|
- C3PCompressionModelV2(
- (analysis_transform): AnalysisTransformProgressiveV2(
- (layers): Sequential(
- (0): AnalysisBlock(
- (layers): ModuleList(
- (0): Conv3d(1, 16, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))
- (1): ReLU()
- (2): Conv3d(16, 16, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
- (3): ReLU()
- (4): Conv3d(16, 16, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
- (5): ReLU()
- )
- )
- (1): AnalysisBlock(
- (layers): ModuleList(
- (0): Conv3d(16, 32, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))
- (1): ReLU()
- (2): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
- (3): ReLU()
- (4): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
- (5): ReLU()
- )
- )
- (2): AnalysisBlock(
- (layers): ModuleList(
- (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))
- (1): ReLU()
- (2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
- (3): ReLU()
- (4): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
- (5): ReLU()
- )
- )
- (3): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
- )
- )
- (synthesis_transform): SynthesisTransformProgressiveV2(
- (layers): Sequential(
- (0): SynthesisBlock(
- (layers): ModuleList(
- (0): ConvTranspose3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), output_padding=(1, 1, 1))
- (1): ReLU()
- (2): ConvTranspose3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
- (3): ReLU()
- (4): ConvTranspose3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
- (5): ReLU()
- )
- )
- (1): SynthesisBlock(
- (layers): ModuleList(
- (0): ConvTranspose3d(64, 32, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), output_padding=(1, 1, 1))
- (1): ReLU()
- (2): ConvTranspose3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
- (3): ReLU()
- (4): ConvTranspose3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
- (5): ReLU()
- )
- )
- (2): SynthesisBlock(
- (layers): ModuleList(
- (0): ConvTranspose3d(32, 16, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), output_padding=(1, 1, 1))
- (1): ReLU()
- (2): ConvTranspose3d(16, 16, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
- (3): ReLU()
- (4): ConvTranspose3d(16, 16, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
- (5): ReLU()
- )
- )
- (3): ConvTranspose3d(16, 1, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
- (4): ReLU()
- )
- )
- (hyper_analysis_transform): HyperAnalysisTransform(
- (layers): Sequential(
- (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
- (1): ReLU()
- (2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))
- (3): ReLU()
- (4): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
- )
- )
- (hyper_synthesis_transform): HyperSynthesisTransform(
- (layers): Sequential(
- (0): ConvTranspose3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
- (1): ReLU()
- (2): ConvTranspose3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), output_padding=(1, 1, 1))
- (3): ReLU()
- (4): ConvTranspose3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
- (5): ReLU()
- )
- )
- (entropy_bottleneck): EntropyBottleneck(
- (_matrices): ParameterList(
- (0): Parameter containing: [torch.cuda.FloatTensor of size 64x3x1 (GPU 0)]
- (1): Parameter containing: [torch.cuda.FloatTensor of size 64x3x3 (GPU 0)]
- (2): Parameter containing: [torch.cuda.FloatTensor of size 64x3x3 (GPU 0)]
- (3): Parameter containing: [torch.cuda.FloatTensor of size 64x1x3 (GPU 0)]
- )
- (_biases): ParameterList(
- (0): Parameter containing: [torch.cuda.FloatTensor of size 64x3x1 (GPU 0)]
- (1): Parameter containing: [torch.cuda.FloatTensor of size 64x3x1 (GPU 0)]
- (2): Parameter containing: [torch.cuda.FloatTensor of size 64x3x1 (GPU 0)]
- (3): Parameter containing: [torch.cuda.FloatTensor of size 64x1x1 (GPU 0)]
- )
- (_factors): ParameterList(
- (0): Parameter containing: [torch.cuda.FloatTensor of size 64x3x1 (GPU 0)]
- (1): Parameter containing: [torch.cuda.FloatTensor of size 64x3x1 (GPU 0)]
- (2): Parameter containing: [torch.cuda.FloatTensor of size 64x3x1 (GPU 0)]
- )
- )
- (conditional_bottleneck): GaussianConditional()
- )
|