L-↓construct.231(@ctx.addr=0xaaaae3ac9aa0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d @ctx.addr=0xaaaae3ac9aa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- }
-
-
- # [No.118] _mul_tensor.232 @ctx.addr=0xaaaae3ad7bf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_232(
- %para2356 : Tensor(F32)[] # x
- , %para2357 : Tensor(F32)[16, 256, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 256, 32, 32] = %2(%para2356, %para2357) #(Tensor(F32)[], Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.119] ↓construct.127 @ctx.addr=0xaaaae3adec40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_127(
- %para2358 : Tensor(F32)[16, 256, 16, 16] # Φoutput
- ) {
- Primitive::Return{prim_type=1}(%para2358) #(Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv2_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(261)/ return output/
- }
-
-
- # [No.120] L-✓construct.124 @ctx.addr=0xaaaae3aeeae0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- funcgraph fg_124[fg_54](
- ) {
- %1 : Tensor(F32)[16, 256, 16, 16] = FuncGraph::fg_231(Bool(1)) #(Bool) # fg_231=L-↓construct.231(@ctx.addr=0xaaaae3aef250) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d @ctx.addr=0xaaaae3aef250
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- }
-
-
- # [No.120] _mul_tensor.233 @ctx.addr=0xaaaae3afa500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_233(
- %para2359 : Tensor(F32)[] # x
- , %para2360 : Tensor(F32)[16, 256, 16, 16] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 256, 16, 16] = %2(%para2359, %para2360) #(Tensor(F32)[], Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.121] ↓construct.129 @ctx.addr=0xaaaae3b01410
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_129(
- %para2361 : Tensor(F32)[16, 512, 16, 16] # Φoutput
- ) {
- Primitive::Return{prim_type=1}(%para2361) #(Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv3_0-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(261)/ return output/
- }
-
-
- # [No.122] L-bool_.130 @ctx.addr=0xaaaae3b13150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(530)/def bool_(x):/
- funcgraph fg_130(
- %para2362 : Bool # x
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para2362, "__bool__") #(Bool, String) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- %2 : Bool = %1() #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- Primitive::Return{prim_type=1}(%2) #(Bool) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- }
-
-
- # [No.123] L-✓construct.131 @ctx.addr=0xaaaae3b13730
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- funcgraph fg_131[fg_59](
- ) {
- %1 : Tensor(F32)[16, 512, 16, 16] = FuncGraph::fg_234(Bool(1)) #(Bool) # fg_234=L-↓construct.234(@ctx.addr=0xaaaae3b16850) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d @ctx.addr=0xaaaae3b16850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- }
-
-
- # [No.124] _mul_tensor.235 @ctx.addr=0xaaaae3b24ae0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_235(
- %para2363 : Tensor(F32)[] # x
- , %para2364 : Tensor(F32)[16, 512, 16, 16] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 512, 16, 16] = %2(%para2363, %para2364) #(Tensor(F32)[], Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.125] ↓construct.134 @ctx.addr=0xaaaae266d6d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_134(
- %para2365 : Tensor(F32)[16, 512, 8, 8] # Φoutput
- ) {
- Primitive::Return{prim_type=1}(%para2365) #(Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/conv3_1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(261)/ return output/
- }
-
-
- # [No.126] L-✓construct.131 @ctx.addr=0xaaaae3b42fb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- funcgraph fg_131[fg_59](
- ) {
- %1 : Tensor(F32)[16, 512, 8, 8] = FuncGraph::fg_234(Bool(1)) #(Bool) # fg_234=L-↓construct.234(@ctx.addr=0xaaaae3b43720) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d @ctx.addr=0xaaaae3b43720
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- }
-
-
- # [No.126] _mul_tensor.236 @ctx.addr=0xaaaae3b4e9a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_236(
- %para2366 : Tensor(F32)[] # x
- , %para2367 : Tensor(F32)[16, 512, 8, 8] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 512, 8, 8] = %2(%para2366, %para2367) #(Tensor(F32)[], Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.127] L-ms_len.138 @ctx.addr=0xaaaae3bb7960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(495)/def ms_len(data):/
- funcgraph fg_138(
- %para2368 : Tuple[I64*2] # data
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para2368, "__len__") #(Tuple[I64*2], String) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(497)/ return data.__len__()/
- %2 : I64 = %1() #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(497)/ return data.__len__()/
- Primitive::Return{prim_type=1}(%2) #(I64) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(497)/ return data.__len__()/
- }
-
-
- # [No.128] _not_equal_scalar.237 @ctx.addr=0xaaaae3b7e3d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(31)/def _not_equal_scalar(x, y):/
- funcgraph fg_237(
- %para2369 : I64 # x
- , %para2370 : I64 # y
- ) {
- %1 : Func = Primitive::resolve{prim_type=1}(NameSpace::Ast, not_) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(0)/
- %2 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- %3 : Func = Primitive::getattr{prim_type=1}(%2, "scalar_eq") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- %4 : Bool = %3(%para2369, %para2370) #(I64, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- %5 : Bool = %1(%4) #(Bool) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b7e4b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- Primitive::Return{prim_type=1}(%5) #(Bool) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- }
-
-
- # [No.129] L-bool_.139 @ctx.addr=0xaaaae3b7e750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(530)/def bool_(x):/
- funcgraph fg_139(
- %para2371 : Bool # x
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para2371, "__bool__") #(Bool, String) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- %2 : Bool = %1() #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- Primitive::Return{prim_type=1}(%2) #(Bool) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- }
-
-
- # [No.130] L-✗construct.141 @ctx.addr=0xaaaae3b7fd40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- funcgraph fg_141[fg_62](
- ) {
- %1 : Tensor(F32)[16, 100] = FuncGraph::fg_238(%para2291) #(Tensor(F32)[16, 32768]) # fg_238=L-↓construct.238(@ctx.addr=0xaaaae3b81230) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b81230
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- }
-
-
- # [No.131] _mul_tensor.239 @ctx.addr=0xaaaae3bb0910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_239(
- %para2372 : Tensor(F32)[] # x
- , %para2373 : Tensor(F32)[16, 100] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 100] = %2(%para2372, %para2373) #(Tensor(F32)[], Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.132] _not_equal_scalar.240 @ctx.addr=0xaaaae3bb8220
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(31)/def _not_equal_scalar(x, y):/
- funcgraph fg_240(
- %para2374 : I64 # x
- , %para2375 : I64 # y
- ) {
- %1 : Func = Primitive::resolve{prim_type=1}(NameSpace::Ast, not_) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(0)/
- %2 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- %3 : Func = Primitive::getattr{prim_type=1}(%2, "scalar_eq") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- %4 : Bool = %3(%para2374, %para2375) #(I64, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- %5 : Bool = %1(%4) #(Bool) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bc3100
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- Primitive::Return{prim_type=1}(%5) #(Bool) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- }
-
-
- # [No.133] L-✗construct.141 @ctx.addr=0xaaaae3bc33d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- funcgraph fg_141[fg_62](
- ) {
- %1 : Tensor(F32)[16, 1] = FuncGraph::fg_238(%para2291) #(Tensor(F32)[16, 100]) # fg_238=L-↓construct.238(@ctx.addr=0xaaaae3bc3660) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bc3660
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 1]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- }
-
-
- # [No.133] ↓astype.144 @ctx.addr=0xaaaae34e6e10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(180)/ if not copy and dtype == x.dtype:/
- funcgraph fg_144[fg_63](
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(182)/ return F.cast(x, dtype)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "cast") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(182)/ return F.cast(x, dtype)/
- %3 : Func = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, check_astype_dtype_const) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(179)/ dtype = check_astype_dtype_const(dtype)/
- %4 : TypeType = %3(%para2213) #(TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(179)/ dtype = check_astype_dtype_const(dtype)/
- %5 : Tensor(F32)[16, 3, 32, 32] = %2(%para2212, %4) #(Tensor(F64)[16, 3, 32, 32], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(182)/ return F.cast(x, dtype)/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 3, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(182)/ return F.cast(x, dtype)/
- }
-
-
- # [No.134] ↓construct.145 @ctx.addr=0xaaaadc8992f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_145(
- %para2376 : Tensor(F32)[16, 64, 32, 32] # Φoutput
- ) {
- Primitive::Return{prim_type=1}(%para2376) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_first-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(261)/ return output/
- }
-
-
- # [No.135] construct.146 @ctx.addr=0xaaaae23b4b20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_146[fg_0](
- %para2377 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_241(%para2377) #(Tensor(F32)[16, 64, 32, 32]) # fg_241=construct.241(@ctx.addr=0xaaaae8590180) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae8590180
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_242(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_242=construct.242(@ctx.addr=0xaaaad8894a50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad8894a50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2377, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_243(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_243=construct.243(@ctx.addr=0xaaaae20bbc20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae20bbc20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_242(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_242=construct.242(@ctx.addr=0xaaaad8894a50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad8894a50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2377, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_244(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_244=construct.244(@ctx.addr=0xaaaae24902b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae24902b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_242(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_242=construct.242(@ctx.addr=0xaaaad8894a50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad8894a50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2377, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_245(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_245=construct.245(@ctx.addr=0xaaaafae7daa0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaafae7daa0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_242(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_242=construct.242(@ctx.addr=0xaaaad8894a50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad8894a50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2377, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_246(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_246=construct.246(@ctx.addr=0xaaaad95f3cf0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad95f3cf0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad9438010
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2377) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf6e8f590
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.136] construct.147 @ctx.addr=0xaaaad61b20e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_147[fg_0](
- %para2378 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_247(%para2378) #(Tensor(F32)[16, 64, 32, 32]) # fg_247=construct.247(@ctx.addr=0xaaaad9a684d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaad9a684d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_248(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_248=construct.248(@ctx.addr=0xaaaaf6f2da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf6f2da90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2378, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_249(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_249=construct.249(@ctx.addr=0xaaaaef7fba40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaef7fba40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_248(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_248=construct.248(@ctx.addr=0xaaaaf6f2da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf6f2da90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2378, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_250(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_250=construct.250(@ctx.addr=0xaaaaf4a7da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf4a7da90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_248(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_248=construct.248(@ctx.addr=0xaaaaf6f2da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf6f2da90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2378, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_251(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_251=construct.251(@ctx.addr=0xaaaadcb97340) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaadcb97340
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_248(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_248=construct.248(@ctx.addr=0xaaaaf6f2da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf6f2da90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2378, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_252(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_252=construct.252(@ctx.addr=0xaaaadd122a30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaadd122a30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae584eaf0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2378) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaedd64db0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.137] construct.148 @ctx.addr=0xaaaae8491460
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_148[fg_0](
- %para2379 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_253(%para2379) #(Tensor(F32)[16, 64, 32, 32]) # fg_253=construct.253(@ctx.addr=0xaaaae9399220) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae9399220
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_254(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_254=construct.254(@ctx.addr=0xaaaae206c2e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae206c2e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2379, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_255(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_255=construct.255(@ctx.addr=0xaaaaf13a67e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf13a67e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_254(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_254=construct.254(@ctx.addr=0xaaaae206c2e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae206c2e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2379, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_256(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_256=construct.256(@ctx.addr=0xaaaae0eefad0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae0eefad0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_254(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_254=construct.254(@ctx.addr=0xaaaae206c2e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae206c2e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2379, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_257(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_257=construct.257(@ctx.addr=0xaaaaed575030) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaed575030
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_254(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_254=construct.254(@ctx.addr=0xaaaae206c2e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae206c2e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2379, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_258(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_258=construct.258(@ctx.addr=0xaaaaebc1ab70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaebc1ab70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf07729b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2379) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaea956290
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.138] _tensor_mul_scalar.259 @ctx.addr=0xaaaadc46c8e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_259(
- %para2380 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2381 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2380, %para2381) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.139] _tensor_add_tensor.260 @ctx.addr=0xaaaaddb31820
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_260(
- %para2382 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2383 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2382, %para2383) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.140] construct.149 @ctx.addr=0xaaaaf733f5e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_149[fg_0](
- %para2384 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_261(%para2384) #(Tensor(F32)[16, 64, 32, 32]) # fg_261=construct.261(@ctx.addr=0xaaaae8602e20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae8602e20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_262(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_262=construct.262(@ctx.addr=0xaaaaf38cb550) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf38cb550
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2384, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_263(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_263=construct.263(@ctx.addr=0xaaaad6a09770) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad6a09770
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_262(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_262=construct.262(@ctx.addr=0xaaaaf38cb550) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf38cb550
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2384, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_264(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_264=construct.264(@ctx.addr=0xaaaad698ba30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad698ba30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_262(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_262=construct.262(@ctx.addr=0xaaaaf38cb550) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf38cb550
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2384, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_265(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_265=construct.265(@ctx.addr=0xaaaae244c860) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae244c860
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_262(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_262=construct.262(@ctx.addr=0xaaaaf38cb550) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf38cb550
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2384, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_266(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_266=construct.266(@ctx.addr=0xaaaae2574220) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2574220
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaafad9f0b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2384) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaec024f50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.141] construct.150 @ctx.addr=0xaaaaf4a6f090
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_150[fg_0](
- %para2385 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_267(%para2385) #(Tensor(F32)[16, 64, 32, 32]) # fg_267=construct.267(@ctx.addr=0xaaaaeedddd10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaeedddd10
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_268(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_268=construct.268(@ctx.addr=0xaaaaf2c0daa0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf2c0daa0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2385, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_269(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_269=construct.269(@ctx.addr=0xaaaae822d9b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae822d9b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_268(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_268=construct.268(@ctx.addr=0xaaaaf2c0daa0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf2c0daa0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2385, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_270(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_270=construct.270(@ctx.addr=0xaaaae8253130) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae8253130
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_268(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_268=construct.268(@ctx.addr=0xaaaaf2c0daa0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf2c0daa0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2385, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_271(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_271=construct.271(@ctx.addr=0xaaaae6de1260) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae6de1260
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_268(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_268=construct.268(@ctx.addr=0xaaaaf2c0daa0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf2c0daa0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2385, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_272(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_272=construct.272(@ctx.addr=0xaaaaed0d6580) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaed0d6580
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2438460
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2385) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae200e410
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.142] construct.151 @ctx.addr=0xaaaae84beba0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_151[fg_0](
- %para2386 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_273(%para2386) #(Tensor(F32)[16, 64, 32, 32]) # fg_273=construct.273(@ctx.addr=0xaaaae1ebb5f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1ebb5f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_274(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_274=construct.274(@ctx.addr=0xaaaae23e5740) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae23e5740
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2386, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_275(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_275=construct.275(@ctx.addr=0xaaaae09191c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae09191c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_274(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_274=construct.274(@ctx.addr=0xaaaae23e5740) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae23e5740
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2386, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_276(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_276=construct.276(@ctx.addr=0xaaaade9cf0f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaade9cf0f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_274(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_274=construct.274(@ctx.addr=0xaaaae23e5740) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae23e5740
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2386, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_277(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_277=construct.277(@ctx.addr=0xaaaae25a60c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae25a60c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_274(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_274=construct.274(@ctx.addr=0xaaaae23e5740) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae23e5740
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2386, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_278(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_278=construct.278(@ctx.addr=0xaaaafae469c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafae469c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae85beaa0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2386) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae23e15a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.143] _tensor_mul_scalar.279 @ctx.addr=0xaaaae238c9a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_279(
- %para2387 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2388 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2387, %para2388) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.144] _tensor_add_tensor.280 @ctx.addr=0xaaaafa6eb140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_280(
- %para2389 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2390 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2389, %para2390) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.145] construct.152 @ctx.addr=0xaaaaf21c8410
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_152[fg_0](
- %para2391 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_281(%para2391) #(Tensor(F32)[16, 64, 32, 32]) # fg_281=construct.281(@ctx.addr=0xaaaae846f480) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae846f480
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_282(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_282=construct.282(@ctx.addr=0xaaaae8177ef0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae8177ef0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2391, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_283(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_283=construct.283(@ctx.addr=0xaaaae6f80070) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae6f80070
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_282(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_282=construct.282(@ctx.addr=0xaaaae8177ef0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae8177ef0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2391, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_284(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_284=construct.284(@ctx.addr=0xaaaad8cbb570) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad8cbb570
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_282(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_282=construct.282(@ctx.addr=0xaaaae8177ef0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae8177ef0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2391, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_285(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_285=construct.285(@ctx.addr=0xaaaaf2aa0790) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf2aa0790
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_282(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_282=construct.282(@ctx.addr=0xaaaae8177ef0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae8177ef0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2391, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_286(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_286=construct.286(@ctx.addr=0xaaaad81b1740) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad81b1740
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad61a80e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2391) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad9a68bd0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.146] construct.153 @ctx.addr=0xaaaaf2aa14f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_153[fg_0](
- %para2392 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_287(%para2392) #(Tensor(F32)[16, 64, 32, 32]) # fg_287=construct.287(@ctx.addr=0xaaaaf0af7ed0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf0af7ed0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_288(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_288=construct.288(@ctx.addr=0xaaaae21c2520) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae21c2520
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2392, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_289(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_289=construct.289(@ctx.addr=0xaaaae1ef34e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1ef34e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_288(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_288=construct.288(@ctx.addr=0xaaaae21c2520) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae21c2520
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2392, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_290(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_290=construct.290(@ctx.addr=0xaaaae1da4fe0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1da4fe0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_288(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_288=construct.288(@ctx.addr=0xaaaae21c2520) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae21c2520
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2392, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_291(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_291=construct.291(@ctx.addr=0xaaaad813e750) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaad813e750
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_288(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_288=construct.288(@ctx.addr=0xaaaae21c2520) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae21c2520
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2392, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_292(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_292=construct.292(@ctx.addr=0xaaaae2345e10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2345e10
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaade447ca0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2392) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae819fec0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.147] construct.154 @ctx.addr=0xaaaafae72b20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_154[fg_0](
- %para2393 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_293(%para2393) #(Tensor(F32)[16, 64, 32, 32]) # fg_293=construct.293(@ctx.addr=0xaaaafaec8440) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafaec8440
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_294(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_294=construct.294(@ctx.addr=0xaaaadd3265d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaadd3265d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2393, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_295(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_295=construct.295(@ctx.addr=0xaaaae0d2cf50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae0d2cf50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_294(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_294=construct.294(@ctx.addr=0xaaaadd3265d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaadd3265d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2393, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_296(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_296=construct.296(@ctx.addr=0xaaaae20cb680) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae20cb680
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_294(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_294=construct.294(@ctx.addr=0xaaaadd3265d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaadd3265d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2393, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_297(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_297=construct.297(@ctx.addr=0xaaaaed0cbe50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaed0cbe50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_294(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_294=construct.294(@ctx.addr=0xaaaadd3265d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaadd3265d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2393, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_298(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_298=construct.298(@ctx.addr=0xaaaafad9bf40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafad9bf40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae23c6640
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2393) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae21fbb30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.148] _tensor_mul_scalar.299 @ctx.addr=0xaaaae24fb8e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_299(
- %para2394 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2395 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2394, %para2395) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.149] _tensor_add_tensor.300 @ctx.addr=0xaaaae1e4a170
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_300(
- %para2396 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2397 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2396, %para2397) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.150] construct.155 @ctx.addr=0xaaaae11c08e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_155[fg_0](
- %para2398 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_301(%para2398) #(Tensor(F32)[16, 64, 32, 32]) # fg_301=construct.301(@ctx.addr=0xaaaae2228520) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2228520
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_302(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_302=construct.302(@ctx.addr=0xaaaae2057000) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2057000
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2398, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_303(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_303=construct.303(@ctx.addr=0xaaaaf13a8960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf13a8960
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_302(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_302=construct.302(@ctx.addr=0xaaaae2057000) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2057000
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2398, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_304(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_304=construct.304(@ctx.addr=0xaaaad8a4b480) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad8a4b480
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_302(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_302=construct.302(@ctx.addr=0xaaaae2057000) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2057000
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2398, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_305(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_305=construct.305(@ctx.addr=0xaaaae850aae0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae850aae0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_302(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_302=construct.302(@ctx.addr=0xaaaae2057000) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2057000
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2398, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_306(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_306=construct.306(@ctx.addr=0xaaaae85a5400) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae85a5400
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaed0c9a70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2398) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaadc746aa0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.151] construct.156 @ctx.addr=0xaaaae5649660
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_156[fg_0](
- %para2399 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_307(%para2399) #(Tensor(F32)[16, 64, 32, 32]) # fg_307=construct.307(@ctx.addr=0xaaaaf709b940) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf709b940
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_308(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_308=construct.308(@ctx.addr=0xaaaafaecadb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaafaecadb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2399, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_309(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_309=construct.309(@ctx.addr=0xaaaae1f164b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1f164b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_308(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_308=construct.308(@ctx.addr=0xaaaafaecadb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaafaecadb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2399, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_310(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_310=construct.310(@ctx.addr=0xaaaae228e300) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae228e300
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_308(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_308=construct.308(@ctx.addr=0xaaaafaecadb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaafaecadb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2399, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_311(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_311=construct.311(@ctx.addr=0xaaaaf0c41530) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf0c41530
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_308(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_308=construct.308(@ctx.addr=0xaaaafaecadb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaafaecadb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2399, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_312(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_312=construct.312(@ctx.addr=0xaaaaec20d780) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaec20d780
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae833e420
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2399) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae81f1770
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.152] construct.157 @ctx.addr=0xaaaaefd0da50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_157[fg_0](
- %para2400 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_313(%para2400) #(Tensor(F32)[16, 64, 32, 32]) # fg_313=construct.313(@ctx.addr=0xaaaafae6ea50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafae6ea50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_314(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_314=construct.314(@ctx.addr=0xaaaaebfb19e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaebfb19e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2400, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_315(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_315=construct.315(@ctx.addr=0xaaaade484200) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaade484200
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_314(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_314=construct.314(@ctx.addr=0xaaaaebfb19e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaebfb19e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2400, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_316(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_316=construct.316(@ctx.addr=0xaaaaec7a5040) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaec7a5040
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_314(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_314=construct.314(@ctx.addr=0xaaaaebfb19e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaebfb19e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2400, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_317(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_317=construct.317(@ctx.addr=0xaaaadd11b840) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaadd11b840
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_314(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_314=construct.314(@ctx.addr=0xaaaaebfb19e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaebfb19e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2400, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_318(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_318=construct.318(@ctx.addr=0xaaaaef19fbe0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaef19fbe0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaeacea050
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2400) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1f84c90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.153] _tensor_mul_scalar.319 @ctx.addr=0xaaaad5c94230
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_319(
- %para2401 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2402 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2401, %para2402) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.154] _tensor_add_tensor.320 @ctx.addr=0xaaaae98a6890
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_320(
- %para2403 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2404 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2403, %para2404) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.155] construct.158 @ctx.addr=0xaaaafaee10d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_158[fg_0](
- %para2405 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_321(%para2405) #(Tensor(F32)[16, 64, 32, 32]) # fg_321=construct.321(@ctx.addr=0xaaaaf532de00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf532de00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_322(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_322=construct.322(@ctx.addr=0xaaaaf0c3f390) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf0c3f390
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2405, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_323(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_323=construct.323(@ctx.addr=0xaaaaf10bad20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf10bad20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_322(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_322=construct.322(@ctx.addr=0xaaaaf0c3f390) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf0c3f390
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2405, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_324(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_324=construct.324(@ctx.addr=0xaaaae9dbc110) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae9dbc110
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_322(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_322=construct.322(@ctx.addr=0xaaaaf0c3f390) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf0c3f390
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2405, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_325(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_325=construct.325(@ctx.addr=0xaaaae1dd5600) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae1dd5600
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_322(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_322=construct.322(@ctx.addr=0xaaaaf0c3f390) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf0c3f390
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2405, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_326(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_326=construct.326(@ctx.addr=0xaaaaf70a36b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf70a36b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae1f7bb30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2405) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaec201e00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.156] construct.159 @ctx.addr=0xaaaaf0c790a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_159[fg_0](
- %para2406 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_327(%para2406) #(Tensor(F32)[16, 64, 32, 32]) # fg_327=construct.327(@ctx.addr=0xaaaaf38ccb60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf38ccb60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_328(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_328=construct.328(@ctx.addr=0xaaaae83ce7a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae83ce7a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2406, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_329(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_329=construct.329(@ctx.addr=0xaaaae84db8f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae84db8f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_328(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_328=construct.328(@ctx.addr=0xaaaae83ce7a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae83ce7a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2406, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_330(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_330=construct.330(@ctx.addr=0xaaaae1de9870) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1de9870
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_328(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_328=construct.328(@ctx.addr=0xaaaae83ce7a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae83ce7a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2406, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_331(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_331=construct.331(@ctx.addr=0xaaaae1da3240) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1da3240
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_328(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_328=construct.328(@ctx.addr=0xaaaae83ce7a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae83ce7a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2406, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_332(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_332=construct.332(@ctx.addr=0xaaaae7196b50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae7196b50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae22ae1c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2406) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae82c7da0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.157] construct.160 @ctx.addr=0xaaaae2472280
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_160[fg_0](
- %para2407 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_333(%para2407) #(Tensor(F32)[16, 64, 32, 32]) # fg_333=construct.333(@ctx.addr=0xaaaae2534e20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2534e20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_334(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_334=construct.334(@ctx.addr=0xaaaae24cb2f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae24cb2f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2407, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_335(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_335=construct.335(@ctx.addr=0xaaaae8a58670) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae8a58670
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_334(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_334=construct.334(@ctx.addr=0xaaaae24cb2f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae24cb2f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2407, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_336(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_336=construct.336(@ctx.addr=0xaaaae2188830) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2188830
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_334(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_334=construct.334(@ctx.addr=0xaaaae24cb2f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae24cb2f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2407, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_337(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_337=construct.337(@ctx.addr=0xaaaafae867e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafae867e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_334(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_334=construct.334(@ctx.addr=0xaaaae24cb2f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae24cb2f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2407, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_338(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_338=construct.338(@ctx.addr=0xaaaae25e8150) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae25e8150
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae8203fd0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2407) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2520610
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.158] _tensor_mul_scalar.339 @ctx.addr=0xaaaaf2cc8c60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_339(
- %para2408 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2409 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2408, %para2409) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.159] _tensor_add_tensor.340 @ctx.addr=0xaaaae23bec70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_340(
- %para2410 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2411 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2410, %para2411) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.160] construct.161 @ctx.addr=0xaaaae84611f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_161[fg_0](
- %para2412 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_341(%para2412) #(Tensor(F32)[16, 64, 32, 32]) # fg_341=construct.341(@ctx.addr=0xaaaae8461330) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae8461330
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_342(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_342=construct.342(@ctx.addr=0xaaaae83c18d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae83c18d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2412, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_343(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_343=construct.343(@ctx.addr=0xaaaaecb0a720) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaecb0a720
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_342(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_342=construct.342(@ctx.addr=0xaaaae83c18d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae83c18d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2412, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_344(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_344=construct.344(@ctx.addr=0xaaaad7cd4700) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad7cd4700
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_342(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_342=construct.342(@ctx.addr=0xaaaae83c18d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae83c18d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2412, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_345(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_345=construct.345(@ctx.addr=0xaaaaf4dd9340) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf4dd9340
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_342(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_342=construct.342(@ctx.addr=0xaaaae83c18d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae83c18d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2412, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_346(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_346=construct.346(@ctx.addr=0xaaaaf13a9c30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf13a9c30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae0dbfa80
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2412) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf42ebec0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.161] construct.162 @ctx.addr=0xaaaae6f7ed40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_162[fg_0](
- %para2413 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_347(%para2413) #(Tensor(F32)[16, 64, 32, 32]) # fg_347=construct.347(@ctx.addr=0xaaaae0cb0db0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae0cb0db0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_348(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_348=construct.348(@ctx.addr=0xaaaae86b5eb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae86b5eb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2413, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_349(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_349=construct.349(@ctx.addr=0xaaaaf49046b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf49046b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_348(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_348=construct.348(@ctx.addr=0xaaaae86b5eb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae86b5eb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2413, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_350(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_350=construct.350(@ctx.addr=0xaaaae5647e70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae5647e70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_348(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_348=construct.348(@ctx.addr=0xaaaae86b5eb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae86b5eb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2413, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_351(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_351=construct.351(@ctx.addr=0xaaaae23d9ea0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae23d9ea0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_348(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_348=construct.348(@ctx.addr=0xaaaae86b5eb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae86b5eb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2413, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_352(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_352=construct.352(@ctx.addr=0xaaaae8344230) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae8344230
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae83cfec0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2413) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae21867c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.162] construct.163 @ctx.addr=0xaaaad813bdf0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_163[fg_0](
- %para2414 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_353(%para2414) #(Tensor(F32)[16, 64, 32, 32]) # fg_353=construct.353(@ctx.addr=0xaaaae1e9d9f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1e9d9f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_354(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_354=construct.354(@ctx.addr=0xaaaafaee3a20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafaee3a20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2414, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_355(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_355=construct.355(@ctx.addr=0xaaaae1f93660) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1f93660
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_354(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_354=construct.354(@ctx.addr=0xaaaafaee3a20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafaee3a20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2414, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_356(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_356=construct.356(@ctx.addr=0xaaaae829ad40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae829ad40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_354(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_354=construct.354(@ctx.addr=0xaaaafaee3a20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafaee3a20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2414, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_357(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_357=construct.357(@ctx.addr=0xaaaaf4275280) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf4275280
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_354(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_354=construct.354(@ctx.addr=0xaaaafaee3a20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafaee3a20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2414, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_358(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_358=construct.358(@ctx.addr=0xaaaaecb144a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaecb144a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaefbc6e20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2414) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaee77ef30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.163] _tensor_mul_scalar.359 @ctx.addr=0xaaaae8228000
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_359(
- %para2415 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2416 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2415, %para2416) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.164] _tensor_add_tensor.360 @ctx.addr=0xaaaae21ed900
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_360(
- %para2417 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2418 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2417, %para2418) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.165] construct.164 @ctx.addr=0xaaaadce2cb40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_164[fg_0](
- %para2419 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_361(%para2419) #(Tensor(F32)[16, 64, 32, 32]) # fg_361=construct.361(@ctx.addr=0xaaaaea1c70a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaea1c70a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_362(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_362=construct.362(@ctx.addr=0xaaaad5a1a810) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad5a1a810
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2419, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_363(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_363=construct.363(@ctx.addr=0xaaaaf2a9ef60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf2a9ef60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_362(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_362=construct.362(@ctx.addr=0xaaaad5a1a810) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad5a1a810
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2419, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_364(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_364=construct.364(@ctx.addr=0xaaaae9f2e7c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae9f2e7c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_362(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_362=construct.362(@ctx.addr=0xaaaad5a1a810) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad5a1a810
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2419, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_365(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_365=construct.365(@ctx.addr=0xaaaaf3e9edb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf3e9edb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_362(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_362=construct.362(@ctx.addr=0xaaaad5a1a810) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad5a1a810
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2419, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_366(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_366=construct.366(@ctx.addr=0xaaaae1df40f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae1df40f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf37bf890
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2419) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae20c76e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.166] construct.165 @ctx.addr=0xaaaae81d7750
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_165[fg_0](
- %para2420 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_367(%para2420) #(Tensor(F32)[16, 64, 32, 32]) # fg_367=construct.367(@ctx.addr=0xaaaae82d0a50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae82d0a50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_368(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_368=construct.368(@ctx.addr=0xaaaae5c1fe60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae5c1fe60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2420, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_369(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_369=construct.369(@ctx.addr=0xaaaaf4271a40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf4271a40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_368(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_368=construct.368(@ctx.addr=0xaaaae5c1fe60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae5c1fe60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2420, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_370(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_370=construct.370(@ctx.addr=0xaaaae8427e20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae8427e20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_368(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_368=construct.368(@ctx.addr=0xaaaae5c1fe60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae5c1fe60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2420, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_371(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_371=construct.371(@ctx.addr=0xaaaaeeddd080) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaeeddd080
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_368(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_368=construct.368(@ctx.addr=0xaaaae5c1fe60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae5c1fe60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2420, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_372(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_372=construct.372(@ctx.addr=0xaaaafab28d20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaafab28d20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae22d4d00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2420) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae229a5d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.167] construct.166 @ctx.addr=0xaaaae210af70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_166[fg_0](
- %para2421 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_373(%para2421) #(Tensor(F32)[16, 64, 32, 32]) # fg_373=construct.373(@ctx.addr=0xaaaae2227400) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2227400
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_374(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_374=construct.374(@ctx.addr=0xaaaaf57fc770) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf57fc770
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2421, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_375(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_375=construct.375(@ctx.addr=0xaaaad8141740) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaad8141740
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_374(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_374=construct.374(@ctx.addr=0xaaaaf57fc770) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf57fc770
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2421, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_376(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_376=construct.376(@ctx.addr=0xaaaae242f620) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae242f620
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_374(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_374=construct.374(@ctx.addr=0xaaaaf57fc770) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf57fc770
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2421, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_377(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_377=construct.377(@ctx.addr=0xaaaae859ecc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae859ecc0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_374(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_374=construct.374(@ctx.addr=0xaaaaf57fc770) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf57fc770
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2421, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_378(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_378=construct.378(@ctx.addr=0xaaaaee102010) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaee102010
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae6a64670
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2421) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf21caf00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.168] _tensor_mul_scalar.379 @ctx.addr=0xaaaae851fad0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_379(
- %para2422 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2423 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2422, %para2423) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.169] _tensor_add_tensor.380 @ctx.addr=0xaaaadec0dd70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_380(
- %para2424 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2425 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2424, %para2425) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.170] construct.167 @ctx.addr=0xaaaaf48c6c60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_167[fg_0](
- %para2426 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_381(%para2426) #(Tensor(F32)[16, 64, 32, 32]) # fg_381=construct.381(@ctx.addr=0xaaaaeda6f160) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaeda6f160
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_382(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_382=construct.382(@ctx.addr=0xaaaaeb587690) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaeb587690
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2426, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_383(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_383=construct.383(@ctx.addr=0xaaaae2072680) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2072680
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_382(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_382=construct.382(@ctx.addr=0xaaaaeb587690) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaeb587690
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2426, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_384(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_384=construct.384(@ctx.addr=0xaaaafaee4ad0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaafaee4ad0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_382(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_382=construct.382(@ctx.addr=0xaaaaeb587690) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaeb587690
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2426, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_385(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_385=construct.385(@ctx.addr=0xaaaae1fd2740) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae1fd2740
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_382(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_382=construct.382(@ctx.addr=0xaaaaeb587690) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaeb587690
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2426, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_386(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_386=construct.386(@ctx.addr=0xaaaae2321630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2321630
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae81bc470
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2426) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf2f75470
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.171] construct.168 @ctx.addr=0xaaaaf1f58560
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_168[fg_0](
- %para2427 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_387(%para2427) #(Tensor(F32)[16, 64, 32, 32]) # fg_387=construct.387(@ctx.addr=0xaaaaf348c810) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf348c810
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_388(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_388=construct.388(@ctx.addr=0xaaaae1e7fee0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1e7fee0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2427, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_389(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_389=construct.389(@ctx.addr=0xaaaae81e11e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae81e11e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_388(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_388=construct.388(@ctx.addr=0xaaaae1e7fee0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1e7fee0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2427, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_390(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_390=construct.390(@ctx.addr=0xaaaaf2e2b270) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf2e2b270
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_388(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_388=construct.388(@ctx.addr=0xaaaae1e7fee0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1e7fee0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2427, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_391(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_391=construct.391(@ctx.addr=0xaaaae83fb760) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae83fb760
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_388(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_388=construct.388(@ctx.addr=0xaaaae1e7fee0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1e7fee0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2427, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_392(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_392=construct.392(@ctx.addr=0xaaaafaeb7cf0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaafaeb7cf0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1f2d230
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2427) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaeda73960
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.172] construct.169 @ctx.addr=0xaaaae6994220
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_169[fg_0](
- %para2428 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_393(%para2428) #(Tensor(F32)[16, 64, 32, 32]) # fg_393=construct.393(@ctx.addr=0xaaaae06cc5a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae06cc5a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_394(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_394=construct.394(@ctx.addr=0xaaaae8d02a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae8d02a90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2428, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_395(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_395=construct.395(@ctx.addr=0xaaaaee8d4ac0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaee8d4ac0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_394(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_394=construct.394(@ctx.addr=0xaaaae8d02a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae8d02a90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2428, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_396(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_396=construct.396(@ctx.addr=0xaaaae237a9c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae237a9c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_394(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_394=construct.394(@ctx.addr=0xaaaae8d02a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae8d02a90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2428, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_397(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_397=construct.397(@ctx.addr=0xaaaaf4dd2fa0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf4dd2fa0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_394(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_394=construct.394(@ctx.addr=0xaaaae8d02a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae8d02a90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2428, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_398(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_398=construct.398(@ctx.addr=0xaaaae9726960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae9726960
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf34c3170
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2428) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae81a0ed0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.173] _tensor_mul_scalar.399 @ctx.addr=0xaaaaea14e370
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_399(
- %para2429 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2430 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2429, %para2430) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.174] _tensor_add_tensor.400 @ctx.addr=0xaaaae1f4a4d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_400(
- %para2431 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2432 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2431, %para2432) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.175] construct.170 @ctx.addr=0xaaaaf2492ef0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_170[fg_0](
- %para2433 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_401(%para2433) #(Tensor(F32)[16, 64, 32, 32]) # fg_401=construct.401(@ctx.addr=0xaaaae591b880) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae591b880
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_402(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_402=construct.402(@ctx.addr=0xaaaae25b80b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae25b80b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2433, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_403(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_403=construct.403(@ctx.addr=0xaaaae97aad20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae97aad20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_402(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_402=construct.402(@ctx.addr=0xaaaae25b80b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae25b80b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2433, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_404(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_404=construct.404(@ctx.addr=0xaaaae1f668c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae1f668c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_402(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_402=construct.402(@ctx.addr=0xaaaae25b80b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae25b80b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2433, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_405(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_405=construct.405(@ctx.addr=0xaaaaf6db98f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf6db98f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_402(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_402=construct.402(@ctx.addr=0xaaaae25b80b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae25b80b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2433, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_406(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_406=construct.406(@ctx.addr=0xaaaaf17cc650) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf17cc650
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaefd10c60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2433) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2301060
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.176] construct.171 @ctx.addr=0xaaaaf10b8d50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_171[fg_0](
- %para2434 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_407(%para2434) #(Tensor(F32)[16, 64, 32, 32]) # fg_407=construct.407(@ctx.addr=0xaaaaf18ae530) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf18ae530
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_408(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_408=construct.408(@ctx.addr=0xaaaae1ede700) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1ede700
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2434, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_409(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_409=construct.409(@ctx.addr=0xaaaae84e1eb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae84e1eb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_408(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_408=construct.408(@ctx.addr=0xaaaae1ede700) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1ede700
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2434, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_410(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_410=construct.410(@ctx.addr=0xaaaae83e33e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae83e33e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_408(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_408=construct.408(@ctx.addr=0xaaaae1ede700) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1ede700
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2434, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_411(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_411=construct.411(@ctx.addr=0xaaaaeabe94a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaeabe94a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_408(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_408=construct.408(@ctx.addr=0xaaaae1ede700) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1ede700
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2434, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_412(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_412=construct.412(@ctx.addr=0xaaaadc85e090) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaadc85e090
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae81f2300
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2434) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae819c7d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.177] construct.172 @ctx.addr=0xaaaad6a035e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_172[fg_0](
- %para2435 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_413(%para2435) #(Tensor(F32)[16, 64, 32, 32]) # fg_413=construct.413(@ctx.addr=0xaaaaf1449230) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf1449230
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_414(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_414=construct.414(@ctx.addr=0xaaaafae718f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafae718f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2435, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_415(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_415=construct.415(@ctx.addr=0xaaaafae19fd0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafae19fd0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_414(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_414=construct.414(@ctx.addr=0xaaaafae718f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafae718f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2435, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_416(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_416=construct.416(@ctx.addr=0xaaaaef3ddd00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaef3ddd00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_414(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_414=construct.414(@ctx.addr=0xaaaafae718f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafae718f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2435, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_417(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_417=construct.417(@ctx.addr=0xaaaaef8014b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaef8014b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_414(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_414=construct.414(@ctx.addr=0xaaaafae718f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafae718f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2435, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_418(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_418=construct.418(@ctx.addr=0xaaaae22bc920) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae22bc920
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae223cdf0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2435) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2276e50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.178] _tensor_mul_scalar.419 @ctx.addr=0xaaaae217ab20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_419(
- %para2436 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2437 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2436, %para2437) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.179] _tensor_add_tensor.420 @ctx.addr=0xaaaafaed8280
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_420(
- %para2438 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2439 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2438, %para2439) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.180] construct.173 @ctx.addr=0xaaaae2485d70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_173[fg_0](
- %para2440 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_421(%para2440) #(Tensor(F32)[16, 64, 32, 32]) # fg_421=construct.421(@ctx.addr=0xaaaaed11abb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaed11abb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_422(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_422=construct.422(@ctx.addr=0xaaaae584c2c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae584c2c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2440, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_423(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_423=construct.423(@ctx.addr=0xaaaaf2f71d00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf2f71d00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_422(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_422=construct.422(@ctx.addr=0xaaaae584c2c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae584c2c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2440, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_424(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_424=construct.424(@ctx.addr=0xaaaaee788540) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaee788540
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_422(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_422=construct.422(@ctx.addr=0xaaaae584c2c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae584c2c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2440, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_425(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_425=construct.425(@ctx.addr=0xaaaadd3a2650) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaadd3a2650
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_422(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_422=construct.422(@ctx.addr=0xaaaae584c2c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae584c2c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2440, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_426(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_426=construct.426(@ctx.addr=0xaaaaec7ab330) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaec7ab330
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae236fb60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2440) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae21325d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.181] construct.174 @ctx.addr=0xaaaaec606420
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_174[fg_0](
- %para2441 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_427(%para2441) #(Tensor(F32)[16, 64, 32, 32]) # fg_427=construct.427(@ctx.addr=0xaaaae20fcda0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae20fcda0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_428(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_428=construct.428(@ctx.addr=0xaaaae81c6e90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae81c6e90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2441, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_429(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_429=construct.429(@ctx.addr=0xaaaae859d120) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae859d120
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_428(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_428=construct.428(@ctx.addr=0xaaaae81c6e90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae81c6e90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2441, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_430(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_430=construct.430(@ctx.addr=0xaaaae121b3e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae121b3e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_428(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_428=construct.428(@ctx.addr=0xaaaae81c6e90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae81c6e90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2441, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_431(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_431=construct.431(@ctx.addr=0xaaaaec0ea9e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaec0ea9e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_428(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_428=construct.428(@ctx.addr=0xaaaae81c6e90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae81c6e90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2441, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_432(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_432=construct.432(@ctx.addr=0xaaaae823beb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae823beb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2287090
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2441) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae224cc70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.182] construct.175 @ctx.addr=0xaaaae21d8f10
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_175[fg_0](
- %para2442 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_433(%para2442) #(Tensor(F32)[16, 64, 32, 32]) # fg_433=construct.433(@ctx.addr=0xaaaae2212d00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2212d00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_434(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_434=construct.434(@ctx.addr=0xaaaae1fdad30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1fdad30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2442, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_435(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_435=construct.435(@ctx.addr=0xaaaae35dcfc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae35dcfc0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_434(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_434=construct.434(@ctx.addr=0xaaaae1fdad30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1fdad30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2442, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_436(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_436=construct.436(@ctx.addr=0xaaaae090f3c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae090f3c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_434(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_434=construct.434(@ctx.addr=0xaaaae1fdad30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1fdad30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2442, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_437(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_437=construct.437(@ctx.addr=0xaaaae564c4c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae564c4c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_434(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_434=construct.434(@ctx.addr=0xaaaae1fdad30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1fdad30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2442, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_438(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_438=construct.438(@ctx.addr=0xaaaae24baf30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae24baf30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaadca63a30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2442) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf0214120
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.183] _tensor_mul_scalar.439 @ctx.addr=0xaaaad8cb1630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_439(
- %para2443 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2444 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2443, %para2444) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.184] _tensor_add_tensor.440 @ctx.addr=0xaaaaebfaba40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_440(
- %para2445 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2446 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2445, %para2446) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.185] construct.176 @ctx.addr=0xaaaae21e55b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_176[fg_0](
- %para2447 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_441(%para2447) #(Tensor(F32)[16, 64, 32, 32]) # fg_441=construct.441(@ctx.addr=0xaaaaf25541c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf25541c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_442(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_442=construct.442(@ctx.addr=0xaaaaf2554670) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf2554670
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2447, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_443(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_443=construct.443(@ctx.addr=0xaaaae2147810) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2147810
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_442(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_442=construct.442(@ctx.addr=0xaaaaf2554670) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf2554670
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2447, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_444(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_444=construct.444(@ctx.addr=0xaaaafadcc2e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaafadcc2e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_442(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_442=construct.442(@ctx.addr=0xaaaaf2554670) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf2554670
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2447, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_445(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_445=construct.445(@ctx.addr=0xaaaae1e2e040) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae1e2e040
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_442(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_442=construct.442(@ctx.addr=0xaaaaf2554670) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf2554670
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2447, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_446(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_446=construct.446(@ctx.addr=0xaaaafae0b870) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaafae0b870
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2554900
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2447) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae258ffe0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.186] construct.177 @ctx.addr=0xaaaafadb51b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_177[fg_0](
- %para2448 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_447(%para2448) #(Tensor(F32)[16, 64, 32, 32]) # fg_447=construct.447(@ctx.addr=0xaaaae1f2c120) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1f2c120
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_448(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_448=construct.448(@ctx.addr=0xaaaaec0e4c00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaec0e4c00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2448, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_449(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_449=construct.449(@ctx.addr=0xaaaaef039920) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaef039920
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_448(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_448=construct.448(@ctx.addr=0xaaaaec0e4c00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaec0e4c00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2448, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_450(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_450=construct.450(@ctx.addr=0xaaaaf229ad40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf229ad40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_448(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_448=construct.448(@ctx.addr=0xaaaaec0e4c00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaec0e4c00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2448, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_451(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_451=construct.451(@ctx.addr=0xaaaaf0848130) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf0848130
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_448(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_448=construct.448(@ctx.addr=0xaaaaec0e4c00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaec0e4c00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2448, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_452(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_452=construct.452(@ctx.addr=0xaaaad88a18c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaad88a18c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaadc684010
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2448) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaef405e70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.187] construct.178 @ctx.addr=0xaaaaf43fe5a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_178[fg_0](
- %para2449 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_453(%para2449) #(Tensor(F32)[16, 64, 32, 32]) # fg_453=construct.453(@ctx.addr=0xaaaaef406580) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaef406580
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_454(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_454=construct.454(@ctx.addr=0xaaaaf22a9910) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf22a9910
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2449, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_455(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_455=construct.455(@ctx.addr=0xaaaadd258120) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaadd258120
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_454(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_454=construct.454(@ctx.addr=0xaaaaf22a9910) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf22a9910
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2449, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_456(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_456=construct.456(@ctx.addr=0xaaaaf258cbf0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf258cbf0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_454(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_454=construct.454(@ctx.addr=0xaaaaf22a9910) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf22a9910
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2449, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_457(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_457=construct.457(@ctx.addr=0xaaaaf09839b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf09839b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_454(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_454=construct.454(@ctx.addr=0xaaaaf22a9910) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf22a9910
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2449, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_458(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_458=construct.458(@ctx.addr=0xaaaaee3f30a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaee3f30a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaea7e4510
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2449) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaea7e4a40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.188] _tensor_mul_scalar.459 @ctx.addr=0xaaaad90aa330
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_459(
- %para2450 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2451 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2450, %para2451) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.189] _tensor_add_tensor.460 @ctx.addr=0xaaaae0aa8ca0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_460(
- %para2452 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2453 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2452, %para2453) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.190] construct.179 @ctx.addr=0xaaaae1f0b8d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_179[fg_0](
- %para2454 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_461(%para2454) #(Tensor(F32)[16, 64, 32, 32]) # fg_461=construct.461(@ctx.addr=0xaaaae1fb46c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae1fb46c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_462(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_462=construct.462(@ctx.addr=0xaaaae83105d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae83105d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2454, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_463(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_463=construct.463(@ctx.addr=0xaaaad74171c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad74171c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_462(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_462=construct.462(@ctx.addr=0xaaaae83105d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae83105d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2454, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_464(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_464=construct.464(@ctx.addr=0xaaaaf2f7d2f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf2f7d2f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_462(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_462=construct.462(@ctx.addr=0xaaaae83105d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae83105d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2454, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_465(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_465=construct.465(@ctx.addr=0xaaaae83ca150) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae83ca150
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_462(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_462=construct.462(@ctx.addr=0xaaaae83105d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae83105d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2454, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_466(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_466=construct.466(@ctx.addr=0xaaaae81d23d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae81d23d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaadeb4a0f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2454) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaadeb4a550
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.191] construct.180 @ctx.addr=0xaaaae55363f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_180[fg_0](
- %para2455 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_467(%para2455) #(Tensor(F32)[16, 64, 32, 32]) # fg_467=construct.467(@ctx.addr=0xaaaadec91680) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaadec91680
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_468(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_468=construct.468(@ctx.addr=0xaaaaebfa5230) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaebfa5230
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2455, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_469(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_469=construct.469(@ctx.addr=0xaaaaf18aa4c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf18aa4c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_468(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_468=construct.468(@ctx.addr=0xaaaaebfa5230) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaebfa5230
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2455, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_470(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_470=construct.470(@ctx.addr=0xaaaae6e11ae0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae6e11ae0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_468(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_468=construct.468(@ctx.addr=0xaaaaebfa5230) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaebfa5230
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2455, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_471(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_471=construct.471(@ctx.addr=0xaaaaea94c210) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaea94c210
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_468(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_468=construct.468(@ctx.addr=0xaaaaebfa5230) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaebfa5230
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2455, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_472(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_472=construct.472(@ctx.addr=0xaaaae70fac00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae70fac00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae6bee740
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2455) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae6beec30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.192] construct.181 @ctx.addr=0xaaaae6be96c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_181[fg_0](
- %para2456 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_473(%para2456) #(Tensor(F32)[16, 64, 32, 32]) # fg_473=construct.473(@ctx.addr=0xaaaae6bea430) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae6bea430
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_474(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_474=construct.474(@ctx.addr=0xaaaade54ff70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaade54ff70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2456, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_475(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_475=construct.475(@ctx.addr=0xaaaad924c590) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaad924c590
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_474(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_474=construct.474(@ctx.addr=0xaaaade54ff70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaade54ff70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2456, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_476(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_476=construct.476(@ctx.addr=0xaaaad8a47090) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaad8a47090
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_474(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_474=construct.474(@ctx.addr=0xaaaade54ff70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaade54ff70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2456, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_477(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_477=construct.477(@ctx.addr=0xaaaadc8a4750) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaadc8a4750
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_474(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_474=construct.474(@ctx.addr=0xaaaade54ff70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaade54ff70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2456, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_478(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_478=construct.478(@ctx.addr=0xaaaaf4ee5ec0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf4ee5ec0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaad90ab8b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2456) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaad90abda0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.193] _tensor_mul_scalar.479 @ctx.addr=0xaaaaf3ab1260
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_479(
- %para2457 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2458 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2457, %para2458) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.194] _tensor_add_tensor.480 @ctx.addr=0xaaaae101a820
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_480(
- %para2459 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2460 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2459, %para2460) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.195] construct.182 @ctx.addr=0xaaaadc689be0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_182[fg_0](
- %para2461 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_481(%para2461) #(Tensor(F32)[16, 64, 32, 32]) # fg_481=construct.481(@ctx.addr=0xaaaae3336bc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae3336bc0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_482(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_482=construct.482(@ctx.addr=0xaaaae33370e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae33370e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2461, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_483(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_483=construct.483(@ctx.addr=0xaaaae5924a60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae5924a60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_482(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_482=construct.482(@ctx.addr=0xaaaae33370e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae33370e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2461, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_484(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_484=construct.484(@ctx.addr=0xaaaae6ee2c20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae6ee2c20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_482(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_482=construct.482(@ctx.addr=0xaaaae33370e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae33370e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2461, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_485(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_485=construct.485(@ctx.addr=0xaaaaedfc64e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaedfc64e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_482(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_482=construct.482(@ctx.addr=0xaaaae33370e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae33370e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2461, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_486(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_486=construct.486(@ctx.addr=0xaaaaf71edcf0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf71edcf0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaef837220
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2461) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaef837710
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.196] construct.183 @ctx.addr=0xaaaae9874e40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_183[fg_0](
- %para2462 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_487(%para2462) #(Tensor(F32)[16, 64, 32, 32]) # fg_487=construct.487(@ctx.addr=0xaaaaef832fb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaef832fb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_488(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_488=construct.488(@ctx.addr=0xaaaae000ed80) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae000ed80
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2462, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_489(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_489=construct.489(@ctx.addr=0xaaaaf49020f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf49020f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_488(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_488=construct.488(@ctx.addr=0xaaaae000ed80) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae000ed80
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2462, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_490(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_490=construct.490(@ctx.addr=0xaaaae5c18d70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae5c18d70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_488(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_488=construct.488(@ctx.addr=0xaaaae000ed80) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae000ed80
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2462, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_491(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_491=construct.491(@ctx.addr=0xaaaadc46b050) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaadc46b050
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_488(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_488=construct.488(@ctx.addr=0xaaaae000ed80) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae000ed80
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2462, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_492(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_492=construct.492(@ctx.addr=0xaaaad5c92fb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaad5c92fb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf0f6cd10
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2462) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf0f6d200
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.197] construct.184 @ctx.addr=0xaaaaf3ee0fa0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_184[fg_0](
- %para2463 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_493(%para2463) #(Tensor(F32)[16, 64, 32, 32]) # fg_493=construct.493(@ctx.addr=0xaaaaf3eea9c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf3eea9c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_494(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_494=construct.494(@ctx.addr=0xaaaad5bc4110) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaad5bc4110
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2463, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_495(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_495=construct.495(@ctx.addr=0xaaaae8537840) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae8537840
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_494(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_494=construct.494(@ctx.addr=0xaaaad5bc4110) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaad5bc4110
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2463, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_496(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_496=construct.496(@ctx.addr=0xaaaafae14bb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafae14bb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_494(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_494=construct.494(@ctx.addr=0xaaaad5bc4110) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaad5bc4110
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2463, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_497(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_497=construct.497(@ctx.addr=0xaaaafa857d90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafa857d90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_494(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_494=construct.494(@ctx.addr=0xaaaad5bc4110) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaad5bc4110
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2463, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_498(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_498=construct.498(@ctx.addr=0xaaaaf1d2a1f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf1d2a1f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaad68c5b70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2463) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaad68c6060
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.198] _tensor_mul_scalar.499 @ctx.addr=0xaaaad74242d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_499(
- %para2464 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2465 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2464, %para2465) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.199] _tensor_add_tensor.500 @ctx.addr=0xaaaad6e44500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_500(
- %para2466 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2467 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2466, %para2467) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.200] construct.185 @ctx.addr=0xaaaae0eee8e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_185[fg_0](
- %para2468 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_501(%para2468) #(Tensor(F32)[16, 64, 32, 32]) # fg_501=construct.501(@ctx.addr=0xaaaaeacb3bb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaeacb3bb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_502(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_502=construct.502(@ctx.addr=0xaaaae2668570) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2668570
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2468, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_503(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_503=construct.503(@ctx.addr=0xaaaaf0c38ba0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf0c38ba0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_502(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_502=construct.502(@ctx.addr=0xaaaae2668570) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2668570
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2468, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_504(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_504=construct.504(@ctx.addr=0xaaaad72a42b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad72a42b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_502(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_502=construct.502(@ctx.addr=0xaaaae2668570) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2668570
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2468, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_505(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_505=construct.505(@ctx.addr=0xaaaad6a02fa0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad6a02fa0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_502(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_502=construct.502(@ctx.addr=0xaaaae2668570) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2668570
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2468, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_506(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_506=construct.506(@ctx.addr=0xaaaaedb25360) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaedb25360
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf71a6cf0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2468) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf709c2a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.201] construct.186 @ctx.addr=0xaaaaf709d480
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_186[fg_0](
- %para2469 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_507(%para2469) #(Tensor(F32)[16, 64, 32, 32]) # fg_507=construct.507(@ctx.addr=0xaaaaf709ca30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaf709ca30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_508(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_508=construct.508(@ctx.addr=0xaaaae0caeec0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae0caeec0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2469, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_509(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_509=construct.509(@ctx.addr=0xaaaaef22ef60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaef22ef60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_508(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_508=construct.508(@ctx.addr=0xaaaae0caeec0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae0caeec0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2469, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_510(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_510=construct.510(@ctx.addr=0xaaaae2577e20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2577e20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_508(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_508=construct.508(@ctx.addr=0xaaaae0caeec0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae0caeec0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2469, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_511(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_511=construct.511(@ctx.addr=0xaaaae21546d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae21546d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_508(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_508=construct.508(@ctx.addr=0xaaaae0caeec0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae0caeec0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2469, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_512(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_512=construct.512(@ctx.addr=0xaaaaee3b5f30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaee3b5f30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae817b210
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2469) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae817b700
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.202] construct.187 @ctx.addr=0xaaaafae4dec0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_187[fg_0](
- %para2470 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_513(%para2470) #(Tensor(F32)[16, 64, 32, 32]) # fg_513=construct.513(@ctx.addr=0xaaaafae4d470) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaafae4d470
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_514(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_514=construct.514(@ctx.addr=0xaaaae24b4b40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae24b4b40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2470, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_515(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_515=construct.515(@ctx.addr=0xaaaaf1e63820) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf1e63820
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_514(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_514=construct.514(@ctx.addr=0xaaaae24b4b40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae24b4b40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2470, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_516(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_516=construct.516(@ctx.addr=0xaaaaf066dee0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf066dee0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_514(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_514=construct.514(@ctx.addr=0xaaaae24b4b40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae24b4b40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2470, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_517(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_517=construct.517(@ctx.addr=0xaaaae841bd80) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae841bd80
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_514(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_514=construct.514(@ctx.addr=0xaaaae24b4b40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae24b4b40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2470, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_518(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_518=construct.518(@ctx.addr=0xaaaae1fc18a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1fc18a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae82c9aa0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2470) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae82c9f90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.203] _tensor_mul_scalar.519 @ctx.addr=0xaaaae23a29e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_519(
- %para2471 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2472 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2471, %para2472) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.204] _tensor_add_tensor.520 @ctx.addr=0xaaaaf0dea030
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_520(
- %para2473 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2474 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2473, %para2474) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.205] construct.188 @ctx.addr=0xaaaae2286150
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_188[fg_0](
- %para2475 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_521(%para2475) #(Tensor(F32)[16, 64, 32, 32]) # fg_521=construct.521(@ctx.addr=0xaaaaee7fe400) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaee7fe400
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_522(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_522=construct.522(@ctx.addr=0xaaaaee7fea20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaee7fea20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2475, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_523(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_523=construct.523(@ctx.addr=0xaaaae258dbf0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae258dbf0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_522(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_522=construct.522(@ctx.addr=0xaaaaee7fea20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaee7fea20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2475, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_524(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_524=construct.524(@ctx.addr=0xaaaae258f120) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae258f120
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_522(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_522=construct.522(@ctx.addr=0xaaaaee7fea20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaee7fea20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2475, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_525(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_525=construct.525(@ctx.addr=0xaaaafae847e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaafae847e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_522(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_522=construct.522(@ctx.addr=0xaaaaee7fea20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaee7fea20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2475, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_526(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_526=construct.526(@ctx.addr=0xaaaaee80db90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaee80db90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae84b75d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2475) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae84b7ac0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.206] construct.189 @ctx.addr=0xaaaae205a960
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_189[fg_0](
- %para2476 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_527(%para2476) #(Tensor(F32)[16, 64, 32, 32]) # fg_527=construct.527(@ctx.addr=0xaaaae84b8250) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae84b8250
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_528(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_528=construct.528(@ctx.addr=0xaaaae20b0e50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae20b0e50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2476, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_529(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_529=construct.529(@ctx.addr=0xaaaafae959b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaafae959b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_528(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_528=construct.528(@ctx.addr=0xaaaae20b0e50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae20b0e50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2476, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_530(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_530=construct.530(@ctx.addr=0xaaaae1e602a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1e602a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_528(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_528=construct.528(@ctx.addr=0xaaaae20b0e50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae20b0e50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2476, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_531(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_531=construct.531(@ctx.addr=0xaaaaeef83ad0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaeef83ad0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_528(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_528=construct.528(@ctx.addr=0xaaaae20b0e50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae20b0e50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2476, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_532(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_532=construct.532(@ctx.addr=0xaaaae1f4e760) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1f4e760
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaebbd5200
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2476) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaebbd56f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.207] construct.190 @ctx.addr=0xaaaae85f96c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_190[fg_0](
- %para2477 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_533(%para2477) #(Tensor(F32)[16, 64, 32, 32]) # fg_533=construct.533(@ctx.addr=0xaaaaebbd5e80) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaebbd5e80
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_534(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_534=construct.534(@ctx.addr=0xaaaae2671ad0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2671ad0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2477, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_535(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_535=construct.535(@ctx.addr=0xaaaae2102600) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2102600
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_534(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_534=construct.534(@ctx.addr=0xaaaae2671ad0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2671ad0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2477, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_536(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_536=construct.536(@ctx.addr=0xaaaae21f2d20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae21f2d20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_534(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_534=construct.534(@ctx.addr=0xaaaae2671ad0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2671ad0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2477, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_537(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_537=construct.537(@ctx.addr=0xaaaae2304fa0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2304fa0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_534(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_534=construct.534(@ctx.addr=0xaaaae2671ad0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2671ad0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2477, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_538(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_538=construct.538(@ctx.addr=0xaaaae259d110) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae259d110
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae23be370
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2477) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae23be860
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.208] _tensor_mul_scalar.539 @ctx.addr=0xaaaae246cb60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_539(
- %para2478 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2479 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2478, %para2479) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.209] _tensor_add_tensor.540 @ctx.addr=0xaaaae22a1c40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_540(
- %para2480 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2481 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2480, %para2481) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.210] construct.191 @ctx.addr=0xaaaad942ff50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_191[fg_0](
- %para2482 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_541(%para2482) #(Tensor(F32)[16, 64, 32, 32]) # fg_541=construct.541(@ctx.addr=0xaaaad59c3850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad59c3850
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_542(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_542=construct.542(@ctx.addr=0xaaaad59c3e40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad59c3e40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2482, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_543(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_543=construct.543(@ctx.addr=0xaaaaf605f440) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf605f440
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_542(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_542=construct.542(@ctx.addr=0xaaaad59c3e40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad59c3e40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2482, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_544(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_544=construct.544(@ctx.addr=0xaaaaf6060a00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf6060a00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_542(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_542=construct.542(@ctx.addr=0xaaaad59c3e40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad59c3e40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2482, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_545(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_545=construct.545(@ctx.addr=0xaaaaeb6d2c80) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaeb6d2c80
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_542(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_542=construct.542(@ctx.addr=0xaaaad59c3e40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad59c3e40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2482, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_546(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_546=construct.546(@ctx.addr=0xaaaad83039f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaad83039f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaeb373d60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2482) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaeb374250
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.211] construct.192 @ctx.addr=0xaaaaeede3b50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_192[fg_0](
- %para2483 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_547(%para2483) #(Tensor(F32)[16, 64, 32, 32]) # fg_547=construct.547(@ctx.addr=0xaaaaeb3749e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaeb3749e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_548(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_548=construct.548(@ctx.addr=0xaaaadc52e590) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaadc52e590
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2483, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_549(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_549=construct.549(@ctx.addr=0xaaaafae32960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaafae32960
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_548(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_548=construct.548(@ctx.addr=0xaaaadc52e590) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaadc52e590
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2483, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_550(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_550=construct.550(@ctx.addr=0xaaaafae33f20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaafae33f20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_548(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_548=construct.548(@ctx.addr=0xaaaadc52e590) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaadc52e590
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2483, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_551(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_551=construct.551(@ctx.addr=0xaaaae0ef2c50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae0ef2c50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_548(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_548=construct.548(@ctx.addr=0xaaaadc52e590) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaadc52e590
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2483, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_552(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_552=construct.552(@ctx.addr=0xaaaae224eb60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae224eb60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae24c8360
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2483) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae24c8850
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.212] construct.193 @ctx.addr=0xaaaae248ceb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_193[fg_0](
- %para2484 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_553(%para2484) #(Tensor(F32)[16, 64, 32, 32]) # fg_553=construct.553(@ctx.addr=0xaaaae248c460) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae248c460
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_554(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_554=construct.554(@ctx.addr=0xaaaae21db380) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae21db380
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2484, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_555(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_555=construct.555(@ctx.addr=0xaaaae81a73c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae81a73c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_554(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_554=construct.554(@ctx.addr=0xaaaae21db380) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae21db380
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2484, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_556(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_556=construct.556(@ctx.addr=0xaaaae2287ec0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2287ec0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_554(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_554=construct.554(@ctx.addr=0xaaaae21db380) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae21db380
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2484, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_557(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_557=construct.557(@ctx.addr=0xaaaaf065fb70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaaf065fb70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_554(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_554=construct.554(@ctx.addr=0xaaaae21db380) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae21db380
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2484, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_558(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_558=construct.558(@ctx.addr=0xaaaae21e7460) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae21e7460
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae24266a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2484) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2426b90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.213] _tensor_mul_scalar.559 @ctx.addr=0xaaaae225ca60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_559(
- %para2485 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2486 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2485, %para2486) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.214] _tensor_add_tensor.560 @ctx.addr=0xaaaae2096180
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_560(
- %para2487 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2488 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2487, %para2488) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.215] construct.194 @ctx.addr=0xaaaae22d0570
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_194[fg_0](
- %para2489 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_561(%para2489) #(Tensor(F32)[16, 64, 32, 32]) # fg_561=construct.561(@ctx.addr=0xaaaae2159b60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2159b60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_562(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_562=construct.562(@ctx.addr=0xaaaae215a180) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae215a180
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2489, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_563(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_563=construct.563(@ctx.addr=0xaaaae1fc7170) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae1fc7170
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_562(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_562=construct.562(@ctx.addr=0xaaaae215a180) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae215a180
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2489, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_564(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_564=construct.564(@ctx.addr=0xaaaaf71a9a60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf71a9a60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_562(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_562=construct.562(@ctx.addr=0xaaaae215a180) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae215a180
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2489, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_565(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_565=construct.565(@ctx.addr=0xaaaae21f6100) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae21f6100
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_562(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_562=construct.562(@ctx.addr=0xaaaae215a180) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae215a180
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2489, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_566(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_566=construct.566(@ctx.addr=0xaaaae23d1610) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae23d1610
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae214a160
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2489) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae214a650
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.216] construct.195 @ctx.addr=0xaaaae2317950
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_195[fg_0](
- %para2490 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_567(%para2490) #(Tensor(F32)[16, 64, 32, 32]) # fg_567=construct.567(@ctx.addr=0xaaaae214ade0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae214ade0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_568(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_568=construct.568(@ctx.addr=0xaaaae2172050) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2172050
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2490, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_569(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_569=construct.569(@ctx.addr=0xaaaaec603770) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaec603770
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_568(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_568=construct.568(@ctx.addr=0xaaaae2172050) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2172050
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2490, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_570(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_570=construct.570(@ctx.addr=0xaaaae24a9c80) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae24a9c80
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_568(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_568=construct.568(@ctx.addr=0xaaaae2172050) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2172050
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2490, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_571(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_571=construct.571(@ctx.addr=0xaaaae121a490) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae121a490
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_568(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_568=construct.568(@ctx.addr=0xaaaae2172050) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2172050
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2490, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_572(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_572=construct.572(@ctx.addr=0xaaaae84ffbc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae84ffbc0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2521830
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2490) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2521d20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.217] construct.196 @ctx.addr=0xaaaae2522f00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_196[fg_0](
- %para2491 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_573(%para2491) #(Tensor(F32)[16, 64, 32, 32]) # fg_573=construct.573(@ctx.addr=0xaaaae25224b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae25224b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_574(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_574=construct.574(@ctx.addr=0xaaaae20a5820) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae20a5820
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2491, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_575(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_575=construct.575(@ctx.addr=0xaaaae81c5ad0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae81c5ad0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_574(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_574=construct.574(@ctx.addr=0xaaaae20a5820) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae20a5820
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2491, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_576(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_576=construct.576(@ctx.addr=0xaaaae1efd5c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1efd5c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_574(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_574=construct.574(@ctx.addr=0xaaaae20a5820) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae20a5820
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2491, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_577(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_577=construct.577(@ctx.addr=0xaaaae1e55d40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1e55d40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_574(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_574=construct.574(@ctx.addr=0xaaaae20a5820) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae20a5820
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2491, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_578(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_578=construct.578(@ctx.addr=0xaaaae84ab7a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae84ab7a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1e62e90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2491) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1e63380
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.218] _tensor_mul_scalar.579 @ctx.addr=0xaaaae205e0a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_579(
- %para2492 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2493 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2492, %para2493) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.219] _tensor_add_tensor.580 @ctx.addr=0xaaaaeedd46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_580(
- %para2494 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2495 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2494, %para2495) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.220] construct.197 @ctx.addr=0xaaaae233f2e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_197[fg_0](
- %para2496 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_581(%para2496) #(Tensor(F32)[16, 64, 32, 32]) # fg_581=construct.581(@ctx.addr=0xaaaaebbe3740) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaebbe3740
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_582(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_582=construct.582(@ctx.addr=0xaaaaebbe3cd0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaebbe3cd0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2496, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_583(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_583=construct.583(@ctx.addr=0xaaaae235c600) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae235c600
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_582(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_582=construct.582(@ctx.addr=0xaaaaebbe3cd0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaebbe3cd0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2496, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_584(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_584=construct.584(@ctx.addr=0xaaaae235dbc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae235dbc0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_582(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_582=construct.582(@ctx.addr=0xaaaaebbe3cd0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaebbe3cd0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2496, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_585(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_585=construct.585(@ctx.addr=0xaaaae82cde60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae82cde60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_582(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_582=construct.582(@ctx.addr=0xaaaaebbe3cd0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaebbe3cd0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2496, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_586(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_586=construct.586(@ctx.addr=0xaaaadca62f00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaadca62f00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaec7b28a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2496) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaec7b2d90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.221] construct.198 @ctx.addr=0xaaaae2443740
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_198[fg_0](
- %para2497 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_587(%para2497) #(Tensor(F32)[16, 64, 32, 32]) # fg_587=construct.587(@ctx.addr=0xaaaaec7b3520) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaec7b3520
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_588(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_588=construct.588(@ctx.addr=0xaaaaec02a790) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaec02a790
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2497, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_589(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_589=construct.589(@ctx.addr=0xaaaae1dc8c60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1dc8c60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_588(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_588=construct.588(@ctx.addr=0xaaaaec02a790) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaec02a790
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2497, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_590(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_590=construct.590(@ctx.addr=0xaaaae1dca220) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae1dca220
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_588(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_588=construct.588(@ctx.addr=0xaaaaec02a790) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaec02a790
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2497, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_591(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_591=construct.591(@ctx.addr=0xaaaae85b82b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae85b82b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_588(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_588=construct.588(@ctx.addr=0xaaaaec02a790) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaaec02a790
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2497, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_592(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_592=construct.592(@ctx.addr=0xaaaae8475e70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae8475e70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae85c7d00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2497) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae85c81f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.222] construct.199 @ctx.addr=0xaaaae85c93d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_199[fg_0](
- %para2498 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_593(%para2498) #(Tensor(F32)[16, 64, 32, 32]) # fg_593=construct.593(@ctx.addr=0xaaaae85c8980) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae85c8980
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_594(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_594=construct.594(@ctx.addr=0xaaaae1f60d10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1f60d10
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2498, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_595(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_595=construct.595(@ctx.addr=0xaaaae2279370) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2279370
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_594(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_594=construct.594(@ctx.addr=0xaaaae1f60d10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1f60d10
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2498, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_596(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_596=construct.596(@ctx.addr=0xaaaae227a930) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae227a930
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_594(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_594=construct.594(@ctx.addr=0xaaaae1f60d10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1f60d10
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2498, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_597(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_597=construct.597(@ctx.addr=0xaaaae1e0eb30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1e0eb30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_594(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_594=construct.594(@ctx.addr=0xaaaae1f60d10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1f60d10
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2498, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_598(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_598=construct.598(@ctx.addr=0xaaaae83cbf20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae83cbf20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1febc70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2498) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae1fec160
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.223] _tensor_mul_scalar.599 @ctx.addr=0xaaaae2395290
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_599(
- %para2499 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2500 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2499, %para2500) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.224] _tensor_add_tensor.600 @ctx.addr=0xaaaafae22740
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_600(
- %para2501 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2502 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2501, %para2502) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.225] construct.200 @ctx.addr=0xaaaaf17caae0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_200[fg_0](
- %para2503 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_601(%para2503) #(Tensor(F32)[16, 64, 32, 32]) # fg_601=construct.601(@ctx.addr=0xaaaafadcf750) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaafadcf750
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_602(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_602=construct.602(@ctx.addr=0xaaaaf1f601b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf1f601b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2503, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_603(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_603=construct.603(@ctx.addr=0xaaaae2680550) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2680550
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_602(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_602=construct.602(@ctx.addr=0xaaaaf1f601b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf1f601b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2503, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_604(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_604=construct.604(@ctx.addr=0xaaaae2681b10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2681b10
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_602(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_602=construct.602(@ctx.addr=0xaaaaf1f601b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf1f601b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2503, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_605(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_605=construct.605(@ctx.addr=0xaaaae26676a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae26676a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_602(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_602=construct.602(@ctx.addr=0xaaaaf1f601b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaaf1f601b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2503, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_606(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_606=construct.606(@ctx.addr=0xaaaae2674a80) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2674a80
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae267a970
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2503) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae267ae60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.226] construct.201 @ctx.addr=0xaaaae267c010
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_201[fg_0](
- %para2504 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_607(%para2504) #(Tensor(F32)[16, 64, 32, 32]) # fg_607=construct.607(@ctx.addr=0xaaaae267b5c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae267b5c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_608(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_608=construct.608(@ctx.addr=0xaaaae2695bb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2695bb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2504, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_609(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_609=construct.609(@ctx.addr=0xaaaae26bae60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae26bae60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_608(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_608=construct.608(@ctx.addr=0xaaaae2695bb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2695bb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2504, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_610(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_610=construct.610(@ctx.addr=0xaaaae26bc420) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae26bc420
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_608(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_608=construct.608(@ctx.addr=0xaaaae2695bb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2695bb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2504, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_611(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_611=construct.611(@ctx.addr=0xaaaae272c860) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae272c860
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_608(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_608=construct.608(@ctx.addr=0xaaaae2695bb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2695bb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2504, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_612(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_612=construct.612(@ctx.addr=0xaaaae2733990) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2733990
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2738ab0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2504) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae27390e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.227] construct.202 @ctx.addr=0xaaaae273a300
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_202[fg_0](
- %para2505 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_613(%para2505) #(Tensor(F32)[16, 64, 32, 32]) # fg_613=construct.613(@ctx.addr=0xaaaae27398b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae27398b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_614(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_614=construct.614(@ctx.addr=0xaaaae2743450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2743450
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2505, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_615(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_615=construct.615(@ctx.addr=0xaaaae275bcb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae275bcb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_614(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_614=construct.614(@ctx.addr=0xaaaae2743450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2743450
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2505, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_616(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_616=construct.616(@ctx.addr=0xaaaae275d270) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae275d270
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_614(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_614=construct.614(@ctx.addr=0xaaaae2743450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2743450
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2505, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_617(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_617=construct.617(@ctx.addr=0xaaaae2773c20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2773c20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_614(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_614=construct.614(@ctx.addr=0xaaaae2743450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2743450
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2505, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_618(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_618=construct.618(@ctx.addr=0xaaaae277ad70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae277ad70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae277fe90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2505) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae27804c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.228] _tensor_mul_scalar.619 @ctx.addr=0xaaaae2789640
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_619(
- %para2506 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2507 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2506, %para2507) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.229] _tensor_add_tensor.620 @ctx.addr=0xaaaae278f4b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_620(
- %para2508 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2509 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2508, %para2509) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.230] construct.203 @ctx.addr=0xaaaae27a3e30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_203[fg_0](
- %para2510 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_621(%para2510) #(Tensor(F32)[16, 64, 32, 32]) # fg_621=construct.621(@ctx.addr=0xaaaae27cb570) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae27cb570
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_622(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_622=construct.622(@ctx.addr=0xaaaae27cbf70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae27cbf70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2510, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_623(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_623=construct.623(@ctx.addr=0xaaaae27e5330) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae27e5330
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_622(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_622=construct.622(@ctx.addr=0xaaaae27cbf70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae27cbf70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2510, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_624(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_624=construct.624(@ctx.addr=0xaaaae27e68f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae27e68f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_622(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_622=construct.622(@ctx.addr=0xaaaae27cbf70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae27cbf70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2510, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_625(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_625=construct.625(@ctx.addr=0xaaaae27fd4c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae27fd4c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_622(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_622=construct.622(@ctx.addr=0xaaaae27cbf70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae27cbf70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2510, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_626(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_626=construct.626(@ctx.addr=0xaaaae28045f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae28045f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2809700
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2510) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2809d30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.231] construct.204 @ctx.addr=0xaaaae280af50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_204[fg_0](
- %para2511 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_627(%para2511) #(Tensor(F32)[16, 64, 32, 32]) # fg_627=construct.627(@ctx.addr=0xaaaae280a500) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae280a500
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_628(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_628=construct.628(@ctx.addr=0xaaaae28140a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae28140a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2511, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_629(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_629=construct.629(@ctx.addr=0xaaaae282c6f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae282c6f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_628(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_628=construct.628(@ctx.addr=0xaaaae28140a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae28140a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2511, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_630(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_630=construct.630(@ctx.addr=0xaaaae282dcb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae282dcb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_628(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_628=construct.628(@ctx.addr=0xaaaae28140a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae28140a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2511, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_631(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_631=construct.631(@ctx.addr=0xaaaae2844860) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2844860
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_628(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_628=construct.628(@ctx.addr=0xaaaae28140a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae28140a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2511, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_632(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_632=construct.632(@ctx.addr=0xaaaae284b9a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae284b9a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2850ab0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2511) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae28510e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.232] construct.205 @ctx.addr=0xaaaae2852300
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_205[fg_0](
- %para2512 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_633(%para2512) #(Tensor(F32)[16, 64, 32, 32]) # fg_633=construct.633(@ctx.addr=0xaaaae28518b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae28518b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_634(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_634=construct.634(@ctx.addr=0xaaaae285b450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae285b450
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2512, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_635(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_635=construct.635(@ctx.addr=0xaaaae2873a80) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2873a80
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_634(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_634=construct.634(@ctx.addr=0xaaaae285b450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae285b450
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2512, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_636(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_636=construct.636(@ctx.addr=0xaaaae2875040) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2875040
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_634(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_634=construct.634(@ctx.addr=0xaaaae285b450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae285b450
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2512, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_637(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_637=construct.637(@ctx.addr=0xaaaae288bc20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae288bc20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_634(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_634=construct.634(@ctx.addr=0xaaaae285b450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae285b450
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2512, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_638(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_638=construct.638(@ctx.addr=0xaaaae2892d50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2892d50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2897e60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2512) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2898490
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.233] _tensor_mul_scalar.639 @ctx.addr=0xaaaae28a1610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_639(
- %para2513 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2514 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2513, %para2514) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.234] _tensor_add_tensor.640 @ctx.addr=0xaaaae28a7290
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_640(
- %para2515 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2516 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2515, %para2516) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.235] construct.206 @ctx.addr=0xaaaae28cb590
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_206[fg_0](
- %para2517 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_641(%para2517) #(Tensor(F32)[16, 64, 32, 32]) # fg_641=construct.641(@ctx.addr=0xaaaae28e1d80) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae28e1d80
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_642(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_642=construct.642(@ctx.addr=0xaaaae28e2780) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae28e2780
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2517, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_643(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_643=construct.643(@ctx.addr=0xaaaae28fb9b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae28fb9b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_642(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_642=construct.642(@ctx.addr=0xaaaae28e2780) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae28e2780
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2517, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_644(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_644=construct.644(@ctx.addr=0xaaaae28fcf70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae28fcf70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_642(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_642=construct.642(@ctx.addr=0xaaaae28e2780) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae28e2780
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2517, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_645(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_645=construct.645(@ctx.addr=0xaaaae2913b20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2913b20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_642(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_642=construct.642(@ctx.addr=0xaaaae28e2780) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae28e2780
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2517, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_646(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_646=construct.646(@ctx.addr=0xaaaae291ac50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae291ac50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae291fd60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2517) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae2920390
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.236] construct.207 @ctx.addr=0xaaaae29215b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_207[fg_0](
- %para2518 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_647(%para2518) #(Tensor(F32)[16, 64, 32, 32]) # fg_647=construct.647(@ctx.addr=0xaaaae2920b60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2920b60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_648(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_648=construct.648(@ctx.addr=0xaaaae292a700) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae292a700
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2518, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_649(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_649=construct.649(@ctx.addr=0xaaaae2942d60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2942d60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_648(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_648=construct.648(@ctx.addr=0xaaaae292a700) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae292a700
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2518, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_650(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_650=construct.650(@ctx.addr=0xaaaae2944320) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2944320
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_648(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_648=construct.648(@ctx.addr=0xaaaae292a700) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae292a700
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2518, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_651(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_651=construct.651(@ctx.addr=0xaaaae295aee0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae295aee0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_648(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_648=construct.648(@ctx.addr=0xaaaae292a700) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae292a700
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2518, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_652(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_652=construct.652(@ctx.addr=0xaaaae29629b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae29629b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2968110
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2518) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae2968600
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.237] construct.208 @ctx.addr=0xaaaae36f8a20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_208[fg_0](
- %para2519 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_653(%para2519) #(Tensor(F32)[16, 64, 32, 32]) # fg_653=construct.653(@ctx.addr=0xaaaae2968d90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae2968d90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_654(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_654=construct.654(@ctx.addr=0xaaaae3700d10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3700d10
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2519, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_655(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_655=construct.655(@ctx.addr=0xaaaae3719350) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3719350
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_654(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_654=construct.654(@ctx.addr=0xaaaae3700d10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3700d10
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2519, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_656(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_656=construct.656(@ctx.addr=0xaaaae371a910) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae371a910
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_654(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_654=construct.654(@ctx.addr=0xaaaae3700d10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3700d10
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2519, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_657(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_657=construct.657(@ctx.addr=0xaaaae3731500) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3731500
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_654(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_654=construct.654(@ctx.addr=0xaaaae3700d10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3700d10
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2519, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_658(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_658=construct.658(@ctx.addr=0xaaaae3738640) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3738640
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae373d750
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2519) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae373dd80
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.238] _tensor_mul_scalar.659 @ctx.addr=0xaaaae3746f00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_659(
- %para2520 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2521 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2520, %para2521) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.239] _tensor_add_tensor.660 @ctx.addr=0xaaaae374cd70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_660(
- %para2522 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2523 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2522, %para2523) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.240] construct.209 @ctx.addr=0xaaaae3770dd0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_209[fg_0](
- %para2524 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_661(%para2524) #(Tensor(F32)[16, 64, 32, 32]) # fg_661=construct.661(@ctx.addr=0xaaaae3785dc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae3785dc0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_662(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_662=construct.662(@ctx.addr=0xaaaae3786820) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae3786820
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2524, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_663(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_663=construct.663(@ctx.addr=0xaaaae379fae0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae379fae0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_662(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_662=construct.662(@ctx.addr=0xaaaae3786820) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae3786820
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2524, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_664(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_664=construct.664(@ctx.addr=0xaaaae37a10a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae37a10a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_662(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_662=construct.662(@ctx.addr=0xaaaae3786820) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae3786820
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2524, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_665(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_665=construct.665(@ctx.addr=0xaaaae37b7c50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae37b7c50
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_662(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_662=construct.662(@ctx.addr=0xaaaae3786820) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae3786820
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2524, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_666(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_666=construct.666(@ctx.addr=0xaaaae37bfa70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae37bfa70
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae37c4b80
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2524) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae37c51b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.241] construct.210 @ctx.addr=0xaaaae37c63d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_210[fg_0](
- %para2525 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_667(%para2525) #(Tensor(F32)[16, 64, 32, 32]) # fg_667=construct.667(@ctx.addr=0xaaaae37c5980) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae37c5980
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_668(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_668=construct.668(@ctx.addr=0xaaaae37cf520) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae37cf520
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2525, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_669(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_669=construct.669(@ctx.addr=0xaaaae37e7b60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae37e7b60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_668(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_668=construct.668(@ctx.addr=0xaaaae37cf520) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae37cf520
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2525, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_670(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_670=construct.670(@ctx.addr=0xaaaae37e9120) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae37e9120
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_668(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_668=construct.668(@ctx.addr=0xaaaae37cf520) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae37cf520
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2525, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_671(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_671=construct.671(@ctx.addr=0xaaaae37ffcf0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae37ffcf0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_668(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_668=construct.668(@ctx.addr=0xaaaae37cf520) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae37cf520
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2525, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_672(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_672=construct.672(@ctx.addr=0xaaaae3806e20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae3806e20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae380bf30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2525) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae380c560
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.242] construct.211 @ctx.addr=0xaaaae380d780
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_211[fg_0](
- %para2526 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_673(%para2526) #(Tensor(F32)[16, 64, 32, 32]) # fg_673=construct.673(@ctx.addr=0xaaaae380cd30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae380cd30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_674(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_674=construct.674(@ctx.addr=0xaaaae38168d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae38168d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2526, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_675(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_675=construct.675(@ctx.addr=0xaaaae382ef10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae382ef10
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_674(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_674=construct.674(@ctx.addr=0xaaaae38168d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae38168d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2526, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_676(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_676=construct.676(@ctx.addr=0xaaaae38304d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae38304d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_674(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_674=construct.674(@ctx.addr=0xaaaae38168d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae38168d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2526, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_677(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_677=construct.677(@ctx.addr=0xaaaae3847080) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3847080
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_674(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_674=construct.674(@ctx.addr=0xaaaae38168d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae38168d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2526, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_678(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_678=construct.678(@ctx.addr=0xaaaae384e1b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae384e1b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae38532c0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2526) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae38538f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.243] _tensor_mul_scalar.679 @ctx.addr=0xaaaae385ca70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_679(
- %para2527 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2528 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2527, %para2528) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.244] _tensor_add_tensor.680 @ctx.addr=0xaaaae38628e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_680(
- %para2529 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2530 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2529, %para2530) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.245] construct.212 @ctx.addr=0xaaaae38868b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_212[fg_0](
- %para2531 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_681(%para2531) #(Tensor(F32)[16, 64, 32, 32]) # fg_681=construct.681(@ctx.addr=0xaaaae389a0a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae389a0a0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_682(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_682=construct.682(@ctx.addr=0xaaaae389ab00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae389ab00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2531, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_683(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_683=construct.683(@ctx.addr=0xaaaae38b3e20) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae38b3e20
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_682(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_682=construct.682(@ctx.addr=0xaaaae389ab00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae389ab00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2531, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_684(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_684=construct.684(@ctx.addr=0xaaaae38b53e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae38b53e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_682(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_682=construct.682(@ctx.addr=0xaaaae389ab00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae389ab00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2531, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_685(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_685=construct.685(@ctx.addr=0xaaaae38cbfb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae38cbfb0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_682(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_682=construct.682(@ctx.addr=0xaaaae389ab00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae389ab00
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2531, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_686(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_686=construct.686(@ctx.addr=0xaaaae38d30e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae38d30e0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae38d81f0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2531) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C @ctx.addr=0xaaaae38d8820
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.246] construct.213 @ctx.addr=0xaaaae38d9a40
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_213[fg_0](
- %para2532 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_687(%para2532) #(Tensor(F32)[16, 64, 32, 32]) # fg_687=construct.687(@ctx.addr=0xaaaae38d8ff0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae38d8ff0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_688(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_688=construct.688(@ctx.addr=0xaaaae38e2b90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae38e2b90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2532, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_689(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_689=construct.689(@ctx.addr=0xaaaae38fb1d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae38fb1d0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_688(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_688=construct.688(@ctx.addr=0xaaaae38e2b90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae38e2b90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2532, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_690(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_690=construct.690(@ctx.addr=0xaaaae38fc790) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae38fc790
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_688(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_688=construct.688(@ctx.addr=0xaaaae38e2b90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae38e2b90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2532, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_691(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_691=construct.691(@ctx.addr=0xaaaae3913350) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae3913350
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_688(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_688=construct.688(@ctx.addr=0xaaaae38e2b90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae38e2b90
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2532, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_692(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_692=construct.692(@ctx.addr=0xaaaae391a480) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae391a480
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae391f590
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2532) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C @ctx.addr=0xaaaae391fbc0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.247] construct.214 @ctx.addr=0xaaaae3920de0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(37)/ def construct(self, x)://
- funcgraph fg_214[fg_0](
- %para2533 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_693(%para2533) #(Tensor(F32)[16, 64, 32, 32]) # fg_693=construct.693(@ctx.addr=0xaaaae3920390) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3920390
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %2 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_694(%1) #(Tensor(F32)[16, 32, 32, 32]) # fg_694=construct.694(@ctx.addr=0xaaaae3929f30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3929f30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(38)/ x1 = self.lrelu(self.conv1(x))//
- %3 : Tuple[Tensor(F32)*2] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2533, %2) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %4 : Tensor(F32)[16, 96, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%3) #(Tuple[Tensor(F32)*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %5 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_695(%4) #(Tensor(F32)[16, 96, 32, 32]) # fg_695=construct.695(@ctx.addr=0xaaaae3942590) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3942590
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %6 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_694(%5) #(Tensor(F32)[16, 32, 32, 32]) # fg_694=construct.694(@ctx.addr=0xaaaae3929f30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3929f30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(39)/ x2 = self.lrelu(self.conv2(self.cat((x, x1))))//
- %7 : Tuple[Tensor(F32)*3] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2533, %2, %6) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %8 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%7) #(Tuple[Tensor(F32)*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_696(%8) #(Tensor(F32)[16, 128, 32, 32]) # fg_696=construct.696(@ctx.addr=0xaaaae3943b30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3943b30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %10 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_694(%9) #(Tensor(F32)[16, 32, 32, 32]) # fg_694=construct.694(@ctx.addr=0xaaaae3929f30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3929f30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(40)/ x3 = self.lrelu(self.conv3(self.cat((x, x1, x2))))//
- %11 : Tuple[Tensor(F32)*4] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2533, %2, %6, %10) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %12 : Tensor(F32)[16, 160, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%11) #(Tuple[Tensor(F32)*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %13 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_697(%12) #(Tensor(F32)[16, 160, 32, 32]) # fg_697=construct.697(@ctx.addr=0xaaaae395a670) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae395a670
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %14 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_694(%13) #(Tensor(F32)[16, 32, 32, 32]) # fg_694=construct.694(@ctx.addr=0xaaaae3929f30) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3929f30
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(41)/ x4 = self.lrelu(self.conv4(self.cat((x, x1, x2, x3))))//
- %15 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-MakeTuple{prim_type=1}(%para2533, %2, %6, %10, %14) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %16 : Tensor(F32)[16, 192, 32, 32] = DoSignaturePrimitive::S-Prim-Concat{prim_type=1}[inputNums=I64(5), axis=I64(1)](%15) #(Tuple[Tensor(F32)*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %17 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_698(%16) #(Tensor(F32)[16, 192, 32, 32]) # fg_698=construct.698(@ctx.addr=0xaaaae39617b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae39617b0
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(42)/ x5 = self.conv5(self.cat((x, x1, x2, x3, x4)))//
- %18 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%17, F32(0.2)) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3966930
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- %19 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-add{prim_type=1}(%18, %para2533) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C @ctx.addr=0xaaaae3966f60
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- Primitive::Return{prim_type=1}(%19) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C
- # In file /home/HEU_535/ESRGAN/src/model/RRDB_Net.py(43)/ return x5 * self.res_beta + x//
- }
-
-
- # [No.248] _tensor_mul_scalar.699 @ctx.addr=0xaaaae3970070
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_699(
- %para2534 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2535 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2534, %para2535) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.249] _tensor_add_tensor.700 @ctx.addr=0xaaaae3975ee0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_700(
- %para2536 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2537 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2536, %para2537) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.250] ms_iter.215 @ctx.addr=0xaaaad95f2670
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(480)/def ms_iter(xs):/
- funcgraph fg_215(
- %para2538 : Tuple[Func*23] # xs
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para2538, "__ms_iter__") #(Tuple[Func*23], String) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(482)/ return xs.__ms_iter__()/
- %2 : Tuple[Func*23] = %1() #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(482)/ return xs.__ms_iter__()/
- Primitive::Return{prim_type=1}(%2) #(Tuple[Func*23]) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(482)/ return xs.__ms_iter__()/
- }
-
-
- # [No.251] ⤾✓construct.216 @ctx.addr=0xaaaaf3089d40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para2539 : Tuple[Func*23] # @cell
- , %para2540 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*23]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaaf4c92850), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf4c92850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.252] L-bool_.217 @ctx.addr=0xaaaae3989a00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(530)/def bool_(x):/
- funcgraph fg_217(
- %para2541 : Bool # x
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para2541, "__bool__") #(Bool, String) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- %2 : Bool = %1() #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- Primitive::Return{prim_type=1}(%2) #(Bool) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- }
-
-
- # [No.253] L-✓construct.218 @ctx.addr=0xaaaae398ad70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_218[fg_96](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para2324, %para2326) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para2325) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_704(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_704=L-↓construct.704(@ctx.addr=0xaaaae39f9890) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d @ctx.addr=0xaaaae39f9890
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.254] L-✓construct.218 @ctx.addr=0xaaaae39bb7b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_218[fg_96](
- ) {
- %1 : Tensor(F32)[16, 64, 64, 64] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para2324, %para2326) #(Tensor(F32)[16, 64, 64, 64], Ref[Tensor(F32)][64, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 64, 64] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para2325) #(Tensor(F32)[16, 64, 64, 64], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 64, 64] = FuncGraph::fg_704(%2) #(Tensor(F32)[16, 64, 64, 64]) # fg_704=L-↓construct.704(@ctx.addr=0xaaaae39f9890) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d @ctx.addr=0xaaaae39f9890
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.254] _mul_tensor.705 @ctx.addr=0xaaaae39d1180
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_705(
- %para2542 : Tensor(F32)[] # x
- , %para2543 : Tensor(F32)[16, 64, 64, 64] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 64, 64] = %2(%para2542, %para2543) #(Tensor(F32)[], Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.255] ↓construct.221 @ctx.addr=0xaaaae3a08490
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_221(
- %para2544 : Tensor(F32)[16, 64, 128, 128] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para2544) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.256] L-✓construct.218 @ctx.addr=0xaaaae39f9640
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_218[fg_96](
- ) {
- %1 : Tensor(F32)[16, 64, 128, 128] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para2324, %para2326) #(Tensor(F32)[16, 64, 128, 128], Ref[Tensor(F32)][64, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 128, 128] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para2325) #(Tensor(F32)[16, 64, 128, 128], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 128, 128] = FuncGraph::fg_704(%2) #(Tensor(F32)[16, 64, 128, 128]) # fg_704=L-↓construct.704(@ctx.addr=0xaaaae39f9890) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d @ctx.addr=0xaaaae39f9890
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.256] _mul_tensor.706 @ctx.addr=0xaaaae3a08ee0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_706(
- %para2545 : Tensor(F32)[] # x
- , %para2546 : Tensor(F32)[16, 64, 128, 128] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 128, 128] = %2(%para2545, %para2546) #(Tensor(F32)[], Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.257] L-✓construct.218 @ctx.addr=0xaaaae3a0ff40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_218[fg_96](
- ) {
- %1 : Tensor(F32)[16, 64, 128, 128] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para2324, %para2326) #(Tensor(F32)[16, 64, 128, 128], Ref[Tensor(F32)][64, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 128, 128] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para2325) #(Tensor(F32)[16, 64, 128, 128], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 128, 128] = FuncGraph::fg_704(%2) #(Tensor(F32)[16, 64, 128, 128]) # fg_704=L-↓construct.704(@ctx.addr=0xaaaae39f9890) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d @ctx.addr=0xaaaae39f9890
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.257] ↓construct.223 @ctx.addr=0xaaaae3a1feb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_223(
- %para2547 : Tensor(F32)[16, 3, 128, 128] # Φoutput
- ) {
- Primitive::Return{prim_type=1}(%para2547) #(Tensor(F32)[16, 3, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/conv_last-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(261)/ return output/
- }
-
-
- # [No.258] ✓↓construct.225 @ctx.addr=0xaaaae3a4f520
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- funcgraph fg_225[fg_13](
- ) {
- %1 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-BatchNorm{prim_type=1}[epsilon=F32(1e-05), momentum=F32(0.1), output_names=["y", "batch_mean", "batch_variance", "reserve_space_1", "reserve_space_2"], format="NCHW", input_names=["x", "scale", "offset", "mean", "variance"], is_training=Bool(1)](%para2186, %para2168, %para2169, %para2170, %para2171) #(Tensor(F32)[16, 64, 64, 64], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- %2 : Tensor(F32)[16, 64, 64, 64] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Tensor(F32)*5], I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d @ctx.addr=0xaaaae3a578a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- Primitive::Return{prim_type=1}(%2) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- }
-
-
- # [No.259] L-↓construct.228 @ctx.addr=0xaaaae3a7ce10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- funcgraph fg_228[fg_49](
- %para2548 : Bool # Φflag
- ) {
- %1 : Bool = FuncGraph::fg_116(%para2548) #(Bool) # fg_116=L-bool_.116(@ctx.addr=0xaaaae3a79710) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d @ctx.addr=0xaaaae3a79710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_707, FuncGraph::fg_708) #(Bool, Func, Func) # fg_707=L-✓↓construct.707(@ctx.addr=0xaaaae3a7e910), fg_708=L-✗↓construct.708 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- %3 : Tensor(F32)[16, 128, 64, 64] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d @ctx.addr=0xaaaae3a7e910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- }
-
-
- # [No.260] L-↓construct.228 @ctx.addr=0xaaaae3aa2660
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- funcgraph fg_228[fg_49](
- %para2549 : Bool # Φflag
- ) {
- %1 : Bool = FuncGraph::fg_116(%para2548) #(Bool) # fg_116=L-bool_.116(@ctx.addr=0xaaaae3a79710) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d @ctx.addr=0xaaaae3a79710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_707, FuncGraph::fg_708) #(Bool, Func, Func) # fg_707=L-✓↓construct.707(@ctx.addr=0xaaaae3aa33f0), fg_708=L-✗↓construct.708 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- %3 : Tensor(F32)[16, 128, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d @ctx.addr=0xaaaae3aa33f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- }
-
-
- # [No.260] L-↓construct.231 @ctx.addr=0xaaaae3ac9aa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- funcgraph fg_231[fg_54](
- %para2550 : Bool # Φflag
- ) {
- %1 : Bool = FuncGraph::fg_123(%para2550) #(Bool) # fg_123=L-bool_.123(@ctx.addr=0xaaaae3ac63a0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d @ctx.addr=0xaaaae3ac63a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_709, FuncGraph::fg_710) #(Bool, Func, Func) # fg_709=L-✓↓construct.709(@ctx.addr=0xaaaae3acb5a0), fg_710=L-✗↓construct.710 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- %3 : Tensor(F32)[16, 256, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d @ctx.addr=0xaaaae3acb5a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- }
-
-
- # [No.261] L-↓construct.231 @ctx.addr=0xaaaae3aef250
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- funcgraph fg_231[fg_54](
- %para2551 : Bool # Φflag
- ) {
- %1 : Bool = FuncGraph::fg_123(%para2550) #(Bool) # fg_123=L-bool_.123(@ctx.addr=0xaaaae3ac63a0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d @ctx.addr=0xaaaae3ac63a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_709, FuncGraph::fg_710) #(Bool, Func, Func) # fg_709=L-✓↓construct.709(@ctx.addr=0xaaaae3aeffe0), fg_710=L-✗↓construct.710 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- %3 : Tensor(F32)[16, 256, 16, 16] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d @ctx.addr=0xaaaae3aeffe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- }
-
-
- # [No.261] L-↓construct.234 @ctx.addr=0xaaaae3b16850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- funcgraph fg_234[fg_59](
- %para2552 : Bool # Φflag
- ) {
- %1 : Bool = FuncGraph::fg_130(%para2552) #(Bool) # fg_130=L-bool_.130(@ctx.addr=0xaaaae3b13150) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d @ctx.addr=0xaaaae3b13150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_711, FuncGraph::fg_712) #(Bool, Func, Func) # fg_711=L-✓↓construct.711(@ctx.addr=0xaaaae3b18350), fg_712=L-✗↓construct.712 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- %3 : Tensor(F32)[16, 512, 16, 16] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d @ctx.addr=0xaaaae3b18350
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- }
-
-
- # [No.262] L-↓construct.234 @ctx.addr=0xaaaae3b43720
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(185)/ if self.use_batch_statistics is None:/
- funcgraph fg_234[fg_59](
- %para2553 : Bool # Φflag
- ) {
- %1 : Bool = FuncGraph::fg_130(%para2552) #(Bool) # fg_130=L-bool_.130(@ctx.addr=0xaaaae3b13150) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d @ctx.addr=0xaaaae3b13150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_711, FuncGraph::fg_712) #(Bool, Func, Func) # fg_711=L-✓↓construct.711(@ctx.addr=0xaaaae3b444b0), fg_712=L-✗↓construct.712 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- %3 : Tensor(F32)[16, 512, 8, 8] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d @ctx.addr=0xaaaae3b444b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- }
-
-
- # [No.262] _logical_not_scala.713 @ctx.addr=0xaaaae3b7e4b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(26)/def _logical_not_scala(x):/
- funcgraph fg_713(
- %para2554 : Bool # x
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "bool_not") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %3 : Func = Primitive::getattr{prim_type=1}(%para2554, "__bool__") #(Bool, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %4 : Bool = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %5 : Bool = %2(%4) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- Primitive::Return{prim_type=1}(%5) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- }
-
-
- # [No.263] L-↓construct.238 @ctx.addr=0xaaaae3b81230
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- funcgraph fg_238[fg_62](
- %para2555 : Tensor(F32)[16, 32768] # Φx
- ) {
- %1 : Bool = FuncGraph::fg_139(Bool(1)) #(Bool) # fg_139=L-bool_.139(@ctx.addr=0xaaaae3b86280) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b86280
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(304)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_714, FuncGraph::fg_715) #(Bool, Func, Func) # fg_714=L-✓↓construct.714(@ctx.addr=0xaaaae3b86510), fg_715=L-✗↓construct.715 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(304)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 100] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b86510
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(304)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(304)/ if self.has_bias:/
- }
-
-
- # [No.264] _logical_not_scala.716 @ctx.addr=0xaaaae3bc3100
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(26)/def _logical_not_scala(x):/
- funcgraph fg_716(
- %para2556 : Bool # x
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "bool_not") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %3 : Func = Primitive::getattr{prim_type=1}(%para2556, "__bool__") #(Bool, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %4 : Bool = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %5 : Bool = %2(%4) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- Primitive::Return{prim_type=1}(%5) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- }
-
-
- # [No.265] L-↓construct.238 @ctx.addr=0xaaaae3bc3660
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(301)/ if len(x_shape) != 2:/
- funcgraph fg_238[fg_62](
- %para2557 : Tensor(F32)[16, 100] # Φx
- ) {
- %1 : Bool = FuncGraph::fg_139(Bool(1)) #(Bool) # fg_139=L-bool_.139(@ctx.addr=0xaaaae3b86280) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b86280
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(304)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_714, FuncGraph::fg_715) #(Bool, Func, Func) # fg_714=L-✓↓construct.714(@ctx.addr=0xaaaae3bc4d90), fg_715=L-✗↓construct.715 #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(304)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 1] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bc4d90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(304)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 1]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(304)/ if self.has_bias:/
- }
-
-
- # [No.265] construct.241 @ctx.addr=0xaaaae8590180
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_241[fg_0](
- %para2558 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2558, %para8, %para7) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaf548ee70) #scope: Default @ctx.addr=0xaaaaf548ee70
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.266] construct.242 @ctx.addr=0xaaaad8894a50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_242(
- %para2559 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaafadafac0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_718, FuncGraph::fg_719) #(Bool, Func, Func) # fg_718=✓construct.718(@ctx.addr=0xaaaaf384db10), fg_719=✗construct.719 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf384db10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.267] construct.243 @ctx.addr=0xaaaae20bbc20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_243[fg_0](
- %para2560 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2560, %para10, %para9) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae855a410) #scope: Default @ctx.addr=0xaaaae855a410
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.268] construct.244 @ctx.addr=0xaaaae24902b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_244[fg_0](
- %para2561 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2561, %para12, %para11) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae242b2f0) #scope: Default @ctx.addr=0xaaaae242b2f0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.269] construct.245 @ctx.addr=0xaaaafae7daa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_245[fg_0](
- %para2562 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2562, %para14, %para13) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaafade31d0) #scope: Default @ctx.addr=0xaaaafade31d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.270] construct.246 @ctx.addr=0xaaaad95f3cf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_246[fg_0](
- %para2563 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2563, %para16, %para15) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaebc17b40) #scope: Default @ctx.addr=0xaaaaebc17b40
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.271] _tensor_mul_scalar.724 @ctx.addr=0xaaaad9438010
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_724(
- %para2564 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2565 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2564, %para2565) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.272] _tensor_add_tensor.725 @ctx.addr=0xaaaaf6e8f590
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_725(
- %para2566 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2567 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2566, %para2567) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.273] construct.247 @ctx.addr=0xaaaad9a684d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_247[fg_0](
- %para2568 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2568, %para18, %para17) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaad95fa5e0) #scope: Default @ctx.addr=0xaaaad95fa5e0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.274] construct.248 @ctx.addr=0xaaaaf6f2da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_248(
- %para2569 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad81ada90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_726, FuncGraph::fg_727) #(Bool, Func, Func) # fg_726=✓construct.726(@ctx.addr=0xaaaae98affc0), fg_727=✗construct.727 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae98affc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.275] construct.249 @ctx.addr=0xaaaaef7fba40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_249[fg_0](
- %para2570 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2570, %para20, %para19) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaad5b77380) #scope: Default @ctx.addr=0xaaaad5b77380
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.276] construct.250 @ctx.addr=0xaaaaf4a7da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_250[fg_0](
- %para2571 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2571, %para22, %para21) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae242a330) #scope: Default @ctx.addr=0xaaaae242a330
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.277] construct.251 @ctx.addr=0xaaaadcb97340
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_251[fg_0](
- %para2572 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2572, %para24, %para23) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaad8a4a8b0) #scope: Default @ctx.addr=0xaaaad8a4a8b0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.278] construct.252 @ctx.addr=0xaaaadd122a30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_252[fg_0](
- %para2573 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2573, %para26, %para25) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaf1defd70) #scope: Default @ctx.addr=0xaaaaf1defd70
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.279] _tensor_mul_scalar.728 @ctx.addr=0xaaaae584eaf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_728(
- %para2574 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2575 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2574, %para2575) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.280] _tensor_add_tensor.729 @ctx.addr=0xaaaaedd64db0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_729(
- %para2576 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2577 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2576, %para2577) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.281] construct.253 @ctx.addr=0xaaaae9399220
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_253[fg_0](
- %para2578 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2578, %para28, %para27) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae5708110) #scope: Default @ctx.addr=0xaaaae5708110
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.282] construct.254 @ctx.addr=0xaaaae206c2e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_254(
- %para2579 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf43fc0a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_730, FuncGraph::fg_731) #(Bool, Func, Func) # fg_730=✓construct.730(@ctx.addr=0xaaaad6044630), fg_731=✗construct.731 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad6044630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.283] construct.255 @ctx.addr=0xaaaaf13a67e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_255[fg_0](
- %para2580 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2580, %para30, %para29) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaaee8050b0) #scope: Default @ctx.addr=0xaaaaee8050b0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.284] construct.256 @ctx.addr=0xaaaae0eefad0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_256[fg_0](
- %para2581 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2581, %para32, %para31) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaaee400a10) #scope: Default @ctx.addr=0xaaaaee400a10
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.285] construct.257 @ctx.addr=0xaaaaed575030
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_257[fg_0](
- %para2582 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2582, %para34, %para33) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf072d9b0) #scope: Default @ctx.addr=0xaaaaf072d9b0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.286] construct.258 @ctx.addr=0xaaaaebc1ab70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_258[fg_0](
- %para2583 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2583, %para36, %para35) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaf083bcc0) #scope: Default @ctx.addr=0xaaaaf083bcc0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.287] _tensor_mul_scalar.732 @ctx.addr=0xaaaaf07729b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_732(
- %para2584 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2585 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2584, %para2585) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.288] _tensor_add_tensor.733 @ctx.addr=0xaaaaea956290
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_733(
- %para2586 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2587 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2586, %para2587) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.289] construct.261 @ctx.addr=0xaaaae8602e20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_261[fg_0](
- %para2588 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2588, %para38, %para37) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae85aecc0) #scope: Default @ctx.addr=0xaaaae85aecc0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.290] construct.262 @ctx.addr=0xaaaaf38cb550
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_262(
- %para2589 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf6dbdef0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_734, FuncGraph::fg_735) #(Bool, Func, Func) # fg_734=✓construct.734(@ctx.addr=0xaaaaea7d9a50), fg_735=✗construct.735 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaea7d9a50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.291] construct.263 @ctx.addr=0xaaaad6a09770
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_263[fg_0](
- %para2590 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2590, %para40, %para39) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaad699bc80) #scope: Default @ctx.addr=0xaaaad699bc80
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.292] construct.264 @ctx.addr=0xaaaad698ba30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_264[fg_0](
- %para2591 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2591, %para42, %para41) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaaea2d34d0) #scope: Default @ctx.addr=0xaaaaea2d34d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.293] construct.265 @ctx.addr=0xaaaae244c860
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_265[fg_0](
- %para2592 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2592, %para44, %para43) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae232f120) #scope: Default @ctx.addr=0xaaaae232f120
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.294] construct.266 @ctx.addr=0xaaaae2574220
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_266[fg_0](
- %para2593 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2593, %para46, %para45) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae23c95c0) #scope: Default @ctx.addr=0xaaaae23c95c0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.295] _tensor_mul_scalar.736 @ctx.addr=0xaaaafad9f0b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_736(
- %para2594 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2595 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2594, %para2595) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.296] _tensor_add_tensor.737 @ctx.addr=0xaaaaec024f50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_737(
- %para2596 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2597 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2596, %para2597) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.297] construct.267 @ctx.addr=0xaaaaeedddd10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_267[fg_0](
- %para2598 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2598, %para48, %para47) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaeeddd610) #scope: Default @ctx.addr=0xaaaaeeddd610
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.298] construct.268 @ctx.addr=0xaaaaf2c0daa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_268(
- %para2599 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1fce140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_738, FuncGraph::fg_739) #(Bool, Func, Func) # fg_738=✓construct.738(@ctx.addr=0xaaaae24fb010), fg_739=✗construct.739 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae24fb010
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.299] construct.269 @ctx.addr=0xaaaae822d9b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_269[fg_0](
- %para2600 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2600, %para50, %para49) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae82b8200) #scope: Default @ctx.addr=0xaaaae82b8200
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.300] construct.270 @ctx.addr=0xaaaae8253130
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_270[fg_0](
- %para2601 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2601, %para52, %para51) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaafae91640) #scope: Default @ctx.addr=0xaaaafae91640
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.301] construct.271 @ctx.addr=0xaaaae6de1260
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_271[fg_0](
- %para2602 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2602, %para54, %para53) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaade9c9d20) #scope: Default @ctx.addr=0xaaaade9c9d20
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.302] construct.272 @ctx.addr=0xaaaaed0d6580
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_272[fg_0](
- %para2603 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2603, %para56, %para55) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaf42ee320) #scope: Default @ctx.addr=0xaaaaf42ee320
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.303] _tensor_mul_scalar.740 @ctx.addr=0xaaaae2438460
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_740(
- %para2604 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2605 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2604, %para2605) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.304] _tensor_add_tensor.741 @ctx.addr=0xaaaae200e410
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_741(
- %para2606 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2607 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2606, %para2607) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.305] construct.273 @ctx.addr=0xaaaae1ebb5f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_273[fg_0](
- %para2608 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2608, %para58, %para57) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae0eef880) #scope: Default @ctx.addr=0xaaaae0eef880
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.306] construct.274 @ctx.addr=0xaaaae23e5740
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_274(
- %para2609 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae22d8130
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_742, FuncGraph::fg_743) #(Bool, Func, Func) # fg_742=✓construct.742(@ctx.addr=0xaaaae229e1e0), fg_743=✗construct.743 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae229e1e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.307] construct.275 @ctx.addr=0xaaaae09191c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_275[fg_0](
- %para2610 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2610, %para60, %para59) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae54de930) #scope: Default @ctx.addr=0xaaaae54de930
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.308] construct.276 @ctx.addr=0xaaaade9cf0f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_276[fg_0](
- %para2611 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2611, %para62, %para61) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaadca67c40) #scope: Default @ctx.addr=0xaaaadca67c40
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.309] construct.277 @ctx.addr=0xaaaae25a60c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_277[fg_0](
- %para2612 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2612, %para64, %para63) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae243a480) #scope: Default @ctx.addr=0xaaaae243a480
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.310] construct.278 @ctx.addr=0xaaaafae469c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_278[fg_0](
- %para2613 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2613, %para66, %para65) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaeeddbce0) #scope: Default @ctx.addr=0xaaaaeeddbce0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.311] _tensor_mul_scalar.744 @ctx.addr=0xaaaae85beaa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_744(
- %para2614 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2615 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2614, %para2615) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.312] _tensor_add_tensor.745 @ctx.addr=0xaaaae23e15a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_745(
- %para2616 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2617 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2616, %para2617) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.313] construct.281 @ctx.addr=0xaaaae846f480
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_281[fg_0](
- %para2618 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2618, %para68, %para67) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae831a3a0) #scope: Default @ctx.addr=0xaaaae831a3a0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.314] construct.282 @ctx.addr=0xaaaae8177ef0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_282(
- %para2619 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaafa5c8140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_746, FuncGraph::fg_747) #(Bool, Func, Func) # fg_746=✓construct.746(@ctx.addr=0xaaaaea7d4b00), fg_747=✗construct.747 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaea7d4b00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.315] construct.283 @ctx.addr=0xaaaae6f80070
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_283[fg_0](
- %para2620 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2620, %para70, %para69) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae6a748f0) #scope: Default @ctx.addr=0xaaaae6a748f0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.316] construct.284 @ctx.addr=0xaaaad8cbb570
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_284[fg_0](
- %para2621 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2621, %para72, %para71) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaadd3277b0) #scope: Default @ctx.addr=0xaaaadd3277b0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.317] construct.285 @ctx.addr=0xaaaaf2aa0790
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_285[fg_0](
- %para2622 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2622, %para74, %para73) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae070e9c0) #scope: Default @ctx.addr=0xaaaae070e9c0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.318] construct.286 @ctx.addr=0xaaaad81b1740
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_286[fg_0](
- %para2623 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2623, %para76, %para75) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaf1d27d00) #scope: Default @ctx.addr=0xaaaaf1d27d00
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.319] _tensor_mul_scalar.748 @ctx.addr=0xaaaad61a80e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_748(
- %para2624 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2625 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2624, %para2625) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.320] _tensor_add_tensor.749 @ctx.addr=0xaaaad9a68bd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_749(
- %para2626 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2627 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2626, %para2627) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.321] construct.287 @ctx.addr=0xaaaaf0af7ed0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_287[fg_0](
- %para2628 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2628, %para78, %para77) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae25b4250) #scope: Default @ctx.addr=0xaaaae25b4250
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.322] construct.288 @ctx.addr=0xaaaae21c2520
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_288(
- %para2629 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaafadf0cb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_750, FuncGraph::fg_751) #(Bool, Func, Func) # fg_750=✓construct.750(@ctx.addr=0xaaaafad9d130), fg_751=✗construct.751 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaafad9d130
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.323] construct.289 @ctx.addr=0xaaaae1ef34e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_289[fg_0](
- %para2630 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2630, %para80, %para79) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaafab1c2c0) #scope: Default @ctx.addr=0xaaaafab1c2c0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.324] construct.290 @ctx.addr=0xaaaae1da4fe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_290[fg_0](
- %para2631 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2631, %para82, %para81) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae1da1190) #scope: Default @ctx.addr=0xaaaae1da1190
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.325] construct.291 @ctx.addr=0xaaaad813e750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_291[fg_0](
- %para2632 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2632, %para84, %para83) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf3ea0170) #scope: Default @ctx.addr=0xaaaaf3ea0170
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.326] construct.292 @ctx.addr=0xaaaae2345e10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_292[fg_0](
- %para2633 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2633, %para86, %para85) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae1f5a8e0) #scope: Default @ctx.addr=0xaaaae1f5a8e0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.327] _tensor_mul_scalar.752 @ctx.addr=0xaaaade447ca0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_752(
- %para2634 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2635 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2634, %para2635) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.328] _tensor_add_tensor.753 @ctx.addr=0xaaaae819fec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_753(
- %para2636 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2637 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2636, %para2637) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.329] construct.293 @ctx.addr=0xaaaafaec8440
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_293[fg_0](
- %para2638 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2638, %para88, %para87) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaade4485a0) #scope: Default @ctx.addr=0xaaaade4485a0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.330] construct.294 @ctx.addr=0xaaaadd3265d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_294(
- %para2639 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf1e6c440
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_754, FuncGraph::fg_755) #(Bool, Func, Func) # fg_754=✓construct.754(@ctx.addr=0xaaaaf17c6070), fg_755=✗construct.755 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf17c6070
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.331] construct.295 @ctx.addr=0xaaaae0d2cf50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_295[fg_0](
- %para2640 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2640, %para90, %para89) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae0f3e820) #scope: Default @ctx.addr=0xaaaae0f3e820
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.332] construct.296 @ctx.addr=0xaaaae20cb680
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_296[fg_0](
- %para2641 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2641, %para92, %para91) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaad783cd70) #scope: Default @ctx.addr=0xaaaad783cd70
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.333] construct.297 @ctx.addr=0xaaaaed0cbe50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_297[fg_0](
- %para2642 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2642, %para94, %para93) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf03cc390) #scope: Default @ctx.addr=0xaaaaf03cc390
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.334] construct.298 @ctx.addr=0xaaaafad9bf40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_298[fg_0](
- %para2643 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2643, %para96, %para95) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae227dcc0) #scope: Default @ctx.addr=0xaaaae227dcc0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.335] _tensor_mul_scalar.756 @ctx.addr=0xaaaae23c6640
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_756(
- %para2644 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2645 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2644, %para2645) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.336] _tensor_add_tensor.757 @ctx.addr=0xaaaae21fbb30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_757(
- %para2646 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2647 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2646, %para2647) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.337] construct.301 @ctx.addr=0xaaaae2228520
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_301[fg_0](
- %para2648 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2648, %para98, %para97) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae210bff0) #scope: Default @ctx.addr=0xaaaae210bff0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.338] construct.302 @ctx.addr=0xaaaae2057000
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_302(
- %para2649 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae22565e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_758, FuncGraph::fg_759) #(Bool, Func, Func) # fg_758=✓construct.758(@ctx.addr=0xaaaae221c5b0), fg_759=✗construct.759 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae221c5b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.339] construct.303 @ctx.addr=0xaaaaf13a8960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_303[fg_0](
- %para2650 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2650, %para100, %para99) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaaf13a8a60) #scope: Default @ctx.addr=0xaaaaf13a8a60
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.340] construct.304 @ctx.addr=0xaaaad8a4b480
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_304[fg_0](
- %para2651 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2651, %para102, %para101) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaad720b510) #scope: Default @ctx.addr=0xaaaad720b510
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.341] construct.305 @ctx.addr=0xaaaae850aae0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_305[fg_0](
- %para2652 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2652, %para104, %para103) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae830d500) #scope: Default @ctx.addr=0xaaaae830d500
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.342] construct.306 @ctx.addr=0xaaaae85a5400
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_306[fg_0](
- %para2653 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2653, %para106, %para105) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae8352b50) #scope: Default @ctx.addr=0xaaaae8352b50
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.343] _tensor_mul_scalar.760 @ctx.addr=0xaaaaed0c9a70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_760(
- %para2654 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2655 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2654, %para2655) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.344] _tensor_add_tensor.761 @ctx.addr=0xaaaadc746aa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_761(
- %para2656 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2657 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2656, %para2657) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.345] construct.307 @ctx.addr=0xaaaaf709b940
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_307[fg_0](
- %para2658 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2658, %para108, %para107) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaadcb9a880) #scope: Default @ctx.addr=0xaaaadcb9a880
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.346] construct.308 @ctx.addr=0xaaaafaecadb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_308(
- %para2659 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae218a8e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_762, FuncGraph::fg_763) #(Bool, Func, Func) # fg_762=✓construct.762(@ctx.addr=0xaaaae214d510), fg_763=✗construct.763 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae214d510
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.347] construct.309 @ctx.addr=0xaaaae1f164b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_309[fg_0](
- %para2660 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2660, %para110, %para109) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae24cd2f0) #scope: Default @ctx.addr=0xaaaae24cd2f0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.348] construct.310 @ctx.addr=0xaaaae228e300
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_310[fg_0](
- %para2661 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2661, %para112, %para111) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae242a960) #scope: Default @ctx.addr=0xaaaae242a960
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.349] construct.311 @ctx.addr=0xaaaaf0c41530
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_311[fg_0](
- %para2662 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2662, %para114, %para113) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaadcc88580) #scope: Default @ctx.addr=0xaaaadcc88580
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.350] construct.312 @ctx.addr=0xaaaaec20d780
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_312[fg_0](
- %para2663 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2663, %para116, %para115) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaf1faa100) #scope: Default @ctx.addr=0xaaaaf1faa100
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.351] _tensor_mul_scalar.764 @ctx.addr=0xaaaae833e420
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_764(
- %para2664 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2665 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2664, %para2665) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.352] _tensor_add_tensor.765 @ctx.addr=0xaaaae81f1770
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_765(
- %para2666 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2667 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2666, %para2667) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.353] construct.313 @ctx.addr=0xaaaafae6ea50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_313[fg_0](
- %para2668 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2668, %para118, %para117) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaee565200) #scope: Default @ctx.addr=0xaaaaee565200
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.354] construct.314 @ctx.addr=0xaaaaebfb19e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_314(
- %para2669 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaefc466d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_766, FuncGraph::fg_767) #(Bool, Func, Func) # fg_766=✓construct.766(@ctx.addr=0xaaaad8cbbdc0), fg_767=✗construct.767 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad8cbbdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.355] construct.315 @ctx.addr=0xaaaade484200
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_315[fg_0](
- %para2670 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2670, %para120, %para119) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaaf34c30b0) #scope: Default @ctx.addr=0xaaaaf34c30b0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.356] construct.316 @ctx.addr=0xaaaaec7a5040
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_316[fg_0](
- %para2671 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2671, %para122, %para121) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaad6a0a120) #scope: Default @ctx.addr=0xaaaad6a0a120
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.357] construct.317 @ctx.addr=0xaaaadd11b840
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_317[fg_0](
- %para2672 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2672, %para124, %para123) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaeb7ea3b0) #scope: Default @ctx.addr=0xaaaaeb7ea3b0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.358] construct.318 @ctx.addr=0xaaaaef19fbe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_318[fg_0](
- %para2673 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2673, %para126, %para125) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaee910e20) #scope: Default @ctx.addr=0xaaaaee910e20
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.359] _tensor_mul_scalar.768 @ctx.addr=0xaaaaeacea050
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_768(
- %para2674 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2675 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2674, %para2675) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.360] _tensor_add_tensor.769 @ctx.addr=0xaaaae1f84c90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_769(
- %para2676 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2677 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2676, %para2677) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.361] construct.321 @ctx.addr=0xaaaaf532de00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_321[fg_0](
- %para2678 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2678, %para128, %para127) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaecfb4970) #scope: Default @ctx.addr=0xaaaaecfb4970
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.362] construct.322 @ctx.addr=0xaaaaf0c3f390
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_322(
- %para2679 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaafa5be180
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_770, FuncGraph::fg_771) #(Bool, Func, Func) # fg_770=✓construct.770(@ctx.addr=0xaaaae8529bb0), fg_771=✗construct.771 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8529bb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.363] construct.323 @ctx.addr=0xaaaaf10bad20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_323[fg_0](
- %para2680 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2680, %para130, %para129) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae584bdc0) #scope: Default @ctx.addr=0xaaaae584bdc0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.364] construct.324 @ctx.addr=0xaaaae9dbc110
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_324[fg_0](
- %para2681 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2681, %para132, %para131) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaafa7bf860) #scope: Default @ctx.addr=0xaaaafa7bf860
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.365] construct.325 @ctx.addr=0xaaaae1dd5600
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_325[fg_0](
- %para2682 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2682, %para134, %para133) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaad6699870) #scope: Default @ctx.addr=0xaaaad6699870
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.366] construct.326 @ctx.addr=0xaaaaf70a36b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_326[fg_0](
- %para2683 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2683, %para136, %para135) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae88d3410) #scope: Default @ctx.addr=0xaaaae88d3410
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.367] _tensor_mul_scalar.772 @ctx.addr=0xaaaae1f7bb30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_772(
- %para2684 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2685 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2684, %para2685) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.368] _tensor_add_tensor.773 @ctx.addr=0xaaaaec201e00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_773(
- %para2686 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2687 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2686, %para2687) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.369] construct.327 @ctx.addr=0xaaaaf38ccb60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_327[fg_0](
- %para2688 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2688, %para138, %para137) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae1ec9f00) #scope: Default @ctx.addr=0xaaaae1ec9f00
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.370] construct.328 @ctx.addr=0xaaaae83ce7a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_328(
- %para2689 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaea1c2850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_774, FuncGraph::fg_775) #(Bool, Func, Func) # fg_774=✓construct.774(@ctx.addr=0xaaaae5e5b020), fg_775=✗construct.775 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae5e5b020
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.371] construct.329 @ctx.addr=0xaaaae84db8f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_329[fg_0](
- %para2690 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2690, %para140, %para139) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae82430d0) #scope: Default @ctx.addr=0xaaaae82430d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.372] construct.330 @ctx.addr=0xaaaae1de9870
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_330[fg_0](
- %para2691 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2691, %para142, %para141) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae1f90a80) #scope: Default @ctx.addr=0xaaaae1f90a80
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.373] construct.331 @ctx.addr=0xaaaae1da3240
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_331[fg_0](
- %para2692 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2692, %para144, %para143) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaad6292f20) #scope: Default @ctx.addr=0xaaaad6292f20
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.374] construct.332 @ctx.addr=0xaaaae7196b50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_332[fg_0](
- %para2693 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2693, %para146, %para145) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaefd0f8d0) #scope: Default @ctx.addr=0xaaaaefd0f8d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.375] _tensor_mul_scalar.776 @ctx.addr=0xaaaae22ae1c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_776(
- %para2694 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2695 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2694, %para2695) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.376] _tensor_add_tensor.777 @ctx.addr=0xaaaae82c7da0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_777(
- %para2696 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2697 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2696, %para2697) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.377] construct.333 @ctx.addr=0xaaaae2534e20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_333[fg_0](
- %para2698 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2698, %para148, %para147) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaeb608c50) #scope: Default @ctx.addr=0xaaaaeb608c50
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.378] construct.334 @ctx.addr=0xaaaae24cb2f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_334(
- %para2699 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaeb6d01a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_778, FuncGraph::fg_779) #(Bool, Func, Func) # fg_778=✓construct.778(@ctx.addr=0xaaaaeb6d02b0), fg_779=✗construct.779 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaeb6d02b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.379] construct.335 @ctx.addr=0xaaaae8a58670
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_335[fg_0](
- %para2700 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2700, %para150, %para149) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaaecd3d070) #scope: Default @ctx.addr=0xaaaaecd3d070
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.380] construct.336 @ctx.addr=0xaaaae2188830
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_336[fg_0](
- %para2701 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2701, %para152, %para151) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae123bb70) #scope: Default @ctx.addr=0xaaaae123bb70
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.381] construct.337 @ctx.addr=0xaaaafae867e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_337[fg_0](
- %para2702 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2702, %para154, %para153) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaafa6eee30) #scope: Default @ctx.addr=0xaaaafa6eee30
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.382] construct.338 @ctx.addr=0xaaaae25e8150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_338[fg_0](
- %para2703 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2703, %para156, %para155) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaad741c920) #scope: Default @ctx.addr=0xaaaad741c920
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.383] _tensor_mul_scalar.780 @ctx.addr=0xaaaae8203fd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_780(
- %para2704 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2705 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2704, %para2705) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.384] _tensor_add_tensor.781 @ctx.addr=0xaaaae2520610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_781(
- %para2706 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2707 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2706, %para2707) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.385] construct.341 @ctx.addr=0xaaaae8461330
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_341[fg_0](
- %para2708 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2708, %para158, %para157) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaf03d05b0) #scope: Default @ctx.addr=0xaaaaf03d05b0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.386] construct.342 @ctx.addr=0xaaaae83c18d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_342(
- %para2709 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae82c0630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_782, FuncGraph::fg_783) #(Bool, Func, Func) # fg_782=✓construct.782(@ctx.addr=0xaaaae826bbb0), fg_783=✗construct.783 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae826bbb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.387] construct.343 @ctx.addr=0xaaaaecb0a720
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_343[fg_0](
- %para2710 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2710, %para160, %para159) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaaecb0a820) #scope: Default @ctx.addr=0xaaaaecb0a820
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.388] construct.344 @ctx.addr=0xaaaad7cd4700
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_344[fg_0](
- %para2711 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2711, %para162, %para161) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaadc863e70) #scope: Default @ctx.addr=0xaaaadc863e70
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.389] construct.345 @ctx.addr=0xaaaaf4dd9340
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_345[fg_0](
- %para2712 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2712, %para164, %para163) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaadd327110) #scope: Default @ctx.addr=0xaaaadd327110
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.390] construct.346 @ctx.addr=0xaaaaf13a9c30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_346[fg_0](
- %para2713 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2713, %para166, %para165) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae1232f00) #scope: Default @ctx.addr=0xaaaae1232f00
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.391] _tensor_mul_scalar.784 @ctx.addr=0xaaaae0dbfa80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_784(
- %para2714 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2715 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2714, %para2715) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.392] _tensor_add_tensor.785 @ctx.addr=0xaaaaf42ebec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_785(
- %para2716 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2717 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2716, %para2717) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.393] construct.347 @ctx.addr=0xaaaae0cb0db0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_347[fg_0](
- %para2718 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2718, %para168, %para167) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaafae0b470) #scope: Default @ctx.addr=0xaaaafae0b470
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.394] construct.348 @ctx.addr=0xaaaae86b5eb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_348(
- %para2719 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae84f8030
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_786, FuncGraph::fg_787) #(Bool, Func, Func) # fg_786=✓construct.786(@ctx.addr=0xaaaae845c970), fg_787=✗construct.787 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae845c970
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.395] construct.349 @ctx.addr=0xaaaaf49046b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_349[fg_0](
- %para2720 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2720, %para170, %para169) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaadec3efb0) #scope: Default @ctx.addr=0xaaaadec3efb0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.396] construct.350 @ctx.addr=0xaaaae5647e70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_350[fg_0](
- %para2721 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2721, %para172, %para171) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaaed0da330) #scope: Default @ctx.addr=0xaaaaed0da330
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.397] construct.351 @ctx.addr=0xaaaae23d9ea0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_351[fg_0](
- %para2722 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2722, %para174, %para173) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae822f3d0) #scope: Default @ctx.addr=0xaaaae822f3d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.398] construct.352 @ctx.addr=0xaaaae8344230
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_352[fg_0](
- %para2723 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2723, %para176, %para175) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae20e5fc0) #scope: Default @ctx.addr=0xaaaae20e5fc0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.399] _tensor_mul_scalar.788 @ctx.addr=0xaaaae83cfec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_788(
- %para2724 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2725 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2724, %para2725) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.400] _tensor_add_tensor.789 @ctx.addr=0xaaaae21867c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_789(
- %para2726 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2727 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2726, %para2727) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.401] construct.353 @ctx.addr=0xaaaae1e9d9f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_353[fg_0](
- %para2728 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2728, %para178, %para177) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae84f51e0) #scope: Default @ctx.addr=0xaaaae84f51e0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.402] construct.354 @ctx.addr=0xaaaafaee3a20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_354(
- %para2729 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae22e32f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_790, FuncGraph::fg_791) #(Bool, Func, Func) # fg_790=✓construct.790(@ctx.addr=0xaaaae22e3440), fg_791=✗construct.791 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae22e3440
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.403] construct.355 @ctx.addr=0xaaaae1f93660
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_355[fg_0](
- %para2730 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2730, %para180, %para179) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae1f93760) #scope: Default @ctx.addr=0xaaaae1f93760
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.404] construct.356 @ctx.addr=0xaaaae829ad40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_356[fg_0](
- %para2731 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2731, %para182, %para181) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae1eea430) #scope: Default @ctx.addr=0xaaaae1eea430
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.405] construct.357 @ctx.addr=0xaaaaf4275280
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_357[fg_0](
- %para2732 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2732, %para184, %para183) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf083d030) #scope: Default @ctx.addr=0xaaaaf083d030
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.406] construct.358 @ctx.addr=0xaaaaecb144a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_358[fg_0](
- %para2733 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2733, %para186, %para185) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae1ff4640) #scope: Default @ctx.addr=0xaaaae1ff4640
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.407] _tensor_mul_scalar.792 @ctx.addr=0xaaaaefbc6e20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_792(
- %para2734 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2735 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2734, %para2735) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.408] _tensor_add_tensor.793 @ctx.addr=0xaaaaee77ef30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_793(
- %para2736 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2737 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2736, %para2737) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.409] construct.361 @ctx.addr=0xaaaaea1c70a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_361[fg_0](
- %para2738 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2738, %para188, %para187) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaea1c71e0) #scope: Default @ctx.addr=0xaaaaea1c71e0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.410] construct.362 @ctx.addr=0xaaaad5a1a810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_362(
- %para2739 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae20badb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_794, FuncGraph::fg_795) #(Bool, Func, Func) # fg_794=✓construct.794(@ctx.addr=0xaaaae20baf00), fg_795=✗construct.795 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae20baf00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.411] construct.363 @ctx.addr=0xaaaaf2a9ef60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_363[fg_0](
- %para2740 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2740, %para190, %para189) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaaf2a9f060) #scope: Default @ctx.addr=0xaaaaf2a9f060
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.412] construct.364 @ctx.addr=0xaaaae9f2e7c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_364[fg_0](
- %para2741 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2741, %para192, %para191) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae935b6f0) #scope: Default @ctx.addr=0xaaaae935b6f0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.413] construct.365 @ctx.addr=0xaaaaf3e9edb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_365[fg_0](
- %para2742 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2742, %para194, %para193) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae213d3c0) #scope: Default @ctx.addr=0xaaaae213d3c0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.414] construct.366 @ctx.addr=0xaaaae1df40f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_366[fg_0](
- %para2743 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2743, %para196, %para195) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaf165c610) #scope: Default @ctx.addr=0xaaaaf165c610
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.415] _tensor_mul_scalar.796 @ctx.addr=0xaaaaf37bf890
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_796(
- %para2744 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2745 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2744, %para2745) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.416] _tensor_add_tensor.797 @ctx.addr=0xaaaae20c76e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_797(
- %para2746 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2747 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2746, %para2747) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.417] construct.367 @ctx.addr=0xaaaae82d0a50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_367[fg_0](
- %para2748 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2748, %para198, %para197) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaadec0e4b0) #scope: Default @ctx.addr=0xaaaadec0e4b0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.418] construct.368 @ctx.addr=0xaaaae5c1fe60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_368(
- %para2749 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf0c348b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_798, FuncGraph::fg_799) #(Bool, Func, Func) # fg_798=✓construct.798(@ctx.addr=0xaaaae0ca95d0), fg_799=✗construct.799 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae0ca95d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.419] construct.369 @ctx.addr=0xaaaaf4271a40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_369[fg_0](
- %para2750 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2750, %para200, %para199) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae23d58e0) #scope: Default @ctx.addr=0xaaaae23d58e0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.420] construct.370 @ctx.addr=0xaaaae8427e20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_370[fg_0](
- %para2751 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2751, %para202, %para201) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae85d0310) #scope: Default @ctx.addr=0xaaaae85d0310
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.421] construct.371 @ctx.addr=0xaaaaeeddd080
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_371[fg_0](
- %para2752 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2752, %para204, %para203) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf072cbf0) #scope: Default @ctx.addr=0xaaaaf072cbf0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.422] construct.372 @ctx.addr=0xaaaafab28d20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_372[fg_0](
- %para2753 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2753, %para206, %para205) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaedd611d0) #scope: Default @ctx.addr=0xaaaaedd611d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.423] _tensor_mul_scalar.800 @ctx.addr=0xaaaae22d4d00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_800(
- %para2754 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2755 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2754, %para2755) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.424] _tensor_add_tensor.801 @ctx.addr=0xaaaae229a5d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_801(
- %para2756 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2757 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2756, %para2757) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.425] construct.373 @ctx.addr=0xaaaae2227400
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_373[fg_0](
- %para2758 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2758, %para208, %para207) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae8613f30) #scope: Default @ctx.addr=0xaaaae8613f30
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.426] construct.374 @ctx.addr=0xaaaaf57fc770
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_374(
- %para2759 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae82b8810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_802, FuncGraph::fg_803) #(Bool, Func, Func) # fg_802=✓construct.802(@ctx.addr=0xaaaae82b8960), fg_803=✗construct.803 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae82b8960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.427] construct.375 @ctx.addr=0xaaaad8141740
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_375[fg_0](
- %para2760 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2760, %para210, %para209) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaad8141840) #scope: Default @ctx.addr=0xaaaad8141840
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.428] construct.376 @ctx.addr=0xaaaae242f620
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_376[fg_0](
- %para2761 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2761, %para212, %para211) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae24de070) #scope: Default @ctx.addr=0xaaaae24de070
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.429] construct.377 @ctx.addr=0xaaaae859ecc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_377[fg_0](
- %para2762 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2762, %para214, %para213) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae1f168d0) #scope: Default @ctx.addr=0xaaaae1f168d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.430] construct.378 @ctx.addr=0xaaaaee102010
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_378[fg_0](
- %para2763 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2763, %para216, %para215) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaafadade30) #scope: Default @ctx.addr=0xaaaafadade30
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.431] _tensor_mul_scalar.804 @ctx.addr=0xaaaae6a64670
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_804(
- %para2764 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2765 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2764, %para2765) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.432] _tensor_add_tensor.805 @ctx.addr=0xaaaaf21caf00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_805(
- %para2766 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2767 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2766, %para2767) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.433] construct.381 @ctx.addr=0xaaaaeda6f160
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_381[fg_0](
- %para2768 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2768, %para218, %para217) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaeda6f2a0) #scope: Default @ctx.addr=0xaaaaeda6f2a0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.434] construct.382 @ctx.addr=0xaaaaeb587690
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_382(
- %para2769 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8571520
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_806, FuncGraph::fg_807) #(Bool, Func, Func) # fg_806=✓construct.806(@ctx.addr=0xaaaae8571620), fg_807=✗construct.807 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8571620
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.435] construct.383 @ctx.addr=0xaaaae2072680
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_383[fg_0](
- %para2770 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2770, %para220, %para219) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae2072780) #scope: Default @ctx.addr=0xaaaae2072780
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.436] construct.384 @ctx.addr=0xaaaafaee4ad0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_384[fg_0](
- %para2771 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2771, %para222, %para221) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae21993a0) #scope: Default @ctx.addr=0xaaaae21993a0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.437] construct.385 @ctx.addr=0xaaaae1fd2740
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_385[fg_0](
- %para2772 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2772, %para224, %para223) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae8629770) #scope: Default @ctx.addr=0xaaaae8629770
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.438] construct.386 @ctx.addr=0xaaaae2321630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_386[fg_0](
- %para2773 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2773, %para226, %para225) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae82c48c0) #scope: Default @ctx.addr=0xaaaae82c48c0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.439] _tensor_mul_scalar.808 @ctx.addr=0xaaaae81bc470
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_808(
- %para2774 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2775 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2774, %para2775) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.440] _tensor_add_tensor.809 @ctx.addr=0xaaaaf2f75470
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_809(
- %para2776 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2777 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2776, %para2777) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.441] construct.387 @ctx.addr=0xaaaaf348c810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_387[fg_0](
- %para2778 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2778, %para228, %para227) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaafae09210) #scope: Default @ctx.addr=0xaaaafae09210
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.442] construct.388 @ctx.addr=0xaaaae1e7fee0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_388(
- %para2779 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadccd4ff0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_810, FuncGraph::fg_811) #(Bool, Func, Func) # fg_810=✓construct.810(@ctx.addr=0xaaaadccd5140), fg_811=✗construct.811 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadccd5140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.443] construct.389 @ctx.addr=0xaaaae81e11e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_389[fg_0](
- %para2780 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2780, %para230, %para229) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae81e12e0) #scope: Default @ctx.addr=0xaaaae81e12e0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.444] construct.390 @ctx.addr=0xaaaaf2e2b270
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_390[fg_0](
- %para2781 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2781, %para232, %para231) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae84d7750) #scope: Default @ctx.addr=0xaaaae84d7750
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.445] construct.391 @ctx.addr=0xaaaae83fb760
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_391[fg_0](
- %para2782 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2782, %para234, %para233) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf71156f0) #scope: Default @ctx.addr=0xaaaaf71156f0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.446] construct.392 @ctx.addr=0xaaaafaeb7cf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_392[fg_0](
- %para2783 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2783, %para236, %para235) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaf42f4e10) #scope: Default @ctx.addr=0xaaaaf42f4e10
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.447] _tensor_mul_scalar.812 @ctx.addr=0xaaaae1f2d230
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_812(
- %para2784 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2785 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2784, %para2785) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.448] _tensor_add_tensor.813 @ctx.addr=0xaaaaeda73960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_813(
- %para2786 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2787 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2786, %para2787) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.449] construct.393 @ctx.addr=0xaaaae06cc5a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_393[fg_0](
- %para2788 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2788, %para238, %para237) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaafa486370) #scope: Default @ctx.addr=0xaaaafa486370
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.450] construct.394 @ctx.addr=0xaaaae8d02a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_394(
- %para2789 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae23b5d10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_814, FuncGraph::fg_815) #(Bool, Func, Func) # fg_814=✓construct.814(@ctx.addr=0xaaaadca71af0), fg_815=✗construct.815 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadca71af0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.451] construct.395 @ctx.addr=0xaaaaee8d4ac0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_395[fg_0](
- %para2790 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2790, %para240, %para239) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaaee8d4bc0) #scope: Default @ctx.addr=0xaaaaee8d4bc0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.452] construct.396 @ctx.addr=0xaaaae237a9c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_396[fg_0](
- %para2791 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2791, %para242, %para241) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaaf1fa86f0) #scope: Default @ctx.addr=0xaaaaf1fa86f0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.453] construct.397 @ctx.addr=0xaaaaf4dd2fa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_397[fg_0](
- %para2792 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2792, %para244, %para243) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae123e5a0) #scope: Default @ctx.addr=0xaaaae123e5a0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.454] construct.398 @ctx.addr=0xaaaae9726960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_398[fg_0](
- %para2793 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2793, %para246, %para245) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaf3854ca0) #scope: Default @ctx.addr=0xaaaaf3854ca0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.455] _tensor_mul_scalar.816 @ctx.addr=0xaaaaf34c3170
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_816(
- %para2794 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2795 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2794, %para2795) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.456] _tensor_add_tensor.817 @ctx.addr=0xaaaae81a0ed0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_817(
- %para2796 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2797 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2796, %para2797) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.457] construct.401 @ctx.addr=0xaaaae591b880
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_401[fg_0](
- %para2798 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2798, %para248, %para247) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae591b9c0) #scope: Default @ctx.addr=0xaaaae591b9c0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.458] construct.402 @ctx.addr=0xaaaae25b80b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_402(
- %para2799 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2351cb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_818, FuncGraph::fg_819) #(Bool, Func, Func) # fg_818=✓construct.818(@ctx.addr=0xaaaad60d96f0), fg_819=✗construct.819 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad60d96f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.459] construct.403 @ctx.addr=0xaaaae97aad20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_403[fg_0](
- %para2800 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2800, %para250, %para249) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae97aae20) #scope: Default @ctx.addr=0xaaaae97aae20
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.460] construct.404 @ctx.addr=0xaaaae1f668c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_404[fg_0](
- %para2801 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2801, %para252, %para251) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae21fc610) #scope: Default @ctx.addr=0xaaaae21fc610
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.461] construct.405 @ctx.addr=0xaaaaf6db98f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_405[fg_0](
- %para2802 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2802, %para254, %para253) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaed1fd0d0) #scope: Default @ctx.addr=0xaaaaed1fd0d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.462] construct.406 @ctx.addr=0xaaaaf17cc650
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_406[fg_0](
- %para2803 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2803, %para256, %para255) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaadec8a1e0) #scope: Default @ctx.addr=0xaaaadec8a1e0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.463] _tensor_mul_scalar.820 @ctx.addr=0xaaaaefd10c60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_820(
- %para2804 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2805 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2804, %para2805) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.464] _tensor_add_tensor.821 @ctx.addr=0xaaaae2301060
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_821(
- %para2806 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2807 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2806, %para2807) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.465] construct.407 @ctx.addr=0xaaaaf18ae530
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_407[fg_0](
- %para2808 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2808, %para258, %para257) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae2031920) #scope: Default @ctx.addr=0xaaaae2031920
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.466] construct.408 @ctx.addr=0xaaaae1ede700
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_408(
- %para2809 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1faf250
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_822, FuncGraph::fg_823) #(Bool, Func, Func) # fg_822=✓construct.822(@ctx.addr=0xaaaae1e07c00), fg_823=✗construct.823 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1e07c00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.467] construct.409 @ctx.addr=0xaaaae84e1eb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_409[fg_0](
- %para2810 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2810, %para260, %para259) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae84e1fb0) #scope: Default @ctx.addr=0xaaaae84e1fb0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.468] construct.410 @ctx.addr=0xaaaae83e33e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_410[fg_0](
- %para2811 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2811, %para262, %para261) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae8437a10) #scope: Default @ctx.addr=0xaaaae8437a10
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.469] construct.411 @ctx.addr=0xaaaaeabe94a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_411[fg_0](
- %para2812 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2812, %para264, %para263) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf0733aa0) #scope: Default @ctx.addr=0xaaaaf0733aa0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.470] construct.412 @ctx.addr=0xaaaadc85e090
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_412[fg_0](
- %para2813 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2813, %para266, %para265) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaad65ad210) #scope: Default @ctx.addr=0xaaaad65ad210
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.471] _tensor_mul_scalar.824 @ctx.addr=0xaaaae81f2300
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_824(
- %para2814 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2815 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2814, %para2815) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.472] _tensor_add_tensor.825 @ctx.addr=0xaaaae819c7d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_825(
- %para2816 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2817 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2816, %para2817) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.473] construct.413 @ctx.addr=0xaaaaf1449230
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_413[fg_0](
- %para2818 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2818, %para268, %para267) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae8296ef0) #scope: Default @ctx.addr=0xaaaae8296ef0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.474] construct.414 @ctx.addr=0xaaaafae718f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_414(
- %para2819 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaee8c4000
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_826, FuncGraph::fg_827) #(Bool, Func, Func) # fg_826=✓construct.826(@ctx.addr=0xaaaaee8c4150), fg_827=✗construct.827 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaee8c4150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.475] construct.415 @ctx.addr=0xaaaafae19fd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_415[fg_0](
- %para2820 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2820, %para270, %para269) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaafae1a0d0) #scope: Default @ctx.addr=0xaaaafae1a0d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.476] construct.416 @ctx.addr=0xaaaaef3ddd00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_416[fg_0](
- %para2821 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2821, %para272, %para271) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaad5f63fc0) #scope: Default @ctx.addr=0xaaaad5f63fc0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.477] construct.417 @ctx.addr=0xaaaaef8014b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_417[fg_0](
- %para2822 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2822, %para274, %para273) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae81d1c40) #scope: Default @ctx.addr=0xaaaae81d1c40
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.478] construct.418 @ctx.addr=0xaaaae22bc920
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_418[fg_0](
- %para2823 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2823, %para276, %para275) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaafad8b7b0) #scope: Default @ctx.addr=0xaaaafad8b7b0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.479] _tensor_mul_scalar.828 @ctx.addr=0xaaaae223cdf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_828(
- %para2824 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2825 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2824, %para2825) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.480] _tensor_add_tensor.829 @ctx.addr=0xaaaae2276e50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_829(
- %para2826 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2827 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2826, %para2827) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.481] construct.421 @ctx.addr=0xaaaaed11abb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_421[fg_0](
- %para2828 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2828, %para278, %para277) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaadc7ca4a0) #scope: Default @ctx.addr=0xaaaadc7ca4a0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.482] construct.422 @ctx.addr=0xaaaae584c2c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_422(
- %para2829 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf0deace0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_830, FuncGraph::fg_831) #(Bool, Func, Func) # fg_830=✓construct.830(@ctx.addr=0xaaaaf0deae30), fg_831=✗construct.831 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf0deae30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.483] construct.423 @ctx.addr=0xaaaaf2f71d00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_423[fg_0](
- %para2830 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2830, %para280, %para279) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaaf2f71e00) #scope: Default @ctx.addr=0xaaaaf2f71e00
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.484] construct.424 @ctx.addr=0xaaaaee788540
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_424[fg_0](
- %para2831 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2831, %para282, %para281) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaaefbc57b0) #scope: Default @ctx.addr=0xaaaaefbc57b0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.485] construct.425 @ctx.addr=0xaaaadd3a2650
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_425[fg_0](
- %para2832 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2832, %para284, %para283) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaef541eb0) #scope: Default @ctx.addr=0xaaaaef541eb0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.486] construct.426 @ctx.addr=0xaaaaec7ab330
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_426[fg_0](
- %para2833 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2833, %para286, %para285) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaad6047760) #scope: Default @ctx.addr=0xaaaad6047760
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.487] _tensor_mul_scalar.832 @ctx.addr=0xaaaae236fb60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_832(
- %para2834 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2835 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2834, %para2835) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.488] _tensor_add_tensor.833 @ctx.addr=0xaaaae21325d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_833(
- %para2836 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2837 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2836, %para2837) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.489] construct.427 @ctx.addr=0xaaaae20fcda0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_427[fg_0](
- %para2838 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2838, %para288, %para287) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae226c9a0) #scope: Default @ctx.addr=0xaaaae226c9a0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.490] construct.428 @ctx.addr=0xaaaae81c6e90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_428(
- %para2839 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1ef13e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_834, FuncGraph::fg_835) #(Bool, Func, Func) # fg_834=✓construct.834(@ctx.addr=0xaaaae1ef1530), fg_835=✗construct.835 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1ef1530
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.491] construct.429 @ctx.addr=0xaaaae859d120
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_429[fg_0](
- %para2840 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2840, %para290, %para289) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae121b090) #scope: Default @ctx.addr=0xaaaae121b090
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.492] construct.430 @ctx.addr=0xaaaae121b3e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_430[fg_0](
- %para2841 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2841, %para292, %para291) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaaebbd70d0) #scope: Default @ctx.addr=0xaaaaebbd70d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.493] construct.431 @ctx.addr=0xaaaaec0ea9e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_431[fg_0](
- %para2842 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2842, %para294, %para293) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf255bd40) #scope: Default @ctx.addr=0xaaaaf255bd40
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.494] construct.432 @ctx.addr=0xaaaae823beb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_432[fg_0](
- %para2843 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2843, %para296, %para295) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaadceffa60) #scope: Default @ctx.addr=0xaaaadceffa60
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.495] _tensor_mul_scalar.836 @ctx.addr=0xaaaae2287090
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_836(
- %para2844 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2845 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2844, %para2845) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.496] _tensor_add_tensor.837 @ctx.addr=0xaaaae224cc70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_837(
- %para2846 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2847 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2846, %para2847) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.497] construct.433 @ctx.addr=0xaaaae2212d00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_433[fg_0](
- %para2848 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2848, %para298, %para297) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaafaebf940) #scope: Default @ctx.addr=0xaaaafaebf940
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.498] construct.434 @ctx.addr=0xaaaae1fdad30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_434(
- %para2849 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad5a1e8a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_838, FuncGraph::fg_839) #(Bool, Func, Func) # fg_838=✓construct.838(@ctx.addr=0xaaaad5a1e9f0), fg_839=✗construct.839 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad5a1e9f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.499] construct.435 @ctx.addr=0xaaaae35dcfc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_435[fg_0](
- %para2850 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2850, %para300, %para299) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae35dd0c0) #scope: Default @ctx.addr=0xaaaae35dd0c0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.500] construct.436 @ctx.addr=0xaaaae090f3c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_436[fg_0](
- %para2851 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2851, %para302, %para301) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaaf3858d20) #scope: Default @ctx.addr=0xaaaaf3858d20
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.501] construct.437 @ctx.addr=0xaaaae564c4c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_437[fg_0](
- %para2852 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2852, %para304, %para303) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf2c16d40) #scope: Default @ctx.addr=0xaaaaf2c16d40
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.502] construct.438 @ctx.addr=0xaaaae24baf30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_438[fg_0](
- %para2853 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2853, %para306, %para305) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae85f13a0) #scope: Default @ctx.addr=0xaaaae85f13a0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.503] _tensor_mul_scalar.840 @ctx.addr=0xaaaadca63a30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_840(
- %para2854 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2855 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2854, %para2855) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.504] _tensor_add_tensor.841 @ctx.addr=0xaaaaf0214120
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_841(
- %para2856 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2857 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2856, %para2857) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.505] construct.441 @ctx.addr=0xaaaaf25541c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_441[fg_0](
- %para2858 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2858, %para308, %para307) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaf2554300) #scope: Default @ctx.addr=0xaaaaf2554300
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.506] construct.442 @ctx.addr=0xaaaaf2554670
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_442(
- %para2859 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaecc2b000
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_842, FuncGraph::fg_843) #(Bool, Func, Func) # fg_842=✓construct.842(@ctx.addr=0xaaaaecc2b150), fg_843=✗construct.843 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaecc2b150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.507] construct.443 @ctx.addr=0xaaaae2147810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_443[fg_0](
- %para2860 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2860, %para310, %para309) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae2147910) #scope: Default @ctx.addr=0xaaaae2147910
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.508] construct.444 @ctx.addr=0xaaaafadcc2e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_444[fg_0](
- %para2861 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2861, %para312, %para311) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae2112550) #scope: Default @ctx.addr=0xaaaae2112550
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.509] construct.445 @ctx.addr=0xaaaae1e2e040
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_445[fg_0](
- %para2862 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2862, %para314, %para313) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf52e3820) #scope: Default @ctx.addr=0xaaaaf52e3820
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.510] construct.446 @ctx.addr=0xaaaafae0b870
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_446[fg_0](
- %para2863 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2863, %para316, %para315) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae35d7640) #scope: Default @ctx.addr=0xaaaae35d7640
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.511] _tensor_mul_scalar.844 @ctx.addr=0xaaaae2554900
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_844(
- %para2864 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2865 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2864, %para2865) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.512] _tensor_add_tensor.845 @ctx.addr=0xaaaae258ffe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_845(
- %para2866 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2867 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2866, %para2867) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.513] construct.447 @ctx.addr=0xaaaae1f2c120
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_447[fg_0](
- %para2868 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2868, %para318, %para317) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaf2e2e480) #scope: Default @ctx.addr=0xaaaaf2e2e480
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.514] construct.448 @ctx.addr=0xaaaaec0e4c00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_448(
- %para2869 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae0d2a130
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_846, FuncGraph::fg_847) #(Bool, Func, Func) # fg_846=✓construct.846(@ctx.addr=0xaaaae0d2a240), fg_847=✗construct.847 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae0d2a240
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.515] construct.449 @ctx.addr=0xaaaaef039920
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_449[fg_0](
- %para2870 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2870, %para320, %para319) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaaef039a20) #scope: Default @ctx.addr=0xaaaaef039a20
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.516] construct.450 @ctx.addr=0xaaaaf229ad40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_450[fg_0](
- %para2871 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2871, %para322, %para321) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaaea945420) #scope: Default @ctx.addr=0xaaaaea945420
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.517] construct.451 @ctx.addr=0xaaaaf0848130
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_451[fg_0](
- %para2872 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2872, %para324, %para323) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae97ade10) #scope: Default @ctx.addr=0xaaaae97ade10
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.518] construct.452 @ctx.addr=0xaaaad88a18c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_452[fg_0](
- %para2873 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2873, %para326, %para325) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae69d0a80) #scope: Default @ctx.addr=0xaaaae69d0a80
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.519] _tensor_mul_scalar.848 @ctx.addr=0xaaaadc684010
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_848(
- %para2874 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2875 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2874, %para2875) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.520] _tensor_add_tensor.849 @ctx.addr=0xaaaaef405e70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_849(
- %para2876 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2877 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2876, %para2877) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.521] construct.453 @ctx.addr=0xaaaaef406580
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_453[fg_0](
- %para2878 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2878, %para328, %para327) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaea94f7d0) #scope: Default @ctx.addr=0xaaaaea94f7d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.522] construct.454 @ctx.addr=0xaaaaf22a9910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_454(
- %para2879 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaecb4b5f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_850, FuncGraph::fg_851) #(Bool, Func, Func) # fg_850=✓construct.850(@ctx.addr=0xaaaaecb4b740), fg_851=✗construct.851 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaecb4b740
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.523] construct.455 @ctx.addr=0xaaaadd258120
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_455[fg_0](
- %para2880 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2880, %para330, %para329) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaad61fb110) #scope: Default @ctx.addr=0xaaaad61fb110
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.524] construct.456 @ctx.addr=0xaaaaf258cbf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_456[fg_0](
- %para2881 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2881, %para332, %para331) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaaf25912d0) #scope: Default @ctx.addr=0xaaaaf25912d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.525] construct.457 @ctx.addr=0xaaaaf09839b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_457[fg_0](
- %para2882 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2882, %para334, %para333) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf6e99bd0) #scope: Default @ctx.addr=0xaaaaf6e99bd0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.526] construct.458 @ctx.addr=0xaaaaee3f30a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_458[fg_0](
- %para2883 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2883, %para336, %para335) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaddb2e900) #scope: Default @ctx.addr=0xaaaaddb2e900
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.527] _tensor_mul_scalar.852 @ctx.addr=0xaaaaea7e4510
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_852(
- %para2884 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2885 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2884, %para2885) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.528] _tensor_add_tensor.853 @ctx.addr=0xaaaaea7e4a40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_853(
- %para2886 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2887 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2886, %para2887) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.529] construct.461 @ctx.addr=0xaaaae1fb46c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_461[fg_0](
- %para2888 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2888, %para338, %para337) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae83baa20) #scope: Default @ctx.addr=0xaaaae83baa20
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.530] construct.462 @ctx.addr=0xaaaae83105d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_462(
- %para2889 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1e71200
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_854, FuncGraph::fg_855) #(Bool, Func, Func) # fg_854=✓construct.854(@ctx.addr=0xaaaae1e71310), fg_855=✗construct.855 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1e71310
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.531] construct.463 @ctx.addr=0xaaaad74171c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_463[fg_0](
- %para2890 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2890, %para340, %para339) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaad74172c0) #scope: Default @ctx.addr=0xaaaad74172c0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.532] construct.464 @ctx.addr=0xaaaaf2f7d2f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_464[fg_0](
- %para2891 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2891, %para342, %para341) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaaf4056b00) #scope: Default @ctx.addr=0xaaaaf4056b00
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.533] construct.465 @ctx.addr=0xaaaae83ca150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_465[fg_0](
- %para2892 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2892, %para344, %para343) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf3998680) #scope: Default @ctx.addr=0xaaaaf3998680
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.534] construct.466 @ctx.addr=0xaaaae81d23d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_466[fg_0](
- %para2893 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2893, %para346, %para345) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaec02a170) #scope: Default @ctx.addr=0xaaaaec02a170
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.535] _tensor_mul_scalar.856 @ctx.addr=0xaaaadeb4a0f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_856(
- %para2894 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2895 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2894, %para2895) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.536] _tensor_add_tensor.857 @ctx.addr=0xaaaadeb4a550
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_857(
- %para2896 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2897 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2896, %para2897) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.537] construct.467 @ctx.addr=0xaaaadec91680
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_467[fg_0](
- %para2898 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2898, %para348, %para347) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae85ebf40) #scope: Default @ctx.addr=0xaaaae85ebf40
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.538] construct.468 @ctx.addr=0xaaaaebfa5230
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_468(
- %para2899 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae0d25610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_858, FuncGraph::fg_859) #(Bool, Func, Func) # fg_858=✓construct.858(@ctx.addr=0xaaaae0d257c0), fg_859=✗construct.859 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae0d257c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.539] construct.469 @ctx.addr=0xaaaaf18aa4c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_469[fg_0](
- %para2900 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2900, %para350, %para349) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaaf18aa5c0) #scope: Default @ctx.addr=0xaaaaf18aa5c0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.540] construct.470 @ctx.addr=0xaaaae6e11ae0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_470[fg_0](
- %para2901 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2901, %para352, %para351) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae6e13e00) #scope: Default @ctx.addr=0xaaaae6e13e00
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.541] construct.471 @ctx.addr=0xaaaaea94c210
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_471[fg_0](
- %para2902 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2902, %para354, %para353) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf229c410) #scope: Default @ctx.addr=0xaaaaf229c410
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.542] construct.472 @ctx.addr=0xaaaae70fac00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_472[fg_0](
- %para2903 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2903, %para356, %para355) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaea28f7e0) #scope: Default @ctx.addr=0xaaaaea28f7e0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.543] _tensor_mul_scalar.860 @ctx.addr=0xaaaae6bee740
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_860(
- %para2904 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2905 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2904, %para2905) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.544] _tensor_add_tensor.861 @ctx.addr=0xaaaae6beec30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_861(
- %para2906 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2907 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2906, %para2907) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.545] construct.473 @ctx.addr=0xaaaae6bea430
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_473[fg_0](
- %para2908 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2908, %para358, %para357) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaade555220) #scope: Default @ctx.addr=0xaaaade555220
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.546] construct.474 @ctx.addr=0xaaaade54ff70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_474(
- %para2909 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8d7fb60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_862, FuncGraph::fg_863) #(Bool, Func, Func) # fg_862=✓construct.862(@ctx.addr=0xaaaae8d7fc70), fg_863=✗construct.863 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8d7fc70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.547] construct.475 @ctx.addr=0xaaaad924c590
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_475[fg_0](
- %para2910 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2910, %para360, %para359) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaad8a51a70) #scope: Default @ctx.addr=0xaaaad8a51a70
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.548] construct.476 @ctx.addr=0xaaaad8a47090
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_476[fg_0](
- %para2911 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2911, %para362, %para361) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaad8a50d50) #scope: Default @ctx.addr=0xaaaad8a50d50
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.549] construct.477 @ctx.addr=0xaaaadc8a4750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_477[fg_0](
- %para2912 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2912, %para364, %para363) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae6ee7f20) #scope: Default @ctx.addr=0xaaaae6ee7f20
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.550] construct.478 @ctx.addr=0xaaaaf4ee5ec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_478[fg_0](
- %para2913 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2913, %para366, %para365) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaad63ecf10) #scope: Default @ctx.addr=0xaaaad63ecf10
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.551] _tensor_mul_scalar.864 @ctx.addr=0xaaaad90ab8b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_864(
- %para2914 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2915 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2914, %para2915) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.552] _tensor_add_tensor.865 @ctx.addr=0xaaaad90abda0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_865(
- %para2916 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2917 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2916, %para2917) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.553] construct.481 @ctx.addr=0xaaaae3336bc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_481[fg_0](
- %para2918 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2918, %para368, %para367) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae3336d00) #scope: Default @ctx.addr=0xaaaae3336d00
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.554] construct.482 @ctx.addr=0xaaaae33370e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_482(
- %para2919 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad7f4ccd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_866, FuncGraph::fg_867) #(Bool, Func, Func) # fg_866=✓construct.866(@ctx.addr=0xaaaad7f4cde0), fg_867=✗construct.867 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad7f4cde0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.555] construct.483 @ctx.addr=0xaaaae5924a60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_483[fg_0](
- %para2920 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2920, %para370, %para369) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae5924b60) #scope: Default @ctx.addr=0xaaaae5924b60
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.556] construct.484 @ctx.addr=0xaaaae6ee2c20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_484[fg_0](
- %para2921 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2921, %para372, %para371) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae6eec640) #scope: Default @ctx.addr=0xaaaae6eec640
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.557] construct.485 @ctx.addr=0xaaaaedfc64e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_485[fg_0](
- %para2922 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2922, %para374, %para373) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf34c6a10) #scope: Default @ctx.addr=0xaaaaf34c6a10
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.558] construct.486 @ctx.addr=0xaaaaf71edcf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_486[fg_0](
- %para2923 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2923, %para376, %para375) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaea1bf4e0) #scope: Default @ctx.addr=0xaaaaea1bf4e0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.559] _tensor_mul_scalar.868 @ctx.addr=0xaaaaef837220
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_868(
- %para2924 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2925 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2924, %para2925) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.560] _tensor_add_tensor.869 @ctx.addr=0xaaaaef837710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_869(
- %para2926 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2927 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2926, %para2927) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.561] construct.487 @ctx.addr=0xaaaaef832fb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_487[fg_0](
- %para2928 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2928, %para378, %para377) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaec6404c0) #scope: Default @ctx.addr=0xaaaaec6404c0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.562] construct.488 @ctx.addr=0xaaaae000ed80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_488(
- %para2929 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaded5f8e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_870, FuncGraph::fg_871) #(Bool, Func, Func) # fg_870=✓construct.870(@ctx.addr=0xaaaaded5f9f0), fg_871=✗construct.871 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaded5f9f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.563] construct.489 @ctx.addr=0xaaaaf49020f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_489[fg_0](
- %para2930 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2930, %para380, %para379) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaaf49021f0) #scope: Default @ctx.addr=0xaaaaf49021f0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.564] construct.490 @ctx.addr=0xaaaae5c18d70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_490[fg_0](
- %para2931 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2931, %para382, %para381) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae5c1d210) #scope: Default @ctx.addr=0xaaaae5c1d210
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.565] construct.491 @ctx.addr=0xaaaadc46b050
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_491[fg_0](
- %para2932 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2932, %para384, %para383) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf11936f0) #scope: Default @ctx.addr=0xaaaaf11936f0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.566] construct.492 @ctx.addr=0xaaaad5c92fb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_492[fg_0](
- %para2933 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2933, %para386, %para385) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaf2592460) #scope: Default @ctx.addr=0xaaaaf2592460
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.567] _tensor_mul_scalar.872 @ctx.addr=0xaaaaf0f6cd10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_872(
- %para2934 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2935 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2934, %para2935) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.568] _tensor_add_tensor.873 @ctx.addr=0xaaaaf0f6d200
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_873(
- %para2936 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2937 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2936, %para2937) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.569] construct.493 @ctx.addr=0xaaaaf3eea9c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_493[fg_0](
- %para2938 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2938, %para388, %para387) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaed73df80) #scope: Default @ctx.addr=0xaaaaed73df80
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.570] construct.494 @ctx.addr=0xaaaad5bc4110
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_494(
- %para2939 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad68d0100
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_874, FuncGraph::fg_875) #(Bool, Func, Func) # fg_874=✓construct.874(@ctx.addr=0xaaaad68d0210), fg_875=✗construct.875 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad68d0210
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.571] construct.495 @ctx.addr=0xaaaae8537840
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_495[fg_0](
- %para2940 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2940, %para390, %para389) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae8537940) #scope: Default @ctx.addr=0xaaaae8537940
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.572] construct.496 @ctx.addr=0xaaaafae14bb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_496[fg_0](
- %para2941 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2941, %para392, %para391) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaaf43af6c0) #scope: Default @ctx.addr=0xaaaaf43af6c0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.573] construct.497 @ctx.addr=0xaaaafa857d90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_497[fg_0](
- %para2942 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2942, %para394, %para393) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaad8a4cf70) #scope: Default @ctx.addr=0xaaaad8a4cf70
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.574] construct.498 @ctx.addr=0xaaaaf1d2a1f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_498[fg_0](
- %para2943 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2943, %para396, %para395) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae34172f0) #scope: Default @ctx.addr=0xaaaae34172f0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.575] _tensor_mul_scalar.876 @ctx.addr=0xaaaad68c5b70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_876(
- %para2944 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2945 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2944, %para2945) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.576] _tensor_add_tensor.877 @ctx.addr=0xaaaad68c6060
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_877(
- %para2946 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2947 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2946, %para2947) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.577] construct.501 @ctx.addr=0xaaaaeacb3bb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_501[fg_0](
- %para2948 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2948, %para398, %para397) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaeacb3cf0) #scope: Default @ctx.addr=0xaaaaeacb3cf0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.578] construct.502 @ctx.addr=0xaaaae2668570
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_502(
- %para2949 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf0c3bad0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_878, FuncGraph::fg_879) #(Bool, Func, Func) # fg_878=✓construct.878(@ctx.addr=0xaaaaf0c3bbe0), fg_879=✗construct.879 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf0c3bbe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.579] construct.503 @ctx.addr=0xaaaaf0c38ba0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_503[fg_0](
- %para2950 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2950, %para400, %para399) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaaf0c38ca0) #scope: Default @ctx.addr=0xaaaaf0c38ca0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.580] construct.504 @ctx.addr=0xaaaad72a42b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_504[fg_0](
- %para2951 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2951, %para402, %para401) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaaf0c39710) #scope: Default @ctx.addr=0xaaaaf0c39710
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.581] construct.505 @ctx.addr=0xaaaad6a02fa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_505[fg_0](
- %para2952 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2952, %para404, %para403) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaade43f980) #scope: Default @ctx.addr=0xaaaade43f980
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.582] construct.506 @ctx.addr=0xaaaaedb25360
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_506[fg_0](
- %para2953 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2953, %para406, %para405) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaad72a2380) #scope: Default @ctx.addr=0xaaaad72a2380
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.583] _tensor_mul_scalar.880 @ctx.addr=0xaaaaf71a6cf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_880(
- %para2954 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2955 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2954, %para2955) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.584] _tensor_add_tensor.881 @ctx.addr=0xaaaaf709c2a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_881(
- %para2956 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2957 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2956, %para2957) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.585] construct.507 @ctx.addr=0xaaaaf709ca30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_507[fg_0](
- %para2958 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2958, %para408, %para407) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae0db8b10) #scope: Default @ctx.addr=0xaaaae0db8b10
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.586] construct.508 @ctx.addr=0xaaaae0caeec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_508(
- %para2959 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae83736c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_882, FuncGraph::fg_883) #(Bool, Func, Func) # fg_882=✓construct.882(@ctx.addr=0xaaaae8373870), fg_883=✗construct.883 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8373870
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.587] construct.509 @ctx.addr=0xaaaaef22ef60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_509[fg_0](
- %para2960 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2960, %para410, %para409) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae25769a0) #scope: Default @ctx.addr=0xaaaae25769a0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.588] construct.510 @ctx.addr=0xaaaae2577e20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_510[fg_0](
- %para2961 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2961, %para412, %para411) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae25773d0) #scope: Default @ctx.addr=0xaaaae25773d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.589] construct.511 @ctx.addr=0xaaaae21546d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_511[fg_0](
- %para2962 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2962, %para414, %para413) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaafadf87e0) #scope: Default @ctx.addr=0xaaaafadf87e0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.590] construct.512 @ctx.addr=0xaaaaee3b5f30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_512[fg_0](
- %para2963 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2963, %para416, %para415) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae247a150) #scope: Default @ctx.addr=0xaaaae247a150
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.591] _tensor_mul_scalar.884 @ctx.addr=0xaaaae817b210
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_884(
- %para2964 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2965 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2964, %para2965) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.592] _tensor_add_tensor.885 @ctx.addr=0xaaaae817b700
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_885(
- %para2966 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2967 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2966, %para2967) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.593] construct.513 @ctx.addr=0xaaaafae4d470
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_513[fg_0](
- %para2968 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2968, %para418, %para417) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae24eeeb0) #scope: Default @ctx.addr=0xaaaae24eeeb0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.594] construct.514 @ctx.addr=0xaaaae24b4b40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_514(
- %para2969 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae25ac500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_886, FuncGraph::fg_887) #(Bool, Func, Func) # fg_886=✓construct.886(@ctx.addr=0xaaaae25ac610), fg_887=✗construct.887 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae25ac610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.595] construct.515 @ctx.addr=0xaaaaf1e63820
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_515[fg_0](
- %para2970 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2970, %para420, %para419) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaaf1e63920) #scope: Default @ctx.addr=0xaaaaf1e63920
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.596] construct.516 @ctx.addr=0xaaaaf066dee0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_516[fg_0](
- %para2971 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2971, %para422, %para421) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaaf1e64390) #scope: Default @ctx.addr=0xaaaaf1e64390
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.597] construct.517 @ctx.addr=0xaaaae841bd80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_517[fg_0](
- %para2972 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2972, %para424, %para423) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae8274790) #scope: Default @ctx.addr=0xaaaae8274790
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.598] construct.518 @ctx.addr=0xaaaae1fc18a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_518[fg_0](
- %para2973 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2973, %para426, %para425) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae85c4ba0) #scope: Default @ctx.addr=0xaaaae85c4ba0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.599] _tensor_mul_scalar.888 @ctx.addr=0xaaaae82c9aa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_888(
- %para2974 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2975 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2974, %para2975) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.600] _tensor_add_tensor.889 @ctx.addr=0xaaaae82c9f90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_889(
- %para2976 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2977 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2976, %para2977) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.601] construct.521 @ctx.addr=0xaaaaee7fe400
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_521[fg_0](
- %para2978 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2978, %para428, %para427) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaee7fe650) #scope: Default @ctx.addr=0xaaaaee7fe650
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.602] construct.522 @ctx.addr=0xaaaaee7fea20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_522(
- %para2979 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf3ea74f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_890, FuncGraph::fg_891) #(Bool, Func, Func) # fg_890=✓construct.890(@ctx.addr=0xaaaaf3ea7600), fg_891=✗construct.891 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf3ea7600
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.603] construct.523 @ctx.addr=0xaaaae258dbf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_523[fg_0](
- %para2980 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2980, %para430, %para429) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae258dcf0) #scope: Default @ctx.addr=0xaaaae258dcf0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.604] construct.524 @ctx.addr=0xaaaae258f120
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_524[fg_0](
- %para2981 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2981, %para432, %para431) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae258e6d0) #scope: Default @ctx.addr=0xaaaae258e6d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.605] construct.525 @ctx.addr=0xaaaafae847e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_525[fg_0](
- %para2982 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2982, %para434, %para433) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae83b7c90) #scope: Default @ctx.addr=0xaaaae83b7c90
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.606] construct.526 @ctx.addr=0xaaaaee80db90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_526[fg_0](
- %para2983 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2983, %para436, %para435) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaafad93170) #scope: Default @ctx.addr=0xaaaafad93170
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.607] _tensor_mul_scalar.892 @ctx.addr=0xaaaae84b75d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_892(
- %para2984 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2985 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2984, %para2985) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.608] _tensor_add_tensor.893 @ctx.addr=0xaaaae84b7ac0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_893(
- %para2986 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2987 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2986, %para2987) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.609] construct.527 @ctx.addr=0xaaaae84b8250
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_527[fg_0](
- %para2988 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2988, %para438, %para437) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae23f7d10) #scope: Default @ctx.addr=0xaaaae23f7d10
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.610] construct.528 @ctx.addr=0xaaaae20b0e50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_528(
- %para2989 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaafae94ca0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_894, FuncGraph::fg_895) #(Bool, Func, Func) # fg_894=✓construct.894(@ctx.addr=0xaaaafae94db0), fg_895=✗construct.895 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaafae94db0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.611] construct.529 @ctx.addr=0xaaaafae959b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_529[fg_0](
- %para2990 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para2990, %para440, %para439) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae1e5ff50) #scope: Default @ctx.addr=0xaaaae1e5ff50
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.612] construct.530 @ctx.addr=0xaaaae1e602a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_530[fg_0](
- %para2991 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para2991, %para442, %para441) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae1e60ac0) #scope: Default @ctx.addr=0xaaaae1e60ac0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.613] construct.531 @ctx.addr=0xaaaaeef83ad0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_531[fg_0](
- %para2992 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para2992, %para444, %para443) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae24309c0) #scope: Default @ctx.addr=0xaaaae24309c0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.614] construct.532 @ctx.addr=0xaaaae1f4e760
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_532[fg_0](
- %para2993 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para2993, %para446, %para445) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaaec7ae3e0) #scope: Default @ctx.addr=0xaaaaec7ae3e0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.615] _tensor_mul_scalar.896 @ctx.addr=0xaaaaebbd5200
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_896(
- %para2994 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2995 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2994, %para2995) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.616] _tensor_add_tensor.897 @ctx.addr=0xaaaaebbd56f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_897(
- %para2996 : Tensor(F32)[16, 64, 32, 32] # x
- , %para2997 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para2996, %para2997) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.617] construct.533 @ctx.addr=0xaaaaebbd5e80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_533[fg_0](
- %para2998 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para2998, %para448, %para447) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaee0ffa30) #scope: Default @ctx.addr=0xaaaaee0ffa30
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.618] construct.534 @ctx.addr=0xaaaae2671ad0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_534(
- %para2999 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2698010
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_898, FuncGraph::fg_899) #(Bool, Func, Func) # fg_898=✓construct.898(@ctx.addr=0xaaaae2698120), fg_899=✗construct.899 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2698120
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.619] construct.535 @ctx.addr=0xaaaae2102600
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_535[fg_0](
- %para3000 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3000, %para450, %para449) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae2102700) #scope: Default @ctx.addr=0xaaaae2102700
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.620] construct.536 @ctx.addr=0xaaaae21f2d20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_536[fg_0](
- %para3001 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3001, %para452, %para451) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae21f22d0) #scope: Default @ctx.addr=0xaaaae21f22d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.621] construct.537 @ctx.addr=0xaaaae2304fa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_537[fg_0](
- %para3002 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3002, %para454, %para453) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae2306e10) #scope: Default @ctx.addr=0xaaaae2306e10
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.622] construct.538 @ctx.addr=0xaaaae259d110
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_538[fg_0](
- %para3003 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3003, %para456, %para455) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae21452f0) #scope: Default @ctx.addr=0xaaaae21452f0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.623] _tensor_mul_scalar.900 @ctx.addr=0xaaaae23be370
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_900(
- %para3004 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3005 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3004, %para3005) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.624] _tensor_add_tensor.901 @ctx.addr=0xaaaae23be860
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_901(
- %para3006 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3007 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3006, %para3007) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.625] construct.541 @ctx.addr=0xaaaad59c3850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_541[fg_0](
- %para3008 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3008, %para458, %para457) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaad59c3aa0) #scope: Default @ctx.addr=0xaaaad59c3aa0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.626] construct.542 @ctx.addr=0xaaaad59c3e40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_542(
- %para3009 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1e505b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_902, FuncGraph::fg_903) #(Bool, Func, Func) # fg_902=✓construct.902(@ctx.addr=0xaaaae1e506c0), fg_903=✗construct.903 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1e506c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.627] construct.543 @ctx.addr=0xaaaaf605f440
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_543[fg_0](
- %para3010 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3010, %para460, %para459) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaaf605f540) #scope: Default @ctx.addr=0xaaaaf605f540
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.628] construct.544 @ctx.addr=0xaaaaf6060a00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_544[fg_0](
- %para3011 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3011, %para462, %para461) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaaf605ffb0) #scope: Default @ctx.addr=0xaaaaf605ffb0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.629] construct.545 @ctx.addr=0xaaaaeb6d2c80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_545[fg_0](
- %para3012 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3012, %para464, %para463) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf143e150) #scope: Default @ctx.addr=0xaaaaf143e150
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.630] construct.546 @ctx.addr=0xaaaad83039f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_546[fg_0](
- %para3013 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3013, %para466, %para465) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae254c2a0) #scope: Default @ctx.addr=0xaaaae254c2a0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.631] _tensor_mul_scalar.904 @ctx.addr=0xaaaaeb373d60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_904(
- %para3014 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3015 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3014, %para3015) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.632] _tensor_add_tensor.905 @ctx.addr=0xaaaaeb374250
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_905(
- %para3016 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3017 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3016, %para3017) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.633] construct.547 @ctx.addr=0xaaaaeb3749e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_547[fg_0](
- %para3018 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3018, %para468, %para467) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae2041550) #scope: Default @ctx.addr=0xaaaae2041550
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.634] construct.548 @ctx.addr=0xaaaadc52e590
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_548(
- %para3019 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae25fc8c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_906, FuncGraph::fg_907) #(Bool, Func, Func) # fg_906=✓construct.906(@ctx.addr=0xaaaae25fc9d0), fg_907=✗construct.907 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae25fc9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.635] construct.549 @ctx.addr=0xaaaafae32960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_549[fg_0](
- %para3020 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3020, %para470, %para469) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaafae32a60) #scope: Default @ctx.addr=0xaaaafae32a60
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.636] construct.550 @ctx.addr=0xaaaafae33f20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_550[fg_0](
- %para3021 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3021, %para472, %para471) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaafae334d0) #scope: Default @ctx.addr=0xaaaafae334d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.637] construct.551 @ctx.addr=0xaaaae0ef2c50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_551[fg_0](
- %para3022 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3022, %para474, %para473) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae258a2d0) #scope: Default @ctx.addr=0xaaaae258a2d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.638] construct.552 @ctx.addr=0xaaaae224eb60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_552[fg_0](
- %para3023 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3023, %para476, %para475) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae219ff50) #scope: Default @ctx.addr=0xaaaae219ff50
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.639] _tensor_mul_scalar.908 @ctx.addr=0xaaaae24c8360
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_908(
- %para3024 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3025 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3024, %para3025) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.640] _tensor_add_tensor.909 @ctx.addr=0xaaaae24c8850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_909(
- %para3026 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3027 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3026, %para3027) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.641] construct.553 @ctx.addr=0xaaaae248c460
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_553[fg_0](
- %para3028 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3028, %para478, %para477) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae21d9ee0) #scope: Default @ctx.addr=0xaaaae21d9ee0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.642] construct.554 @ctx.addr=0xaaaae21db380
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_554(
- %para3029 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae22137b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_910, FuncGraph::fg_911) #(Bool, Func, Func) # fg_910=✓construct.910(@ctx.addr=0xaaaae22138c0), fg_911=✗construct.911 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae22138c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.643] construct.555 @ctx.addr=0xaaaae81a73c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_555[fg_0](
- %para3030 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3030, %para480, %para479) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae81a74c0) #scope: Default @ctx.addr=0xaaaae81a74c0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.644] construct.556 @ctx.addr=0xaaaae2287ec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_556[fg_0](
- %para3031 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3031, %para482, %para481) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae81a7f30) #scope: Default @ctx.addr=0xaaaae81a7f30
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.645] construct.557 @ctx.addr=0xaaaaf065fb70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_557[fg_0](
- %para3032 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3032, %para484, %para483) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf0725580) #scope: Default @ctx.addr=0xaaaaf0725580
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.646] construct.558 @ctx.addr=0xaaaae21e7460
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_558[fg_0](
- %para3033 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3033, %para486, %para485) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae2220840) #scope: Default @ctx.addr=0xaaaae2220840
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.647] _tensor_mul_scalar.912 @ctx.addr=0xaaaae24266a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_912(
- %para3034 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3035 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3034, %para3035) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.648] _tensor_add_tensor.913 @ctx.addr=0xaaaae2426b90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_913(
- %para3036 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3037 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3036, %para3037) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.649] construct.561 @ctx.addr=0xaaaae2159b60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_561[fg_0](
- %para3038 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3038, %para488, %para487) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae2159db0) #scope: Default @ctx.addr=0xaaaae2159db0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.650] construct.562 @ctx.addr=0xaaaae215a180
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_562(
- %para3039 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf1773bb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_914, FuncGraph::fg_915) #(Bool, Func, Func) # fg_914=✓construct.914(@ctx.addr=0xaaaaf1773cc0), fg_915=✗construct.915 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf1773cc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.651] construct.563 @ctx.addr=0xaaaae1fc7170
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_563[fg_0](
- %para3040 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3040, %para490, %para489) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae1fc7270) #scope: Default @ctx.addr=0xaaaae1fc7270
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.652] construct.564 @ctx.addr=0xaaaaf71a9a60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_564[fg_0](
- %para3041 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3041, %para492, %para491) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaaf71a9010) #scope: Default @ctx.addr=0xaaaaf71a9010
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.653] construct.565 @ctx.addr=0xaaaae21f6100
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_565[fg_0](
- %para3042 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3042, %para494, %para493) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae1e1dd30) #scope: Default @ctx.addr=0xaaaae1e1dd30
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.654] construct.566 @ctx.addr=0xaaaae23d1610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_566[fg_0](
- %para3043 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3043, %para496, %para495) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae23c1020) #scope: Default @ctx.addr=0xaaaae23c1020
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.655] _tensor_mul_scalar.916 @ctx.addr=0xaaaae214a160
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_916(
- %para3044 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3045 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3044, %para3045) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.656] _tensor_add_tensor.917 @ctx.addr=0xaaaae214a650
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_917(
- %para3046 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3047 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3046, %para3047) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.657] construct.567 @ctx.addr=0xaaaae214ade0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_567[fg_0](
- %para3048 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3048, %para498, %para497) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae1fa8060) #scope: Default @ctx.addr=0xaaaae1fa8060
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.658] construct.568 @ctx.addr=0xaaaae2172050
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_568(
- %para3049 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8216850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_918, FuncGraph::fg_919) #(Bool, Func, Func) # fg_918=✓construct.918(@ctx.addr=0xaaaae8216960), fg_919=✗construct.919 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8216960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.659] construct.569 @ctx.addr=0xaaaaec603770
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_569[fg_0](
- %para3050 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3050, %para500, %para499) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae24a9930) #scope: Default @ctx.addr=0xaaaae24a9930
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.660] construct.570 @ctx.addr=0xaaaae24a9c80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_570[fg_0](
- %para3051 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3051, %para502, %para501) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae24aa4a0) #scope: Default @ctx.addr=0xaaaae24aa4a0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.661] construct.571 @ctx.addr=0xaaaae121a490
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_571[fg_0](
- %para3052 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3052, %para504, %para503) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaaf4dd0830) #scope: Default @ctx.addr=0xaaaaf4dd0830
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.662] construct.572 @ctx.addr=0xaaaae84ffbc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_572[fg_0](
- %para3053 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3053, %para506, %para505) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae8501a10) #scope: Default @ctx.addr=0xaaaae8501a10
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.663] _tensor_mul_scalar.920 @ctx.addr=0xaaaae2521830
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_920(
- %para3054 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3055 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3054, %para3055) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.664] _tensor_add_tensor.921 @ctx.addr=0xaaaae2521d20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_921(
- %para3056 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3057 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3056, %para3057) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.665] construct.573 @ctx.addr=0xaaaae25224b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_573[fg_0](
- %para3058 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3058, %para508, %para507) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae20a4380) #scope: Default @ctx.addr=0xaaaae20a4380
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.666] construct.574 @ctx.addr=0xaaaae20a5820
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_574(
- %para3059 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2409190
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_922, FuncGraph::fg_923) #(Bool, Func, Func) # fg_922=✓construct.922(@ctx.addr=0xaaaae24092a0), fg_923=✗construct.923 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae24092a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.667] construct.575 @ctx.addr=0xaaaae81c5ad0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_575[fg_0](
- %para3060 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3060, %para510, %para509) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae81c5bd0) #scope: Default @ctx.addr=0xaaaae81c5bd0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.668] construct.576 @ctx.addr=0xaaaae1efd5c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_576[fg_0](
- %para3061 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3061, %para512, %para511) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae81c6640) #scope: Default @ctx.addr=0xaaaae81c6640
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.669] construct.577 @ctx.addr=0xaaaae1e55d40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_577[fg_0](
- %para3062 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3062, %para514, %para513) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae2205df0) #scope: Default @ctx.addr=0xaaaae2205df0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.670] construct.578 @ctx.addr=0xaaaae84ab7a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_578[fg_0](
- %para3063 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3063, %para516, %para515) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae84ad5b0) #scope: Default @ctx.addr=0xaaaae84ad5b0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.671] _tensor_mul_scalar.924 @ctx.addr=0xaaaae1e62e90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_924(
- %para3064 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3065 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3064, %para3065) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.672] _tensor_add_tensor.925 @ctx.addr=0xaaaae1e63380
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_925(
- %para3066 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3067 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3066, %para3067) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.673] construct.581 @ctx.addr=0xaaaaebbe3740
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_581[fg_0](
- %para3068 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3068, %para518, %para517) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaaebbe3990) #scope: Default @ctx.addr=0xaaaaebbe3990
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.674] construct.582 @ctx.addr=0xaaaaebbe3cd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_582(
- %para3069 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae81d3460
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_926, FuncGraph::fg_927) #(Bool, Func, Func) # fg_926=✓construct.926(@ctx.addr=0xaaaae81d3570), fg_927=✗construct.927 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae81d3570
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.675] construct.583 @ctx.addr=0xaaaae235c600
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_583[fg_0](
- %para3070 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3070, %para520, %para519) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae235c700) #scope: Default @ctx.addr=0xaaaae235c700
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.676] construct.584 @ctx.addr=0xaaaae235dbc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_584[fg_0](
- %para3071 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3071, %para522, %para521) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae235d170) #scope: Default @ctx.addr=0xaaaae235d170
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.677] construct.585 @ctx.addr=0xaaaae82cde60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_585[fg_0](
- %para3072 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3072, %para524, %para523) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae1eb7440) #scope: Default @ctx.addr=0xaaaae1eb7440
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.678] construct.586 @ctx.addr=0xaaaadca62f00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_586[fg_0](
- %para3073 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3073, %para526, %para525) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae1dab930) #scope: Default @ctx.addr=0xaaaae1dab930
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.679] _tensor_mul_scalar.928 @ctx.addr=0xaaaaec7b28a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_928(
- %para3074 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3075 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3074, %para3075) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.680] _tensor_add_tensor.929 @ctx.addr=0xaaaaec7b2d90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_929(
- %para3076 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3077 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3076, %para3077) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.681] construct.587 @ctx.addr=0xaaaaec7b3520
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_587[fg_0](
- %para3078 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3078, %para528, %para527) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae817f480) #scope: Default @ctx.addr=0xaaaae817f480
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.682] construct.588 @ctx.addr=0xaaaaec02a790
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_588(
- %para3079 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8448690
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_930, FuncGraph::fg_931) #(Bool, Func, Func) # fg_930=✓construct.930(@ctx.addr=0xaaaae84487a0), fg_931=✗construct.931 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae84487a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.683] construct.589 @ctx.addr=0xaaaae1dc8c60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_589[fg_0](
- %para3080 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3080, %para530, %para529) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae1dc8d60) #scope: Default @ctx.addr=0xaaaae1dc8d60
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.684] construct.590 @ctx.addr=0xaaaae1dca220
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_590[fg_0](
- %para3081 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3081, %para532, %para531) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae1dc97d0) #scope: Default @ctx.addr=0xaaaae1dc97d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.685] construct.591 @ctx.addr=0xaaaae85b82b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_591[fg_0](
- %para3082 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3082, %para534, %para533) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae85ba160) #scope: Default @ctx.addr=0xaaaae85ba160
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.686] construct.592 @ctx.addr=0xaaaae8475e70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_592[fg_0](
- %para3083 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3083, %para536, %para535) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae8477c80) #scope: Default @ctx.addr=0xaaaae8477c80
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.687] _tensor_mul_scalar.932 @ctx.addr=0xaaaae85c7d00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_932(
- %para3084 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3085 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3084, %para3085) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.688] _tensor_add_tensor.933 @ctx.addr=0xaaaae85c81f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_933(
- %para3086 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3087 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3086, %para3087) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.689] construct.593 @ctx.addr=0xaaaae85c8980
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_593[fg_0](
- %para3088 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3088, %para538, %para537) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaad741aa60) #scope: Default @ctx.addr=0xaaaad741aa60
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.690] construct.594 @ctx.addr=0xaaaae1f60d10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_594(
- %para3089 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaafae7aa60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_934, FuncGraph::fg_935) #(Bool, Func, Func) # fg_934=✓construct.934(@ctx.addr=0xaaaafae7ab70), fg_935=✗construct.935 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaafae7ab70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.691] construct.595 @ctx.addr=0xaaaae2279370
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_595[fg_0](
- %para3090 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3090, %para540, %para539) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae2279470) #scope: Default @ctx.addr=0xaaaae2279470
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.692] construct.596 @ctx.addr=0xaaaae227a930
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_596[fg_0](
- %para3091 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3091, %para542, %para541) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae2279ee0) #scope: Default @ctx.addr=0xaaaae2279ee0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.693] construct.597 @ctx.addr=0xaaaae1e0eb30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_597[fg_0](
- %para3092 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3092, %para544, %para543) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae1e109e0) #scope: Default @ctx.addr=0xaaaae1e109e0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.694] construct.598 @ctx.addr=0xaaaae83cbf20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_598[fg_0](
- %para3093 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3093, %para546, %para545) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae84f01f0) #scope: Default @ctx.addr=0xaaaae84f01f0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.695] _tensor_mul_scalar.936 @ctx.addr=0xaaaae1febc70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_936(
- %para3094 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3095 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3094, %para3095) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.696] _tensor_add_tensor.937 @ctx.addr=0xaaaae1fec160
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_937(
- %para3096 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3097 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3096, %para3097) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.697] construct.601 @ctx.addr=0xaaaafadcf750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_601[fg_0](
- %para3098 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3098, %para548, %para547) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaafadcf9a0) #scope: Default @ctx.addr=0xaaaafadcf9a0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.698] construct.602 @ctx.addr=0xaaaaf1f601b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_602(
- %para3099 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf1659d70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_938, FuncGraph::fg_939) #(Bool, Func, Func) # fg_938=✓construct.938(@ctx.addr=0xaaaaf1659e80), fg_939=✗construct.939 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf1659e80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.699] construct.603 @ctx.addr=0xaaaae2680550
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_603[fg_0](
- %para3100 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3100, %para550, %para549) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae2680650) #scope: Default @ctx.addr=0xaaaae2680650
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.700] construct.604 @ctx.addr=0xaaaae2681b10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_604[fg_0](
- %para3101 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3101, %para552, %para551) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae26810c0) #scope: Default @ctx.addr=0xaaaae26810c0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.701] construct.605 @ctx.addr=0xaaaae26676a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_605[fg_0](
- %para3102 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3102, %para554, %para553) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae25f5ff0) #scope: Default @ctx.addr=0xaaaae25f5ff0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.702] construct.606 @ctx.addr=0xaaaae2674a80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_606[fg_0](
- %para3103 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3103, %para556, %para555) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae26768d0) #scope: Default @ctx.addr=0xaaaae26768d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.703] _tensor_mul_scalar.940 @ctx.addr=0xaaaae267a970
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_940(
- %para3104 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3105 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3104, %para3105) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.704] _tensor_add_tensor.941 @ctx.addr=0xaaaae267ae60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_941(
- %para3106 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3107 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3106, %para3107) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.705] construct.607 @ctx.addr=0xaaaae267b5c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_607[fg_0](
- %para3108 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3108, %para558, %para557) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae2694710) #scope: Default @ctx.addr=0xaaaae2694710
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.706] construct.608 @ctx.addr=0xaaaae2695bb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_608(
- %para3109 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae25ffa20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_942, FuncGraph::fg_943) #(Bool, Func, Func) # fg_942=✓construct.942(@ctx.addr=0xaaaae25ffb30), fg_943=✗construct.943 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae25ffb30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.707] construct.609 @ctx.addr=0xaaaae26bae60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_609[fg_0](
- %para3110 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3110, %para560, %para559) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae26baf60) #scope: Default @ctx.addr=0xaaaae26baf60
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.708] construct.610 @ctx.addr=0xaaaae26bc420
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_610[fg_0](
- %para3111 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3111, %para562, %para561) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae26bb9d0) #scope: Default @ctx.addr=0xaaaae26bb9d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.709] construct.611 @ctx.addr=0xaaaae272c860
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_611[fg_0](
- %para3112 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3112, %para564, %para563) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae272ed70) #scope: Default @ctx.addr=0xaaaae272ed70
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.710] construct.612 @ctx.addr=0xaaaae2733990
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_612[fg_0](
- %para3113 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3113, %para566, %para565) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae2735e40) #scope: Default @ctx.addr=0xaaaae2735e40
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.711] _tensor_mul_scalar.944 @ctx.addr=0xaaaae2738ab0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_944(
- %para3114 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3115 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3114, %para3115) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.712] _tensor_add_tensor.945 @ctx.addr=0xaaaae27390e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_945(
- %para3116 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3117 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3116, %para3117) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.713] construct.613 @ctx.addr=0xaaaae27398b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_613[fg_0](
- %para3118 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3118, %para568, %para567) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae2742010) #scope: Default @ctx.addr=0xaaaae2742010
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.714] construct.614 @ctx.addr=0xaaaae2743450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_614(
- %para3119 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae274d6a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_946, FuncGraph::fg_947) #(Bool, Func, Func) # fg_946=✓construct.946(@ctx.addr=0xaaaae274d7f0), fg_947=✗construct.947 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae274d7f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.715] construct.615 @ctx.addr=0xaaaae275bcb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_615[fg_0](
- %para3120 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3120, %para570, %para569) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae275bdb0) #scope: Default @ctx.addr=0xaaaae275bdb0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.716] construct.616 @ctx.addr=0xaaaae275d270
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_616[fg_0](
- %para3121 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3121, %para572, %para571) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae275c820) #scope: Default @ctx.addr=0xaaaae275c820
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.717] construct.617 @ctx.addr=0xaaaae2773c20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_617[fg_0](
- %para3122 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3122, %para574, %para573) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae27761b0) #scope: Default @ctx.addr=0xaaaae27761b0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.718] construct.618 @ctx.addr=0xaaaae277ad70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_618[fg_0](
- %para3123 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3123, %para576, %para575) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae277d2a0) #scope: Default @ctx.addr=0xaaaae277d2a0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.719] _tensor_mul_scalar.948 @ctx.addr=0xaaaae277fe90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_948(
- %para3124 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3125 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3124, %para3125) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.720] _tensor_add_tensor.949 @ctx.addr=0xaaaae27804c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_949(
- %para3126 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3127 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3126, %para3127) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.721] construct.621 @ctx.addr=0xaaaae27cb570
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_621[fg_0](
- %para3128 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3128, %para578, %para577) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae27cb800) #scope: Default @ctx.addr=0xaaaae27cb800
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.722] construct.622 @ctx.addr=0xaaaae27cbf70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_622(
- %para3129 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae27d7290
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_950, FuncGraph::fg_951) #(Bool, Func, Func) # fg_950=✓construct.950(@ctx.addr=0xaaaae27d73e0), fg_951=✗construct.951 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae27d73e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.723] construct.623 @ctx.addr=0xaaaae27e5330
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_623[fg_0](
- %para3130 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3130, %para580, %para579) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae27e5430) #scope: Default @ctx.addr=0xaaaae27e5430
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.724] construct.624 @ctx.addr=0xaaaae27e68f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_624[fg_0](
- %para3131 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3131, %para582, %para581) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae27e5ea0) #scope: Default @ctx.addr=0xaaaae27e5ea0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.725] construct.625 @ctx.addr=0xaaaae27fd4c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_625[fg_0](
- %para3132 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3132, %para584, %para583) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae27ffad0) #scope: Default @ctx.addr=0xaaaae27ffad0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.726] construct.626 @ctx.addr=0xaaaae28045f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_626[fg_0](
- %para3133 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3133, %para586, %para585) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae2806ba0) #scope: Default @ctx.addr=0xaaaae2806ba0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.727] _tensor_mul_scalar.952 @ctx.addr=0xaaaae2809700
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_952(
- %para3134 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3135 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3134, %para3135) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.728] _tensor_add_tensor.953 @ctx.addr=0xaaaae2809d30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_953(
- %para3136 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3137 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3136, %para3137) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.729] construct.627 @ctx.addr=0xaaaae280a500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_627[fg_0](
- %para3138 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3138, %para588, %para587) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae2812c60) #scope: Default @ctx.addr=0xaaaae2812c60
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.730] construct.628 @ctx.addr=0xaaaae28140a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_628(
- %para3139 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae281e2f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_954, FuncGraph::fg_955) #(Bool, Func, Func) # fg_954=✓construct.954(@ctx.addr=0xaaaae281e440), fg_955=✗construct.955 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae281e440
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.731] construct.629 @ctx.addr=0xaaaae282c6f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_629[fg_0](
- %para3140 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3140, %para590, %para589) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae282c7f0) #scope: Default @ctx.addr=0xaaaae282c7f0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.732] construct.630 @ctx.addr=0xaaaae282dcb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_630[fg_0](
- %para3141 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3141, %para592, %para591) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae282d260) #scope: Default @ctx.addr=0xaaaae282d260
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.733] construct.631 @ctx.addr=0xaaaae2844860
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_631[fg_0](
- %para3142 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3142, %para594, %para593) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae2846ef0) #scope: Default @ctx.addr=0xaaaae2846ef0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.734] construct.632 @ctx.addr=0xaaaae284b9a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_632[fg_0](
- %para3143 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3143, %para596, %para595) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae284dfd0) #scope: Default @ctx.addr=0xaaaae284dfd0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.735] _tensor_mul_scalar.956 @ctx.addr=0xaaaae2850ab0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_956(
- %para3144 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3145 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3144, %para3145) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.736] _tensor_add_tensor.957 @ctx.addr=0xaaaae28510e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_957(
- %para3146 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3147 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3146, %para3147) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.737] construct.633 @ctx.addr=0xaaaae28518b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_633[fg_0](
- %para3148 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3148, %para598, %para597) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae285a010) #scope: Default @ctx.addr=0xaaaae285a010
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.738] construct.634 @ctx.addr=0xaaaae285b450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_634(
- %para3149 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae28656c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_958, FuncGraph::fg_959) #(Bool, Func, Func) # fg_958=✓construct.958(@ctx.addr=0xaaaae2865810), fg_959=✗construct.959 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2865810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.739] construct.635 @ctx.addr=0xaaaae2873a80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_635[fg_0](
- %para3150 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3150, %para600, %para599) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae2873b80) #scope: Default @ctx.addr=0xaaaae2873b80
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.740] construct.636 @ctx.addr=0xaaaae2875040
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_636[fg_0](
- %para3151 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3151, %para602, %para601) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae28745f0) #scope: Default @ctx.addr=0xaaaae28745f0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.741] construct.637 @ctx.addr=0xaaaae288bc20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_637[fg_0](
- %para3152 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3152, %para604, %para603) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae288e330) #scope: Default @ctx.addr=0xaaaae288e330
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.742] construct.638 @ctx.addr=0xaaaae2892d50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_638[fg_0](
- %para3153 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3153, %para606, %para605) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae2895400) #scope: Default @ctx.addr=0xaaaae2895400
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.743] _tensor_mul_scalar.960 @ctx.addr=0xaaaae2897e60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_960(
- %para3154 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3155 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3154, %para3155) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.744] _tensor_add_tensor.961 @ctx.addr=0xaaaae2898490
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_961(
- %para3156 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3157 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3156, %para3157) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.745] construct.641 @ctx.addr=0xaaaae28e1d80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_641[fg_0](
- %para3158 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3158, %para608, %para607) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae28e2010) #scope: Default @ctx.addr=0xaaaae28e2010
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.746] construct.642 @ctx.addr=0xaaaae28e2780
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_642(
- %para3159 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae28ed910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_962, FuncGraph::fg_963) #(Bool, Func, Func) # fg_962=✓construct.962(@ctx.addr=0xaaaae28eda60), fg_963=✗construct.963 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae28eda60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.747] construct.643 @ctx.addr=0xaaaae28fb9b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_643[fg_0](
- %para3160 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3160, %para610, %para609) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae28fbab0) #scope: Default @ctx.addr=0xaaaae28fbab0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.748] construct.644 @ctx.addr=0xaaaae28fcf70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_644[fg_0](
- %para3161 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3161, %para612, %para611) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae28fc520) #scope: Default @ctx.addr=0xaaaae28fc520
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.749] construct.645 @ctx.addr=0xaaaae2913b20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_645[fg_0](
- %para3162 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3162, %para614, %para613) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae29162b0) #scope: Default @ctx.addr=0xaaaae29162b0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.750] construct.646 @ctx.addr=0xaaaae291ac50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_646[fg_0](
- %para3163 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3163, %para616, %para615) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae291d380) #scope: Default @ctx.addr=0xaaaae291d380
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.751] _tensor_mul_scalar.964 @ctx.addr=0xaaaae291fd60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_964(
- %para3164 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3165 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3164, %para3165) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.752] _tensor_add_tensor.965 @ctx.addr=0xaaaae2920390
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_965(
- %para3166 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3167 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3166, %para3167) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.753] construct.647 @ctx.addr=0xaaaae2920b60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_647[fg_0](
- %para3168 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3168, %para618, %para617) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae29292c0) #scope: Default @ctx.addr=0xaaaae29292c0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.754] construct.648 @ctx.addr=0xaaaae292a700
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_648(
- %para3169 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae29349a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_966, FuncGraph::fg_967) #(Bool, Func, Func) # fg_966=✓construct.966(@ctx.addr=0xaaaae2934af0), fg_967=✗construct.967 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2934af0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.755] construct.649 @ctx.addr=0xaaaae2942d60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_649[fg_0](
- %para3170 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3170, %para620, %para619) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae2942e60) #scope: Default @ctx.addr=0xaaaae2942e60
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.756] construct.650 @ctx.addr=0xaaaae2944320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_650[fg_0](
- %para3171 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3171, %para622, %para621) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae29438d0) #scope: Default @ctx.addr=0xaaaae29438d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.757] construct.651 @ctx.addr=0xaaaae295aee0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_651[fg_0](
- %para3172 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3172, %para624, %para623) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae295d6f0) #scope: Default @ctx.addr=0xaaaae295d6f0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.758] construct.652 @ctx.addr=0xaaaae29629b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_652[fg_0](
- %para3173 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3173, %para626, %para625) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae2964800) #scope: Default @ctx.addr=0xaaaae2964800
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.759] _tensor_mul_scalar.968 @ctx.addr=0xaaaae2968110
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_968(
- %para3174 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3175 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3174, %para3175) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.760] _tensor_add_tensor.969 @ctx.addr=0xaaaae2968600
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_969(
- %para3176 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3177 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3176, %para3177) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.761] construct.653 @ctx.addr=0xaaaae2968d90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_653[fg_0](
- %para3178 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3178, %para628, %para627) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae36ff8d0) #scope: Default @ctx.addr=0xaaaae36ff8d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.762] construct.654 @ctx.addr=0xaaaae3700d10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_654(
- %para3179 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae370af50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_970, FuncGraph::fg_971) #(Bool, Func, Func) # fg_970=✓construct.970(@ctx.addr=0xaaaae370b0a0), fg_971=✗construct.971 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae370b0a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.763] construct.655 @ctx.addr=0xaaaae3719350
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_655[fg_0](
- %para3180 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3180, %para630, %para629) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae3719450) #scope: Default @ctx.addr=0xaaaae3719450
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.764] construct.656 @ctx.addr=0xaaaae371a910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_656[fg_0](
- %para3181 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3181, %para632, %para631) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae3719ec0) #scope: Default @ctx.addr=0xaaaae3719ec0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.765] construct.657 @ctx.addr=0xaaaae3731500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_657[fg_0](
- %para3182 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3182, %para634, %para633) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae3733d90) #scope: Default @ctx.addr=0xaaaae3733d90
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.766] construct.658 @ctx.addr=0xaaaae3738640
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_658[fg_0](
- %para3183 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3183, %para636, %para635) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae373ae70) #scope: Default @ctx.addr=0xaaaae373ae70
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.767] _tensor_mul_scalar.972 @ctx.addr=0xaaaae373d750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_972(
- %para3184 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3185 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3184, %para3185) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.768] _tensor_add_tensor.973 @ctx.addr=0xaaaae373dd80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_973(
- %para3186 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3187 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3186, %para3187) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.769] construct.661 @ctx.addr=0xaaaae3785dc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_661[fg_0](
- %para3188 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3188, %para638, %para637) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae3786050) #scope: Default @ctx.addr=0xaaaae3786050
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.770] construct.662 @ctx.addr=0xaaaae3786820
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_662(
- %para3189 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae3791a40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_974, FuncGraph::fg_975) #(Bool, Func, Func) # fg_974=✓construct.974(@ctx.addr=0xaaaae3791b90), fg_975=✗construct.975 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae3791b90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.771] construct.663 @ctx.addr=0xaaaae379fae0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_663[fg_0](
- %para3190 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3190, %para640, %para639) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae379fbe0) #scope: Default @ctx.addr=0xaaaae379fbe0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.772] construct.664 @ctx.addr=0xaaaae37a10a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_664[fg_0](
- %para3191 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3191, %para642, %para641) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae37a0650) #scope: Default @ctx.addr=0xaaaae37a0650
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.773] construct.665 @ctx.addr=0xaaaae37b7c50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_665[fg_0](
- %para3192 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3192, %para644, %para643) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae37bb260) #scope: Default @ctx.addr=0xaaaae37bb260
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.774] construct.666 @ctx.addr=0xaaaae37bfa70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_666[fg_0](
- %para3193 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3193, %para646, %para645) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae37c2320) #scope: Default @ctx.addr=0xaaaae37c2320
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.775] _tensor_mul_scalar.976 @ctx.addr=0xaaaae37c4b80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_976(
- %para3194 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3195 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3194, %para3195) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.776] _tensor_add_tensor.977 @ctx.addr=0xaaaae37c51b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_977(
- %para3196 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3197 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3196, %para3197) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.777] construct.667 @ctx.addr=0xaaaae37c5980
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_667[fg_0](
- %para3198 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3198, %para648, %para647) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae37ce0e0) #scope: Default @ctx.addr=0xaaaae37ce0e0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.778] construct.668 @ctx.addr=0xaaaae37cf520
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_668(
- %para3199 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae37d9760
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_978, FuncGraph::fg_979) #(Bool, Func, Func) # fg_978=✓construct.978(@ctx.addr=0xaaaae37d98b0), fg_979=✗construct.979 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae37d98b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.779] construct.669 @ctx.addr=0xaaaae37e7b60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_669[fg_0](
- %para3200 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3200, %para650, %para649) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae37e7c60) #scope: Default @ctx.addr=0xaaaae37e7c60
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.780] construct.670 @ctx.addr=0xaaaae37e9120
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_670[fg_0](
- %para3201 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3201, %para652, %para651) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae37e86d0) #scope: Default @ctx.addr=0xaaaae37e86d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.781] construct.671 @ctx.addr=0xaaaae37ffcf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_671[fg_0](
- %para3202 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3202, %para654, %para653) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae3802680) #scope: Default @ctx.addr=0xaaaae3802680
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.782] construct.672 @ctx.addr=0xaaaae3806e20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_672[fg_0](
- %para3203 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3203, %para656, %para655) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae3809750) #scope: Default @ctx.addr=0xaaaae3809750
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.783] _tensor_mul_scalar.980 @ctx.addr=0xaaaae380bf30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_980(
- %para3204 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3205 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3204, %para3205) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.784] _tensor_add_tensor.981 @ctx.addr=0xaaaae380c560
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_981(
- %para3206 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3207 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3206, %para3207) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.785] construct.673 @ctx.addr=0xaaaae380cd30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_673[fg_0](
- %para3208 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3208, %para658, %para657) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae3815490) #scope: Default @ctx.addr=0xaaaae3815490
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.786] construct.674 @ctx.addr=0xaaaae38168d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_674(
- %para3209 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae3820b10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_982, FuncGraph::fg_983) #(Bool, Func, Func) # fg_982=✓construct.982(@ctx.addr=0xaaaae3820c60), fg_983=✗construct.983 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae3820c60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.787] construct.675 @ctx.addr=0xaaaae382ef10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_675[fg_0](
- %para3210 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3210, %para660, %para659) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae382f010) #scope: Default @ctx.addr=0xaaaae382f010
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.788] construct.676 @ctx.addr=0xaaaae38304d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_676[fg_0](
- %para3211 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3211, %para662, %para661) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae382fa80) #scope: Default @ctx.addr=0xaaaae382fa80
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.789] construct.677 @ctx.addr=0xaaaae3847080
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_677[fg_0](
- %para3212 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3212, %para664, %para663) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae3849a90) #scope: Default @ctx.addr=0xaaaae3849a90
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.790] construct.678 @ctx.addr=0xaaaae384e1b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_678[fg_0](
- %para3213 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3213, %para666, %para665) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae3850b60) #scope: Default @ctx.addr=0xaaaae3850b60
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.791] _tensor_mul_scalar.984 @ctx.addr=0xaaaae38532c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_984(
- %para3214 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3215 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3214, %para3215) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.792] _tensor_add_tensor.985 @ctx.addr=0xaaaae38538f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_985(
- %para3216 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3217 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3216, %para3217) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.793] construct.681 @ctx.addr=0xaaaae389a0a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_681[fg_0](
- %para3218 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3218, %para668, %para667) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae389a330) #scope: Default @ctx.addr=0xaaaae389a330
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.794] construct.682 @ctx.addr=0xaaaae389ab00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_682(
- %para3219 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae38a5d80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_986, FuncGraph::fg_987) #(Bool, Func, Func) # fg_986=✓construct.986(@ctx.addr=0xaaaae38a5ed0), fg_987=✗construct.987 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae38a5ed0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.795] construct.683 @ctx.addr=0xaaaae38b3e20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_683[fg_0](
- %para3220 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3220, %para670, %para669) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae38b3f20) #scope: Default @ctx.addr=0xaaaae38b3f20
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.796] construct.684 @ctx.addr=0xaaaae38b53e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_684[fg_0](
- %para3221 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3221, %para672, %para671) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae38b4990) #scope: Default @ctx.addr=0xaaaae38b4990
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.797] construct.685 @ctx.addr=0xaaaae38cbfb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_685[fg_0](
- %para3222 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3222, %para674, %para673) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae38cea40) #scope: Default @ctx.addr=0xaaaae38cea40
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.798] construct.686 @ctx.addr=0xaaaae38d30e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_686[fg_0](
- %para3223 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3223, %para676, %para675) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae38d5b10) #scope: Default @ctx.addr=0xaaaae38d5b10
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.799] _tensor_mul_scalar.988 @ctx.addr=0xaaaae38d81f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_988(
- %para3224 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3225 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3224, %para3225) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.800] _tensor_add_tensor.989 @ctx.addr=0xaaaae38d8820
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_989(
- %para3226 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3227 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3226, %para3227) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.801] construct.687 @ctx.addr=0xaaaae38d8ff0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_687[fg_0](
- %para3228 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3228, %para678, %para677) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae38e1750) #scope: Default @ctx.addr=0xaaaae38e1750
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.802] construct.688 @ctx.addr=0xaaaae38e2b90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_688(
- %para3229 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae38ecdd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_990, FuncGraph::fg_991) #(Bool, Func, Func) # fg_990=✓construct.990(@ctx.addr=0xaaaae38ecf20), fg_991=✗construct.991 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae38ecf20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.803] construct.689 @ctx.addr=0xaaaae38fb1d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_689[fg_0](
- %para3230 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3230, %para680, %para679) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae38fb2d0) #scope: Default @ctx.addr=0xaaaae38fb2d0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.804] construct.690 @ctx.addr=0xaaaae38fc790
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_690[fg_0](
- %para3231 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3231, %para682, %para681) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae38fbd40) #scope: Default @ctx.addr=0xaaaae38fbd40
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.805] construct.691 @ctx.addr=0xaaaae3913350
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_691[fg_0](
- %para3232 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3232, %para684, %para683) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae3915e60) #scope: Default @ctx.addr=0xaaaae3915e60
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.806] construct.692 @ctx.addr=0xaaaae391a480
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_692[fg_0](
- %para3233 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3233, %para686, %para685) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae391cf30) #scope: Default @ctx.addr=0xaaaae391cf30
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.807] _tensor_mul_scalar.992 @ctx.addr=0xaaaae391f590
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_992(
- %para3234 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3235 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3234, %para3235) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.808] _tensor_add_tensor.993 @ctx.addr=0xaaaae391fbc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_993(
- %para3236 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3237 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3236, %para3237) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.809] construct.693 @ctx.addr=0xaaaae3920390
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_693[fg_0](
- %para3238 : Tensor(F32)[16, 64, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_717(%para3238, %para688, %para687) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 64, 3, 3]) # fg_717=L-construct.717(@ctx.addr=0xaaaae3928af0) #scope: Default @ctx.addr=0xaaaae3928af0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.810] construct.694 @ctx.addr=0xaaaae3929f30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(325)/ def construct(self, x):/
- funcgraph fg_694(
- %para3239 : Tensor(F32)[16, 32, 32, 32] # x
- ) {
- %1 : Bool = DoSignaturePrimitive::S-Prim-less_equal{prim_type=1}(F32(0.2), I64(1)) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae39341d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %2 : Bool = FuncGraph::fg_29(%1) #(Bool) # fg_29=bool_.29(@ctx.addr=0xaaaadc76ea90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76ea90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %3 : Func = Primitive::Switch{prim_type=1}(%2, FuncGraph::fg_994, FuncGraph::fg_995) #(Bool, Func, Func) # fg_994=✓construct.994(@ctx.addr=0xaaaae3934320), fg_995=✗construct.995 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- %4 : Tensor(F32)[16, 32, 32, 32] = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae3934320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%4) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.811] construct.695 @ctx.addr=0xaaaae3942590
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_695[fg_0](
- %para3240 : Tensor(F32)[16, 96, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_720(%para3240, %para690, %para689) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 96, 3, 3]) # fg_720=L-construct.720(@ctx.addr=0xaaaae3942690) #scope: Default @ctx.addr=0xaaaae3942690
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.812] construct.696 @ctx.addr=0xaaaae3943b30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_696[fg_0](
- %para3241 : Tensor(F32)[16, 128, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_721(%para3241, %para692, %para691) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 128, 3, 3]) # fg_721=L-construct.721(@ctx.addr=0xaaaae39430e0) #scope: Default @ctx.addr=0xaaaae39430e0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.813] construct.697 @ctx.addr=0xaaaae395a670
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_697[fg_0](
- %para3242 : Tensor(F32)[16, 160, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_722(%para3242, %para694, %para693) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32], Ref[Tensor(F32)][32, 160, 3, 3]) # fg_722=L-construct.722(@ctx.addr=0xaaaae395d200) #scope: Default @ctx.addr=0xaaaae395d200
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.814] construct.698 @ctx.addr=0xaaaae39617b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_698[fg_0](
- %para3243 : Tensor(F32)[16, 192, 32, 32] # x
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_723(%para3243, %para696, %para695) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64], Ref[Tensor(F32)][64, 192, 3, 3]) # fg_723=L-construct.723(@ctx.addr=0xaaaae39642e0) #scope: Default @ctx.addr=0xaaaae39642e0
- #
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.815] _tensor_mul_scalar.996 @ctx.addr=0xaaaae3966930
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(65)/def _tensor_mul_scalar(x, y):/
- funcgraph fg_996(
- %para3244 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3245 : F32 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3244, %para3245) #(Tensor(F32)[16, 64, 32, 32], F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(72)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.816] _tensor_add_tensor.997 @ctx.addr=0xaaaae3966f60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(183)/def _tensor_add_tensor(x, y):/
- funcgraph fg_997(
- %para3246 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3247 : Tensor(F32)[16, 64, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "add") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2(%para3246, %para3247) #(Tensor(F32)[16, 64, 32, 32], Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/add_impl.py(194)/ return F.add(x, y)/
- }
-
-
- # [No.817] hasnext.701 @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(490)/def hasnext(it):/
- funcgraph fg_701(
- %para3248 : Tuple[] # it
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para3248, "__ms_hasnext__") #(Tuple[], String) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(492)/ return it.__ms_hasnext__()/
- %2 : Bool = %1() #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(492)/ return it.__ms_hasnext__()/
- Primitive::Return{prim_type=1}(%2) #(Bool) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(492)/ return it.__ms_hasnext__()/
- }
-
-
- # [No.818] ⥁✓construct.702 @ctx.addr=0xaaaaf4c92850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*22]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*23]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*22] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*22]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae2090460
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*22]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae2064b30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaebd8cc70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*22], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaaef9b2fd0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaef9b2fd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.819] L-↓construct.704 @ctx.addr=0xaaaae39f9890
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_704(
- %para3249 : Tensor(F32)[16, 64, 128, 128] # Φoutput
- ) {
- Primitive::Return{prim_type=1}(%para3249) #(Tensor(F32)[16, 64, 128, 128]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/HRconv-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(261)/ return output/
- }
-
-
- # [No.820] _tuple_getitem_by_number.999 @ctx.addr=0xaaaae3a578a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_999(
- %para3250 : Tuple[Tensor(F32)*5] # data
- , %para3251 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tensor(F32)[16, 64, 64, 64] = %2(%para3250, %para3251) #(Tuple[Tensor(F32)*5], I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.821] L-✓↓construct.707 @ctx.addr=0xaaaae3a7e910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- funcgraph fg_707[fg_49](
- ) {
- %1 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-BatchNorm{prim_type=1}[epsilon=F32(1e-05), momentum=F32(0.1), output_names=["y", "batch_mean", "batch_variance", "reserve_space_1", "reserve_space_2"], format="NCHW", input_names=["x", "scale", "offset", "mean", "variance"], is_training=Bool(1)](%para2246, %para2247, %para2248, %para2249, %para2250) #(Tensor(F32)[16, 128, 64, 64], Ref[Tensor(F32)][128], Ref[Tensor(F32)][128], Ref[Tensor(F32)][128], Ref[Tensor(F32)][128]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- %2 : Tensor(F32)[16, 128, 64, 64] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Tensor(F32)*5], I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d @ctx.addr=0xaaaae3a81ec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- Primitive::Return{prim_type=1}(%2) #(Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- }
-
-
- # [No.822] L-✓↓construct.707 @ctx.addr=0xaaaae3aa33f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- funcgraph fg_707[fg_49](
- ) {
- %1 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-BatchNorm{prim_type=1}[epsilon=F32(1e-05), momentum=F32(0.1), output_names=["y", "batch_mean", "batch_variance", "reserve_space_1", "reserve_space_2"], format="NCHW", input_names=["x", "scale", "offset", "mean", "variance"], is_training=Bool(1)](%para2246, %para2247, %para2248, %para2249, %para2250) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][128], Ref[Tensor(F32)][128], Ref[Tensor(F32)][128], Ref[Tensor(F32)][128]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- %2 : Tensor(F32)[16, 128, 32, 32] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Tensor(F32)*5], I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d @ctx.addr=0xaaaae3aa5ca0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- Primitive::Return{prim_type=1}(%2) #(Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn1_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- }
-
-
- # [No.822] L-✓↓construct.709 @ctx.addr=0xaaaae3acb5a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- funcgraph fg_709[fg_54](
- ) {
- %1 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-BatchNorm{prim_type=1}[epsilon=F32(1e-05), momentum=F32(0.1), output_names=["y", "batch_mean", "batch_variance", "reserve_space_1", "reserve_space_2"], format="NCHW", input_names=["x", "scale", "offset", "mean", "variance"], is_training=Bool(1)](%para2260, %para2261, %para2262, %para2263, %para2264) #(Tensor(F32)[16, 256, 32, 32], Ref[Tensor(F32)][256], Ref[Tensor(F32)][256], Ref[Tensor(F32)][256], Ref[Tensor(F32)][256]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- %2 : Tensor(F32)[16, 256, 32, 32] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Tensor(F32)*5], I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d @ctx.addr=0xaaaae3aceb50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- Primitive::Return{prim_type=1}(%2) #(Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- }
-
-
- # [No.823] L-✓↓construct.709 @ctx.addr=0xaaaae3aeffe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- funcgraph fg_709[fg_54](
- ) {
- %1 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-BatchNorm{prim_type=1}[epsilon=F32(1e-05), momentum=F32(0.1), output_names=["y", "batch_mean", "batch_variance", "reserve_space_1", "reserve_space_2"], format="NCHW", input_names=["x", "scale", "offset", "mean", "variance"], is_training=Bool(1)](%para2260, %para2261, %para2262, %para2263, %para2264) #(Tensor(F32)[16, 256, 16, 16], Ref[Tensor(F32)][256], Ref[Tensor(F32)][256], Ref[Tensor(F32)][256], Ref[Tensor(F32)][256]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- %2 : Tensor(F32)[16, 256, 16, 16] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Tensor(F32)*5], I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d @ctx.addr=0xaaaae3af2890
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- Primitive::Return{prim_type=1}(%2) #(Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn2_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- }
-
-
- # [No.823] L-✓↓construct.711 @ctx.addr=0xaaaae3b18350
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- funcgraph fg_711[fg_59](
- ) {
- %1 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-BatchNorm{prim_type=1}[epsilon=F32(1e-05), momentum=F32(0.1), output_names=["y", "batch_mean", "batch_variance", "reserve_space_1", "reserve_space_2"], format="NCHW", input_names=["x", "scale", "offset", "mean", "variance"], is_training=Bool(1)](%para2274, %para2275, %para2276, %para2277, %para2278) #(Tensor(F32)[16, 512, 16, 16], Ref[Tensor(F32)][512], Ref[Tensor(F32)][512], Ref[Tensor(F32)][512], Ref[Tensor(F32)][512]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- %2 : Tensor(F32)[16, 512, 16, 16] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Tensor(F32)*5], I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d @ctx.addr=0xaaaae3b1b900
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- Primitive::Return{prim_type=1}(%2) #(Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- }
-
-
- # [No.824] L-✓↓construct.711 @ctx.addr=0xaaaae3b444b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(190)/ if flag:/
- funcgraph fg_711[fg_59](
- ) {
- %1 : Tuple[Tensor(F32)*5] = DoSignaturePrimitive::S-Prim-BatchNorm{prim_type=1}[epsilon=F32(1e-05), momentum=F32(0.1), output_names=["y", "batch_mean", "batch_variance", "reserve_space_1", "reserve_space_2"], format="NCHW", input_names=["x", "scale", "offset", "mean", "variance"], is_training=Bool(1)](%para2274, %para2275, %para2276, %para2277, %para2278) #(Tensor(F32)[16, 512, 8, 8], Ref[Tensor(F32)][512], Ref[Tensor(F32)][512], Ref[Tensor(F32)][512], Ref[Tensor(F32)][512]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- %2 : Tensor(F32)[16, 512, 8, 8] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Tensor(F32)*5], I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d @ctx.addr=0xaaaae3b46d60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- Primitive::Return{prim_type=1}(%2) #(Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn3_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/normalization.py(191)/ return self.bn_train(x,/
- }
-
-
- # [No.824] L-bool_.139 @ctx.addr=0xaaaae3b86280
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(530)/def bool_(x):/
- funcgraph fg_139(
- %para3252 : Bool # x
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para2371, "__bool__") #(Bool, String) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- %2 : Bool = %1() #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- Primitive::Return{prim_type=1}(%2) #(Bool) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- }
-
-
- # [No.824] L-✓↓construct.714 @ctx.addr=0xaaaae3b86510
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(304)/ if self.has_bias:/
- funcgraph fg_714[fg_238](
- ) {
- %1 : Tensor(F32)[16, 100] = DoSignaturePrimitive::S-Prim-MatMul{prim_type=1}[input_names=["x1", "x2"], output_names=["output"], transpose_a=Bool(0), transpose_x2=Bool(1), transpose_x1=Bool(0), transpose_b=Bool(1)](%para2555, %para2293) #(Tensor(F32)[16, 32768], Ref[Tensor(F32)][100, 32768]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(303)/ x = self.matmul(x, self.weight)/
- %2 : Tensor(F32)[16, 100] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para2292) #(Tensor(F32)[16, 100], Ref[Tensor(F32)][100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(305)/ x = self.bias_add(x, self.bias)/
- %3 : Tensor(F32)[16, 100] = FuncGraph::fg_1000(%2) #(Tensor(F32)[16, 100]) # fg_1000=L-↓↓construct.1000(@ctx.addr=0xaaaae3b87860) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b87860
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(304)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(304)/ if self.has_bias:/
- }
-
-
- # [No.825] L-✓↓construct.714 @ctx.addr=0xaaaae3bc4d90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(304)/ if self.has_bias:/
- funcgraph fg_714[fg_238](
- ) {
- %1 : Tensor(F32)[16, 1] = DoSignaturePrimitive::S-Prim-MatMul{prim_type=1}[input_names=["x1", "x2"], output_names=["output"], transpose_a=Bool(0), transpose_x2=Bool(1), transpose_x1=Bool(0), transpose_b=Bool(1)](%para2555, %para2293) #(Tensor(F32)[16, 100], Ref[Tensor(F32)][1, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(303)/ x = self.matmul(x, self.weight)/
- %2 : Tensor(F32)[16, 1] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para2292) #(Tensor(F32)[16, 1], Ref[Tensor(F32)][1]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(305)/ x = self.bias_add(x, self.bias)/
- %3 : Tensor(F32)[16, 1] = FuncGraph::fg_1000(%2) #(Tensor(F32)[16, 1]) # fg_1000=L-↓↓construct.1000(@ctx.addr=0xaaaae3bca620) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bca620
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(304)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 1]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(304)/ if self.has_bias:/
- }
-
-
- # [No.825] L-construct.717 @ctx.addr=0xaaaaf548ee70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3253 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3254 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3255 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae24b00f0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae24b00f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.826] _less_equal_scala.1004 @ctx.addr=0xaaaafadafac0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1004(
- %para3256 : F32 # x
- , %para3257 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3256, %para3257) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.827] ✓construct.718 @ctx.addr=0xaaaaf384db10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_718[fg_242](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2559) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2559) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae21c3240
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2559) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1005(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1005=↓construct.1005(@ctx.addr=0xaaaae23f21d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae23f21d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.828] L-construct.720 @ctx.addr=0xaaaae855a410
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3258 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3259 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3260 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae846eef0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae846eef0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.829] L-construct.721 @ctx.addr=0xaaaae242b2f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3261 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3262 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3263 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae23b6280), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae23b6280
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.830] L-construct.722 @ctx.addr=0xaaaafade31d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3264 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3265 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3266 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaefe277b0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaefe277b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.831] L-construct.723 @ctx.addr=0xaaaaebc17b40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3267 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3268 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3269 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaf4dd6b50), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf4dd6b50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.832] L-construct.717 @ctx.addr=0xaaaad95fa5e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3270 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3271 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3272 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaad61f0b50), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaad61f0b50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.832] _less_equal_scala.1018 @ctx.addr=0xaaaad81ada90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1018(
- %para3273 : F32 # x
- , %para3274 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3273, %para3274) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.833] ✓construct.726 @ctx.addr=0xaaaae98affc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_726[fg_248](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2569) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2569) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc77e2c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2569) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1019(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1019=↓construct.1019(@ctx.addr=0xaaaadc76fb80) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc76fb80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.834] L-construct.720 @ctx.addr=0xaaaad5b77380
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3275 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3276 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3277 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaaebd8f2b0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaaebd8f2b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.834] L-construct.721 @ctx.addr=0xaaaae242a330
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3278 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3279 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3280 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaafae3cf80), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaafae3cf80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.834] L-construct.722 @ctx.addr=0xaaaad8a4a8b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3281 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3282 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3283 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaade47afb0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaade47afb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.834] L-construct.723 @ctx.addr=0xaaaaf1defd70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3284 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3285 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3286 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaedb24610), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaedb24610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.834] L-construct.717 @ctx.addr=0xaaaae5708110
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3287 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3288 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3289 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae2217040), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae2217040
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.834] _less_equal_scala.1020 @ctx.addr=0xaaaaf43fc0a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1020(
- %para3290 : F32 # x
- , %para3291 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3290, %para3291) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.835] ✓construct.730 @ctx.addr=0xaaaad6044630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_730[fg_254](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2579) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2579) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaafa4d6300
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2579) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1021(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1021=↓construct.1021(@ctx.addr=0xaaaae8454cd0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8454cd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.836] L-construct.720 @ctx.addr=0xaaaaee8050b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3292 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3293 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3294 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae82b1f50), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae82b1f50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.836] L-construct.721 @ctx.addr=0xaaaaee400a10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3295 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3296 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3297 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae83c4030), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae83c4030
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.836] L-construct.722 @ctx.addr=0xaaaaf072d9b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3298 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3299 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3300 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaf0470160), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaf0470160
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.836] L-construct.723 @ctx.addr=0xaaaaf083bcc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3301 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3302 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3303 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae8d8a240), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae8d8a240
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.836] L-construct.717 @ctx.addr=0xaaaae85aecc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3304 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3305 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3306 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae8504e50), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae8504e50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.836] _less_equal_scala.1022 @ctx.addr=0xaaaaf6dbdef0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1022(
- %para3307 : F32 # x
- , %para3308 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3307, %para3308) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.837] ✓construct.734 @ctx.addr=0xaaaaea7d9a50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_734[fg_262](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2589) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2589) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf4dda610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2589) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1023(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1023=↓construct.1023(@ctx.addr=0xaaaaf4c92fc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf4c92fc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.838] L-construct.720 @ctx.addr=0xaaaad699bc80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3309 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3310 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3311 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaad6a0c6e0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaad6a0c6e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.838] L-construct.721 @ctx.addr=0xaaaaea2d34d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3312 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3313 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3314 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae837ea20), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae837ea20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.838] L-construct.722 @ctx.addr=0xaaaae232f120
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3315 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3316 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3317 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaf58346a0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaf58346a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.838] L-construct.723 @ctx.addr=0xaaaae23c95c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3318 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3319 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3320 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae2238cf0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae2238cf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.838] L-construct.717 @ctx.addr=0xaaaaeeddd610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3321 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3322 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3323 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaaf4a6ed50), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaaf4a6ed50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.838] _less_equal_scala.1024 @ctx.addr=0xaaaae1fce140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1024(
- %para3324 : F32 # x
- , %para3325 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3324, %para3325) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.839] ✓construct.738 @ctx.addr=0xaaaae24fb010
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_738[fg_268](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2599) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2599) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2331780
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2599) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1025(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1025=↓construct.1025(@ctx.addr=0xaaaae123f9b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae123f9b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.840] L-construct.720 @ctx.addr=0xaaaae82b8200
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3326 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3327 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3328 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae82b8ef0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae82b8ef0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.840] L-construct.721 @ctx.addr=0xaaaafae91640
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3329 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3330 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3331 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaafaee6f90), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaafaee6f90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.840] L-construct.722 @ctx.addr=0xaaaade9c9d20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3332 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3333 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3334 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaad669e940), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaad669e940
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.840] L-construct.723 @ctx.addr=0xaaaaf42ee320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3335 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3336 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3337 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae84d3950), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae84d3950
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.840] L-construct.717 @ctx.addr=0xaaaae0eef880
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3338 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3339 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3340 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaaebc15c90), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaaebc15c90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.840] _less_equal_scala.1026 @ctx.addr=0xaaaae22d8130
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1026(
- %para3341 : F32 # x
- , %para3342 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3341, %para3342) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.841] ✓construct.742 @ctx.addr=0xaaaae229e1e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_742[fg_274](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2609) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2609) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1f071d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2609) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1027(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1027=↓construct.1027(@ctx.addr=0xaaaae06c4c80) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae06c4c80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.842] L-construct.720 @ctx.addr=0xaaaae54de930
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3343 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3344 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3345 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaaded24200), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaaded24200
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.842] L-construct.721 @ctx.addr=0xaaaadca67c40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3346 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3347 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3348 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaf2550140), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaf2550140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.842] L-construct.722 @ctx.addr=0xaaaae243a480
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3349 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3350 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3351 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae238ac90), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae238ac90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.842] L-construct.723 @ctx.addr=0xaaaaeeddbce0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3352 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3353 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3354 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaf2115ed0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf2115ed0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.842] L-construct.717 @ctx.addr=0xaaaae831a3a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3355 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3356 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3357 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaafaea0de0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaafaea0de0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.842] _less_equal_scala.1028 @ctx.addr=0xaaaafa5c8140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1028(
- %para3358 : F32 # x
- , %para3359 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3358, %para3359) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.843] ✓construct.746 @ctx.addr=0xaaaaea7d4b00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_746[fg_282](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2619) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2619) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1d926c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2619) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1029(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1029=↓construct.1029(@ctx.addr=0xaaaae6de1910) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae6de1910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.844] L-construct.720 @ctx.addr=0xaaaae6a748f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3360 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3361 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3362 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae5655ee0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae5655ee0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.844] L-construct.721 @ctx.addr=0xaaaadd3277b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3363 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3364 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3365 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaf0c761a0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaf0c761a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.844] L-construct.722 @ctx.addr=0xaaaae070e9c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3366 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3367 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3368 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae1dfa0c0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae1dfa0c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.844] L-construct.723 @ctx.addr=0xaaaaf1d27d00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3369 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3370 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3371 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaf48cd230), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf48cd230
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.844] L-construct.717 @ctx.addr=0xaaaae25b4250
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3372 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3373 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3374 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae238bbf0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae238bbf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.844] _less_equal_scala.1030 @ctx.addr=0xaaaafadf0cb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1030(
- %para3375 : F32 # x
- , %para3376 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3375, %para3376) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.845] ✓construct.750 @ctx.addr=0xaaaafad9d130
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_750[fg_288](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2629) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2629) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad741cfc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2629) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1031(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1031=↓construct.1031(@ctx.addr=0xaaaad6a33160) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad6a33160
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.846] L-construct.720 @ctx.addr=0xaaaafab1c2c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3377 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3378 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3379 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaad5f695a0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaad5f695a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.846] L-construct.721 @ctx.addr=0xaaaae1da1190
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3380 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3381 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3382 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae1dbfa60), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1dbfa60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.846] L-construct.722 @ctx.addr=0xaaaaf3ea0170
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3383 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3384 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3385 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaf2f75020), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaf2f75020
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.846] L-construct.723 @ctx.addr=0xaaaae1f5a8e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3386 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3387 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3388 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae8508a10), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae8508a10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.846] L-construct.717 @ctx.addr=0xaaaade4485a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3389 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3390 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3391 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaade438500), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaade438500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.846] _less_equal_scala.1032 @ctx.addr=0xaaaaf1e6c440
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1032(
- %para3392 : F32 # x
- , %para3393 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3392, %para3393) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.847] ✓construct.754 @ctx.addr=0xaaaaf17c6070
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_754[fg_294](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2639) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2639) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf5833fd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2639) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1033(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1033=↓construct.1033(@ctx.addr=0xaaaaeb92bbe0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaeb92bbe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.848] L-construct.720 @ctx.addr=0xaaaae0f3e820
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3394 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3395 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3396 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaadec89ce0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaadec89ce0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.848] L-construct.721 @ctx.addr=0xaaaad783cd70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3397 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3398 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3399 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae86056a0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae86056a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.848] L-construct.722 @ctx.addr=0xaaaaf03cc390
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3400 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3401 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3402 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaf48caab0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaf48caab0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.848] L-construct.723 @ctx.addr=0xaaaae227dcc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3403 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3404 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3405 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae1ecb6e0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae1ecb6e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.848] L-construct.717 @ctx.addr=0xaaaae210bff0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3406 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3407 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3408 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaad7f58460), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaad7f58460
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.848] _less_equal_scala.1034 @ctx.addr=0xaaaae22565e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1034(
- %para3409 : F32 # x
- , %para3410 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3409, %para3410) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.849] ✓construct.758 @ctx.addr=0xaaaae221c5b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_758[fg_302](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2649) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2649) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae85507f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2649) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1035(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1035=↓construct.1035(@ctx.addr=0xaaaae1ef8fc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1ef8fc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.850] L-construct.720 @ctx.addr=0xaaaaf13a8a60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3411 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3412 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3413 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae20afa10), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae20afa10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.850] L-construct.721 @ctx.addr=0xaaaad720b510
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3414 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3415 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3416 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae232afe0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae232afe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.850] L-construct.722 @ctx.addr=0xaaaae830d500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3417 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3418 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3419 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae6cc16e0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae6cc16e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.850] L-construct.723 @ctx.addr=0xaaaae8352b50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3420 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3421 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3422 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaafaed95e0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaafaed95e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.850] L-construct.717 @ctx.addr=0xaaaadcb9a880
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3423 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3424 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3425 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaaf2eacde0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaaf2eacde0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.850] _less_equal_scala.1036 @ctx.addr=0xaaaae218a8e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1036(
- %para3426 : F32 # x
- , %para3427 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3426, %para3427) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.851] ✓construct.762 @ctx.addr=0xaaaae214d510
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_762[fg_308](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2659) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2659) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae85c2880
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2659) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1037(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1037=↓construct.1037(@ctx.addr=0xaaaae2004450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2004450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.852] L-construct.720 @ctx.addr=0xaaaae24cd2f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3428 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3429 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3430 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae24cd3f0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae24cd3f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.852] L-construct.721 @ctx.addr=0xaaaae242a960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3431 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3432 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3433 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae2490fa0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae2490fa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.852] L-construct.722 @ctx.addr=0xaaaadcc88580
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3434 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3435 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3436 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae6f7be00), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae6f7be00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.852] L-construct.723 @ctx.addr=0xaaaaf1faa100
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3437 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3438 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3439 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaf1dec950), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf1dec950
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.852] L-construct.717 @ctx.addr=0xaaaaee565200
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3440 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3441 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3442 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaad9a2a140), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaad9a2a140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.852] _less_equal_scala.1038 @ctx.addr=0xaaaaefc466d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1038(
- %para3443 : F32 # x
- , %para3444 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3443, %para3444) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.853] ✓construct.766 @ctx.addr=0xaaaad8cbbdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_766[fg_314](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2669) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2669) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf0336340
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2669) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1039(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1039=↓construct.1039(@ctx.addr=0xaaaaec636cd0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaec636cd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.854] L-construct.720 @ctx.addr=0xaaaaf34c30b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3445 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3446 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3447 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaaf0e658a0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaaf0e658a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.854] L-construct.721 @ctx.addr=0xaaaad6a0a120
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3448 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3449 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3450 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaad9252a10), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaad9252a10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.854] L-construct.722 @ctx.addr=0xaaaaeb7ea3b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3451 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3452 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3453 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaf460da40), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaf460da40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.854] L-construct.723 @ctx.addr=0xaaaaee910e20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3454 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3455 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3456 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaf5f9d2e0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf5f9d2e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.854] L-construct.717 @ctx.addr=0xaaaaecfb4970
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3457 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3458 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3459 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaaf70a2a90), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaaf70a2a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.854] _less_equal_scala.1040 @ctx.addr=0xaaaafa5be180
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1040(
- %para3460 : F32 # x
- , %para3461 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3460, %para3461) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.855] ✓construct.770 @ctx.addr=0xaaaae8529bb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_770[fg_322](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2679) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2679) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1013a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2679) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1041(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1041=↓construct.1041(@ctx.addr=0xaaaae2536110) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2536110
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.856] L-construct.720 @ctx.addr=0xaaaae584bdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3462 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3463 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3464 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaaddb2db20), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaaddb2db20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.856] L-construct.721 @ctx.addr=0xaaaafa7bf860
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3465 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3466 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3467 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaea1c1ea0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaea1c1ea0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.856] L-construct.722 @ctx.addr=0xaaaad6699870
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3468 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3469 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3470 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaf2123a70), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaf2123a70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.856] L-construct.723 @ctx.addr=0xaaaae88d3410
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3471 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3472 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3473 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaad5a1dec0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaad5a1dec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.856] L-construct.717 @ctx.addr=0xaaaae1ec9f00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3474 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3475 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3476 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaaf126ccc0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaaf126ccc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.856] _less_equal_scala.1042 @ctx.addr=0xaaaaea1c2850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1042(
- %para3477 : F32 # x
- , %para3478 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3477, %para3478) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.857] ✓construct.774 @ctx.addr=0xaaaae5e5b020
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_774[fg_328](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2689) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2689) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae84872a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2689) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1043(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1043=↓construct.1043(@ctx.addr=0xaaaaf733e510) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf733e510
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.858] L-construct.720 @ctx.addr=0xaaaae82430d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3479 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3480 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3481 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae82431d0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae82431d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.858] L-construct.721 @ctx.addr=0xaaaae1f90a80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3482 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3483 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3484 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaf363d3c0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaf363d3c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.858] L-construct.722 @ctx.addr=0xaaaad6292f20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3485 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3486 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3487 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaf4d0b350), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaf4d0b350
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.858] L-construct.723 @ctx.addr=0xaaaaefd0f8d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3488 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3489 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3490 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae85cccd0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae85cccd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.858] L-construct.717 @ctx.addr=0xaaaaeb608c50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3491 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3492 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3493 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae84a6610), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae84a6610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.858] _less_equal_scala.1044 @ctx.addr=0xaaaaeb6d01a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1044(
- %para3494 : F32 # x
- , %para3495 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3494, %para3495) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.859] ✓construct.778 @ctx.addr=0xaaaaeb6d02b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_778[fg_334](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2699) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2699) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae200ded0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2699) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1045(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1045=↓construct.1045(@ctx.addr=0xaaaae24e76c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae24e76c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.860] L-construct.720 @ctx.addr=0xaaaaecd3d070
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3496 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3497 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3498 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae240f3a0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae240f3a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.860] L-construct.721 @ctx.addr=0xaaaae123bb70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3499 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3500 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3501 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaefbd02f0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaefbd02f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.860] L-construct.722 @ctx.addr=0xaaaafa6eee30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3502 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3503 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3504 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaeb204850), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaeb204850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.860] L-construct.723 @ctx.addr=0xaaaad741c920
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3505 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3506 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3507 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae2111be0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae2111be0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.860] L-construct.717 @ctx.addr=0xaaaaf03d05b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3508 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3509 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3510 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae10f04f0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae10f04f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.860] _less_equal_scala.1046 @ctx.addr=0xaaaae82c0630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1046(
- %para3511 : F32 # x
- , %para3512 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3511, %para3512) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.861] ✓construct.782 @ctx.addr=0xaaaae826bbb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_782[fg_342](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2709) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2709) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1f39eb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2709) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1047(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1047=↓construct.1047(@ctx.addr=0xaaaae208c810) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae208c810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.862] L-construct.720 @ctx.addr=0xaaaaecb0a820
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3513 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3514 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3515 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaad72a9490), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaad72a9490
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.862] L-construct.721 @ctx.addr=0xaaaadc863e70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3516 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3517 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3518 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae2244aa0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae2244aa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.862] L-construct.722 @ctx.addr=0xaaaadd327110
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3519 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3520 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3521 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaef033fe0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaef033fe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.862] L-construct.723 @ctx.addr=0xaaaae1232f00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3522 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3523 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3524 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae837e040), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae837e040
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.862] L-construct.717 @ctx.addr=0xaaaafae0b470
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3525 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3526 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3527 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae1f2bcd0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1f2bcd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.862] _less_equal_scala.1048 @ctx.addr=0xaaaae84f8030
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1048(
- %para3528 : F32 # x
- , %para3529 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3528, %para3529) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.863] ✓construct.786 @ctx.addr=0xaaaae845c970
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_786[fg_348](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2719) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2719) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8308570
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2719) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1049(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1049=↓construct.1049(@ctx.addr=0xaaaad82f82f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad82f82f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.864] L-construct.720 @ctx.addr=0xaaaadec3efb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3530 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3531 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3532 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae5656570), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae5656570
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.864] L-construct.721 @ctx.addr=0xaaaaed0da330
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3533 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3534 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3535 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae1dd4f10), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1dd4f10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.864] L-construct.722 @ctx.addr=0xaaaae822f3d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3536 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3537 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3538 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaef54e090), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaef54e090
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.864] L-construct.723 @ctx.addr=0xaaaae20e5fc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3539 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3540 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3541 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaea9f5da0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaea9f5da0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.864] L-construct.717 @ctx.addr=0xaaaae84f51e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3542 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3543 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3544 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae85093b0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae85093b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.864] _less_equal_scala.1050 @ctx.addr=0xaaaae22e32f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1050(
- %para3545 : F32 # x
- , %para3546 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3545, %para3546) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.865] ✓construct.790 @ctx.addr=0xaaaae22e3440
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_790[fg_354](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2729) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2729) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8568e10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2729) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1051(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1051=↓construct.1051(@ctx.addr=0xaaaae1fba760) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1fba760
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.866] L-construct.720 @ctx.addr=0xaaaae1f93760
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3547 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3548 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3549 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaafae1b2b0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaafae1b2b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.866] L-construct.721 @ctx.addr=0xaaaae1eea430
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3550 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3551 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3552 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaad94298b0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaad94298b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.866] L-construct.722 @ctx.addr=0xaaaaf083d030
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3553 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3554 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3555 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaad9437630), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaad9437630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.866] L-construct.723 @ctx.addr=0xaaaae1ff4640
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3556 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3557 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3558 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae85919b0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae85919b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.866] L-construct.717 @ctx.addr=0xaaaaea1c71e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3559 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3560 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3561 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaad8248910), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaad8248910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.866] _less_equal_scala.1052 @ctx.addr=0xaaaae20badb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1052(
- %para3562 : F32 # x
- , %para3563 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3562, %para3563) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.867] ✓construct.794 @ctx.addr=0xaaaae20baf00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_794[fg_362](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2739) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2739) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae24ad6c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2739) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1053(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1053=↓construct.1053(@ctx.addr=0xaaaae2536a10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2536a10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.868] L-construct.720 @ctx.addr=0xaaaaf2a9f060
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3564 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3565 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3566 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae87ac860), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae87ac860
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.868] L-construct.721 @ctx.addr=0xaaaae935b6f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3567 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3568 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3569 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae7195b70), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae7195b70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.868] L-construct.722 @ctx.addr=0xaaaae213d3c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3570 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3571 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3572 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaedd6d880), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaedd6d880
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.868] L-construct.723 @ctx.addr=0xaaaaf165c610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3573 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3574 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3575 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaf2497350), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf2497350
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.868] L-construct.717 @ctx.addr=0xaaaadec0e4b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3576 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3577 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3578 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaadec0e700), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaadec0e700
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.868] _less_equal_scala.1054 @ctx.addr=0xaaaaf0c348b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1054(
- %para3579 : F32 # x
- , %para3580 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3579, %para3580) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.869] ✓construct.798 @ctx.addr=0xaaaae0ca95d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_798[fg_368](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2749) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2749) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8e93770
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2749) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1055(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1055=↓construct.1055(@ctx.addr=0xaaaae8e93560) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8e93560
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.870] L-construct.720 @ctx.addr=0xaaaae23d58e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3581 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3582 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3583 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae22ba110), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae22ba110
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.870] L-construct.721 @ctx.addr=0xaaaae85d0310
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3584 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3585 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3586 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae851a800), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae851a800
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.870] L-construct.722 @ctx.addr=0xaaaaf072cbf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3587 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3588 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3589 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaade43b8f0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaade43b8f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.870] L-construct.723 @ctx.addr=0xaaaaedd611d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3590 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3591 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3592 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaafae70ae0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaafae70ae0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.870] L-construct.717 @ctx.addr=0xaaaae8613f30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3593 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3594 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3595 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae1dc1270), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1dc1270
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.870] _less_equal_scala.1056 @ctx.addr=0xaaaae82b8810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1056(
- %para3596 : F32 # x
- , %para3597 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3596, %para3597) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.871] ✓construct.802 @ctx.addr=0xaaaae82b8960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_802[fg_374](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2759) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2759) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad6a292f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2759) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1057(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1057=↓construct.1057(@ctx.addr=0xaaaafae92ae0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaafae92ae0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.872] L-construct.720 @ctx.addr=0xaaaad8141840
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3598 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3599 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3600 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae2566480), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae2566480
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.872] L-construct.721 @ctx.addr=0xaaaae24de070
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3601 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3602 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3603 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaf71b5480), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaf71b5480
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.872] L-construct.722 @ctx.addr=0xaaaae1f168d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3604 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3605 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3606 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae243dfa0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae243dfa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.872] L-construct.723 @ctx.addr=0xaaaafadade30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3607 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3608 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3609 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaedde9bb0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaedde9bb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.872] L-construct.717 @ctx.addr=0xaaaaeda6f2a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3610 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3611 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3612 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae1f6d9b0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1f6d9b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.872] _less_equal_scala.1058 @ctx.addr=0xaaaae8571520
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1058(
- %para3613 : F32 # x
- , %para3614 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3613, %para3614) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.873] ✓construct.806 @ctx.addr=0xaaaae8571620
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_806[fg_382](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2769) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2769) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae548d600
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2769) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1059(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1059=↓construct.1059(@ctx.addr=0xaaaaf076ac40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf076ac40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.874] L-construct.720 @ctx.addr=0xaaaae2072780
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3615 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3616 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3617 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae20729d0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae20729d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.874] L-construct.721 @ctx.addr=0xaaaae21993a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3618 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3619 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3620 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaf21c8e00), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaf21c8e00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.874] L-construct.722 @ctx.addr=0xaaaae8629770
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3621 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3622 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3623 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaee563a80), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaee563a80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.874] L-construct.723 @ctx.addr=0xaaaae82c48c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3624 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3625 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3626 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae2598740), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae2598740
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.874] L-construct.717 @ctx.addr=0xaaaafae09210
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3627 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3628 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3629 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae1f29940), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1f29940
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.874] _less_equal_scala.1060 @ctx.addr=0xaaaadccd4ff0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1060(
- %para3630 : F32 # x
- , %para3631 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3630, %para3631) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.875] ✓construct.810 @ctx.addr=0xaaaadccd5140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_810[fg_388](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2779) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2779) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaafadaa110
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2779) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1061(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1061=↓construct.1061(@ctx.addr=0xaaaae22e4c40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae22e4c40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.876] L-construct.720 @ctx.addr=0xaaaae81e12e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3632 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3633 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3634 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae207b2b0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae207b2b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.876] L-construct.721 @ctx.addr=0xaaaae84d7750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3635 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3636 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3637 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae1f80930), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1f80930
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.876] L-construct.722 @ctx.addr=0xaaaaf71156f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3638 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3639 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3640 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaf05e8ac0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaf05e8ac0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.876] L-construct.723 @ctx.addr=0xaaaaf42f4e10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3641 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3642 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3643 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae24ec650), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae24ec650
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.876] L-construct.717 @ctx.addr=0xaaaafa486370
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3644 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3645 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3646 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaaf71fce60), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaaf71fce60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.876] _less_equal_scala.1062 @ctx.addr=0xaaaae23b5d10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1062(
- %para3647 : F32 # x
- , %para3648 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3647, %para3648) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.877] ✓construct.814 @ctx.addr=0xaaaadca71af0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_814[fg_394](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2789) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2789) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc861920
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2789) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1063(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1063=↓construct.1063(@ctx.addr=0xaaaae33399e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae33399e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.878] L-construct.720 @ctx.addr=0xaaaaee8d4bc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3649 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3650 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3651 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaadd113c10), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaadd113c10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.878] L-construct.721 @ctx.addr=0xaaaaf1fa86f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3652 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3653 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3654 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaecb0c2a0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaecb0c2a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.878] L-construct.722 @ctx.addr=0xaaaae123e5a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3655 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3656 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3657 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae8d82970), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae8d82970
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.878] L-construct.723 @ctx.addr=0xaaaaf3854ca0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3658 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3659 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3660 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae8434c40), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae8434c40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.878] L-construct.717 @ctx.addr=0xaaaae591b9c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3661 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3662 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3663 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaadc5a3e80), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaadc5a3e80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.878] _less_equal_scala.1064 @ctx.addr=0xaaaae2351cb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1064(
- %para3664 : F32 # x
- , %para3665 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3664, %para3665) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.879] ✓construct.818 @ctx.addr=0xaaaad60d96f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_818[fg_402](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2799) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2799) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf36da8f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2799) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1065(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1065=↓construct.1065(@ctx.addr=0xaaaae0711050) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae0711050
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.880] L-construct.720 @ctx.addr=0xaaaae97aae20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3666 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3667 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3668 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaad6e43970), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaad6e43970
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.880] L-construct.721 @ctx.addr=0xaaaae21fc610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3669 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3670 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3671 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaf37bc200), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaf37bc200
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.880] L-construct.722 @ctx.addr=0xaaaaed1fd0d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3672 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3673 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3674 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaad68d1fd0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaad68d1fd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.880] L-construct.723 @ctx.addr=0xaaaadec8a1e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3675 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3676 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3677 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaad9b46af0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaad9b46af0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.880] L-construct.717 @ctx.addr=0xaaaae2031920
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3678 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3679 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3680 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae1fdcf10), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1fdcf10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.880] _less_equal_scala.1066 @ctx.addr=0xaaaae1faf250
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1066(
- %para3681 : F32 # x
- , %para3682 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3681, %para3682) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.881] ✓construct.822 @ctx.addr=0xaaaae1e07c00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_822[fg_408](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2809) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2809) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae82b6d50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2809) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1067(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1067=↓construct.1067(@ctx.addr=0xaaaae8409820) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8409820
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.882] L-construct.720 @ctx.addr=0xaaaae84e1fb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3683 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3684 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3685 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae84e2200), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae84e2200
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.882] L-construct.721 @ctx.addr=0xaaaae8437a10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3686 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3687 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3688 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae6ccf2c0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae6ccf2c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.882] L-construct.722 @ctx.addr=0xaaaaf0733aa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3689 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3690 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3691 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaf0733600), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaf0733600
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.882] L-construct.723 @ctx.addr=0xaaaad65ad210
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3692 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3693 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3694 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaded21120), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaded21120
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.882] L-construct.717 @ctx.addr=0xaaaae8296ef0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3695 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3696 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3697 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae8297140), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae8297140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.882] _less_equal_scala.1068 @ctx.addr=0xaaaaee8c4000
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1068(
- %para3698 : F32 # x
- , %para3699 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3698, %para3699) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.883] ✓construct.826 @ctx.addr=0xaaaaee8c4150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_826[fg_414](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2819) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2819) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8737210
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2819) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1069(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1069=↓construct.1069(@ctx.addr=0xaaaae1f3ae40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1f3ae40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.884] L-construct.720 @ctx.addr=0xaaaafae1a0d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3700 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3701 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3702 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaafae1a320), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaafae1a320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.884] L-construct.721 @ctx.addr=0xaaaad5f63fc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3703 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3704 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3705 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae85912b0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae85912b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.884] L-construct.722 @ctx.addr=0xaaaae81d1c40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3706 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3707 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3708 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae2359750), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae2359750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.884] L-construct.723 @ctx.addr=0xaaaafad8b7b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3709 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3710 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3711 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae1fd4320), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae1fd4320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.884] L-construct.717 @ctx.addr=0xaaaadc7ca4a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3712 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3713 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3714 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaaea9f5780), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaaea9f5780
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.884] _less_equal_scala.1070 @ctx.addr=0xaaaaf0deace0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1070(
- %para3715 : F32 # x
- , %para3716 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3715, %para3716) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.885] ✓construct.830 @ctx.addr=0xaaaaf0deae30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_830[fg_422](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2829) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2829) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae0ca5210
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2829) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1071(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1071=↓construct.1071(@ctx.addr=0xaaaaf0c3edf0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf0c3edf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.886] L-construct.720 @ctx.addr=0xaaaaf2f71e00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3717 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3718 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3719 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaaf2f72050), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaaf2f72050
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.886] L-construct.721 @ctx.addr=0xaaaaefbc57b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3720 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3721 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3722 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae9354e30), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae9354e30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.886] L-construct.722 @ctx.addr=0xaaaaef541eb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3723 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3724 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3725 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaea2cc1c0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea2cc1c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.886] L-construct.723 @ctx.addr=0xaaaad6047760
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3726 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3727 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3728 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaf3e9f640), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf3e9f640
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.886] L-construct.717 @ctx.addr=0xaaaae226c9a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3729 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3730 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3731 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae21be320), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae21be320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.886] _less_equal_scala.1072 @ctx.addr=0xaaaae1ef13e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1072(
- %para3732 : F32 # x
- , %para3733 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3732, %para3733) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.887] ✓construct.834 @ctx.addr=0xaaaae1ef1530
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_834[fg_428](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2839) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2839) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1ef17c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2839) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1073(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1073=↓construct.1073(@ctx.addr=0xaaaaf241aa50) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf241aa50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.888] L-construct.720 @ctx.addr=0xaaaae121b090
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3734 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3735 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3736 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae121b190), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae121b190
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.888] L-construct.721 @ctx.addr=0xaaaaebbd70d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3737 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3738 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3739 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaade9c12f0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaade9c12f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.888] L-construct.722 @ctx.addr=0xaaaaf255bd40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3740 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3741 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3742 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaad824cb00), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaad824cb00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.888] L-construct.723 @ctx.addr=0xaaaadceffa60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3743 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3744 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3745 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae24ff480), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae24ff480
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.888] L-construct.717 @ctx.addr=0xaaaafaebf940
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3746 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3747 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3748 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaafaebfb90), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaafaebfb90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.888] _less_equal_scala.1074 @ctx.addr=0xaaaad5a1e8a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1074(
- %para3749 : F32 # x
- , %para3750 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3749, %para3750) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.889] ✓construct.838 @ctx.addr=0xaaaad5a1e9f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_838[fg_434](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2849) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2849) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae82391a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2849) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1075(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1075=↓construct.1075(@ctx.addr=0xaaaae828df70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae828df70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.890] L-construct.720 @ctx.addr=0xaaaae35dd0c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3751 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3752 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3753 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae35dd1c0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae35dd1c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.890] L-construct.721 @ctx.addr=0xaaaaf3858d20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3754 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3755 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3756 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaf03c8300), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaf03c8300
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.890] L-construct.722 @ctx.addr=0xaaaaf2c16d40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3757 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3758 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3759 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae22b5cf0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae22b5cf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.890] L-construct.723 @ctx.addr=0xaaaae85f13a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3760 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3761 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3762 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaebd89530), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaebd89530
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.890] L-construct.717 @ctx.addr=0xaaaaf2554300
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3763 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3764 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3765 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaaf1277310), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaaf1277310
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.890] _less_equal_scala.1076 @ctx.addr=0xaaaaecc2b000
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1076(
- %para3766 : F32 # x
- , %para3767 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3766, %para3767) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.891] ✓construct.842 @ctx.addr=0xaaaaecc2b150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_842[fg_442](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2859) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2859) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaecc26840
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2859) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1077(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1077=↓construct.1077(@ctx.addr=0xaaaaf740b220) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf740b220
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.892] L-construct.720 @ctx.addr=0xaaaae2147910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3768 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3769 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3770 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae2147b60), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae2147b60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.892] L-construct.721 @ctx.addr=0xaaaae2112550
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3771 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3772 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3773 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaad66e15f0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaad66e15f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.892] L-construct.722 @ctx.addr=0xaaaaf52e3820
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3774 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3775 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3776 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaf12795e0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaf12795e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.892] L-construct.723 @ctx.addr=0xaaaae35d7640
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3777 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3778 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3779 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae84da4d0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae84da4d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.892] L-construct.717 @ctx.addr=0xaaaaf2e2e480
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3780 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3781 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3782 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaad933a850), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaad933a850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.892] _less_equal_scala.1078 @ctx.addr=0xaaaae0d2a130
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1078(
- %para3783 : F32 # x
- , %para3784 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3783, %para3784) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.893] ✓construct.846 @ctx.addr=0xaaaae0d2a240
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_846[fg_448](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2869) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2869) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1e42940
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2869) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1079(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1079=↓construct.1079(@ctx.addr=0xaaaaec854b40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaec854b40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.894] L-construct.720 @ctx.addr=0xaaaaef039a20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3785 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3786 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3787 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaaef039c70), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaaef039c70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.894] L-construct.721 @ctx.addr=0xaaaaea945420
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3788 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3789 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3790 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaf2aa55c0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaf2aa55c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.894] L-construct.722 @ctx.addr=0xaaaae97ade10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3791 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3792 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3793 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae9db8440), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae9db8440
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.894] L-construct.723 @ctx.addr=0xaaaae69d0a80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3794 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3795 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3796 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaafa6f60c0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaafa6f60c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.894] L-construct.717 @ctx.addr=0xaaaaea94f7d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3797 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3798 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3799 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaaea94fa20), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaaea94fa20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.894] _less_equal_scala.1080 @ctx.addr=0xaaaaecb4b5f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1080(
- %para3800 : F32 # x
- , %para3801 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3800, %para3801) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.895] ✓construct.850 @ctx.addr=0xaaaaecb4b740
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_850[fg_454](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2879) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2879) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaec644000
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2879) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1081(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1081=↓construct.1081(@ctx.addr=0xaaaaecb47270) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaecb47270
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.896] L-construct.720 @ctx.addr=0xaaaad61fb110
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3802 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3803 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3804 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaad61fb360), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaad61fb360
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.896] L-construct.721 @ctx.addr=0xaaaaf25912d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3805 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3806 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3807 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaf4906540), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaf4906540
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.896] L-construct.722 @ctx.addr=0xaaaaf6e99bd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3808 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3809 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3810 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaf0476980), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaf0476980
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.896] L-construct.723 @ctx.addr=0xaaaaddb2e900
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3811 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3812 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3813 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae8d88230), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae8d88230
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.896] L-construct.717 @ctx.addr=0xaaaae83baa20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3814 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3815 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3816 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae83bab60), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae83bab60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.896] _less_equal_scala.1082 @ctx.addr=0xaaaae1e71200
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1082(
- %para3817 : F32 # x
- , %para3818 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3817, %para3818) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.897] ✓construct.854 @ctx.addr=0xaaaae1e71310
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_854[fg_462](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2889) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2889) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae861ae00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2889) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1083(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1083=↓construct.1083(@ctx.addr=0xaaaae1dc83e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1dc83e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.898] L-construct.720 @ctx.addr=0xaaaad74172c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3819 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3820 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3821 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaad7417510), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaad7417510
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.898] L-construct.721 @ctx.addr=0xaaaaf4056b00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3822 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3823 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3824 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae1236040), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1236040
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.898] L-construct.722 @ctx.addr=0xaaaaf3998680
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3825 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3826 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3827 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae8222b10), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae8222b10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.898] L-construct.723 @ctx.addr=0xaaaaec02a170
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3828 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3829 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3830 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaeede3030), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaeede3030
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.898] L-construct.717 @ctx.addr=0xaaaae85ebf40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3831 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3832 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3833 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae85ec190), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae85ec190
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.898] _less_equal_scala.1084 @ctx.addr=0xaaaae0d25610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1084(
- %para3834 : F32 # x
- , %para3835 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3834, %para3835) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.899] ✓construct.858 @ctx.addr=0xaaaae0d257c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_858[fg_468](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2899) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2899) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad7f563f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2899) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1085(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1085=↓construct.1085(@ctx.addr=0xaaaadc5b39f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadc5b39f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.900] L-construct.720 @ctx.addr=0xaaaaf18aa5c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3836 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3837 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3838 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaaf18aa810), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaaf18aa810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.900] L-construct.721 @ctx.addr=0xaaaae6e13e00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3839 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3840 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3841 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaf17823d0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaf17823d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.900] L-construct.722 @ctx.addr=0xaaaaf229c410
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3842 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3843 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3844 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaf6e9a950), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaf6e9a950
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.900] L-construct.723 @ctx.addr=0xaaaaea28f7e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3845 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3846 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3847 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaef83c1f0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaef83c1f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.900] L-construct.717 @ctx.addr=0xaaaade555220
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3848 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3849 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3850 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaade555470), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaade555470
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.900] _less_equal_scala.1086 @ctx.addr=0xaaaae8d7fb60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1086(
- %para3851 : F32 # x
- , %para3852 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3851, %para3852) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.901] ✓construct.862 @ctx.addr=0xaaaae8d7fc70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_862[fg_474](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2909) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2909) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8d0ec10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2909) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1087(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1087=↓construct.1087(@ctx.addr=0xaaaae8d7f140) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8d7f140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.902] L-construct.720 @ctx.addr=0xaaaad8a51a70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3853 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3854 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3855 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaad8a51cc0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaad8a51cc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.902] L-construct.721 @ctx.addr=0xaaaad8a50d50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3856 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3857 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3858 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaf0c7bde0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaf0c7bde0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.902] L-construct.722 @ctx.addr=0xaaaae6ee7f20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3859 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3860 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3861 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae6ee3a00), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae6ee3a00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.902] L-construct.723 @ctx.addr=0xaaaad63ecf10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3862 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3863 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3864 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaade47dc00), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaade47dc00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.902] L-construct.717 @ctx.addr=0xaaaae3336d00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3865 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3866 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3867 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae3332bf0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae3332bf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.902] _less_equal_scala.1088 @ctx.addr=0xaaaad7f4ccd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1088(
- %para3868 : F32 # x
- , %para3869 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3868, %para3869) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.903] ✓construct.866 @ctx.addr=0xaaaad7f4cde0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_866[fg_482](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2919) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2919) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad7211b80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2919) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1089(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1089=↓construct.1089(@ctx.addr=0xaaaad7216350) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad7216350
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.904] L-construct.720 @ctx.addr=0xaaaae5924b60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3870 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3871 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3872 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae6eebe70), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae6eebe70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.904] L-construct.721 @ctx.addr=0xaaaae6eec640
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3873 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3874 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3875 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaefe1f5d0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaefe1f5d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.904] L-construct.722 @ctx.addr=0xaaaaf34c6a10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3876 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3877 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3878 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae8a5a610), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae8a5a610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.904] L-construct.723 @ctx.addr=0xaaaaea1bf4e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3879 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3880 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3881 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaf0262c40), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0262c40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.904] L-construct.717 @ctx.addr=0xaaaaec6404c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3882 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3883 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3884 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaaec640710), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaaec640710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.904] _less_equal_scala.1090 @ctx.addr=0xaaaaded5f8e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1090(
- %para3885 : F32 # x
- , %para3886 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3885, %para3886) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.905] ✓construct.870 @ctx.addr=0xaaaaded5f9f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_870[fg_488](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2929) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2929) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaafa7c7990
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2929) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1091(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1091=↓construct.1091(@ctx.addr=0xaaaade487d10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaade487d10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.906] L-construct.720 @ctx.addr=0xaaaaf49021f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3887 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3888 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3889 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaaf4902440), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaaf4902440
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.906] L-construct.721 @ctx.addr=0xaaaae5c1d210
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3890 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3891 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3892 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaf4ef1400), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaf4ef1400
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.906] L-construct.722 @ctx.addr=0xaaaaf11936f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3893 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3894 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3895 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaf5035450), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaf5035450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.906] L-construct.723 @ctx.addr=0xaaaaf2592460
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3896 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3897 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3898 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaf4ef08c0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf4ef08c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.906] L-construct.717 @ctx.addr=0xaaaaed73df80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3899 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3900 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3901 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaad8898760), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaad8898760
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.906] _less_equal_scala.1092 @ctx.addr=0xaaaad68d0100
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1092(
- %para3902 : F32 # x
- , %para3903 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3902, %para3903) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.907] ✓construct.874 @ctx.addr=0xaaaad68d0210
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_874[fg_494](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2939) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2939) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaadec8cb50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2939) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1093(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1093=↓construct.1093(@ctx.addr=0xaaaaf2669e00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf2669e00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.908] L-construct.720 @ctx.addr=0xaaaae8537940
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3904 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3905 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3906 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaaf43aeea0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaaf43aeea0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.908] L-construct.721 @ctx.addr=0xaaaaf43af6c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3907 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3908 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3909 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaec932dd0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaec932dd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.908] L-construct.722 @ctx.addr=0xaaaad8a4cf70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3910 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3911 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3912 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaeb1f8360), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaeb1f8360
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.908] L-construct.723 @ctx.addr=0xaaaae34172f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3913 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3914 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3915 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaadc6805d0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadc6805d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.908] L-construct.717 @ctx.addr=0xaaaaeacb3cf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3916 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3917 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3918 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae2668fc0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae2668fc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.908] _less_equal_scala.1094 @ctx.addr=0xaaaaf0c3bad0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1094(
- %para3919 : F32 # x
- , %para3920 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3919, %para3920) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.909] ✓construct.878 @ctx.addr=0xaaaaf0c3bbe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_878[fg_502](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2949) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2949) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaed0caba0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2949) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1095(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1095=↓construct.1095(@ctx.addr=0xaaaaf0c3c7e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf0c3c7e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.910] L-construct.720 @ctx.addr=0xaaaaf0c38ca0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3921 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3922 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3923 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaaf0c38ef0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaaf0c38ef0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.910] L-construct.721 @ctx.addr=0xaaaaf0c39710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3924 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3925 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3926 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaad82fef90), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaad82fef90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.910] L-construct.722 @ctx.addr=0xaaaade43f980
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3927 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3928 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3929 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaade4403d0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaade4403d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.910] L-construct.723 @ctx.addr=0xaaaad72a2380
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3930 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3931 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3932 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaf17d3b90), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf17d3b90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.910] L-construct.717 @ctx.addr=0xaaaae0db8b10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3933 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3934 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3935 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae0db8d60), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae0db8d60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.910] _less_equal_scala.1096 @ctx.addr=0xaaaae83736c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1096(
- %para3936 : F32 # x
- , %para3937 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3936, %para3937) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.911] ✓construct.882 @ctx.addr=0xaaaae8373870
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_882[fg_508](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2959) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2959) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2509c10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2959) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1097(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1097=↓construct.1097(@ctx.addr=0xaaaae8519a70) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8519a70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.912] L-construct.720 @ctx.addr=0xaaaae25769a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3938 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3939 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3940 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae2576bb0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae2576bb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.912] L-construct.721 @ctx.addr=0xaaaae25773d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3941 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3942 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3943 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae24401d0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae24401d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.912] L-construct.722 @ctx.addr=0xaaaafadf87e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3944 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3945 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3946 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaafadf9250), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafadf9250
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.912] L-construct.723 @ctx.addr=0xaaaae247a150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3947 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3948 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3949 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae247ac20), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae247ac20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.912] L-construct.717 @ctx.addr=0xaaaae24eeeb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3950 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3951 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3952 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae24ef100), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae24ef100
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.912] _less_equal_scala.1098 @ctx.addr=0xaaaae25ac500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1098(
- %para3953 : F32 # x
- , %para3954 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3953, %para3954) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.913] ✓construct.886 @ctx.addr=0xaaaae25ac610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_886[fg_514](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2969) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2969) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2275af0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2969) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1099(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1099=↓construct.1099(@ctx.addr=0xaaaae25ad230) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae25ad230
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.914] L-construct.720 @ctx.addr=0xaaaaf1e63920
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3955 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3956 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3957 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaaf1e63b70), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaaf1e63b70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.914] L-construct.721 @ctx.addr=0xaaaaf1e64390
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3958 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3959 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3960 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae20c0180), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae20c0180
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.914] L-construct.722 @ctx.addr=0xaaaae8274790
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3961 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3962 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3963 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae8275260), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae8275260
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.914] L-construct.723 @ctx.addr=0xaaaae85c4ba0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3964 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3965 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3966 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae85c5640), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae85c5640
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.914] L-construct.717 @ctx.addr=0xaaaaee7fe650
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3967 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3968 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3969 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaad8142a20), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaad8142a20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.914] _less_equal_scala.1100 @ctx.addr=0xaaaaf3ea74f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1100(
- %para3970 : F32 # x
- , %para3971 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3970, %para3971) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.915] ✓construct.890 @ctx.addr=0xaaaaf3ea7600
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_890[fg_522](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2979) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2979) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf248f960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2979) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1101(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1101=↓construct.1101(@ctx.addr=0xaaaaf3ea8220) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf3ea8220
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.916] L-construct.720 @ctx.addr=0xaaaae258dcf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3972 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3973 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3974 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae258df40), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae258df40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.916] L-construct.721 @ctx.addr=0xaaaae258e6d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3975 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3976 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3977 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae82bb630), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae82bb630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.916] L-construct.722 @ctx.addr=0xaaaae83b7c90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3978 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3979 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3980 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae83b8700), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae83b8700
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.916] L-construct.723 @ctx.addr=0xaaaafad93170
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3981 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3982 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para3983 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaafad93c70), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaafad93c70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.916] L-construct.717 @ctx.addr=0xaaaae23f7d10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para3984 : Tensor(F32)[16, 64, 32, 32] # x
- , %para3985 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para3986 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae23f7f60), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae23f7f60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.916] _less_equal_scala.1102 @ctx.addr=0xaaaafae94ca0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1102(
- %para3987 : F32 # x
- , %para3988 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para3987, %para3988) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.917] ✓construct.894 @ctx.addr=0xaaaafae94db0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_894[fg_528](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2989) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2989) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaafae94fc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2989) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1103(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1103=↓construct.1103(@ctx.addr=0xaaaad6a2abf0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaad6a2abf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.918] L-construct.720 @ctx.addr=0xaaaae1e5ff50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para3989 : Tensor(F32)[16, 96, 32, 32] # x
- , %para3990 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para3991 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae1e60050), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e60050
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.918] L-construct.721 @ctx.addr=0xaaaae1e60ac0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para3992 : Tensor(F32)[16, 128, 32, 32] # x
- , %para3993 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para3994 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae8362a50), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae8362a50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.918] L-construct.722 @ctx.addr=0xaaaae24309c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para3995 : Tensor(F32)[16, 160, 32, 32] # x
- , %para3996 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para3997 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae2431460), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae2431460
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.918] L-construct.723 @ctx.addr=0xaaaaec7ae3e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para3998 : Tensor(F32)[16, 192, 32, 32] # x
- , %para3999 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4000 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaec7aee80), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaec7aee80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.918] L-construct.717 @ctx.addr=0xaaaaee0ffa30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4001 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4002 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4003 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaaee0ffc80), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaaee0ffc80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.918] _less_equal_scala.1104 @ctx.addr=0xaaaae2698010
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1104(
- %para4004 : F32 # x
- , %para4005 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4004, %para4005) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.919] ✓construct.898 @ctx.addr=0xaaaae2698120
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_898[fg_534](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para2999) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para2999) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf362eb10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para2999) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1105(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1105=↓construct.1105(@ctx.addr=0xaaaae2698d40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2698d40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.920] L-construct.720 @ctx.addr=0xaaaae2102700
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4006 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4007 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4008 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae2102950), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae2102950
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.920] L-construct.721 @ctx.addr=0xaaaae21f22d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4009 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4010 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4011 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae2291320), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae2291320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.920] L-construct.722 @ctx.addr=0xaaaae2306e10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4012 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4013 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4014 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae24d0900), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae24d0900
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.920] L-construct.723 @ctx.addr=0xaaaae21452f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4015 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4016 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4017 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae2145df0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae2145df0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.920] L-construct.717 @ctx.addr=0xaaaad59c3aa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4018 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4019 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4020 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaad59c4890), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaad59c4890
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.920] _less_equal_scala.1106 @ctx.addr=0xaaaae1e505b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1106(
- %para4021 : F32 # x
- , %para4022 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4021, %para4022) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.921] ✓construct.902 @ctx.addr=0xaaaae1e506c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_902[fg_542](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3009) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3009) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1e51d30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3009) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1107(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1107=↓construct.1107(@ctx.addr=0xaaaae1e512e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae1e512e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.922] L-construct.720 @ctx.addr=0xaaaaf605f540
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4023 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4024 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4025 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaaf605f790), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaaf605f790
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.922] L-construct.721 @ctx.addr=0xaaaaf605ffb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4026 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4027 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4028 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaee787bd0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaee787bd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.922] L-construct.722 @ctx.addr=0xaaaaf143e150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4029 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4030 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4031 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaf43bd770), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaf43bd770
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.922] L-construct.723 @ctx.addr=0xaaaae254c2a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4032 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4033 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4034 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae254cd50), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae254cd50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.922] L-construct.717 @ctx.addr=0xaaaae2041550
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4035 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4036 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4037 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae20417a0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae20417a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.922] _less_equal_scala.1108 @ctx.addr=0xaaaae25fc8c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1108(
- %para4038 : F32 # x
- , %para4039 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4038, %para4039) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.923] ✓construct.906 @ctx.addr=0xaaaae25fc9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_906[fg_548](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3019) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3019) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2642030
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3019) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1109(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1109=↓construct.1109(@ctx.addr=0xaaaae25fd5f0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae25fd5f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.924] L-construct.720 @ctx.addr=0xaaaafae32a60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4040 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4041 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4042 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaafae32cb0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaafae32cb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.924] L-construct.721 @ctx.addr=0xaaaafae334d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4043 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4044 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4045 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae22c1700), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae22c1700
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.924] L-construct.722 @ctx.addr=0xaaaae258a2d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4046 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4047 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4048 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae258ad70), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae258ad70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.924] L-construct.723 @ctx.addr=0xaaaae219ff50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4049 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4050 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4051 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae21a0a00), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae21a0a00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.924] L-construct.717 @ctx.addr=0xaaaae21d9ee0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4052 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4053 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4054 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae21da130), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae21da130
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.924] _less_equal_scala.1110 @ctx.addr=0xaaaae22137b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1110(
- %para4055 : F32 # x
- , %para4056 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4055, %para4056) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.925] ✓construct.910 @ctx.addr=0xaaaae22138c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_910[fg_554](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3029) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3029) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2214f10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3029) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1111(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1111=↓construct.1111(@ctx.addr=0xaaaae22144c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae22144c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.926] L-construct.720 @ctx.addr=0xaaaae81a74c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4057 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4058 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4059 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae81a7710), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae81a7710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.926] L-construct.721 @ctx.addr=0xaaaae81a7f30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4060 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4061 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4062 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae265c900), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae265c900
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.926] L-construct.722 @ctx.addr=0xaaaaf0725580
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4063 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4064 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4065 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaf0726390), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaf0726390
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.926] L-construct.723 @ctx.addr=0xaaaae2220840
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4066 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4067 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4068 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae2221620), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae2221620
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.926] L-construct.717 @ctx.addr=0xaaaae2159db0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4069 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4070 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4071 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae1f41bc0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1f41bc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.926] _less_equal_scala.1112 @ctx.addr=0xaaaaf1773bb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1112(
- %para4072 : F32 # x
- , %para4073 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4072, %para4073) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.927] ✓construct.914 @ctx.addr=0xaaaaf1773cc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_914[fg_562](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3039) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3039) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf1775330
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3039) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1113(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1113=↓construct.1113(@ctx.addr=0xaaaaf17748e0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf17748e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.928] L-construct.720 @ctx.addr=0xaaaae1fc7270
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4074 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4075 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4076 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae1fc74c0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1fc74c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.928] L-construct.721 @ctx.addr=0xaaaaf71a9010
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4077 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4078 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4079 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae8358f40), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae8358f40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.928] L-construct.722 @ctx.addr=0xaaaae1e1dd30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4080 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4081 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4082 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae1e1e7a0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae1e1e7a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.928] L-construct.723 @ctx.addr=0xaaaae23c1020
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4083 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4084 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4085 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae23c1ad0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae23c1ad0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.928] L-construct.717 @ctx.addr=0xaaaae1fa8060
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4086 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4087 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4088 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae1fa82b0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1fa82b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.928] _less_equal_scala.1114 @ctx.addr=0xaaaae8216850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1114(
- %para4089 : F32 # x
- , %para4090 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4089, %para4090) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.929] ✓construct.918 @ctx.addr=0xaaaae8216960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_918[fg_568](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3049) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3049) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8216b70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3049) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1115(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1115=↓construct.1115(@ctx.addr=0xaaaaec6041c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaec6041c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.930] L-construct.720 @ctx.addr=0xaaaae24a9930
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4091 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4092 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4093 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae24a9a30), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae24a9a30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.930] L-construct.721 @ctx.addr=0xaaaae24aa4a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4094 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4095 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4096 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae85648d0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae85648d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.930] L-construct.722 @ctx.addr=0xaaaaf4dd0830
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4097 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4098 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4099 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaaf4dd1390), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaf4dd1390
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.930] L-construct.723 @ctx.addr=0xaaaae8501a10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4100 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4101 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4102 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaafad95a00), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaafad95a00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.930] L-construct.717 @ctx.addr=0xaaaae20a4380
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4103 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4104 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4105 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae20a45d0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae20a45d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.930] _less_equal_scala.1116 @ctx.addr=0xaaaae2409190
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1116(
- %para4106 : F32 # x
- , %para4107 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4106, %para4107) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.931] ✓construct.922 @ctx.addr=0xaaaae24092a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_922[fg_574](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3059) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3059) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae240a910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3059) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1117(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1117=↓construct.1117(@ctx.addr=0xaaaae2409ec0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2409ec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.932] L-construct.720 @ctx.addr=0xaaaae81c5bd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4108 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4109 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4110 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae81c5e20), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae81c5e20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.932] L-construct.721 @ctx.addr=0xaaaae81c6640
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4111 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4112 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4113 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae84019f0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae84019f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.932] L-construct.722 @ctx.addr=0xaaaae2205df0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4114 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4115 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4116 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae2206810), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae2206810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.932] L-construct.723 @ctx.addr=0xaaaae84ad5b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4117 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4118 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4119 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaaefc3db90), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaefc3db90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.932] L-construct.717 @ctx.addr=0xaaaaebbe3990
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4120 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4121 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4122 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaaebbe4720), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaaebbe4720
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.932] _less_equal_scala.1118 @ctx.addr=0xaaaae81d3460
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1118(
- %para4123 : F32 # x
- , %para4124 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4123, %para4124) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.933] ✓construct.926 @ctx.addr=0xaaaae81d3570
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_926[fg_582](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3069) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3069) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae81d4be0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3069) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1119(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1119=↓construct.1119(@ctx.addr=0xaaaae81d4190) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae81d4190
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.934] L-construct.720 @ctx.addr=0xaaaae235c700
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4125 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4126 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4127 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae235c950), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae235c950
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.934] L-construct.721 @ctx.addr=0xaaaae235d170
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4128 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4129 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4130 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaaf3632560), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaaf3632560
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.934] L-construct.722 @ctx.addr=0xaaaae1eb7440
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4131 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4132 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4133 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae1eb7ee0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae1eb7ee0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.934] L-construct.723 @ctx.addr=0xaaaae1dab930
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4134 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4135 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4136 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae1dac430), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae1dac430
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.934] L-construct.717 @ctx.addr=0xaaaae817f480
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4137 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4138 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4139 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae817f6d0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae817f6d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.934] _less_equal_scala.1120 @ctx.addr=0xaaaae8448690
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1120(
- %para4140 : F32 # x
- , %para4141 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4140, %para4141) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.935] ✓construct.930 @ctx.addr=0xaaaae84487a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_930[fg_588](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3079) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3079) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaaf2a66280
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3079) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1121(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1121=↓construct.1121(@ctx.addr=0xaaaae84493c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae84493c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.936] L-construct.720 @ctx.addr=0xaaaae1dc8d60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4142 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4143 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4144 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae1dc8fb0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1dc8fb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.936] L-construct.721 @ctx.addr=0xaaaae1dc97d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4145 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4146 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4147 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaafaea8280), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaafaea8280
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.936] L-construct.722 @ctx.addr=0xaaaae85ba160
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4148 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4149 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4150 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae85bac00), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae85bac00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.936] L-construct.723 @ctx.addr=0xaaaae8477c80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4151 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4152 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4153 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae82780a0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae82780a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.936] L-construct.717 @ctx.addr=0xaaaad741aa60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4154 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4155 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4156 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaad741acb0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaad741acb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.936] _less_equal_scala.1122 @ctx.addr=0xaaaafae7aa60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1122(
- %para4157 : F32 # x
- , %para4158 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4157, %para4158) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.937] ✓construct.934 @ctx.addr=0xaaaafae7ab70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_934[fg_594](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3089) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3089) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae829f2d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3089) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1123(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1123=↓construct.1123(@ctx.addr=0xaaaae829e880) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae829e880
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.938] L-construct.720 @ctx.addr=0xaaaae2279470
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4159 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4160 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4161 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae22796c0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae22796c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.938] L-construct.721 @ctx.addr=0xaaaae2279ee0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4162 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4163 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4164 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae849ddb0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae849ddb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.938] L-construct.722 @ctx.addr=0xaaaae1e109e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4165 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4166 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4167 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae83f1450), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae83f1450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.938] L-construct.723 @ctx.addr=0xaaaae84f01f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4168 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4169 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4170 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae84f0cf0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae84f0cf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.938] L-construct.717 @ctx.addr=0xaaaafadcf9a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4171 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4172 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4173 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaaf1f60c00), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaaf1f60c00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.938] _less_equal_scala.1124 @ctx.addr=0xaaaaf1659d70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1124(
- %para4174 : F32 # x
- , %para4175 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4174, %para4175) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.939] ✓construct.938 @ctx.addr=0xaaaaf1659e80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_938[fg_602](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3099) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3099) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae8347cf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3099) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1125(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1125=↓construct.1125(@ctx.addr=0xaaaae83472a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae83472a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.940] L-construct.720 @ctx.addr=0xaaaae2680650
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4176 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4177 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4178 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae26808a0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae26808a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.940] L-construct.721 @ctx.addr=0xaaaae26810c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4179 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4180 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4181 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae2661d40), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae2661d40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.940] L-construct.722 @ctx.addr=0xaaaae25f5ff0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4182 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4183 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4184 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae25f6c70), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae25f6c70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.940] L-construct.723 @ctx.addr=0xaaaae26768d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4185 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4186 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4187 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae26775e0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae26775e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.940] L-construct.717 @ctx.addr=0xaaaae2694710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4188 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4189 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4190 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae2694960), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae2694960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.940] _less_equal_scala.1126 @ctx.addr=0xaaaae25ffa20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1126(
- %para4191 : F32 # x
- , %para4192 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4191, %para4192) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.941] ✓construct.942 @ctx.addr=0xaaaae25ffb30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_942[fg_608](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3109) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3109) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae26011a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3109) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1127(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1127=↓construct.1127(@ctx.addr=0xaaaae2600750) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2600750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.942] L-construct.720 @ctx.addr=0xaaaae26baf60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4193 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4194 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4195 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae26bb1b0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae26bb1b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.942] L-construct.721 @ctx.addr=0xaaaae26bb9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4196 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4197 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4198 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae2726e00), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae2726e00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.942] L-construct.722 @ctx.addr=0xaaaae272ed70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4199 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4200 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4201 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae272dce0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae272dce0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.942] L-construct.723 @ctx.addr=0xaaaae2735e40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4202 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4203 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4204 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae2734db0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae2734db0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.942] L-construct.717 @ctx.addr=0xaaaae2742010
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4205 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4206 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4207 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae2742260), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae2742260
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.942] _less_equal_scala.1128 @ctx.addr=0xaaaae274d6a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1128(
- %para4208 : F32 # x
- , %para4209 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4208, %para4209) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.943] ✓construct.946 @ctx.addr=0xaaaae274d7f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_946[fg_614](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3119) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3119) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae274f0a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3119) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1129(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1129=↓construct.1129(@ctx.addr=0xaaaae274e650) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae274e650
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.944] L-construct.720 @ctx.addr=0xaaaae275bdb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4210 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4211 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4212 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae275c000), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae275c000
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.944] L-construct.721 @ctx.addr=0xaaaae275c820
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4213 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4214 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4215 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae276e1d0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae276e1d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.944] L-construct.722 @ctx.addr=0xaaaae27761b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4216 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4217 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4218 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae27750a0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae27750a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.944] L-construct.723 @ctx.addr=0xaaaae277d2a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4219 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4220 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4221 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae277c190), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae277c190
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.944] L-construct.717 @ctx.addr=0xaaaae27cb800
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4222 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4223 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4224 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae27cc9c0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae27cc9c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.944] _less_equal_scala.1130 @ctx.addr=0xaaaae27d7290
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1130(
- %para4225 : F32 # x
- , %para4226 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4225, %para4226) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.945] ✓construct.950 @ctx.addr=0xaaaae27d73e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_950[fg_622](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3129) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3129) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae27d8c50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3129) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1131(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1131=↓construct.1131(@ctx.addr=0xaaaae27d8200) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae27d8200
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.946] L-construct.720 @ctx.addr=0xaaaae27e5430
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4227 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4228 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4229 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae27e5680), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae27e5680
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.946] L-construct.721 @ctx.addr=0xaaaae27e5ea0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4230 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4231 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4232 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae27f7a60), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae27f7a60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.946] L-construct.722 @ctx.addr=0xaaaae27ffad0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4233 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4234 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4235 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae27fe940), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae27fe940
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.946] L-construct.723 @ctx.addr=0xaaaae2806ba0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4236 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4237 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4238 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae2805a10), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae2805a10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.946] L-construct.717 @ctx.addr=0xaaaae2812c60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4239 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4240 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4241 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae2812eb0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae2812eb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.946] _less_equal_scala.1132 @ctx.addr=0xaaaae281e2f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1132(
- %para4242 : F32 # x
- , %para4243 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4242, %para4243) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.947] ✓construct.954 @ctx.addr=0xaaaae281e440
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_954[fg_628](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3139) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3139) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae281fcf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3139) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1133(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1133=↓construct.1133(@ctx.addr=0xaaaae281f2a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae281f2a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.948] L-construct.720 @ctx.addr=0xaaaae282c7f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4244 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4245 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4246 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae282ca40), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae282ca40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.948] L-construct.721 @ctx.addr=0xaaaae282d260
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4247 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4248 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4249 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae283ee20), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae283ee20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.948] L-construct.722 @ctx.addr=0xaaaae2846ef0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4250 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4251 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4252 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae2845ce0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae2845ce0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.948] L-construct.723 @ctx.addr=0xaaaae284dfd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4253 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4254 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4255 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae284cdc0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae284cdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.948] L-construct.717 @ctx.addr=0xaaaae285a010
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4256 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4257 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4258 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae285a260), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae285a260
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.948] _less_equal_scala.1134 @ctx.addr=0xaaaae28656c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1134(
- %para4259 : F32 # x
- , %para4260 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4259, %para4260) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.949] ✓construct.958 @ctx.addr=0xaaaae2865810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_958[fg_634](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3149) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3149) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2867080
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3149) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1135(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1135=↓construct.1135(@ctx.addr=0xaaaae2866630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2866630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.950] L-construct.720 @ctx.addr=0xaaaae2873b80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4261 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4262 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4263 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae2873dd0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae2873dd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.950] L-construct.721 @ctx.addr=0xaaaae28745f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4264 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4265 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4266 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae28861c0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae28861c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.950] L-construct.722 @ctx.addr=0xaaaae288e330
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4267 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4268 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4269 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae288d0a0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae288d0a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.950] L-construct.723 @ctx.addr=0xaaaae2895400
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4270 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4271 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4272 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae2894170), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae2894170
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.950] L-construct.717 @ctx.addr=0xaaaae28e2010
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4273 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4274 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4275 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae28e31d0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae28e31d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.950] _less_equal_scala.1136 @ctx.addr=0xaaaae28ed910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1136(
- %para4276 : F32 # x
- , %para4277 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4276, %para4277) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.951] ✓construct.962 @ctx.addr=0xaaaae28eda60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_962[fg_642](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3159) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3159) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae28ef2d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3159) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1137(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1137=↓construct.1137(@ctx.addr=0xaaaae28ee880) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae28ee880
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.952] L-construct.720 @ctx.addr=0xaaaae28fbab0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4278 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4279 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4280 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae28fbd00), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae28fbd00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.952] L-construct.721 @ctx.addr=0xaaaae28fc520
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4281 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4282 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4283 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae290e0e0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae290e0e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.952] L-construct.722 @ctx.addr=0xaaaae29162b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4284 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4285 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4286 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae2914fa0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae2914fa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.952] L-construct.723 @ctx.addr=0xaaaae291d380
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4287 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4288 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4289 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae291c070), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae291c070
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.952] L-construct.717 @ctx.addr=0xaaaae29292c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4290 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4291 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4292 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae2929510), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae2929510
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.952] _less_equal_scala.1138 @ctx.addr=0xaaaae29349a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1138(
- %para4293 : F32 # x
- , %para4294 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4293, %para4294) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.953] ✓construct.966 @ctx.addr=0xaaaae2934af0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_966[fg_648](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3169) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3169) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2936360
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3169) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1139(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1139=↓construct.1139(@ctx.addr=0xaaaae2935910) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae2935910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.954] L-construct.720 @ctx.addr=0xaaaae2942e60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4295 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4296 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4297 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae29430b0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae29430b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.954] L-construct.721 @ctx.addr=0xaaaae29438d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4298 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4299 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4300 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae29554a0), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae29554a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.954] L-construct.722 @ctx.addr=0xaaaae295d6f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4301 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4302 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4303 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae295e210), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae295e210
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.954] L-construct.723 @ctx.addr=0xaaaae2964800
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4304 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4305 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4306 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae2965300), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae2965300
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.954] L-construct.717 @ctx.addr=0xaaaae36ff8d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4307 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4308 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4309 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae36ffb20), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae36ffb20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.954] _less_equal_scala.1140 @ctx.addr=0xaaaae370af50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1140(
- %para4310 : F32 # x
- , %para4311 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4310, %para4311) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.955] ✓construct.970 @ctx.addr=0xaaaae370b0a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_970[fg_654](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3179) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3179) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae370c950
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3179) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1141(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1141=↓construct.1141(@ctx.addr=0xaaaae370bf00) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae370bf00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.956] L-construct.720 @ctx.addr=0xaaaae3719450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4312 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4313 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4314 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae37196a0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae37196a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.956] L-construct.721 @ctx.addr=0xaaaae3719ec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4315 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4316 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4317 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae372ba90), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae372ba90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.956] L-construct.722 @ctx.addr=0xaaaae3733d90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4318 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4319 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4320 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae3732980), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae3732980
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.956] L-construct.723 @ctx.addr=0xaaaae373ae70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4321 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4322 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4323 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae3739a60), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae3739a60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.956] L-construct.717 @ctx.addr=0xaaaae3786050
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4324 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4325 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4326 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae3787270), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae3787270
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.956] _less_equal_scala.1142 @ctx.addr=0xaaaae3791a40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1142(
- %para4327 : F32 # x
- , %para4328 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4327, %para4328) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.957] ✓construct.974 @ctx.addr=0xaaaae3791b90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_974[fg_662](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3189) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3189) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae3793400
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3189) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1143(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1143=↓construct.1143(@ctx.addr=0xaaaae37929b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae37929b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.958] L-construct.720 @ctx.addr=0xaaaae379fbe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4329 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4330 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4331 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae379fe30), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae379fe30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.958] L-construct.721 @ctx.addr=0xaaaae37a0650
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4332 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4333 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4334 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae37b2210), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae37b2210
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.958] L-construct.722 @ctx.addr=0xaaaae37bb260
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4335 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4336 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4337 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae37b9e10), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae37b9e10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.958] L-construct.723 @ctx.addr=0xaaaae37c2320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4338 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4339 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4340 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae37c0e90), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae37c0e90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.958] L-construct.717 @ctx.addr=0xaaaae37ce0e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4341 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4342 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4343 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae37ce330), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae37ce330
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.958] _less_equal_scala.1144 @ctx.addr=0xaaaae37d9760
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1144(
- %para4344 : F32 # x
- , %para4345 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4344, %para4345) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.959] ✓construct.978 @ctx.addr=0xaaaae37d98b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_978[fg_668](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3199) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3199) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae37db160
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3199) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1145(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1145=↓construct.1145(@ctx.addr=0xaaaae37da710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae37da710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.960] L-construct.720 @ctx.addr=0xaaaae37e7c60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4346 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4347 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4348 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae37e7eb0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae37e7eb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.960] L-construct.721 @ctx.addr=0xaaaae37e86d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4349 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4350 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4351 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae37fa290), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae37fa290
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.960] L-construct.722 @ctx.addr=0xaaaae3802680
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4352 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4353 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4354 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae3801170), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae3801170
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.960] L-construct.723 @ctx.addr=0xaaaae3809750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4355 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4356 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4357 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae3808240), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae3808240
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.960] L-construct.717 @ctx.addr=0xaaaae3815490
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4358 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4359 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4360 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae38156e0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae38156e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.960] _less_equal_scala.1146 @ctx.addr=0xaaaae3820b10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1146(
- %para4361 : F32 # x
- , %para4362 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4361, %para4362) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.961] ✓construct.982 @ctx.addr=0xaaaae3820c60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_982[fg_674](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3209) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3209) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae3822510
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3209) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1147(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1147=↓construct.1147(@ctx.addr=0xaaaae3821ac0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae3821ac0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.962] L-construct.720 @ctx.addr=0xaaaae382f010
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4363 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4364 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4365 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae382f260), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae382f260
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.962] L-construct.721 @ctx.addr=0xaaaae382fa80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4366 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4367 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4368 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae3841640), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae3841640
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.962] L-construct.722 @ctx.addr=0xaaaae3849a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4369 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4370 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4371 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae3848500), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae3848500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.962] L-construct.723 @ctx.addr=0xaaaae3850b60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4372 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4373 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4374 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae384f5d0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae384f5d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.962] L-construct.717 @ctx.addr=0xaaaae389a330
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4375 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4376 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4377 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae389b550), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae389b550
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.962] _less_equal_scala.1148 @ctx.addr=0xaaaae38a5d80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1148(
- %para4378 : F32 # x
- , %para4379 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4378, %para4379) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.963] ✓construct.986 @ctx.addr=0xaaaae38a5ed0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_986[fg_682](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3219) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3219) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae38a7740
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3219) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1149(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1149=↓construct.1149(@ctx.addr=0xaaaae38a6cf0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae38a6cf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.964] L-construct.720 @ctx.addr=0xaaaae38b3f20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4380 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4381 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4382 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae38b4170), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae38b4170
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.964] L-construct.721 @ctx.addr=0xaaaae38b4990
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4383 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4384 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4385 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae38c6550), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae38c6550
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.964] L-construct.722 @ctx.addr=0xaaaae38cea40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4386 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4387 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4388 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae38cd430), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae38cd430
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.964] L-construct.723 @ctx.addr=0xaaaae38d5b10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4389 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4390 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4391 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae38d4500), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae38d4500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.964] L-construct.717 @ctx.addr=0xaaaae38e1750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4392 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4393 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4394 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae38e19a0), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae38e19a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.964] _less_equal_scala.1150 @ctx.addr=0xaaaae38ecdd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1150(
- %para4395 : F32 # x
- , %para4396 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4395, %para4396) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.965] ✓construct.990 @ctx.addr=0xaaaae38ecf20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_990[fg_688](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3229) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3229) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae38ee7d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3229) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1151(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1151=↓construct.1151(@ctx.addr=0xaaaae38edd80) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae38edd80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.966] L-construct.720 @ctx.addr=0xaaaae38fb2d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4397 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4398 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4399 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae38fb520), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae38fb520
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.966] L-construct.721 @ctx.addr=0xaaaae38fbd40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4400 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4401 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4402 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae390d910), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae390d910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.966] L-construct.722 @ctx.addr=0xaaaae3915e60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4403 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4404 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4405 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae39147d0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae39147d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.966] L-construct.723 @ctx.addr=0xaaaae391cf30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4406 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4407 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4408 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae391b8a0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae391b8a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.966] L-construct.717 @ctx.addr=0xaaaae3928af0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_717(
- %para4409 : Tensor(F32)[16, 64, 32, 32] # x
- , %para4410 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.bias
- , %para4411 : Ref[Tensor(F32)][32, 64, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv1.weight
- ) {
- %1 : Bool = FuncGraph::fg_1001(Bool(1)) #(Bool) # fg_1001=L-bool_.1001(@ctx.addr=0xaaaae1decdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1002, FuncGraph::fg_1003) #(Bool, Func, Func) # fg_1002=L-✓construct.1002(@ctx.addr=0xaaaae3928d40), fg_1003=L-✗construct.1003 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae3928d40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.966] _less_equal_scala.1152 @ctx.addr=0xaaaae39341d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(26)/def _less_equal_scala(x, y):/
- funcgraph fg_1152(
- %para4412 : F32 # x
- , %para4413 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_le") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- %3 : Bool = %2(%para4412, %para4413) #(F32, I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/less_equal_impl.py(37)/ return F.scalar_le(x, y)/
- }
-
-
- # [No.967] ✓construct.994 @ctx.addr=0xaaaae3934320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_994[fg_694](
- ) {
- %1 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %2 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %3 : Tensor(F32)[] = DoSignaturePrimitive::S-Prim-ScalarToArray{prim_type=1}(F32(0.2)) #(F32) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %4 : Func = ClassType() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %5 : TypeType = %4(%para3239) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %6 : Tensor(F32)[] = %2(%3, %5) #(Tensor(F32)[], TypeType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(326)/ alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))/
- %7 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-mul{prim_type=1}(%6, %para3239) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae3935b90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %8 : Tensor(F32)[16, 32, 32, 32] = %1(%7, %para3239) #(Tensor(F32)[16, 32, 32, 32], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(328)/ out = P.Maximum()(alpha_array * x, x)/
- %9 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1153(%8) #(Tensor(F32)[16, 32, 32, 32]) # fg_1153=↓construct.1153(@ctx.addr=0xaaaae3935140) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU @ctx.addr=0xaaaae3935140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- Primitive::Return{prim_type=1}(%9) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- }
-
-
- # [No.968] L-construct.720 @ctx.addr=0xaaaae3942690
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_720(
- %para4414 : Tensor(F32)[16, 96, 32, 32] # x
- , %para4415 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.bias
- , %para4416 : Ref[Tensor(F32)][32, 96, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv2.weight
- ) {
- %1 : Bool = FuncGraph::fg_1006(Bool(1)) #(Bool) # fg_1006=L-bool_.1006(@ctx.addr=0xaaaae122f630) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1007, FuncGraph::fg_1008) #(Bool, Func, Func) # fg_1007=L-✓construct.1007(@ctx.addr=0xaaaae39428e0), fg_1008=L-✗construct.1008 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae39428e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.968] L-construct.721 @ctx.addr=0xaaaae39430e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_721(
- %para4417 : Tensor(F32)[16, 128, 32, 32] # x
- , %para4418 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.bias
- , %para4419 : Ref[Tensor(F32)][32, 128, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv3.weight
- ) {
- %1 : Bool = FuncGraph::fg_1009(Bool(1)) #(Bool) # fg_1009=L-bool_.1009(@ctx.addr=0xaaaae239b9d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1010, FuncGraph::fg_1011) #(Bool, Func, Func) # fg_1010=L-✓construct.1010(@ctx.addr=0xaaaae3954c20), fg_1011=L-✗construct.1011 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae3954c20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.968] L-construct.722 @ctx.addr=0xaaaae395d200
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_722(
- %para4420 : Tensor(F32)[16, 160, 32, 32] # x
- , %para4421 : Ref[Tensor(F32)][32] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.bias
- , %para4422 : Ref[Tensor(F32)][32, 160, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv4.weight
- ) {
- %1 : Bool = FuncGraph::fg_1012(Bool(1)) #(Bool) # fg_1012=L-bool_.1012(@ctx.addr=0xaaaafa48da90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1013, FuncGraph::fg_1014) #(Bool, Func, Func) # fg_1013=L-✓construct.1013(@ctx.addr=0xaaaae395baf0), fg_1014=L-✗construct.1014 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaae395baf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.968] L-construct.723 @ctx.addr=0xaaaae39642e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(257)/ def construct(self, x):/
- funcgraph fg_723(
- %para4423 : Tensor(F32)[16, 192, 32, 32] # x
- , %para4424 : Ref[Tensor(F32)][64] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.bias
- , %para4425 : Ref[Tensor(F32)][64, 192, 3, 3] # L-network.network.G.RRDB_trunk.0.RDB3.conv5.weight
- ) {
- %1 : Bool = FuncGraph::fg_1015(Bool(1)) #(Bool) # fg_1015=L-bool_.1015(@ctx.addr=0xaaaaf0c762b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1016, FuncGraph::fg_1017) #(Bool, Func, Func) # fg_1016=L-✓construct.1016(@ctx.addr=0xaaaae3962bd0), fg_1017=L-✗construct.1017 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaae3962bd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.968] tuple_hasnext.1154 @ctx.addr=0xaaaaedfc9bb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(805)/def tuple_hasnext(xs):/
- funcgraph fg_1154(
- %para4426 : Tuple[] # xs
- ) {
- %1 : Func = Primitive::resolve{prim_type=1}(NameSpace::Ast, gt) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(0)/
- %2 : Func = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, len) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(807)/ return len(xs) > 0/
- %3 : I64 = %2(%para4426) #(Tuple[]) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell @ctx.addr=0xaaaade559a60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(807)/ return len(xs) > 0/
- %4 : Bool = %1(%3, I64(0)) #(I64, I64) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell @ctx.addr=0xaaaae397ee20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(807)/ return len(xs) > 0/
- Primitive::Return{prim_type=1}(%4) #(Bool) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(807)/ return len(xs) > 0/
- }
-
-
- # [No.969] ms_next.998 @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(485)/def ms_next(it):/
- funcgraph fg_998(
- %para4427 : Tuple[Func] # it
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para4427, "__ms_next__") #(Tuple[Func], String) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(487)/ return it.__ms_next__()/
- %2 : Tuple[Func,Tuple[]] = %1() #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(487)/ return it.__ms_next__()/
- Primitive::Return{prim_type=1}(%2) #(Tuple[Func,Tuple[]]) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(487)/ return it.__ms_next__()/
- }
-
-
- # [No.970] _tuple_getitem_by_number.1155 @ctx.addr=0xaaaae2090460
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1155(
- %para4428 : Tuple[Func,Tuple[Func*22]] # data
- , %para4429 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*22] = %2(%para4428, %para4429) #(Tuple[Func,Tuple[Func*22]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*22]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.971] _tuple_getitem_by_number.1156 @ctx.addr=0xaaaae2064b30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1156(
- %para4430 : Tuple[Func,Tuple[Func*22]] # data
- , %para4431 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4430, %para4431) #(Tuple[Func,Tuple[Func*22]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.972] ⤾✓construct.216 @ctx.addr=0xaaaaef9b2fd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4432 : Tuple[Func*22] # @cell
- , %para4433 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*22]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaaf0986870), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf0986870
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.972] _tuple_getitem_by_number.1157 @ctx.addr=0xaaaae3a81ec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1157(
- %para4434 : Tuple[Tensor(F32)*5] # data
- , %para4435 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tensor(F32)[16, 128, 64, 64] = %2(%para4434, %para4435) #(Tuple[Tensor(F32)*5], I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 128, 64, 64]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.973] _tuple_getitem_by_number.1158 @ctx.addr=0xaaaae3aa5ca0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1158(
- %para4436 : Tuple[Tensor(F32)*5] # data
- , %para4437 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tensor(F32)[16, 128, 32, 32] = %2(%para4436, %para4437) #(Tuple[Tensor(F32)*5], I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 128, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.974] _tuple_getitem_by_number.1159 @ctx.addr=0xaaaae3aceb50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1159(
- %para4438 : Tuple[Tensor(F32)*5] # data
- , %para4439 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tensor(F32)[16, 256, 32, 32] = %2(%para4438, %para4439) #(Tuple[Tensor(F32)*5], I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 256, 32, 32]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.975] _tuple_getitem_by_number.1160 @ctx.addr=0xaaaae3af2890
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1160(
- %para4440 : Tuple[Tensor(F32)*5] # data
- , %para4441 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tensor(F32)[16, 256, 16, 16] = %2(%para4440, %para4441) #(Tuple[Tensor(F32)*5], I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 256, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.976] _tuple_getitem_by_number.1161 @ctx.addr=0xaaaae3b1b900
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1161(
- %para4442 : Tuple[Tensor(F32)*5] # data
- , %para4443 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tensor(F32)[16, 512, 16, 16] = %2(%para4442, %para4443) #(Tuple[Tensor(F32)*5], I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 512, 16, 16]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.977] _tuple_getitem_by_number.1162 @ctx.addr=0xaaaae3b46d60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1162(
- %para4444 : Tuple[Tensor(F32)*5] # data
- , %para4445 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tensor(F32)[16, 512, 8, 8] = %2(%para4444, %para4445) #(Tuple[Tensor(F32)*5], I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 512, 8, 8]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/bn0_1-BatchNorm2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.978] L-↓↓construct.1000 @ctx.addr=0xaaaae3b87860
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(304)/ if self.has_bias:/
- funcgraph fg_1000[fg_62](
- %para4446 : Tensor(F32)[16, 100] # Φx
- ) {
- %1 : Bool = FuncGraph::fg_139(Bool(0)) #(Bool) # fg_139=L-bool_.139(@ctx.addr=0xaaaae3b7e750) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b7e750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(306)/ if self.activation_flag:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1163, FuncGraph::fg_1164) #(Bool, Func, Func) # fg_1163=L-✓↓↓construct.1163, fg_1164=L-✗↓↓construct.1164(@ctx.addr=0xaaaae3b98b40) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(306)/ if self.activation_flag:/
- %3 : Tensor(F32)[16, 100] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b98b40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(306)/ if self.activation_flag:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(306)/ if self.activation_flag:/
- }
-
-
- # [No.979] L-↓↓construct.1000 @ctx.addr=0xaaaae3bca620
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(304)/ if self.has_bias:/
- funcgraph fg_1000[fg_62](
- %para4447 : Tensor(F32)[16, 1] # Φx
- ) {
- %1 : Bool = FuncGraph::fg_139(Bool(0)) #(Bool) # fg_139=L-bool_.139(@ctx.addr=0xaaaae3b7e750) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b7e750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(306)/ if self.activation_flag:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_1163, FuncGraph::fg_1164) #(Bool, Func, Func) # fg_1163=L-✓↓↓construct.1163, fg_1164=L-✗↓↓construct.1164(@ctx.addr=0xaaaae3bca870) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(306)/ if self.activation_flag:/
- %3 : Tensor(F32)[16, 1] = %2() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bca870
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(306)/ if self.activation_flag:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 1]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(306)/ if self.activation_flag:/
- }
-
-
- # [No.979] L-bool_.1001 @ctx.addr=0xaaaae1decdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(530)/def bool_(x):/
- funcgraph fg_1001(
- %para4448 : Bool # x
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para4448, "__bool__") #(Bool, String) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- %2 : Bool = %1() #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- Primitive::Return{prim_type=1}(%2) #(Bool) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- }
-
-
- # [No.980] L-✓construct.1002 @ctx.addr=0xaaaae24b00f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.981] _mul_tensor.1166 @ctx.addr=0xaaaae21c3240
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1166(
- %para4449 : Tensor(F32)[] # x
- , %para4450 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4449, %para4450) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.982] ↓construct.1005 @ctx.addr=0xaaaae23f21d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1005(
- %para4451 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4451) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.983] L-bool_.1006 @ctx.addr=0xaaaae122f630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(530)/def bool_(x):/
- funcgraph fg_1006(
- %para4452 : Bool # x
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para4452, "__bool__") #(Bool, String) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- %2 : Bool = %1() #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- Primitive::Return{prim_type=1}(%2) #(Bool) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- }
-
-
- # [No.984] L-✓construct.1007 @ctx.addr=0xaaaae846eef0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.985] L-bool_.1009 @ctx.addr=0xaaaae239b9d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(530)/def bool_(x):/
- funcgraph fg_1009(
- %para4453 : Bool # x
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para4453, "__bool__") #(Bool, String) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- %2 : Bool = %1() #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- Primitive::Return{prim_type=1}(%2) #(Bool) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- }
-
-
- # [No.986] L-✓construct.1010 @ctx.addr=0xaaaae23b6280
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.987] L-bool_.1012 @ctx.addr=0xaaaafa48da90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(530)/def bool_(x):/
- funcgraph fg_1012(
- %para4454 : Bool # x
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para4454, "__bool__") #(Bool, String) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- %2 : Bool = %1() #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- Primitive::Return{prim_type=1}(%2) #(Bool) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- }
-
-
- # [No.988] L-✓construct.1013 @ctx.addr=0xaaaaefe277b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.989] L-bool_.1015 @ctx.addr=0xaaaaf0c762b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(530)/def bool_(x):/
- funcgraph fg_1015(
- %para4455 : Bool # x
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para4455, "__bool__") #(Bool, String) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- %2 : Bool = %1() #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- Primitive::Return{prim_type=1}(%2) #(Bool) #scope: Default/optimizer-Adam
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(532)/ return x.__bool__()/
- }
-
-
- # [No.990] L-✓construct.1016 @ctx.addr=0xaaaaf4dd6b50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.991] L-✓construct.1002 @ctx.addr=0xaaaad61f0b50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.991] _mul_tensor.1171 @ctx.addr=0xaaaadc77e2c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1171(
- %para4456 : Tensor(F32)[] # x
- , %para4457 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4456, %para4457) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.992] ↓construct.1019 @ctx.addr=0xaaaadc76fb80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1019(
- %para4458 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4458) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.993] L-✓construct.1007 @ctx.addr=0xaaaaebd8f2b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.993] L-✓construct.1010 @ctx.addr=0xaaaafae3cf80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.993] L-✓construct.1013 @ctx.addr=0xaaaade47afb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.993] L-✓construct.1016 @ctx.addr=0xaaaaedb24610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.993] L-✓construct.1002 @ctx.addr=0xaaaae2217040
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.993] _mul_tensor.1172 @ctx.addr=0xaaaafa4d6300
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1172(
- %para4459 : Tensor(F32)[] # x
- , %para4460 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4459, %para4460) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.994] ↓construct.1021 @ctx.addr=0xaaaae8454cd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1021(
- %para4461 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4461) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.995] L-✓construct.1007 @ctx.addr=0xaaaae82b1f50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.995] L-✓construct.1010 @ctx.addr=0xaaaae83c4030
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.995] L-✓construct.1013 @ctx.addr=0xaaaaf0470160
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.995] L-✓construct.1016 @ctx.addr=0xaaaae8d8a240
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.995] L-✓construct.1002 @ctx.addr=0xaaaae8504e50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.995] _mul_tensor.1173 @ctx.addr=0xaaaaf4dda610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1173(
- %para4462 : Tensor(F32)[] # x
- , %para4463 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4462, %para4463) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.996] ↓construct.1023 @ctx.addr=0xaaaaf4c92fc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1023(
- %para4464 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4464) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.997] L-✓construct.1007 @ctx.addr=0xaaaad6a0c6e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.997] L-✓construct.1010 @ctx.addr=0xaaaae837ea20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.997] L-✓construct.1013 @ctx.addr=0xaaaaf58346a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.997] L-✓construct.1016 @ctx.addr=0xaaaae2238cf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.997] L-✓construct.1002 @ctx.addr=0xaaaaf4a6ed50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.997] _mul_tensor.1174 @ctx.addr=0xaaaae2331780
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1174(
- %para4465 : Tensor(F32)[] # x
- , %para4466 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4465, %para4466) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.998] ↓construct.1025 @ctx.addr=0xaaaae123f9b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1025(
- %para4467 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4467) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.999] L-✓construct.1007 @ctx.addr=0xaaaae82b8ef0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.999] L-✓construct.1010 @ctx.addr=0xaaaafaee6f90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.999] L-✓construct.1013 @ctx.addr=0xaaaad669e940
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.999] L-✓construct.1016 @ctx.addr=0xaaaae84d3950
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.999] L-✓construct.1002 @ctx.addr=0xaaaaebc15c90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.999] _mul_tensor.1175 @ctx.addr=0xaaaae1f071d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1175(
- %para4468 : Tensor(F32)[] # x
- , %para4469 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4468, %para4469) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1000] ↓construct.1027 @ctx.addr=0xaaaae06c4c80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1027(
- %para4470 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4470) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/1-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1001] L-✓construct.1007 @ctx.addr=0xaaaaded24200
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1001] L-✓construct.1010 @ctx.addr=0xaaaaf2550140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1001] L-✓construct.1013 @ctx.addr=0xaaaae238ac90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1001] L-✓construct.1016 @ctx.addr=0xaaaaf2115ed0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1001] L-✓construct.1002 @ctx.addr=0xaaaafaea0de0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1001] _mul_tensor.1176 @ctx.addr=0xaaaae1d926c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1176(
- %para4471 : Tensor(F32)[] # x
- , %para4472 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4471, %para4472) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1002] ↓construct.1029 @ctx.addr=0xaaaae6de1910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1029(
- %para4473 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4473) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1003] L-✓construct.1007 @ctx.addr=0xaaaae5655ee0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1003] L-✓construct.1010 @ctx.addr=0xaaaaf0c761a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1003] L-✓construct.1013 @ctx.addr=0xaaaae1dfa0c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1003] L-✓construct.1016 @ctx.addr=0xaaaaf48cd230
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1003] L-✓construct.1002 @ctx.addr=0xaaaae238bbf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1003] _mul_tensor.1177 @ctx.addr=0xaaaad741cfc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1177(
- %para4474 : Tensor(F32)[] # x
- , %para4475 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4474, %para4475) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1004] ↓construct.1031 @ctx.addr=0xaaaad6a33160
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1031(
- %para4476 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4476) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1005] L-✓construct.1007 @ctx.addr=0xaaaad5f695a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1005] L-✓construct.1010 @ctx.addr=0xaaaae1dbfa60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1005] L-✓construct.1013 @ctx.addr=0xaaaaf2f75020
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1005] L-✓construct.1016 @ctx.addr=0xaaaae8508a10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1005] L-✓construct.1002 @ctx.addr=0xaaaade438500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1005] _mul_tensor.1178 @ctx.addr=0xaaaaf5833fd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1178(
- %para4477 : Tensor(F32)[] # x
- , %para4478 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4477, %para4478) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1006] ↓construct.1033 @ctx.addr=0xaaaaeb92bbe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1033(
- %para4479 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4479) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/2-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1007] L-✓construct.1007 @ctx.addr=0xaaaadec89ce0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1007] L-✓construct.1010 @ctx.addr=0xaaaae86056a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1007] L-✓construct.1013 @ctx.addr=0xaaaaf48caab0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1007] L-✓construct.1016 @ctx.addr=0xaaaae1ecb6e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1007] L-✓construct.1002 @ctx.addr=0xaaaad7f58460
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1007] _mul_tensor.1179 @ctx.addr=0xaaaae85507f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1179(
- %para4480 : Tensor(F32)[] # x
- , %para4481 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4480, %para4481) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1008] ↓construct.1035 @ctx.addr=0xaaaae1ef8fc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1035(
- %para4482 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4482) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1009] L-✓construct.1007 @ctx.addr=0xaaaae20afa10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1009] L-✓construct.1010 @ctx.addr=0xaaaae232afe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1009] L-✓construct.1013 @ctx.addr=0xaaaae6cc16e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1009] L-✓construct.1016 @ctx.addr=0xaaaafaed95e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1009] L-✓construct.1002 @ctx.addr=0xaaaaf2eacde0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1009] _mul_tensor.1180 @ctx.addr=0xaaaae85c2880
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1180(
- %para4483 : Tensor(F32)[] # x
- , %para4484 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4483, %para4484) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1010] ↓construct.1037 @ctx.addr=0xaaaae2004450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1037(
- %para4485 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4485) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1011] L-✓construct.1007 @ctx.addr=0xaaaae24cd3f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1011] L-✓construct.1010 @ctx.addr=0xaaaae2490fa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1011] L-✓construct.1013 @ctx.addr=0xaaaae6f7be00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1011] L-✓construct.1016 @ctx.addr=0xaaaaf1dec950
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1011] L-✓construct.1002 @ctx.addr=0xaaaad9a2a140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1011] _mul_tensor.1181 @ctx.addr=0xaaaaf0336340
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1181(
- %para4486 : Tensor(F32)[] # x
- , %para4487 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4486, %para4487) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1012] ↓construct.1039 @ctx.addr=0xaaaaec636cd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1039(
- %para4488 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4488) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/3-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1013] L-✓construct.1007 @ctx.addr=0xaaaaf0e658a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1013] L-✓construct.1010 @ctx.addr=0xaaaad9252a10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1013] L-✓construct.1013 @ctx.addr=0xaaaaf460da40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1013] L-✓construct.1016 @ctx.addr=0xaaaaf5f9d2e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1013] L-✓construct.1002 @ctx.addr=0xaaaaf70a2a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1013] _mul_tensor.1182 @ctx.addr=0xaaaae1013a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1182(
- %para4489 : Tensor(F32)[] # x
- , %para4490 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4489, %para4490) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1014] ↓construct.1041 @ctx.addr=0xaaaae2536110
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1041(
- %para4491 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4491) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1015] L-✓construct.1007 @ctx.addr=0xaaaaddb2db20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1015] L-✓construct.1010 @ctx.addr=0xaaaaea1c1ea0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1015] L-✓construct.1013 @ctx.addr=0xaaaaf2123a70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1015] L-✓construct.1016 @ctx.addr=0xaaaad5a1dec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1015] L-✓construct.1002 @ctx.addr=0xaaaaf126ccc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1015] _mul_tensor.1183 @ctx.addr=0xaaaae84872a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1183(
- %para4492 : Tensor(F32)[] # x
- , %para4493 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4492, %para4493) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1016] ↓construct.1043 @ctx.addr=0xaaaaf733e510
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1043(
- %para4494 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4494) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1017] L-✓construct.1007 @ctx.addr=0xaaaae82431d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1017] L-✓construct.1010 @ctx.addr=0xaaaaf363d3c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1017] L-✓construct.1013 @ctx.addr=0xaaaaf4d0b350
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1017] L-✓construct.1016 @ctx.addr=0xaaaae85cccd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1017] L-✓construct.1002 @ctx.addr=0xaaaae84a6610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1017] _mul_tensor.1184 @ctx.addr=0xaaaae200ded0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1184(
- %para4495 : Tensor(F32)[] # x
- , %para4496 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4495, %para4496) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1018] ↓construct.1045 @ctx.addr=0xaaaae24e76c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1045(
- %para4497 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4497) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/4-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1019] L-✓construct.1007 @ctx.addr=0xaaaae240f3a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1019] L-✓construct.1010 @ctx.addr=0xaaaaefbd02f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1019] L-✓construct.1013 @ctx.addr=0xaaaaeb204850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1019] L-✓construct.1016 @ctx.addr=0xaaaae2111be0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1019] L-✓construct.1002 @ctx.addr=0xaaaae10f04f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1019] _mul_tensor.1185 @ctx.addr=0xaaaae1f39eb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1185(
- %para4498 : Tensor(F32)[] # x
- , %para4499 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4498, %para4499) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1020] ↓construct.1047 @ctx.addr=0xaaaae208c810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1047(
- %para4500 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4500) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1021] L-✓construct.1007 @ctx.addr=0xaaaad72a9490
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1021] L-✓construct.1010 @ctx.addr=0xaaaae2244aa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1021] L-✓construct.1013 @ctx.addr=0xaaaaef033fe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1021] L-✓construct.1016 @ctx.addr=0xaaaae837e040
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1021] L-✓construct.1002 @ctx.addr=0xaaaae1f2bcd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1021] _mul_tensor.1186 @ctx.addr=0xaaaae8308570
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1186(
- %para4501 : Tensor(F32)[] # x
- , %para4502 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4501, %para4502) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1022] ↓construct.1049 @ctx.addr=0xaaaad82f82f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1049(
- %para4503 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4503) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1023] L-✓construct.1007 @ctx.addr=0xaaaae5656570
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1023] L-✓construct.1010 @ctx.addr=0xaaaae1dd4f10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1023] L-✓construct.1013 @ctx.addr=0xaaaaef54e090
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1023] L-✓construct.1016 @ctx.addr=0xaaaaea9f5da0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1023] L-✓construct.1002 @ctx.addr=0xaaaae85093b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1023] _mul_tensor.1187 @ctx.addr=0xaaaae8568e10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1187(
- %para4504 : Tensor(F32)[] # x
- , %para4505 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4504, %para4505) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1024] ↓construct.1051 @ctx.addr=0xaaaae1fba760
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1051(
- %para4506 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4506) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/5-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1025] L-✓construct.1007 @ctx.addr=0xaaaafae1b2b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1025] L-✓construct.1010 @ctx.addr=0xaaaad94298b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1025] L-✓construct.1013 @ctx.addr=0xaaaad9437630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1025] L-✓construct.1016 @ctx.addr=0xaaaae85919b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1025] L-✓construct.1002 @ctx.addr=0xaaaad8248910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1025] _mul_tensor.1188 @ctx.addr=0xaaaae24ad6c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1188(
- %para4507 : Tensor(F32)[] # x
- , %para4508 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4507, %para4508) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1026] ↓construct.1053 @ctx.addr=0xaaaae2536a10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1053(
- %para4509 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4509) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1027] L-✓construct.1007 @ctx.addr=0xaaaae87ac860
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1027] L-✓construct.1010 @ctx.addr=0xaaaae7195b70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1027] L-✓construct.1013 @ctx.addr=0xaaaaedd6d880
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1027] L-✓construct.1016 @ctx.addr=0xaaaaf2497350
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1027] L-✓construct.1002 @ctx.addr=0xaaaadec0e700
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1027] _mul_tensor.1189 @ctx.addr=0xaaaae8e93770
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1189(
- %para4510 : Tensor(F32)[] # x
- , %para4511 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4510, %para4511) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1028] ↓construct.1055 @ctx.addr=0xaaaae8e93560
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1055(
- %para4512 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4512) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1029] L-✓construct.1007 @ctx.addr=0xaaaae22ba110
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1029] L-✓construct.1010 @ctx.addr=0xaaaae851a800
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1029] L-✓construct.1013 @ctx.addr=0xaaaade43b8f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1029] L-✓construct.1016 @ctx.addr=0xaaaafae70ae0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1029] L-✓construct.1002 @ctx.addr=0xaaaae1dc1270
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1029] _mul_tensor.1190 @ctx.addr=0xaaaad6a292f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1190(
- %para4513 : Tensor(F32)[] # x
- , %para4514 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4513, %para4514) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1030] ↓construct.1057 @ctx.addr=0xaaaafae92ae0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1057(
- %para4515 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4515) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/6-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1031] L-✓construct.1007 @ctx.addr=0xaaaae2566480
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1031] L-✓construct.1010 @ctx.addr=0xaaaaf71b5480
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1031] L-✓construct.1013 @ctx.addr=0xaaaae243dfa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1031] L-✓construct.1016 @ctx.addr=0xaaaaedde9bb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1031] L-✓construct.1002 @ctx.addr=0xaaaae1f6d9b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1031] _mul_tensor.1191 @ctx.addr=0xaaaae548d600
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1191(
- %para4516 : Tensor(F32)[] # x
- , %para4517 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4516, %para4517) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1032] ↓construct.1059 @ctx.addr=0xaaaaf076ac40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1059(
- %para4518 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4518) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1033] L-✓construct.1007 @ctx.addr=0xaaaae20729d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1033] L-✓construct.1010 @ctx.addr=0xaaaaf21c8e00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1033] L-✓construct.1013 @ctx.addr=0xaaaaee563a80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1033] L-✓construct.1016 @ctx.addr=0xaaaae2598740
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1033] L-✓construct.1002 @ctx.addr=0xaaaae1f29940
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1033] _mul_tensor.1192 @ctx.addr=0xaaaafadaa110
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1192(
- %para4519 : Tensor(F32)[] # x
- , %para4520 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4519, %para4520) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1034] ↓construct.1061 @ctx.addr=0xaaaae22e4c40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1061(
- %para4521 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4521) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1035] L-✓construct.1007 @ctx.addr=0xaaaae207b2b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1035] L-✓construct.1010 @ctx.addr=0xaaaae1f80930
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1035] L-✓construct.1013 @ctx.addr=0xaaaaf05e8ac0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1035] L-✓construct.1016 @ctx.addr=0xaaaae24ec650
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1035] L-✓construct.1002 @ctx.addr=0xaaaaf71fce60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1035] _mul_tensor.1193 @ctx.addr=0xaaaadc861920
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1193(
- %para4522 : Tensor(F32)[] # x
- , %para4523 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4522, %para4523) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1036] ↓construct.1063 @ctx.addr=0xaaaae33399e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1063(
- %para4524 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4524) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/7-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1037] L-✓construct.1007 @ctx.addr=0xaaaadd113c10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1037] L-✓construct.1010 @ctx.addr=0xaaaaecb0c2a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1037] L-✓construct.1013 @ctx.addr=0xaaaae8d82970
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1037] L-✓construct.1016 @ctx.addr=0xaaaae8434c40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1037] L-✓construct.1002 @ctx.addr=0xaaaadc5a3e80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1037] _mul_tensor.1194 @ctx.addr=0xaaaaf36da8f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1194(
- %para4525 : Tensor(F32)[] # x
- , %para4526 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4525, %para4526) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1038] ↓construct.1065 @ctx.addr=0xaaaae0711050
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1065(
- %para4527 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4527) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1039] L-✓construct.1007 @ctx.addr=0xaaaad6e43970
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1039] L-✓construct.1010 @ctx.addr=0xaaaaf37bc200
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1039] L-✓construct.1013 @ctx.addr=0xaaaad68d1fd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1039] L-✓construct.1016 @ctx.addr=0xaaaad9b46af0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1039] L-✓construct.1002 @ctx.addr=0xaaaae1fdcf10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1039] _mul_tensor.1195 @ctx.addr=0xaaaae82b6d50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1195(
- %para4528 : Tensor(F32)[] # x
- , %para4529 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4528, %para4529) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1040] ↓construct.1067 @ctx.addr=0xaaaae8409820
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1067(
- %para4530 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4530) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1041] L-✓construct.1007 @ctx.addr=0xaaaae84e2200
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1041] L-✓construct.1010 @ctx.addr=0xaaaae6ccf2c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1041] L-✓construct.1013 @ctx.addr=0xaaaaf0733600
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1041] L-✓construct.1016 @ctx.addr=0xaaaaded21120
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1041] L-✓construct.1002 @ctx.addr=0xaaaae8297140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1041] _mul_tensor.1196 @ctx.addr=0xaaaae8737210
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1196(
- %para4531 : Tensor(F32)[] # x
- , %para4532 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4531, %para4532) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1042] ↓construct.1069 @ctx.addr=0xaaaae1f3ae40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1069(
- %para4533 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4533) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/8-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1043] L-✓construct.1007 @ctx.addr=0xaaaafae1a320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1043] L-✓construct.1010 @ctx.addr=0xaaaae85912b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1043] L-✓construct.1013 @ctx.addr=0xaaaae2359750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1043] L-✓construct.1016 @ctx.addr=0xaaaae1fd4320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1043] L-✓construct.1002 @ctx.addr=0xaaaaea9f5780
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1043] _mul_tensor.1197 @ctx.addr=0xaaaae0ca5210
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1197(
- %para4534 : Tensor(F32)[] # x
- , %para4535 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4534, %para4535) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1044] ↓construct.1071 @ctx.addr=0xaaaaf0c3edf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1071(
- %para4536 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4536) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1045] L-✓construct.1007 @ctx.addr=0xaaaaf2f72050
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1045] L-✓construct.1010 @ctx.addr=0xaaaae9354e30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1045] L-✓construct.1013 @ctx.addr=0xaaaaea2cc1c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1045] L-✓construct.1016 @ctx.addr=0xaaaaf3e9f640
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1045] L-✓construct.1002 @ctx.addr=0xaaaae21be320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1045] _mul_tensor.1198 @ctx.addr=0xaaaae1ef17c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1198(
- %para4537 : Tensor(F32)[] # x
- , %para4538 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4537, %para4538) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1046] ↓construct.1073 @ctx.addr=0xaaaaf241aa50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1073(
- %para4539 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4539) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1047] L-✓construct.1007 @ctx.addr=0xaaaae121b190
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1047] L-✓construct.1010 @ctx.addr=0xaaaade9c12f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1047] L-✓construct.1013 @ctx.addr=0xaaaad824cb00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1047] L-✓construct.1016 @ctx.addr=0xaaaae24ff480
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1047] L-✓construct.1002 @ctx.addr=0xaaaafaebfb90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1047] _mul_tensor.1199 @ctx.addr=0xaaaae82391a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1199(
- %para4540 : Tensor(F32)[] # x
- , %para4541 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4540, %para4541) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1048] ↓construct.1075 @ctx.addr=0xaaaae828df70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1075(
- %para4542 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4542) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/9-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1049] L-✓construct.1007 @ctx.addr=0xaaaae35dd1c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1049] L-✓construct.1010 @ctx.addr=0xaaaaf03c8300
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1049] L-✓construct.1013 @ctx.addr=0xaaaae22b5cf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1049] L-✓construct.1016 @ctx.addr=0xaaaaebd89530
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1049] L-✓construct.1002 @ctx.addr=0xaaaaf1277310
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1049] _mul_tensor.1200 @ctx.addr=0xaaaaecc26840
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1200(
- %para4543 : Tensor(F32)[] # x
- , %para4544 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4543, %para4544) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1050] ↓construct.1077 @ctx.addr=0xaaaaf740b220
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1077(
- %para4545 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4545) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1051] L-✓construct.1007 @ctx.addr=0xaaaae2147b60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1051] L-✓construct.1010 @ctx.addr=0xaaaad66e15f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1051] L-✓construct.1013 @ctx.addr=0xaaaaf12795e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1051] L-✓construct.1016 @ctx.addr=0xaaaae84da4d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1051] L-✓construct.1002 @ctx.addr=0xaaaad933a850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1051] _mul_tensor.1201 @ctx.addr=0xaaaae1e42940
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1201(
- %para4546 : Tensor(F32)[] # x
- , %para4547 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4546, %para4547) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1052] ↓construct.1079 @ctx.addr=0xaaaaec854b40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1079(
- %para4548 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4548) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1053] L-✓construct.1007 @ctx.addr=0xaaaaef039c70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1053] L-✓construct.1010 @ctx.addr=0xaaaaf2aa55c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1053] L-✓construct.1013 @ctx.addr=0xaaaae9db8440
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1053] L-✓construct.1016 @ctx.addr=0xaaaafa6f60c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1053] L-✓construct.1002 @ctx.addr=0xaaaaea94fa20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1053] _mul_tensor.1202 @ctx.addr=0xaaaaec644000
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1202(
- %para4549 : Tensor(F32)[] # x
- , %para4550 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4549, %para4550) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1054] ↓construct.1081 @ctx.addr=0xaaaaecb47270
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1081(
- %para4551 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4551) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/10-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1055] L-✓construct.1007 @ctx.addr=0xaaaad61fb360
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1055] L-✓construct.1010 @ctx.addr=0xaaaaf4906540
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1055] L-✓construct.1013 @ctx.addr=0xaaaaf0476980
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1055] L-✓construct.1016 @ctx.addr=0xaaaae8d88230
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1055] L-✓construct.1002 @ctx.addr=0xaaaae83bab60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1055] _mul_tensor.1203 @ctx.addr=0xaaaae861ae00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1203(
- %para4552 : Tensor(F32)[] # x
- , %para4553 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4552, %para4553) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1056] ↓construct.1083 @ctx.addr=0xaaaae1dc83e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1083(
- %para4554 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4554) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1057] L-✓construct.1007 @ctx.addr=0xaaaad7417510
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1057] L-✓construct.1010 @ctx.addr=0xaaaae1236040
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1057] L-✓construct.1013 @ctx.addr=0xaaaae8222b10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1057] L-✓construct.1016 @ctx.addr=0xaaaaeede3030
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1057] L-✓construct.1002 @ctx.addr=0xaaaae85ec190
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1057] _mul_tensor.1204 @ctx.addr=0xaaaad7f563f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1204(
- %para4555 : Tensor(F32)[] # x
- , %para4556 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4555, %para4556) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1058] ↓construct.1085 @ctx.addr=0xaaaadc5b39f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1085(
- %para4557 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4557) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1059] L-✓construct.1007 @ctx.addr=0xaaaaf18aa810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1059] L-✓construct.1010 @ctx.addr=0xaaaaf17823d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1059] L-✓construct.1013 @ctx.addr=0xaaaaf6e9a950
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1059] L-✓construct.1016 @ctx.addr=0xaaaaef83c1f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1059] L-✓construct.1002 @ctx.addr=0xaaaade555470
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1059] _mul_tensor.1205 @ctx.addr=0xaaaae8d0ec10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1205(
- %para4558 : Tensor(F32)[] # x
- , %para4559 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4558, %para4559) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1060] ↓construct.1087 @ctx.addr=0xaaaae8d7f140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1087(
- %para4560 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4560) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/11-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1061] L-✓construct.1007 @ctx.addr=0xaaaad8a51cc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1061] L-✓construct.1010 @ctx.addr=0xaaaaf0c7bde0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1061] L-✓construct.1013 @ctx.addr=0xaaaae6ee3a00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1061] L-✓construct.1016 @ctx.addr=0xaaaade47dc00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1061] L-✓construct.1002 @ctx.addr=0xaaaae3332bf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1061] _mul_tensor.1206 @ctx.addr=0xaaaad7211b80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1206(
- %para4561 : Tensor(F32)[] # x
- , %para4562 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4561, %para4562) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1062] ↓construct.1089 @ctx.addr=0xaaaad7216350
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1089(
- %para4563 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4563) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1063] L-✓construct.1007 @ctx.addr=0xaaaae6eebe70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1063] L-✓construct.1010 @ctx.addr=0xaaaaefe1f5d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1063] L-✓construct.1013 @ctx.addr=0xaaaae8a5a610
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1063] L-✓construct.1016 @ctx.addr=0xaaaaf0262c40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1063] L-✓construct.1002 @ctx.addr=0xaaaaec640710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1063] _mul_tensor.1207 @ctx.addr=0xaaaafa7c7990
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1207(
- %para4564 : Tensor(F32)[] # x
- , %para4565 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4564, %para4565) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1064] ↓construct.1091 @ctx.addr=0xaaaade487d10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1091(
- %para4566 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4566) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1065] L-✓construct.1007 @ctx.addr=0xaaaaf4902440
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1065] L-✓construct.1010 @ctx.addr=0xaaaaf4ef1400
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1065] L-✓construct.1013 @ctx.addr=0xaaaaf5035450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1065] L-✓construct.1016 @ctx.addr=0xaaaaf4ef08c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1065] L-✓construct.1002 @ctx.addr=0xaaaad8898760
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1065] _mul_tensor.1208 @ctx.addr=0xaaaadec8cb50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1208(
- %para4567 : Tensor(F32)[] # x
- , %para4568 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4567, %para4568) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1066] ↓construct.1093 @ctx.addr=0xaaaaf2669e00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1093(
- %para4569 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4569) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/12-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1067] L-✓construct.1007 @ctx.addr=0xaaaaf43aeea0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1067] L-✓construct.1010 @ctx.addr=0xaaaaec932dd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1067] L-✓construct.1013 @ctx.addr=0xaaaaeb1f8360
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1067] L-✓construct.1016 @ctx.addr=0xaaaadc6805d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1067] L-✓construct.1002 @ctx.addr=0xaaaae2668fc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1067] _mul_tensor.1209 @ctx.addr=0xaaaaed0caba0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1209(
- %para4570 : Tensor(F32)[] # x
- , %para4571 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4570, %para4571) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1068] ↓construct.1095 @ctx.addr=0xaaaaf0c3c7e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1095(
- %para4572 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4572) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1069] L-✓construct.1007 @ctx.addr=0xaaaaf0c38ef0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1069] L-✓construct.1010 @ctx.addr=0xaaaad82fef90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1069] L-✓construct.1013 @ctx.addr=0xaaaade4403d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1069] L-✓construct.1016 @ctx.addr=0xaaaaf17d3b90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1069] L-✓construct.1002 @ctx.addr=0xaaaae0db8d60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1069] _mul_tensor.1210 @ctx.addr=0xaaaae2509c10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1210(
- %para4573 : Tensor(F32)[] # x
- , %para4574 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4573, %para4574) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1070] ↓construct.1097 @ctx.addr=0xaaaae8519a70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1097(
- %para4575 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4575) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1071] L-✓construct.1007 @ctx.addr=0xaaaae2576bb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1071] L-✓construct.1010 @ctx.addr=0xaaaae24401d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1071] L-✓construct.1013 @ctx.addr=0xaaaafadf9250
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1071] L-✓construct.1016 @ctx.addr=0xaaaae247ac20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1071] L-✓construct.1002 @ctx.addr=0xaaaae24ef100
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1071] _mul_tensor.1211 @ctx.addr=0xaaaae2275af0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1211(
- %para4576 : Tensor(F32)[] # x
- , %para4577 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4576, %para4577) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1072] ↓construct.1099 @ctx.addr=0xaaaae25ad230
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1099(
- %para4578 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4578) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/13-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1073] L-✓construct.1007 @ctx.addr=0xaaaaf1e63b70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1073] L-✓construct.1010 @ctx.addr=0xaaaae20c0180
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1073] L-✓construct.1013 @ctx.addr=0xaaaae8275260
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1073] L-✓construct.1016 @ctx.addr=0xaaaae85c5640
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1073] L-✓construct.1002 @ctx.addr=0xaaaad8142a20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1073] _mul_tensor.1212 @ctx.addr=0xaaaaf248f960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1212(
- %para4579 : Tensor(F32)[] # x
- , %para4580 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4579, %para4580) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1074] ↓construct.1101 @ctx.addr=0xaaaaf3ea8220
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1101(
- %para4581 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4581) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1075] L-✓construct.1007 @ctx.addr=0xaaaae258df40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1075] L-✓construct.1010 @ctx.addr=0xaaaae82bb630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1075] L-✓construct.1013 @ctx.addr=0xaaaae83b8700
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1075] L-✓construct.1016 @ctx.addr=0xaaaafad93c70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1075] L-✓construct.1002 @ctx.addr=0xaaaae23f7f60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1075] _mul_tensor.1213 @ctx.addr=0xaaaafae94fc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1213(
- %para4582 : Tensor(F32)[] # x
- , %para4583 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4582, %para4583) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1076] ↓construct.1103 @ctx.addr=0xaaaad6a2abf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1103(
- %para4584 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4584) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1077] L-✓construct.1007 @ctx.addr=0xaaaae1e60050
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1077] L-✓construct.1010 @ctx.addr=0xaaaae8362a50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1077] L-✓construct.1013 @ctx.addr=0xaaaae2431460
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1077] L-✓construct.1016 @ctx.addr=0xaaaaec7aee80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1077] L-✓construct.1002 @ctx.addr=0xaaaaee0ffc80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1077] _mul_tensor.1214 @ctx.addr=0xaaaaf362eb10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1214(
- %para4585 : Tensor(F32)[] # x
- , %para4586 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4585, %para4586) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1078] ↓construct.1105 @ctx.addr=0xaaaae2698d40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1105(
- %para4587 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4587) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/14-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1079] L-✓construct.1007 @ctx.addr=0xaaaae2102950
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1079] L-✓construct.1010 @ctx.addr=0xaaaae2291320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1079] L-✓construct.1013 @ctx.addr=0xaaaae24d0900
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1079] L-✓construct.1016 @ctx.addr=0xaaaae2145df0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1079] L-✓construct.1002 @ctx.addr=0xaaaad59c4890
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1079] _mul_tensor.1215 @ctx.addr=0xaaaae1e51d30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1215(
- %para4588 : Tensor(F32)[] # x
- , %para4589 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4588, %para4589) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1080] ↓construct.1107 @ctx.addr=0xaaaae1e512e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1107(
- %para4590 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4590) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1081] L-✓construct.1007 @ctx.addr=0xaaaaf605f790
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1081] L-✓construct.1010 @ctx.addr=0xaaaaee787bd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1081] L-✓construct.1013 @ctx.addr=0xaaaaf43bd770
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1081] L-✓construct.1016 @ctx.addr=0xaaaae254cd50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1081] L-✓construct.1002 @ctx.addr=0xaaaae20417a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1081] _mul_tensor.1216 @ctx.addr=0xaaaae2642030
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1216(
- %para4591 : Tensor(F32)[] # x
- , %para4592 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4591, %para4592) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1082] ↓construct.1109 @ctx.addr=0xaaaae25fd5f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1109(
- %para4593 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4593) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1083] L-✓construct.1007 @ctx.addr=0xaaaafae32cb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1083] L-✓construct.1010 @ctx.addr=0xaaaae22c1700
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1083] L-✓construct.1013 @ctx.addr=0xaaaae258ad70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1083] L-✓construct.1016 @ctx.addr=0xaaaae21a0a00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1083] L-✓construct.1002 @ctx.addr=0xaaaae21da130
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1083] _mul_tensor.1217 @ctx.addr=0xaaaae2214f10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1217(
- %para4594 : Tensor(F32)[] # x
- , %para4595 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4594, %para4595) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1084] ↓construct.1111 @ctx.addr=0xaaaae22144c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1111(
- %para4596 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4596) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/15-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1085] L-✓construct.1007 @ctx.addr=0xaaaae81a7710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1085] L-✓construct.1010 @ctx.addr=0xaaaae265c900
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1085] L-✓construct.1013 @ctx.addr=0xaaaaf0726390
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1085] L-✓construct.1016 @ctx.addr=0xaaaae2221620
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1085] L-✓construct.1002 @ctx.addr=0xaaaae1f41bc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1085] _mul_tensor.1218 @ctx.addr=0xaaaaf1775330
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1218(
- %para4597 : Tensor(F32)[] # x
- , %para4598 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4597, %para4598) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1086] ↓construct.1113 @ctx.addr=0xaaaaf17748e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1113(
- %para4599 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4599) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1087] L-✓construct.1007 @ctx.addr=0xaaaae1fc74c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1087] L-✓construct.1010 @ctx.addr=0xaaaae8358f40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1087] L-✓construct.1013 @ctx.addr=0xaaaae1e1e7a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1087] L-✓construct.1016 @ctx.addr=0xaaaae23c1ad0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1087] L-✓construct.1002 @ctx.addr=0xaaaae1fa82b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1087] _mul_tensor.1219 @ctx.addr=0xaaaae8216b70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1219(
- %para4600 : Tensor(F32)[] # x
- , %para4601 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4600, %para4601) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1088] ↓construct.1115 @ctx.addr=0xaaaaec6041c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1115(
- %para4602 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4602) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1089] L-✓construct.1007 @ctx.addr=0xaaaae24a9a30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1089] L-✓construct.1010 @ctx.addr=0xaaaae85648d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1089] L-✓construct.1013 @ctx.addr=0xaaaaf4dd1390
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1089] L-✓construct.1016 @ctx.addr=0xaaaafad95a00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1089] L-✓construct.1002 @ctx.addr=0xaaaae20a45d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1089] _mul_tensor.1220 @ctx.addr=0xaaaae240a910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1220(
- %para4603 : Tensor(F32)[] # x
- , %para4604 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4603, %para4604) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1090] ↓construct.1117 @ctx.addr=0xaaaae2409ec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1117(
- %para4605 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4605) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/16-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1091] L-✓construct.1007 @ctx.addr=0xaaaae81c5e20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1091] L-✓construct.1010 @ctx.addr=0xaaaae84019f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1091] L-✓construct.1013 @ctx.addr=0xaaaae2206810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1091] L-✓construct.1016 @ctx.addr=0xaaaaefc3db90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1091] L-✓construct.1002 @ctx.addr=0xaaaaebbe4720
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1091] _mul_tensor.1221 @ctx.addr=0xaaaae81d4be0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1221(
- %para4606 : Tensor(F32)[] # x
- , %para4607 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4606, %para4607) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1092] ↓construct.1119 @ctx.addr=0xaaaae81d4190
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1119(
- %para4608 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4608) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1093] L-✓construct.1007 @ctx.addr=0xaaaae235c950
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1093] L-✓construct.1010 @ctx.addr=0xaaaaf3632560
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1093] L-✓construct.1013 @ctx.addr=0xaaaae1eb7ee0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1093] L-✓construct.1016 @ctx.addr=0xaaaae1dac430
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1093] L-✓construct.1002 @ctx.addr=0xaaaae817f6d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1093] _mul_tensor.1222 @ctx.addr=0xaaaaf2a66280
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1222(
- %para4609 : Tensor(F32)[] # x
- , %para4610 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4609, %para4610) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1094] ↓construct.1121 @ctx.addr=0xaaaae84493c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1121(
- %para4611 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4611) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1095] L-✓construct.1007 @ctx.addr=0xaaaae1dc8fb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1095] L-✓construct.1010 @ctx.addr=0xaaaafaea8280
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1095] L-✓construct.1013 @ctx.addr=0xaaaae85bac00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1095] L-✓construct.1016 @ctx.addr=0xaaaae82780a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1095] L-✓construct.1002 @ctx.addr=0xaaaad741acb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1095] _mul_tensor.1223 @ctx.addr=0xaaaae829f2d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1223(
- %para4612 : Tensor(F32)[] # x
- , %para4613 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4612, %para4613) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1096] ↓construct.1123 @ctx.addr=0xaaaae829e880
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1123(
- %para4614 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4614) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/17-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1097] L-✓construct.1007 @ctx.addr=0xaaaae22796c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1097] L-✓construct.1010 @ctx.addr=0xaaaae849ddb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1097] L-✓construct.1013 @ctx.addr=0xaaaae83f1450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1097] L-✓construct.1016 @ctx.addr=0xaaaae84f0cf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1097] L-✓construct.1002 @ctx.addr=0xaaaaf1f60c00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1097] _mul_tensor.1224 @ctx.addr=0xaaaae8347cf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1224(
- %para4615 : Tensor(F32)[] # x
- , %para4616 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4615, %para4616) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1098] ↓construct.1125 @ctx.addr=0xaaaae83472a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1125(
- %para4617 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4617) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1099] L-✓construct.1007 @ctx.addr=0xaaaae26808a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1099] L-✓construct.1010 @ctx.addr=0xaaaae2661d40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1099] L-✓construct.1013 @ctx.addr=0xaaaae25f6c70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1099] L-✓construct.1016 @ctx.addr=0xaaaae26775e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1099] L-✓construct.1002 @ctx.addr=0xaaaae2694960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1099] _mul_tensor.1225 @ctx.addr=0xaaaae26011a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1225(
- %para4618 : Tensor(F32)[] # x
- , %para4619 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4618, %para4619) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1100] ↓construct.1127 @ctx.addr=0xaaaae2600750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1127(
- %para4620 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4620) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1101] L-✓construct.1007 @ctx.addr=0xaaaae26bb1b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1101] L-✓construct.1010 @ctx.addr=0xaaaae2726e00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1101] L-✓construct.1013 @ctx.addr=0xaaaae272dce0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1101] L-✓construct.1016 @ctx.addr=0xaaaae2734db0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1101] L-✓construct.1002 @ctx.addr=0xaaaae2742260
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1101] _mul_tensor.1226 @ctx.addr=0xaaaae274f0a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1226(
- %para4621 : Tensor(F32)[] # x
- , %para4622 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4621, %para4622) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1102] ↓construct.1129 @ctx.addr=0xaaaae274e650
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1129(
- %para4623 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4623) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/18-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1103] L-✓construct.1007 @ctx.addr=0xaaaae275c000
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1103] L-✓construct.1010 @ctx.addr=0xaaaae276e1d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1103] L-✓construct.1013 @ctx.addr=0xaaaae27750a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1103] L-✓construct.1016 @ctx.addr=0xaaaae277c190
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1103] L-✓construct.1002 @ctx.addr=0xaaaae27cc9c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1103] _mul_tensor.1227 @ctx.addr=0xaaaae27d8c50
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1227(
- %para4624 : Tensor(F32)[] # x
- , %para4625 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4624, %para4625) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1104] ↓construct.1131 @ctx.addr=0xaaaae27d8200
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1131(
- %para4626 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4626) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1105] L-✓construct.1007 @ctx.addr=0xaaaae27e5680
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1105] L-✓construct.1010 @ctx.addr=0xaaaae27f7a60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1105] L-✓construct.1013 @ctx.addr=0xaaaae27fe940
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1105] L-✓construct.1016 @ctx.addr=0xaaaae2805a10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1105] L-✓construct.1002 @ctx.addr=0xaaaae2812eb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1105] _mul_tensor.1228 @ctx.addr=0xaaaae281fcf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1228(
- %para4627 : Tensor(F32)[] # x
- , %para4628 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4627, %para4628) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1106] ↓construct.1133 @ctx.addr=0xaaaae281f2a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1133(
- %para4629 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4629) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1107] L-✓construct.1007 @ctx.addr=0xaaaae282ca40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1107] L-✓construct.1010 @ctx.addr=0xaaaae283ee20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1107] L-✓construct.1013 @ctx.addr=0xaaaae2845ce0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1107] L-✓construct.1016 @ctx.addr=0xaaaae284cdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1107] L-✓construct.1002 @ctx.addr=0xaaaae285a260
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1107] _mul_tensor.1229 @ctx.addr=0xaaaae2867080
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1229(
- %para4630 : Tensor(F32)[] # x
- , %para4631 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4630, %para4631) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1108] ↓construct.1135 @ctx.addr=0xaaaae2866630
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1135(
- %para4632 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4632) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/19-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1109] L-✓construct.1007 @ctx.addr=0xaaaae2873dd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1109] L-✓construct.1010 @ctx.addr=0xaaaae28861c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1109] L-✓construct.1013 @ctx.addr=0xaaaae288d0a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1109] L-✓construct.1016 @ctx.addr=0xaaaae2894170
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1109] L-✓construct.1002 @ctx.addr=0xaaaae28e31d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1109] _mul_tensor.1230 @ctx.addr=0xaaaae28ef2d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1230(
- %para4633 : Tensor(F32)[] # x
- , %para4634 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4633, %para4634) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1110] ↓construct.1137 @ctx.addr=0xaaaae28ee880
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1137(
- %para4635 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4635) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1111] L-✓construct.1007 @ctx.addr=0xaaaae28fbd00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1111] L-✓construct.1010 @ctx.addr=0xaaaae290e0e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1111] L-✓construct.1013 @ctx.addr=0xaaaae2914fa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1111] L-✓construct.1016 @ctx.addr=0xaaaae291c070
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1111] L-✓construct.1002 @ctx.addr=0xaaaae2929510
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1111] _mul_tensor.1231 @ctx.addr=0xaaaae2936360
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1231(
- %para4636 : Tensor(F32)[] # x
- , %para4637 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4636, %para4637) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1112] ↓construct.1139 @ctx.addr=0xaaaae2935910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1139(
- %para4638 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4638) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1113] L-✓construct.1007 @ctx.addr=0xaaaae29430b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1113] L-✓construct.1010 @ctx.addr=0xaaaae29554a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1113] L-✓construct.1013 @ctx.addr=0xaaaae295e210
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1113] L-✓construct.1016 @ctx.addr=0xaaaae2965300
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1113] L-✓construct.1002 @ctx.addr=0xaaaae36ffb20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1113] _mul_tensor.1232 @ctx.addr=0xaaaae370c950
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1232(
- %para4639 : Tensor(F32)[] # x
- , %para4640 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4639, %para4640) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1114] ↓construct.1141 @ctx.addr=0xaaaae370bf00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1141(
- %para4641 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4641) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/20-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1115] L-✓construct.1007 @ctx.addr=0xaaaae37196a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1115] L-✓construct.1010 @ctx.addr=0xaaaae372ba90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1115] L-✓construct.1013 @ctx.addr=0xaaaae3732980
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1115] L-✓construct.1016 @ctx.addr=0xaaaae3739a60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1115] L-✓construct.1002 @ctx.addr=0xaaaae3787270
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1115] _mul_tensor.1233 @ctx.addr=0xaaaae3793400
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1233(
- %para4642 : Tensor(F32)[] # x
- , %para4643 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4642, %para4643) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1116] ↓construct.1143 @ctx.addr=0xaaaae37929b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1143(
- %para4644 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4644) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1117] L-✓construct.1007 @ctx.addr=0xaaaae379fe30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1117] L-✓construct.1010 @ctx.addr=0xaaaae37b2210
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1117] L-✓construct.1013 @ctx.addr=0xaaaae37b9e10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1117] L-✓construct.1016 @ctx.addr=0xaaaae37c0e90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1117] L-✓construct.1002 @ctx.addr=0xaaaae37ce330
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1117] _mul_tensor.1234 @ctx.addr=0xaaaae37db160
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1234(
- %para4645 : Tensor(F32)[] # x
- , %para4646 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4645, %para4646) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1118] ↓construct.1145 @ctx.addr=0xaaaae37da710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1145(
- %para4647 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4647) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1119] L-✓construct.1007 @ctx.addr=0xaaaae37e7eb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1119] L-✓construct.1010 @ctx.addr=0xaaaae37fa290
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1119] L-✓construct.1013 @ctx.addr=0xaaaae3801170
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1119] L-✓construct.1016 @ctx.addr=0xaaaae3808240
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1119] L-✓construct.1002 @ctx.addr=0xaaaae38156e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1119] _mul_tensor.1235 @ctx.addr=0xaaaae3822510
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1235(
- %para4648 : Tensor(F32)[] # x
- , %para4649 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4648, %para4649) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1120] ↓construct.1147 @ctx.addr=0xaaaae3821ac0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1147(
- %para4650 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4650) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/21-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1121] L-✓construct.1007 @ctx.addr=0xaaaae382f260
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1121] L-✓construct.1010 @ctx.addr=0xaaaae3841640
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1121] L-✓construct.1013 @ctx.addr=0xaaaae3848500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1121] L-✓construct.1016 @ctx.addr=0xaaaae384f5d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1121] L-✓construct.1002 @ctx.addr=0xaaaae389b550
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1121] _mul_tensor.1236 @ctx.addr=0xaaaae38a7740
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1236(
- %para4651 : Tensor(F32)[] # x
- , %para4652 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4651, %para4652) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1122] ↓construct.1149 @ctx.addr=0xaaaae38a6cf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1149(
- %para4653 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4653) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1123] L-✓construct.1007 @ctx.addr=0xaaaae38b4170
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1123] L-✓construct.1010 @ctx.addr=0xaaaae38c6550
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1123] L-✓construct.1013 @ctx.addr=0xaaaae38cd430
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1123] L-✓construct.1016 @ctx.addr=0xaaaae38d4500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1123] L-✓construct.1002 @ctx.addr=0xaaaae38e19a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1123] _mul_tensor.1237 @ctx.addr=0xaaaae38ee7d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1237(
- %para4654 : Tensor(F32)[] # x
- , %para4655 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4654, %para4655) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1124] ↓construct.1151 @ctx.addr=0xaaaae38edd80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1151(
- %para4656 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4656) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB2-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1125] L-✓construct.1007 @ctx.addr=0xaaaae38fb520
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1125] L-✓construct.1010 @ctx.addr=0xaaaae390d910
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1125] L-✓construct.1013 @ctx.addr=0xaaaae39147d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1125] L-✓construct.1016 @ctx.addr=0xaaaae391b8a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1125] L-✓construct.1002 @ctx.addr=0xaaaae3928d40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1002[fg_717](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3253, %para3255) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][32, 64, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3254) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1165(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1165=L-↓construct.1165(@ctx.addr=0xaaaae81d8710) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1125] _mul_tensor.1238 @ctx.addr=0xaaaae3935b90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(43)/def _mul_tensor(x, y):/
- funcgraph fg_1238(
- %para4657 : Tensor(F32)[] # x
- , %para4658 : Tensor(F32)[16, 32, 32, 32] # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tensor_mul") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- %3 : Tensor(F32)[16, 32, 32, 32] = %2(%para4657, %para4658) #(Tensor(F32)[], Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB1-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/mul_impl.py(50)/ return F.tensor_mul(x, y)/
- }
-
-
- # [No.1126] ↓construct.1153 @ctx.addr=0xaaaae3935140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(327)/ if self.alpha <= 1:/
- funcgraph fg_1153(
- %para4659 : Tensor(F32)[16, 32, 32, 32] # Φout
- ) {
- Primitive::Return{prim_type=1}(%para4659) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/22-RRDB/RDB3-ResidualDenseBlock_5C/lrelu-LeakyReLU
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/activation.py(331)/ return out/
- }
-
-
- # [No.1127] L-✓construct.1007 @ctx.addr=0xaaaae39428e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1007[fg_720](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3258, %para3260) #(Tensor(F32)[16, 96, 32, 32], Ref[Tensor(F32)][32, 96, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3259) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1167(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1167=L-↓construct.1167(@ctx.addr=0xaaaae1e17960) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1127] L-✓construct.1010 @ctx.addr=0xaaaae3954c20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1010[fg_721](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3261, %para3263) #(Tensor(F32)[16, 128, 32, 32], Ref[Tensor(F32)][32, 128, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3262) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1168(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1168=L-↓construct.1168(@ctx.addr=0xaaaae1e05a90) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1127] L-✓construct.1013 @ctx.addr=0xaaaae395baf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1013[fg_722](
- ) {
- %1 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(32), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3264, %para3266) #(Tensor(F32)[16, 160, 32, 32], Ref[Tensor(F32)][32, 160, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 32, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3265) #(Tensor(F32)[16, 32, 32, 32], Ref[Tensor(F32)][32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 32, 32, 32] = FuncGraph::fg_1169(%2) #(Tensor(F32)[16, 32, 32, 32]) # fg_1169=L-↓construct.1169(@ctx.addr=0xaaaaea7a46d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1127] L-✓construct.1016 @ctx.addr=0xaaaae3962bd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1016[fg_723](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-Conv2D{prim_type=1}[pad_list=(I64(1), I64(1), I64(1), I64(1)), out_channel=I64(64), output_names=["output"], mode=I64(1), pad_mode=I64(0), pad=(I64(1), I64(1), I64(1), I64(1)), groups=I64(1), format="NCHW", group=I64(1), kernel_size=(I64(3), I64(3)), stride=(I64(1), I64(1), I64(1), I64(1)), input_names=["x", "w"], dilation=(I64(1), I64(1), I64(1), I64(1))](%para3267, %para3269) #(Tensor(F32)[16, 192, 32, 32], Ref[Tensor(F32)][64, 192, 3, 3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(258)/ output = self.conv2d(x, self.weight)/
- %2 : Tensor(F32)[16, 64, 32, 32] = DoSignaturePrimitive::S-Prim-BiasAdd{prim_type=1}[input_names=["x", "b"], output_names=["output"], format="NCHW"](%1, %para3268) #(Tensor(F32)[16, 64, 32, 32], Ref[Tensor(F32)][64]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(260)/ output = self.bias_add(output, self.bias)/
- %3 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1170(%2) #(Tensor(F32)[16, 64, 32, 32]) # fg_1170=L-↓construct.1170(@ctx.addr=0xaaaadcc890b0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- }
-
-
- # [No.1127] ms_len.93 @ctx.addr=0xaaaade559a60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(495)/def ms_len(data):/
- funcgraph fg_93(
- %para4660 : Tuple[] # data
- ) {
- %1 : Func = Primitive::getattr{prim_type=1}(%para2323, "__len__") #(Tuple[], String) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(497)/ return data.__len__()/
- %2 : I64 = %1() #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(497)/ return data.__len__()/
- Primitive::Return{prim_type=1}(%2) #(I64) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(497)/ return data.__len__()/
- }
-
-
- # [No.1127] _greater_scalar.1239 @ctx.addr=0xaaaae397ee20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/greater_impl.py(26)/def _greater_scalar(x, y):/
- funcgraph fg_1239(
- %para4661 : I64 # x
- , %para4662 : I64 # y
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/greater_impl.py(37)/ return F.scalar_gt(x, y)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "scalar_gt") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/greater_impl.py(37)/ return F.scalar_gt(x, y)/
- %3 : Bool = %2(%para4661, %para4662) #(I64, I64) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/greater_impl.py(37)/ return F.scalar_gt(x, y)/
- Primitive::Return{prim_type=1}(%3) #(Bool) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/greater_impl.py(37)/ return F.scalar_gt(x, y)/
- }
-
-
- # [No.1128] tuple_next.1240 @ctx.addr=0xaaaae3876600
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(800)/def tuple_next(xs):/
- funcgraph fg_1240(
- %para4663 : Tuple[Func] # xs
- ) {
- %1 : Func = Primitive::resolve{prim_type=1}(NameSpace::CommonOPS, MakeTuple) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(802)/ return xs[0], tail(xs)/
- %2 : Func = Primitive::resolve{prim_type=1}(NameSpace::CommonOPS, getitem) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(802)/ return xs[0], tail(xs)/
- %3 : Func = %2(%para4663, I64(0)) #(Tuple[Func], I64) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell @ctx.addr=0xaaaae3884660
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(802)/ return xs[0], tail(xs)/
- %4 : Func = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, tail) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(802)/ return xs[0], tail(xs)/
- %5 : Tuple[] = %4(%para4663) #(Tuple[Func]) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell @ctx.addr=0xaaaae3885a10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(802)/ return xs[0], tail(xs)/
- %6 : Tuple[Func,Tuple[]] = %1(%3, %5) #(Func, Tuple[]) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(802)/ return xs[0], tail(xs)/
- Primitive::Return{prim_type=1}(%6) #(Tuple[Func,Tuple[]]) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(802)/ return xs[0], tail(xs)/
- }
-
-
- # [No.1129] ⥁✓construct.702 @ctx.addr=0xaaaaf0986870
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*21]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*22]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*21] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*21]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae22d35c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*21]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae2299f40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf1779fc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*21], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaafaed7360) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaafaed7360
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1129] L-✗↓↓construct.1164 @ctx.addr=0xaaaae3b98b40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(306)/ if self.activation_flag:/
- funcgraph fg_1164[fg_1000](
- ) {
- %1 : Tensor(F32)[16, 100] = FuncGraph::fg_1241(%para4446) #(Tensor(F32)[16, 100]) # fg_1241=L-↓↓↓construct.1241(@ctx.addr=0xaaaae3b99470) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b99470
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(306)/ if self.activation_flag:/
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(306)/ if self.activation_flag:/
- }
-
-
- # [No.1130] L-✗↓↓construct.1164 @ctx.addr=0xaaaae3bca870
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(306)/ if self.activation_flag:/
- funcgraph fg_1164[fg_1000](
- ) {
- %1 : Tensor(F32)[16, 1] = FuncGraph::fg_1241(%para4446) #(Tensor(F32)[16, 1]) # fg_1241=L-↓↓↓construct.1241(@ctx.addr=0xaaaae3bcbb60) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bcbb60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(306)/ if self.activation_flag:/
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 1]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(306)/ if self.activation_flag:/
- }
-
-
- # [No.1130] L-↓construct.1165 @ctx.addr=0xaaaae81d8710
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1165(
- %para4664 : Tensor(F32)[16, 32, 32, 32] # Φoutput
- ) {
- Primitive::Return{prim_type=1}(%para4664) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv1-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(261)/ return output/
- }
-
-
- # [No.1131] L-↓construct.1167 @ctx.addr=0xaaaae1e17960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1167(
- %para4665 : Tensor(F32)[16, 32, 32, 32] # Φoutput
- ) {
- Primitive::Return{prim_type=1}(%para4665) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv2-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(261)/ return output/
- }
-
-
- # [No.1132] L-↓construct.1168 @ctx.addr=0xaaaae1e05a90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1168(
- %para4666 : Tensor(F32)[16, 32, 32, 32] # Φoutput
- ) {
- Primitive::Return{prim_type=1}(%para4666) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv3-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(261)/ return output/
- }
-
-
- # [No.1133] L-↓construct.1169 @ctx.addr=0xaaaaea7a46d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1169(
- %para4667 : Tensor(F32)[16, 32, 32, 32] # Φoutput
- ) {
- Primitive::Return{prim_type=1}(%para4667) #(Tensor(F32)[16, 32, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv4-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(261)/ return output/
- }
-
-
- # [No.1134] L-↓construct.1170 @ctx.addr=0xaaaadcc890b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(259)/ if self.has_bias:/
- funcgraph fg_1170(
- %para4668 : Tensor(F32)[16, 64, 32, 32] # Φoutput
- ) {
- Primitive::Return{prim_type=1}(%para4668) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell/0-RRDB/RDB3-ResidualDenseBlock_5C/conv5-Conv2d
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/conv.py(261)/ return output/
- }
-
-
- # [No.1135] _tuple_getitem_by_number.1242 @ctx.addr=0xaaaae3884660
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1242(
- %para4669 : Tuple[Func] # data
- , %para4670 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4669, %para4670) #(Tuple[Func], I64) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1136] tail.1243 @ctx.addr=0xaaaae3885a10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(802)/ return xs[0], tail(xs)/
- funcgraph fg_1243(
- %para4671 : Tuple[Func] # 1244
- ) {
- %1 : Tuple[] = Primitive::MakeTuple{prim_type=1}() #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(802)/ return xs[0], tail(xs)/
- Primitive::Return{prim_type=1}(%1) #(Tuple[]) #scope: Default/G-GeneratorLossCell/perception_criterion-PerceptualLoss/loss_network-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/_extends/parse/standard_method.py(802)/ return xs[0], tail(xs)/
- }
-
-
- # [No.1137] _tuple_getitem_by_number.1245 @ctx.addr=0xaaaae22d35c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1245(
- %para4672 : Tuple[Func,Tuple[Func*21]] # data
- , %para4673 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*21] = %2(%para4672, %para4673) #(Tuple[Func,Tuple[Func*21]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*21]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1138] _tuple_getitem_by_number.1246 @ctx.addr=0xaaaae2299f40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1246(
- %para4674 : Tuple[Func,Tuple[Func*21]] # data
- , %para4675 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4674, %para4675) #(Tuple[Func,Tuple[Func*21]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1139] ⤾✓construct.216 @ctx.addr=0xaaaafaed7360
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4676 : Tuple[Func*21] # @cell
- , %para4677 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*21]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaaea79be80), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaea79be80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1139] L-↓↓↓construct.1241 @ctx.addr=0xaaaae3b99470
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(306)/ if self.activation_flag:/
- funcgraph fg_1241[fg_62](
- %para4678 : Tensor(F32)[16, 100] # Φx
- ) {
- %1 : Tuple[I64*2] = DoSignaturePrimitive::S-Prim-Shape{prim_type=1}(%para2291) #(Tensor(F32)[16, 32768]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- %2 : I64 = FuncGraph::fg_138(%1) #(Tuple[I64*2]) # fg_138=L-ms_len.138(@ctx.addr=0xaaaae3bb7960) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bb7960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- %3 : Bool = DoSignaturePrimitive::S-Prim-not_equal{prim_type=1}(%2, I64(2)) #(I64, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b9acd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- %4 : Bool = FuncGraph::fg_139(%3) #(Bool) # fg_139=L-bool_.139(@ctx.addr=0xaaaae3b7e750) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b7e750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- %5 : Func = Primitive::Switch{prim_type=1}(%4, FuncGraph::fg_1247, FuncGraph::fg_1248) #(Bool, Func, Func) # fg_1247=L-✓↓↓↓construct.1247, fg_1248=L-✗↓↓↓construct.1248(@ctx.addr=0xaaaae3b9b320) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- %6 : Tensor(F32)[16, 100] = %5() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b9b320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- Primitive::Return{prim_type=1}(%6) #(Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- }
-
-
- # [No.1140] L-↓↓↓construct.1241 @ctx.addr=0xaaaae3bcbb60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(306)/ if self.activation_flag:/
- funcgraph fg_1241[fg_62](
- %para4679 : Tensor(F32)[16, 1] # Φx
- ) {
- %1 : Tuple[I64*2] = DoSignaturePrimitive::S-Prim-Shape{prim_type=1}(%para2291) #(Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- %2 : I64 = FuncGraph::fg_138(%1) #(Tuple[I64*2]) # fg_138=L-ms_len.138(@ctx.addr=0xaaaae3bb7960) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bb7960
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- %3 : Bool = DoSignaturePrimitive::S-Prim-not_equal{prim_type=1}(%2, I64(2)) #(I64, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bd6810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- %4 : Bool = FuncGraph::fg_139(%3) #(Bool) # fg_139=L-bool_.139(@ctx.addr=0xaaaae3b7e750) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b7e750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- %5 : Func = Primitive::Switch{prim_type=1}(%4, FuncGraph::fg_1247, FuncGraph::fg_1248) #(Bool, Func, Func) # fg_1247=L-✓↓↓↓construct.1247, fg_1248=L-✗↓↓↓construct.1248(@ctx.addr=0xaaaae3bd6e60) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- %6 : Tensor(F32)[16, 1] = %5() #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bd6e60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- Primitive::Return{prim_type=1}(%6) #(Tensor(F32)[16, 1]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- }
-
-
- # [No.1140] ⥁✓construct.702 @ctx.addr=0xaaaaea79be80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*20]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*21]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*20] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*20]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf70a3be0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*20]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae20bc220
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae1f68670
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*20], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaae8306d10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae8306d10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1140] _not_equal_scalar.1249 @ctx.addr=0xaaaae3b9acd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(31)/def _not_equal_scalar(x, y):/
- funcgraph fg_1249(
- %para4680 : I64 # x
- , %para4681 : I64 # y
- ) {
- %1 : Func = Primitive::resolve{prim_type=1}(NameSpace::Ast, not_) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(0)/
- %2 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- %3 : Func = Primitive::getattr{prim_type=1}(%2, "scalar_eq") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- %4 : Bool = %3(%para4680, %para4681) #(I64, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- %5 : Bool = %1(%4) #(Bool) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3b9b050
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- Primitive::Return{prim_type=1}(%5) #(Bool) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- }
-
-
- # [No.1141] L-✗↓↓↓construct.1248 @ctx.addr=0xaaaae3b9b320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- funcgraph fg_1248[fg_1241](
- ) {
- %1 : Tensor(F32)[16, 100] = FuncGraph::fg_1250(%para4678) #(Tensor(F32)[16, 100]) # fg_1250=L-↓↓↓↓construct.1250(@ctx.addr=0xaaaae3bd70f0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bd70f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 100]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- }
-
-
- # [No.1142] _not_equal_scalar.1251 @ctx.addr=0xaaaae3bd6810
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(31)/def _not_equal_scalar(x, y):/
- funcgraph fg_1251(
- %para4682 : I64 # x
- , %para4683 : I64 # y
- ) {
- %1 : Func = Primitive::resolve{prim_type=1}(NameSpace::Ast, not_) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(0)/
- %2 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- %3 : Func = Primitive::getattr{prim_type=1}(%2, "scalar_eq") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- %4 : Bool = %3(%para4682, %para4683) #(I64, I64) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- %5 : Bool = %1(%4) #(Bool) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bd6b90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- Primitive::Return{prim_type=1}(%5) #(Bool) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/not_equal_impl.py(42)/ return not F.scalar_eq(x, y)/
- }
-
-
- # [No.1143] L-✗↓↓↓construct.1248 @ctx.addr=0xaaaae3bd6e60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- funcgraph fg_1248[fg_1241](
- ) {
- %1 : Tensor(F32)[16, 1] = FuncGraph::fg_1250(%para4678) #(Tensor(F32)[16, 1]) # fg_1250=L-↓↓↓↓construct.1250(@ctx.addr=0xaaaae3bd70f0) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense @ctx.addr=0xaaaae3bd70f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 1]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- }
-
-
- # [No.1143] _tuple_getitem_by_number.1252 @ctx.addr=0xaaaaf70a3be0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1252(
- %para4684 : Tuple[Func,Tuple[Func*20]] # data
- , %para4685 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*20] = %2(%para4684, %para4685) #(Tuple[Func,Tuple[Func*20]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*20]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1144] _tuple_getitem_by_number.1253 @ctx.addr=0xaaaae20bc220
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1253(
- %para4686 : Tuple[Func,Tuple[Func*20]] # data
- , %para4687 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4686, %para4687) #(Tuple[Func,Tuple[Func*20]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1145] ⤾✓construct.216 @ctx.addr=0xaaaae8306d10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4688 : Tuple[Func*20] # @cell
- , %para4689 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*20]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaae1e06db0), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae1e06db0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1145] _logical_not_scala.1254 @ctx.addr=0xaaaae3b9b050
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(26)/def _logical_not_scala(x):/
- funcgraph fg_1254(
- %para4690 : Bool # x
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "bool_not") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %3 : Func = Primitive::getattr{prim_type=1}(%para4690, "__bool__") #(Bool, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %4 : Bool = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %5 : Bool = %2(%4) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- Primitive::Return{prim_type=1}(%5) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- }
-
-
- # [No.1146] L-↓↓↓↓construct.1250 @ctx.addr=0xaaaae3bd70f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(308)/ if len(x_shape) != 2:/
- funcgraph fg_1250(
- %para4691 : Tensor(F32)[16, 1] # Φx
- ) {
- Primitive::Return{prim_type=1}(%para4691) #(Tensor(F32)[16, 1]) #scope: Default/G-GeneratorLossCell/D-VGGStyleDiscriminator128/linear2-Dense
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/basic.py(311)/ return x/
- }
-
-
- # [No.1147] _logical_not_scala.1255 @ctx.addr=0xaaaae3bd6b90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(26)/def _logical_not_scala(x):/
- funcgraph fg_1255(
- %para4692 : Bool # x
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "bool_not") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %3 : Func = Primitive::getattr{prim_type=1}(%para4692, "__bool__") #(Bool, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %4 : Bool = %3() #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- %5 : Bool = %2(%4) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- Primitive::Return{prim_type=1}(%5) #(Bool) #scope: Default/G-GeneratorLossCell/G-RRDBNet
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/logic_not_impl.py(36)/ return F.bool_not(x.__bool__())/
- }
-
-
- # [No.1148] ⥁✓construct.702 @ctx.addr=0xaaaae1e06db0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*19]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*20]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*19] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*19]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaafaa41a20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*19]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae11c0820
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaadd25dfa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*19], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaae203d320) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae203d320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1148] _tuple_getitem_by_number.1256 @ctx.addr=0xaaaafaa41a20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1256(
- %para4693 : Tuple[Func,Tuple[Func*19]] # data
- , %para4694 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*19] = %2(%para4693, %para4694) #(Tuple[Func,Tuple[Func*19]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*19]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1149] _tuple_getitem_by_number.1257 @ctx.addr=0xaaaae11c0820
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1257(
- %para4695 : Tuple[Func,Tuple[Func*19]] # data
- , %para4696 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4695, %para4696) #(Tuple[Func,Tuple[Func*19]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1150] ⤾✓construct.216 @ctx.addr=0xaaaae203d320
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4697 : Tuple[Func*19] # @cell
- , %para4698 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*19]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaaf1de8140), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf1de8140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1150] ⥁✓construct.702 @ctx.addr=0xaaaaf1de8140
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*18]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*19]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*18] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*18]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae248f840
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*18]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaadd121b10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaadec3a420
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*18], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaaedde3480) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaedde3480
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1150] _tuple_getitem_by_number.1258 @ctx.addr=0xaaaae248f840
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1258(
- %para4699 : Tuple[Func,Tuple[Func*18]] # data
- , %para4700 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*18] = %2(%para4699, %para4700) #(Tuple[Func,Tuple[Func*18]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*18]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1151] _tuple_getitem_by_number.1259 @ctx.addr=0xaaaadd121b10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1259(
- %para4701 : Tuple[Func,Tuple[Func*18]] # data
- , %para4702 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4701, %para4702) #(Tuple[Func,Tuple[Func*18]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1152] ⤾✓construct.216 @ctx.addr=0xaaaaedde3480
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4703 : Tuple[Func*18] # @cell
- , %para4704 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*18]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaadc7c3760), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaadc7c3760
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1152] ⥁✓construct.702 @ctx.addr=0xaaaadc7c3760
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*17]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*18]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*17] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*17]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae1f27ed0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*17]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaef7fbba0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaed7bde00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*17], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaae85f5bd0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae85f5bd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1152] _tuple_getitem_by_number.1260 @ctx.addr=0xaaaae1f27ed0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1260(
- %para4705 : Tuple[Func,Tuple[Func*17]] # data
- , %para4706 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*17] = %2(%para4705, %para4706) #(Tuple[Func,Tuple[Func*17]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*17]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1153] _tuple_getitem_by_number.1261 @ctx.addr=0xaaaaef7fbba0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1261(
- %para4707 : Tuple[Func,Tuple[Func*17]] # data
- , %para4708 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4707, %para4708) #(Tuple[Func,Tuple[Func*17]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1154] ⤾✓construct.216 @ctx.addr=0xaaaae85f5bd0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4709 : Tuple[Func*17] # @cell
- , %para4710 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*17]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaaf4a7d530), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf4a7d530
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1154] ⥁✓construct.702 @ctx.addr=0xaaaaf4a7d530
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*16]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*17]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*16] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*16]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae2503880
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*16]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae1f2e520
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee562fc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*16], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaadd31eff0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaadd31eff0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1154] _tuple_getitem_by_number.1262 @ctx.addr=0xaaaae2503880
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1262(
- %para4711 : Tuple[Func,Tuple[Func*16]] # data
- , %para4712 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*16] = %2(%para4711, %para4712) #(Tuple[Func,Tuple[Func*16]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*16]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1155] _tuple_getitem_by_number.1263 @ctx.addr=0xaaaae1f2e520
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1263(
- %para4713 : Tuple[Func,Tuple[Func*16]] # data
- , %para4714 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4713, %para4714) #(Tuple[Func,Tuple[Func*16]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1156] ⤾✓construct.216 @ctx.addr=0xaaaadd31eff0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4715 : Tuple[Func*16] # @cell
- , %para4716 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*16]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaafade1f40), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaafade1f40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1156] ⥁✓construct.702 @ctx.addr=0xaaaafade1f40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*15]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*16]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*15] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*15]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae818b000
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*15]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae818ab60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaefa4f520
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*15], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaae83aed10) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae83aed10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1156] _tuple_getitem_by_number.1264 @ctx.addr=0xaaaae818b000
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1264(
- %para4717 : Tuple[Func,Tuple[Func*15]] # data
- , %para4718 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*15] = %2(%para4717, %para4718) #(Tuple[Func,Tuple[Func*15]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*15]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1157] _tuple_getitem_by_number.1265 @ctx.addr=0xaaaae818ab60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1265(
- %para4719 : Tuple[Func,Tuple[Func*15]] # data
- , %para4720 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4719, %para4720) #(Tuple[Func,Tuple[Func*15]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1158] ⤾✓construct.216 @ctx.addr=0xaaaae83aed10
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4721 : Tuple[Func*15] # @cell
- , %para4722 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*15]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaafaedd6a0), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaafaedd6a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1158] ⥁✓construct.702 @ctx.addr=0xaaaafaedd6a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*14]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*15]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*14] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*14]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae84cce30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*14]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae834a570
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaed73b7b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*14], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaae8221e40) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae8221e40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1158] _tuple_getitem_by_number.1266 @ctx.addr=0xaaaae84cce30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1266(
- %para4723 : Tuple[Func,Tuple[Func*14]] # data
- , %para4724 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*14] = %2(%para4723, %para4724) #(Tuple[Func,Tuple[Func*14]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*14]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1159] _tuple_getitem_by_number.1267 @ctx.addr=0xaaaae834a570
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1267(
- %para4725 : Tuple[Func,Tuple[Func*14]] # data
- , %para4726 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4725, %para4726) #(Tuple[Func,Tuple[Func*14]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1160] ⤾✓construct.216 @ctx.addr=0xaaaae8221e40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4727 : Tuple[Func*14] # @cell
- , %para4728 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*14]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaae24f9fe0), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae24f9fe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1160] ⥁✓construct.702 @ctx.addr=0xaaaae24f9fe0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*13]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*14]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*13] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*13]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae83859b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*13]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae0153340
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae8380080
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*13], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaaecfb06a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaecfb06a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1160] _tuple_getitem_by_number.1268 @ctx.addr=0xaaaae83859b0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1268(
- %para4729 : Tuple[Func,Tuple[Func*13]] # data
- , %para4730 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*13] = %2(%para4729, %para4730) #(Tuple[Func,Tuple[Func*13]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*13]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1161] _tuple_getitem_by_number.1269 @ctx.addr=0xaaaae0153340
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1269(
- %para4731 : Tuple[Func,Tuple[Func*13]] # data
- , %para4732 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4731, %para4732) #(Tuple[Func,Tuple[Func*13]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1162] ⤾✓construct.216 @ctx.addr=0xaaaaecfb06a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4733 : Tuple[Func*13] # @cell
- , %para4734 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*13]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaae23affa0), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae23affa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1162] ⥁✓construct.702 @ctx.addr=0xaaaae23affa0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*12]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*13]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*12] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*12]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae986a700
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*12]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaadc866520
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae250d460
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*12], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaaf1c1edb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf1c1edb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1162] _tuple_getitem_by_number.1270 @ctx.addr=0xaaaae986a700
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1270(
- %para4735 : Tuple[Func,Tuple[Func*12]] # data
- , %para4736 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*12] = %2(%para4735, %para4736) #(Tuple[Func,Tuple[Func*12]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*12]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1163] _tuple_getitem_by_number.1271 @ctx.addr=0xaaaadc866520
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1271(
- %para4737 : Tuple[Func,Tuple[Func*12]] # data
- , %para4738 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4737, %para4738) #(Tuple[Func,Tuple[Func*12]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1164] ⤾✓construct.216 @ctx.addr=0xaaaaf1c1edb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4739 : Tuple[Func*12] # @cell
- , %para4740 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*12]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaaf0f70d00), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf0f70d00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1164] ⥁✓construct.702 @ctx.addr=0xaaaaf0f70d00
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*11]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*12]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*11] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*11]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae205d830
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*11]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae2009150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae2573890
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*11], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaae1014fc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae1014fc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1164] _tuple_getitem_by_number.1272 @ctx.addr=0xaaaae205d830
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1272(
- %para4741 : Tuple[Func,Tuple[Func*11]] # data
- , %para4742 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*11] = %2(%para4741, %para4742) #(Tuple[Func,Tuple[Func*11]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*11]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1165] _tuple_getitem_by_number.1273 @ctx.addr=0xaaaae2009150
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1273(
- %para4743 : Tuple[Func,Tuple[Func*11]] # data
- , %para4744 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4743, %para4744) #(Tuple[Func,Tuple[Func*11]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1166] ⤾✓construct.216 @ctx.addr=0xaaaae1014fc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4745 : Tuple[Func*11] # @cell
- , %para4746 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*11]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaadcab5470), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaadcab5470
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1166] ⥁✓construct.702 @ctx.addr=0xaaaadcab5470
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*10]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*11]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*10] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*10]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaeee1f660
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*10]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaeee1bac0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaad9b96ae0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*10], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaafa5c13c0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaafa5c13c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1166] _tuple_getitem_by_number.1274 @ctx.addr=0xaaaaeee1f660
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1274(
- %para4747 : Tuple[Func,Tuple[Func*10]] # data
- , %para4748 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*10] = %2(%para4747, %para4748) #(Tuple[Func,Tuple[Func*10]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*10]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1167] _tuple_getitem_by_number.1275 @ctx.addr=0xaaaaeee1bac0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1275(
- %para4749 : Tuple[Func,Tuple[Func*10]] # data
- , %para4750 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4749, %para4750) #(Tuple[Func,Tuple[Func*10]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1168] ⤾✓construct.216 @ctx.addr=0xaaaafa5c13c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4751 : Tuple[Func*10] # @cell
- , %para4752 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*10]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaae26aa840), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae26aa840
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1168] ⥁✓construct.702 @ctx.addr=0xaaaae26aa840
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*9]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*10]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*9] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*9]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae0efb560
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*9]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf48cea80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf4dde750
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*9], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaaf0c3cdc0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf0c3cdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1168] _tuple_getitem_by_number.1276 @ctx.addr=0xaaaae0efb560
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1276(
- %para4753 : Tuple[Func,Tuple[Func*9]] # data
- , %para4754 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*9] = %2(%para4753, %para4754) #(Tuple[Func,Tuple[Func*9]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*9]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1169] _tuple_getitem_by_number.1277 @ctx.addr=0xaaaaf48cea80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1277(
- %para4755 : Tuple[Func,Tuple[Func*9]] # data
- , %para4756 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4755, %para4756) #(Tuple[Func,Tuple[Func*9]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1170] ⤾✓construct.216 @ctx.addr=0xaaaaf0c3cdc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4757 : Tuple[Func*9] # @cell
- , %para4758 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*9]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaae22848e0), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae22848e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1170] ⥁✓construct.702 @ctx.addr=0xaaaae22848e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*8]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*9]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*8] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*8]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae1224be0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*8]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae1225540
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaedd66cb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*8], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaae2266470) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae2266470
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1170] _tuple_getitem_by_number.1278 @ctx.addr=0xaaaae1224be0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1278(
- %para4759 : Tuple[Func,Tuple[Func*8]] # data
- , %para4760 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*8] = %2(%para4759, %para4760) #(Tuple[Func,Tuple[Func*8]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*8]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1171] _tuple_getitem_by_number.1279 @ctx.addr=0xaaaae1225540
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1279(
- %para4761 : Tuple[Func,Tuple[Func*8]] # data
- , %para4762 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4761, %para4762) #(Tuple[Func,Tuple[Func*8]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1172] ⤾✓construct.216 @ctx.addr=0xaaaae2266470
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4763 : Tuple[Func*8] # @cell
- , %para4764 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*8]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaae21b9e30), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae21b9e30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1172] ⥁✓construct.702 @ctx.addr=0xaaaae21b9e30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*7]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*8]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*7] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*7]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaadd31fec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*7]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaadd3207d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaad6995330
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*7], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaae2096fb0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae2096fb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1172] _tuple_getitem_by_number.1280 @ctx.addr=0xaaaadd31fec0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1280(
- %para4765 : Tuple[Func,Tuple[Func*7]] # data
- , %para4766 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*7] = %2(%para4765, %para4766) #(Tuple[Func,Tuple[Func*7]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*7]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1173] _tuple_getitem_by_number.1281 @ctx.addr=0xaaaadd3207d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1281(
- %para4767 : Tuple[Func,Tuple[Func*7]] # data
- , %para4768 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4767, %para4768) #(Tuple[Func,Tuple[Func*7]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1174] ⤾✓construct.216 @ctx.addr=0xaaaae2096fb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4769 : Tuple[Func*7] # @cell
- , %para4770 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*7]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaae22cf6f0), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae22cf6f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1174] ⥁✓construct.702 @ctx.addr=0xaaaae22cf6f0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*6]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*7]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*6] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*6]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae5e67530
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*6]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae5e67e90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaeb60ddc0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*6], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaaeedd5500) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaeedd5500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1174] _tuple_getitem_by_number.1282 @ctx.addr=0xaaaae5e67530
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1282(
- %para4771 : Tuple[Func,Tuple[Func*6]] # data
- , %para4772 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*6] = %2(%para4771, %para4772) #(Tuple[Func,Tuple[Func*6]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*6]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1175] _tuple_getitem_by_number.1283 @ctx.addr=0xaaaae5e67e90
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1283(
- %para4773 : Tuple[Func,Tuple[Func*6]] # data
- , %para4774 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4773, %para4774) #(Tuple[Func,Tuple[Func*6]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1176] ⤾✓construct.216 @ctx.addr=0xaaaaeedd5500
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4775 : Tuple[Func*6] # @cell
- , %para4776 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*6]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaae233e460), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae233e460
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1176] ⥁✓construct.702 @ctx.addr=0xaaaae233e460
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*5]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*6]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*5] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*5]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf4a74c60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*5]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf4a755c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf4a76eb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*5], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaafae235d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaafae235d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1176] _tuple_getitem_by_number.1284 @ctx.addr=0xaaaaf4a74c60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1284(
- %para4777 : Tuple[Func,Tuple[Func*5]] # data
- , %para4778 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*5] = %2(%para4777, %para4778) #(Tuple[Func,Tuple[Func*5]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*5]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1177] _tuple_getitem_by_number.1285 @ctx.addr=0xaaaaf4a755c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1285(
- %para4779 : Tuple[Func,Tuple[Func*5]] # data
- , %para4780 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4779, %para4780) #(Tuple[Func,Tuple[Func*5]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1178] ⤾✓construct.216 @ctx.addr=0xaaaafae235d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4781 : Tuple[Func*5] # @cell
- , %para4782 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*5]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaaf17c9c60), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf17c9c60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1178] ⥁✓construct.702 @ctx.addr=0xaaaaf17c9c60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*4]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*5]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*4] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*4]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaf2eb3e70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*4]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaade3df1d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaade3e0ac0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*4], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaae27906a0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae27906a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1178] _tuple_getitem_by_number.1286 @ctx.addr=0xaaaaf2eb3e70
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1286(
- %para4783 : Tuple[Func,Tuple[Func*4]] # data
- , %para4784 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*4] = %2(%para4783, %para4784) #(Tuple[Func,Tuple[Func*4]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*4]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1179] _tuple_getitem_by_number.1287 @ctx.addr=0xaaaade3df1d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1287(
- %para4785 : Tuple[Func,Tuple[Func*4]] # data
- , %para4786 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4785, %para4786) #(Tuple[Func,Tuple[Func*4]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1180] ⤾✓construct.216 @ctx.addr=0xaaaae27906a0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4787 : Tuple[Func*4] # @cell
- , %para4788 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*4]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaae27a2fb0), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae27a2fb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1180] ⥁✓construct.702 @ctx.addr=0xaaaae27a2fb0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*3]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*4]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*3] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*3]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae27c58e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*3]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae27c6240
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae27c7b30
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*3], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaae28a8480) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae28a8480
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1180] _tuple_getitem_by_number.1288 @ctx.addr=0xaaaae27c58e0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1288(
- %para4789 : Tuple[Func,Tuple[Func*3]] # data
- , %para4790 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*3] = %2(%para4789, %para4790) #(Tuple[Func,Tuple[Func*3]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*3]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1181] _tuple_getitem_by_number.1289 @ctx.addr=0xaaaae27c6240
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1289(
- %para4791 : Tuple[Func,Tuple[Func*3]] # data
- , %para4792 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4791, %para4792) #(Tuple[Func,Tuple[Func*3]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1182] ⤾✓construct.216 @ctx.addr=0xaaaae28a8480
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4793 : Tuple[Func*3] # @cell
- , %para4794 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*3]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaae28baf40), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae28baf40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1182] ⥁✓construct.702 @ctx.addr=0xaaaae28baf40
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func*2]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*3]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func*2] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func*2]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae28dc180
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func*2]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae28dcae0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae28de3d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func*2], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaae374df60) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae374df60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1182] _tuple_getitem_by_number.1290 @ctx.addr=0xaaaae28dc180
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1290(
- %para4795 : Tuple[Func,Tuple[Func*2]] # data
- , %para4796 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func*2] = %2(%para4795, %para4796) #(Tuple[Func,Tuple[Func*2]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func*2]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1183] _tuple_getitem_by_number.1291 @ctx.addr=0xaaaae28dcae0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1291(
- %para4797 : Tuple[Func,Tuple[Func*2]] # data
- , %para4798 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4797, %para4798) #(Tuple[Func,Tuple[Func*2]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1184] ⤾✓construct.216 @ctx.addr=0xaaaae374df60
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4799 : Tuple[Func*2] # @cell
- , %para4800 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func*2]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaae37607c0), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae37607c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1184] ⥁✓construct.702 @ctx.addr=0xaaaae37607c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[Func]] = FuncGraph::fg_998(%para2539) #(Tuple[Func*2]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[Func] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[Func]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae37802c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[Func]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3780c20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3782510
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[Func], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaae3863ad0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3863ad0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1184] _tuple_getitem_by_number.1292 @ctx.addr=0xaaaae37802c0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1292(
- %para4801 : Tuple[Func,Tuple[Func]] # data
- , %para4802 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[Func] = %2(%para4801, %para4802) #(Tuple[Func,Tuple[Func]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[Func]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1185] _tuple_getitem_by_number.1293 @ctx.addr=0xaaaae3780c20
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1293(
- %para4803 : Tuple[Func,Tuple[Func]] # data
- , %para4804 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4803, %para4804) #(Tuple[Func,Tuple[Func]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1186] ⤾✓construct.216 @ctx.addr=0xaaaae3863ad0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4805 : Tuple[Func] # @cell
- , %para4806 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[Func]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702(@ctx.addr=0xaaaae3876330), fg_703=↓✓construct.703 #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876330
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1186] ⥁✓construct.702 @ctx.addr=0xaaaae3876330
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_702[fg_216](
- ) {
- %1 : Tuple[Func,Tuple[]] = FuncGraph::fg_998(%para2539) #(Tuple[Func]) # fg_998=ms_next.998(@ctx.addr=0xaaaae3876450) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3876450
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Tuple[] = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(1)) #(Tuple[Func,Tuple[]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3894620
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Func = DoSignaturePrimitive::S-Prim-getitem{prim_type=1}(%1, I64(0)) #(Tuple[Func,Tuple[]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3894f80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %4 : Tensor(F32)[16, 64, 32, 32] = %3(%para2540) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae3896870
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(235)/ input_data = cell(input_data)/
- %5 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_216(%2, %4) #(Tuple[], Tensor(F32)[16, 64, 32, 32]) # fg_216=⤾✓construct.216(@ctx.addr=0xaaaae39770d0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae39770d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%5) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1186] _tuple_getitem_by_number.1294 @ctx.addr=0xaaaae3894620
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1294(
- %para4807 : Tuple[Func,Tuple[]] # data
- , %para4808 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Tuple[] = %2(%para4807, %para4808) #(Tuple[Func,Tuple[]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Tuple[]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1187] _tuple_getitem_by_number.1295 @ctx.addr=0xaaaae3894f80
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(75)/def _tuple_getitem_by_number(data, number_index):/
- funcgraph fg_1295(
- %para4809 : Tuple[Func,Tuple[]] # data
- , %para4810 : I64 # number_index
- ) {
- %1 : ExternalType = Primitive::resolve{prim_type=1}(NameSpace::SymbolStr, F) #(ExternalType, ExternalType) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %2 : Func = Primitive::getattr{prim_type=1}(%1, "tuple_getitem") #(ExternalType, String) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- %3 : Func = %2(%para4809, %para4810) #(Tuple[Func,Tuple[]], I64) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- Primitive::Return{prim_type=1}(%3) #(Func) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/ops/composite/multitype_ops/getitem_impl.py(86)/ return F.tuple_getitem(data, number_index)/
- }
-
-
- # [No.1188] ⤾✓construct.216 @ctx.addr=0xaaaae39770d0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_216(
- %para4811 : Tuple[] # @cell
- , %para4812 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- %1 : Bool = FuncGraph::fg_701(%para2539) #(Tuple[]) # fg_701=hasnext.701(@ctx.addr=0xaaaaee9e1850) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaaee9e1850
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %2 : Func = Primitive::Switch{prim_type=1}(%1, FuncGraph::fg_702, FuncGraph::fg_703) #(Bool, Func, Func) # fg_702=⥁✓construct.702, fg_703=↓✓construct.703(@ctx.addr=0xaaaadc46ecf0) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- %3 : Tensor(F32)[16, 64, 32, 32] = %2() #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaadc46ecf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%3) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1188] ↓✓construct.703 @ctx.addr=0xaaaadc46ecf0
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_703[fg_216](
- ) {
- %1 : Tensor(F32)[16, 64, 32, 32] = FuncGraph::fg_1296(%para2540) #(Tensor(F32)[16, 64, 32, 32]) # fg_1296=↓construct.1296(@ctx.addr=0xaaaae397f070) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell @ctx.addr=0xaaaae397f070
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- Primitive::Return{prim_type=1}(%1) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- }
-
-
- # [No.1189] ↓construct.1296 @ctx.addr=0xaaaae397f070
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(234)/ for cell in self.cell_list:/
- funcgraph fg_1296(
- %para4813 : Tensor(F32)[16, 64, 32, 32] # Φinput_data
- ) {
- Primitive::Return{prim_type=1}(%para4813) #(Tensor(F32)[16, 64, 32, 32]) #scope: Default/G-GeneratorLossCell/G-RRDBNet/RRDB_trunk-SequentialCell
- # In file /root/archiconda3/envs/wks/lib/python3.7/site-packages/mindspore/nn/layer/container.py(236)/ return input_data/
- }
-
-
- # num of total function graphs: 1962
|