| CifNetForImageClassification( | |
| (resnet): CifNetModel( | |
| (embedder): CifNetEmbeddings( | |
| (embedder): CifNetConvLayer( | |
| (convolution): Conv2d(3, 16, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (pooler): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) | |
| ) | |
| (encoder): CifNetEncoder( | |
| (stages): ModuleList( | |
| (0): CifNetStage( | |
| (layers): Sequential( | |
| (0): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (1): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (2): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (3): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (4): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (5): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (6): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (7): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (1): CifNetStage( | |
| (layers): Sequential( | |
| (0): CifNetBasicLayer( | |
| (shortcut): CifNetShortCut( | |
| (convolution): Conv2d(16, 32, kernel_size=(1, 1), stride=(2, 2), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| ) | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (1): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (2): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (3): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (4): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (5): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (6): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (7): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (8): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (9): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (10): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (11): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (2): CifNetStage( | |
| (layers): Sequential( | |
| (0): CifNetBasicLayer( | |
| (shortcut): CifNetShortCut( | |
| (convolution): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| ) | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (1): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (2): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (3): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (4): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (5): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (6): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (7): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (8): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (9): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (10): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (11): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (12): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (13): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (14): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (15): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (3): CifNetStage( | |
| (layers): Sequential( | |
| (0): CifNetBasicLayer( | |
| (shortcut): CifNetShortCut( | |
| (convolution): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| ) | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (1): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (2): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (3): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (4): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (5): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (6): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (7): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (8): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (9): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (10): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| (11): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (pooler): AdaptiveAvgPool2d(output_size=(1, 1)) | |
| ) | |
| (classifier): Sequential( | |
| (0): Flatten(start_dim=1, end_dim=-1) | |
| (1): Linear(in_features=128, out_features=10, bias=True) | |
| ) | |
| ) | |
| ---------------------------------------------------------------- | |
| Layer (type) Output Shape Param # | |
| ================================================================ | |
| Conv2d-1 [4, 16, 112, 112] 2,352 | |
| BatchNorm2d-2 [4, 16, 112, 112] 32 | |
| SiLU-3 [4, 16, 112, 112] 0 | |
| CifNetConvLayer-4 [4, 16, 112, 112] 0 | |
| MaxPool2d-5 [4, 16, 56, 56] 0 | |
| CifNetEmbeddings-6 [4, 16, 56, 56] 0 | |
| Conv2d-7 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-8 [4, 16, 56, 56] 32 | |
| SiLU-9 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-10 [4, 16, 56, 56] 0 | |
| Conv2d-11 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-12 [4, 16, 56, 56] 32 | |
| SiLU-13 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-14 [4, 16, 56, 56] 0 | |
| Identity-15 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-16 [4, 16, 56, 56] 0 | |
| Conv2d-17 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-18 [4, 16, 56, 56] 32 | |
| SiLU-19 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-20 [4, 16, 56, 56] 0 | |
| Conv2d-21 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-22 [4, 16, 56, 56] 32 | |
| SiLU-23 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-24 [4, 16, 56, 56] 0 | |
| Identity-25 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-26 [4, 16, 56, 56] 0 | |
| Conv2d-27 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-28 [4, 16, 56, 56] 32 | |
| SiLU-29 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-30 [4, 16, 56, 56] 0 | |
| Conv2d-31 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-32 [4, 16, 56, 56] 32 | |
| SiLU-33 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-34 [4, 16, 56, 56] 0 | |
| Identity-35 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-36 [4, 16, 56, 56] 0 | |
| Conv2d-37 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-38 [4, 16, 56, 56] 32 | |
| SiLU-39 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-40 [4, 16, 56, 56] 0 | |
| Conv2d-41 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-42 [4, 16, 56, 56] 32 | |
| SiLU-43 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-44 [4, 16, 56, 56] 0 | |
| Identity-45 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-46 [4, 16, 56, 56] 0 | |
| Conv2d-47 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-48 [4, 16, 56, 56] 32 | |
| SiLU-49 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-50 [4, 16, 56, 56] 0 | |
| Conv2d-51 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-52 [4, 16, 56, 56] 32 | |
| SiLU-53 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-54 [4, 16, 56, 56] 0 | |
| Identity-55 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-56 [4, 16, 56, 56] 0 | |
| Conv2d-57 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-58 [4, 16, 56, 56] 32 | |
| SiLU-59 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-60 [4, 16, 56, 56] 0 | |
| Conv2d-61 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-62 [4, 16, 56, 56] 32 | |
| SiLU-63 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-64 [4, 16, 56, 56] 0 | |
| Identity-65 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-66 [4, 16, 56, 56] 0 | |
| Conv2d-67 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-68 [4, 16, 56, 56] 32 | |
| SiLU-69 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-70 [4, 16, 56, 56] 0 | |
| Conv2d-71 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-72 [4, 16, 56, 56] 32 | |
| SiLU-73 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-74 [4, 16, 56, 56] 0 | |
| Identity-75 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-76 [4, 16, 56, 56] 0 | |
| Conv2d-77 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-78 [4, 16, 56, 56] 32 | |
| SiLU-79 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-80 [4, 16, 56, 56] 0 | |
| Conv2d-81 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-82 [4, 16, 56, 56] 32 | |
| SiLU-83 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-84 [4, 16, 56, 56] 0 | |
| Identity-85 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-86 [4, 16, 56, 56] 0 | |
| CifNetStage-87 [4, 16, 56, 56] 0 | |
| Conv2d-88 [4, 32, 28, 28] 4,608 | |
| BatchNorm2d-89 [4, 32, 28, 28] 64 | |
| SiLU-90 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-91 [4, 32, 28, 28] 0 | |
| Conv2d-92 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-93 [4, 32, 28, 28] 64 | |
| SiLU-94 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-95 [4, 32, 28, 28] 0 | |
| Conv2d-96 [4, 32, 28, 28] 512 | |
| BatchNorm2d-97 [4, 32, 28, 28] 64 | |
| CifNetShortCut-98 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-99 [4, 32, 28, 28] 0 | |
| Conv2d-100 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-101 [4, 32, 28, 28] 64 | |
| SiLU-102 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-103 [4, 32, 28, 28] 0 | |
| Conv2d-104 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-105 [4, 32, 28, 28] 64 | |
| SiLU-106 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-107 [4, 32, 28, 28] 0 | |
| Identity-108 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-109 [4, 32, 28, 28] 0 | |
| Conv2d-110 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-111 [4, 32, 28, 28] 64 | |
| SiLU-112 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-113 [4, 32, 28, 28] 0 | |
| Conv2d-114 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-115 [4, 32, 28, 28] 64 | |
| SiLU-116 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-117 [4, 32, 28, 28] 0 | |
| Identity-118 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-119 [4, 32, 28, 28] 0 | |
| Conv2d-120 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-121 [4, 32, 28, 28] 64 | |
| SiLU-122 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-123 [4, 32, 28, 28] 0 | |
| Conv2d-124 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-125 [4, 32, 28, 28] 64 | |
| SiLU-126 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-127 [4, 32, 28, 28] 0 | |
| Identity-128 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-129 [4, 32, 28, 28] 0 | |
| Conv2d-130 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-131 [4, 32, 28, 28] 64 | |
| SiLU-132 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-133 [4, 32, 28, 28] 0 | |
| Conv2d-134 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-135 [4, 32, 28, 28] 64 | |
| SiLU-136 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-137 [4, 32, 28, 28] 0 | |
| Identity-138 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-139 [4, 32, 28, 28] 0 | |
| Conv2d-140 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-141 [4, 32, 28, 28] 64 | |
| SiLU-142 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-143 [4, 32, 28, 28] 0 | |
| Conv2d-144 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-145 [4, 32, 28, 28] 64 | |
| SiLU-146 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-147 [4, 32, 28, 28] 0 | |
| Identity-148 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-149 [4, 32, 28, 28] 0 | |
| Conv2d-150 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-151 [4, 32, 28, 28] 64 | |
| SiLU-152 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-153 [4, 32, 28, 28] 0 | |
| Conv2d-154 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-155 [4, 32, 28, 28] 64 | |
| SiLU-156 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-157 [4, 32, 28, 28] 0 | |
| Identity-158 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-159 [4, 32, 28, 28] 0 | |
| Conv2d-160 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-161 [4, 32, 28, 28] 64 | |
| SiLU-162 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-163 [4, 32, 28, 28] 0 | |
| Conv2d-164 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-165 [4, 32, 28, 28] 64 | |
| SiLU-166 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-167 [4, 32, 28, 28] 0 | |
| Identity-168 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-169 [4, 32, 28, 28] 0 | |
| Conv2d-170 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-171 [4, 32, 28, 28] 64 | |
| SiLU-172 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-173 [4, 32, 28, 28] 0 | |
| Conv2d-174 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-175 [4, 32, 28, 28] 64 | |
| SiLU-176 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-177 [4, 32, 28, 28] 0 | |
| Identity-178 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-179 [4, 32, 28, 28] 0 | |
| Conv2d-180 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-181 [4, 32, 28, 28] 64 | |
| SiLU-182 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-183 [4, 32, 28, 28] 0 | |
| Conv2d-184 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-185 [4, 32, 28, 28] 64 | |
| SiLU-186 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-187 [4, 32, 28, 28] 0 | |
| Identity-188 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-189 [4, 32, 28, 28] 0 | |
| Conv2d-190 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-191 [4, 32, 28, 28] 64 | |
| SiLU-192 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-193 [4, 32, 28, 28] 0 | |
| Conv2d-194 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-195 [4, 32, 28, 28] 64 | |
| SiLU-196 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-197 [4, 32, 28, 28] 0 | |
| Identity-198 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-199 [4, 32, 28, 28] 0 | |
| Conv2d-200 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-201 [4, 32, 28, 28] 64 | |
| SiLU-202 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-203 [4, 32, 28, 28] 0 | |
| Conv2d-204 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-205 [4, 32, 28, 28] 64 | |
| SiLU-206 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-207 [4, 32, 28, 28] 0 | |
| Identity-208 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-209 [4, 32, 28, 28] 0 | |
| CifNetStage-210 [4, 32, 28, 28] 0 | |
| Conv2d-211 [4, 64, 14, 14] 18,432 | |
| BatchNorm2d-212 [4, 64, 14, 14] 128 | |
| SiLU-213 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-214 [4, 64, 14, 14] 0 | |
| Conv2d-215 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-216 [4, 64, 14, 14] 128 | |
| SiLU-217 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-218 [4, 64, 14, 14] 0 | |
| Conv2d-219 [4, 64, 14, 14] 2,048 | |
| BatchNorm2d-220 [4, 64, 14, 14] 128 | |
| CifNetShortCut-221 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-222 [4, 64, 14, 14] 0 | |
| Conv2d-223 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-224 [4, 64, 14, 14] 128 | |
| SiLU-225 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-226 [4, 64, 14, 14] 0 | |
| Conv2d-227 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-228 [4, 64, 14, 14] 128 | |
| SiLU-229 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-230 [4, 64, 14, 14] 0 | |
| Identity-231 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-232 [4, 64, 14, 14] 0 | |
| Conv2d-233 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-234 [4, 64, 14, 14] 128 | |
| SiLU-235 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-236 [4, 64, 14, 14] 0 | |
| Conv2d-237 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-238 [4, 64, 14, 14] 128 | |
| SiLU-239 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-240 [4, 64, 14, 14] 0 | |
| Identity-241 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-242 [4, 64, 14, 14] 0 | |
| Conv2d-243 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-244 [4, 64, 14, 14] 128 | |
| SiLU-245 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-246 [4, 64, 14, 14] 0 | |
| Conv2d-247 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-248 [4, 64, 14, 14] 128 | |
| SiLU-249 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-250 [4, 64, 14, 14] 0 | |
| Identity-251 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-252 [4, 64, 14, 14] 0 | |
| Conv2d-253 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-254 [4, 64, 14, 14] 128 | |
| SiLU-255 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-256 [4, 64, 14, 14] 0 | |
| Conv2d-257 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-258 [4, 64, 14, 14] 128 | |
| SiLU-259 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-260 [4, 64, 14, 14] 0 | |
| Identity-261 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-262 [4, 64, 14, 14] 0 | |
| Conv2d-263 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-264 [4, 64, 14, 14] 128 | |
| SiLU-265 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-266 [4, 64, 14, 14] 0 | |
| Conv2d-267 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-268 [4, 64, 14, 14] 128 | |
| SiLU-269 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-270 [4, 64, 14, 14] 0 | |
| Identity-271 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-272 [4, 64, 14, 14] 0 | |
| Conv2d-273 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-274 [4, 64, 14, 14] 128 | |
| SiLU-275 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-276 [4, 64, 14, 14] 0 | |
| Conv2d-277 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-278 [4, 64, 14, 14] 128 | |
| SiLU-279 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-280 [4, 64, 14, 14] 0 | |
| Identity-281 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-282 [4, 64, 14, 14] 0 | |
| Conv2d-283 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-284 [4, 64, 14, 14] 128 | |
| SiLU-285 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-286 [4, 64, 14, 14] 0 | |
| Conv2d-287 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-288 [4, 64, 14, 14] 128 | |
| SiLU-289 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-290 [4, 64, 14, 14] 0 | |
| Identity-291 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-292 [4, 64, 14, 14] 0 | |
| Conv2d-293 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-294 [4, 64, 14, 14] 128 | |
| SiLU-295 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-296 [4, 64, 14, 14] 0 | |
| Conv2d-297 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-298 [4, 64, 14, 14] 128 | |
| SiLU-299 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-300 [4, 64, 14, 14] 0 | |
| Identity-301 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-302 [4, 64, 14, 14] 0 | |
| Conv2d-303 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-304 [4, 64, 14, 14] 128 | |
| SiLU-305 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-306 [4, 64, 14, 14] 0 | |
| Conv2d-307 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-308 [4, 64, 14, 14] 128 | |
| SiLU-309 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-310 [4, 64, 14, 14] 0 | |
| Identity-311 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-312 [4, 64, 14, 14] 0 | |
| Conv2d-313 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-314 [4, 64, 14, 14] 128 | |
| SiLU-315 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-316 [4, 64, 14, 14] 0 | |
| Conv2d-317 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-318 [4, 64, 14, 14] 128 | |
| SiLU-319 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-320 [4, 64, 14, 14] 0 | |
| Identity-321 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-322 [4, 64, 14, 14] 0 | |
| Conv2d-323 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-324 [4, 64, 14, 14] 128 | |
| SiLU-325 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-326 [4, 64, 14, 14] 0 | |
| Conv2d-327 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-328 [4, 64, 14, 14] 128 | |
| SiLU-329 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-330 [4, 64, 14, 14] 0 | |
| Identity-331 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-332 [4, 64, 14, 14] 0 | |
| Conv2d-333 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-334 [4, 64, 14, 14] 128 | |
| SiLU-335 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-336 [4, 64, 14, 14] 0 | |
| Conv2d-337 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-338 [4, 64, 14, 14] 128 | |
| SiLU-339 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-340 [4, 64, 14, 14] 0 | |
| Identity-341 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-342 [4, 64, 14, 14] 0 | |
| Conv2d-343 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-344 [4, 64, 14, 14] 128 | |
| SiLU-345 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-346 [4, 64, 14, 14] 0 | |
| Conv2d-347 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-348 [4, 64, 14, 14] 128 | |
| SiLU-349 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-350 [4, 64, 14, 14] 0 | |
| Identity-351 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-352 [4, 64, 14, 14] 0 | |
| Conv2d-353 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-354 [4, 64, 14, 14] 128 | |
| SiLU-355 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-356 [4, 64, 14, 14] 0 | |
| Conv2d-357 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-358 [4, 64, 14, 14] 128 | |
| SiLU-359 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-360 [4, 64, 14, 14] 0 | |
| Identity-361 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-362 [4, 64, 14, 14] 0 | |
| Conv2d-363 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-364 [4, 64, 14, 14] 128 | |
| SiLU-365 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-366 [4, 64, 14, 14] 0 | |
| Conv2d-367 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-368 [4, 64, 14, 14] 128 | |
| SiLU-369 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-370 [4, 64, 14, 14] 0 | |
| Identity-371 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-372 [4, 64, 14, 14] 0 | |
| CifNetStage-373 [4, 64, 14, 14] 0 | |
| Conv2d-374 [4, 128, 7, 7] 73,728 | |
| BatchNorm2d-375 [4, 128, 7, 7] 256 | |
| SiLU-376 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-377 [4, 128, 7, 7] 0 | |
| Conv2d-378 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-379 [4, 128, 7, 7] 256 | |
| SiLU-380 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-381 [4, 128, 7, 7] 0 | |
| Conv2d-382 [4, 128, 7, 7] 8,192 | |
| BatchNorm2d-383 [4, 128, 7, 7] 256 | |
| CifNetShortCut-384 [4, 128, 7, 7] 0 | |
| CifNetBasicLayer-385 [4, 128, 7, 7] 0 | |
| Conv2d-386 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-387 [4, 128, 7, 7] 256 | |
| SiLU-388 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-389 [4, 128, 7, 7] 0 | |
| Conv2d-390 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-391 [4, 128, 7, 7] 256 | |
| SiLU-392 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-393 [4, 128, 7, 7] 0 | |
| Identity-394 [4, 128, 7, 7] 0 | |
| CifNetBasicLayer-395 [4, 128, 7, 7] 0 | |
| Conv2d-396 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-397 [4, 128, 7, 7] 256 | |
| SiLU-398 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-399 [4, 128, 7, 7] 0 | |
| Conv2d-400 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-401 [4, 128, 7, 7] 256 | |
| SiLU-402 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-403 [4, 128, 7, 7] 0 | |
| Identity-404 [4, 128, 7, 7] 0 | |
| CifNetBasicLayer-405 [4, 128, 7, 7] 0 | |
| Conv2d-406 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-407 [4, 128, 7, 7] 256 | |
| SiLU-408 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-409 [4, 128, 7, 7] 0 | |
| Conv2d-410 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-411 [4, 128, 7, 7] 256 | |
| SiLU-412 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-413 [4, 128, 7, 7] 0 | |
| Identity-414 [4, 128, 7, 7] 0 | |
| CifNetBasicLayer-415 [4, 128, 7, 7] 0 | |
| Conv2d-416 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-417 [4, 128, 7, 7] 256 | |
| SiLU-418 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-419 [4, 128, 7, 7] 0 | |
| Conv2d-420 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-421 [4, 128, 7, 7] 256 | |
| SiLU-422 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-423 [4, 128, 7, 7] 0 | |
| Identity-424 [4, 128, 7, 7] 0 | |
| CifNetBasicLayer-425 [4, 128, 7, 7] 0 | |
| Conv2d-426 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-427 [4, 128, 7, 7] 256 | |
| SiLU-428 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-429 [4, 128, 7, 7] 0 | |
| Conv2d-430 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-431 [4, 128, 7, 7] 256 | |
| SiLU-432 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-433 [4, 128, 7, 7] 0 | |
| Identity-434 [4, 128, 7, 7] 0 | |
| CifNetBasicLayer-435 [4, 128, 7, 7] 0 | |
| Conv2d-436 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-437 [4, 128, 7, 7] 256 | |
| SiLU-438 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-439 [4, 128, 7, 7] 0 | |
| Conv2d-440 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-441 [4, 128, 7, 7] 256 | |
| SiLU-442 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-443 [4, 128, 7, 7] 0 | |
| Identity-444 [4, 128, 7, 7] 0 | |
| CifNetBasicLayer-445 [4, 128, 7, 7] 0 | |
| Conv2d-446 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-447 [4, 128, 7, 7] 256 | |
| SiLU-448 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-449 [4, 128, 7, 7] 0 | |
| Conv2d-450 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-451 [4, 128, 7, 7] 256 | |
| SiLU-452 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-453 [4, 128, 7, 7] 0 | |
| Identity-454 [4, 128, 7, 7] 0 | |
| CifNetBasicLayer-455 [4, 128, 7, 7] 0 | |
| Conv2d-456 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-457 [4, 128, 7, 7] 256 | |
| SiLU-458 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-459 [4, 128, 7, 7] 0 | |
| Conv2d-460 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-461 [4, 128, 7, 7] 256 | |
| SiLU-462 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-463 [4, 128, 7, 7] 0 | |
| Identity-464 [4, 128, 7, 7] 0 | |
| CifNetBasicLayer-465 [4, 128, 7, 7] 0 | |
| Conv2d-466 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-467 [4, 128, 7, 7] 256 | |
| SiLU-468 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-469 [4, 128, 7, 7] 0 | |
| Conv2d-470 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-471 [4, 128, 7, 7] 256 | |
| SiLU-472 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-473 [4, 128, 7, 7] 0 | |
| Identity-474 [4, 128, 7, 7] 0 | |
| CifNetBasicLayer-475 [4, 128, 7, 7] 0 | |
| Conv2d-476 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-477 [4, 128, 7, 7] 256 | |
| SiLU-478 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-479 [4, 128, 7, 7] 0 | |
| Conv2d-480 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-481 [4, 128, 7, 7] 256 | |
| SiLU-482 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-483 [4, 128, 7, 7] 0 | |
| Identity-484 [4, 128, 7, 7] 0 | |
| CifNetBasicLayer-485 [4, 128, 7, 7] 0 | |
| Conv2d-486 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-487 [4, 128, 7, 7] 256 | |
| SiLU-488 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-489 [4, 128, 7, 7] 0 | |
| Conv2d-490 [4, 128, 7, 7] 147,456 | |
| BatchNorm2d-491 [4, 128, 7, 7] 256 | |
| SiLU-492 [4, 128, 7, 7] 0 | |
| CifNetConvLayer-493 [4, 128, 7, 7] 0 | |
| Identity-494 [4, 128, 7, 7] 0 | |
| CifNetBasicLayer-495 [4, 128, 7, 7] 0 | |
| CifNetStage-496 [4, 128, 7, 7] 0 | |
| CifNetEncoder-497 [[-1, 128, 7, 7]] 0 | |
| AdaptiveAvgPool2d-498 [4, 128, 1, 1] 0 | |
| CifNetModel-499 [[-1, 128, 7, 7], [-1, 128, 1, 1]] 0 | |
| Flatten-500 [4, 128] 0 | |
| Linear-501 [4, 10] 1,290 | |
| ================================================================ | |
| Total params: 4,907,034 | |
| Trainable params: 4,907,034 | |
| Non-trainable params: 0 | |
| ---------------------------------------------------------------- | |
| Input size (MB): 2.30 | |
| Forward/backward pass size (MB): 337.79 | |
| Params size (MB): 18.72 | |
| Estimated Total Size (MB): 358.81 | |
| ---------------------------------------------------------------- | |