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- # Copyright 2022 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
-
- from mindspore import nn
- import mindspore.ops as ops
- from .submodules import conv
- from .submodules import deconv
- from .submodules import predict_flow
- from .submodules import Upsample
-
-
- class FlowNetS(nn.Cell):
- def __init__(self, input_channels=12, batchNorm=True):
- super(FlowNetS, self).__init__()
-
- self.batchNorm = batchNorm
- self.conv1 = conv(self.batchNorm, input_channels, 64, kernel_size=7, stride=2)
- self.conv2 = conv(self.batchNorm, 64, 128, kernel_size=5, stride=2)
- self.conv3 = conv(self.batchNorm, 128, 256, kernel_size=5, stride=2)
- self.conv3_1 = conv(self.batchNorm, 256, 256)
- self.conv4 = conv(self.batchNorm, 256, 512, stride=2)
- self.conv4_1 = conv(self.batchNorm, 512, 512)
- self.conv5 = conv(self.batchNorm, 512, 512, stride=2)
- self.conv5_1 = conv(self.batchNorm, 512, 512)
- self.conv6 = conv(self.batchNorm, 512, 1024, stride=2)
- self.conv6_1 = conv(self.batchNorm, 1024, 1024)
-
- self.deconv5 = deconv(1024, 512)
- self.deconv4 = deconv(1026, 256)
- self.deconv3 = deconv(770, 128)
- self.deconv2 = deconv(386, 64)
-
- self.predict_flow6 = predict_flow(1024)
- self.predict_flow5 = predict_flow(1026)
- self.predict_flow4 = predict_flow(770)
- self.predict_flow3 = predict_flow(386)
- self.predict_flow2 = predict_flow(194)
-
- self.upsampled_flow6_to_5 = nn.Conv2dTranspose(2, 2, 4, 2, pad_mode='pad', padding=1, has_bias=False)
- self.upsampled_flow5_to_4 = nn.Conv2dTranspose(2, 2, 4, 2, pad_mode='pad', padding=1, has_bias=False)
- self.upsampled_flow4_to_3 = nn.Conv2dTranspose(2, 2, 4, 2, pad_mode='pad', padding=1, has_bias=False)
- self.upsampled_flow3_to_2 = nn.Conv2dTranspose(2, 2, 4, 2, pad_mode='pad', padding=1, has_bias=False)
-
- self.concat_op = ops.Concat(1)
-
- for c in self.cells():
- if isinstance(c, nn.Conv2d):
- if c.bias_init is not None:
- c.bias_init = 'Uniform'
- c.weight_init = 'XavierUniform'
-
- if isinstance(c, nn.Conv2dTranspose):
- if c.bias_init is not None:
- c.bias_init = 'Uniform'
- c.weight_init = 'XavierUniform'
-
- self.upsample1 = Upsample(scale_factor=4, mode='bilinear')
-
- def construct(self, x):
- out_conv1 = self.conv1(x)
-
- out_conv2 = self.conv2(out_conv1)
- out_conv3 = self.conv3_1(self.conv3(out_conv2))
- out_conv4 = self.conv4_1(self.conv4(out_conv3))
- out_conv5 = self.conv5_1(self.conv5(out_conv4))
- out_conv6 = self.conv6_1(self.conv6(out_conv5))
-
- flow6 = self.predict_flow6(out_conv6)
- flow6_up = self.upsampled_flow6_to_5(flow6)
- out_deconv5 = self.deconv5(out_conv6)
-
- concat5 = self.concat_op((out_conv5, out_deconv5, flow6_up))
- flow5 = self.predict_flow5(concat5)
- flow5_up = self.upsampled_flow5_to_4(flow5)
- out_deconv4 = self.deconv4(concat5)
-
- concat4 = self.concat_op((out_conv4, out_deconv4, flow5_up))
- flow4 = self.predict_flow4(concat4)
- flow4_up = self.upsampled_flow4_to_3(flow4)
- out_deconv3 = self.deconv3(concat4)
-
- concat3 = self.concat_op((out_conv3, out_deconv3, flow4_up))
- flow3 = self.predict_flow3(concat3)
- flow3_up = self.upsampled_flow3_to_2(flow3)
- out_deconv2 = self.deconv2(concat3)
-
- concat2 = self.concat_op((out_conv2, out_deconv2, flow3_up))
- flow2 = self.predict_flow2(concat2)
-
- if self.training:
- return flow2, flow3, flow4, flow5, flow6
- return flow2,
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