| 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() | |
| ) | |
| ) | |
| ) | |
| (8): 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() | |
| ) | |
| ) | |
| ) | |
| (9): 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() | |
| ) | |
| ) | |
| ) | |
| (10): 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() | |
| ) | |
| ) | |
| ) | |
| (11): 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() | |
| ) | |
| ) | |
| ) | |
| (12): 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() | |
| ) | |
| ) | |
| ) | |
| (13): 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() | |
| ) | |
| ) | |
| ) | |
| (14): 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() | |
| ) | |
| ) | |
| ) | |
| (15): 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() | |
| ) | |
| ) | |
| ) | |
| (16): 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() | |
| ) | |
| ) | |
| ) | |
| (17): 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() | |
| ) | |
| ) | |
| ) | |
| (12): 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() | |
| ) | |
| ) | |
| ) | |
| (13): 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() | |
| ) | |
| ) | |
| ) | |
| (14): 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() | |
| ) | |
| ) | |
| ) | |
| (15): 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() | |
| ) | |
| ) | |
| ) | |
| (16): 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() | |
| ) | |
| ) | |
| ) | |
| (17): 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() | |
| ) | |
| ) | |
| ) | |
| (16): 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() | |
| ) | |
| ) | |
| ) | |
| (17): 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() | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (pooler): AdaptiveAvgPool2d(output_size=(1, 1)) | |
| ) | |
| (classifier): Sequential( | |
| (0): Flatten(start_dim=1, end_dim=-1) | |
| (1): Linear(in_features=64, 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 | |
| Conv2d-87 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-88 [4, 16, 56, 56] 32 | |
| SiLU-89 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-90 [4, 16, 56, 56] 0 | |
| Conv2d-91 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-92 [4, 16, 56, 56] 32 | |
| SiLU-93 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-94 [4, 16, 56, 56] 0 | |
| Identity-95 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-96 [4, 16, 56, 56] 0 | |
| Conv2d-97 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-98 [4, 16, 56, 56] 32 | |
| SiLU-99 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-100 [4, 16, 56, 56] 0 | |
| Conv2d-101 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-102 [4, 16, 56, 56] 32 | |
| SiLU-103 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-104 [4, 16, 56, 56] 0 | |
| Identity-105 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-106 [4, 16, 56, 56] 0 | |
| Conv2d-107 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-108 [4, 16, 56, 56] 32 | |
| SiLU-109 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-110 [4, 16, 56, 56] 0 | |
| Conv2d-111 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-112 [4, 16, 56, 56] 32 | |
| SiLU-113 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-114 [4, 16, 56, 56] 0 | |
| Identity-115 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-116 [4, 16, 56, 56] 0 | |
| Conv2d-117 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-118 [4, 16, 56, 56] 32 | |
| SiLU-119 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-120 [4, 16, 56, 56] 0 | |
| Conv2d-121 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-122 [4, 16, 56, 56] 32 | |
| SiLU-123 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-124 [4, 16, 56, 56] 0 | |
| Identity-125 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-126 [4, 16, 56, 56] 0 | |
| Conv2d-127 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-128 [4, 16, 56, 56] 32 | |
| SiLU-129 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-130 [4, 16, 56, 56] 0 | |
| Conv2d-131 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-132 [4, 16, 56, 56] 32 | |
| SiLU-133 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-134 [4, 16, 56, 56] 0 | |
| Identity-135 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-136 [4, 16, 56, 56] 0 | |
| Conv2d-137 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-138 [4, 16, 56, 56] 32 | |
| SiLU-139 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-140 [4, 16, 56, 56] 0 | |
| Conv2d-141 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-142 [4, 16, 56, 56] 32 | |
| SiLU-143 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-144 [4, 16, 56, 56] 0 | |
| Identity-145 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-146 [4, 16, 56, 56] 0 | |
| Conv2d-147 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-148 [4, 16, 56, 56] 32 | |
| SiLU-149 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-150 [4, 16, 56, 56] 0 | |
| Conv2d-151 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-152 [4, 16, 56, 56] 32 | |
| SiLU-153 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-154 [4, 16, 56, 56] 0 | |
| Identity-155 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-156 [4, 16, 56, 56] 0 | |
| Conv2d-157 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-158 [4, 16, 56, 56] 32 | |
| SiLU-159 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-160 [4, 16, 56, 56] 0 | |
| Conv2d-161 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-162 [4, 16, 56, 56] 32 | |
| SiLU-163 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-164 [4, 16, 56, 56] 0 | |
| Identity-165 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-166 [4, 16, 56, 56] 0 | |
| Conv2d-167 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-168 [4, 16, 56, 56] 32 | |
| SiLU-169 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-170 [4, 16, 56, 56] 0 | |
| Conv2d-171 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-172 [4, 16, 56, 56] 32 | |
| SiLU-173 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-174 [4, 16, 56, 56] 0 | |
| Identity-175 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-176 [4, 16, 56, 56] 0 | |
| Conv2d-177 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-178 [4, 16, 56, 56] 32 | |
| SiLU-179 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-180 [4, 16, 56, 56] 0 | |
| Conv2d-181 [4, 16, 56, 56] 2,304 | |
| BatchNorm2d-182 [4, 16, 56, 56] 32 | |
| SiLU-183 [4, 16, 56, 56] 0 | |
| CifNetConvLayer-184 [4, 16, 56, 56] 0 | |
| Identity-185 [4, 16, 56, 56] 0 | |
| CifNetBasicLayer-186 [4, 16, 56, 56] 0 | |
| CifNetStage-187 [4, 16, 56, 56] 0 | |
| Conv2d-188 [4, 32, 28, 28] 4,608 | |
| BatchNorm2d-189 [4, 32, 28, 28] 64 | |
| SiLU-190 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-191 [4, 32, 28, 28] 0 | |
| Conv2d-192 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-193 [4, 32, 28, 28] 64 | |
| SiLU-194 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-195 [4, 32, 28, 28] 0 | |
| Conv2d-196 [4, 32, 28, 28] 512 | |
| BatchNorm2d-197 [4, 32, 28, 28] 64 | |
| CifNetShortCut-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 | |
| Conv2d-210 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-211 [4, 32, 28, 28] 64 | |
| SiLU-212 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-213 [4, 32, 28, 28] 0 | |
| Conv2d-214 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-215 [4, 32, 28, 28] 64 | |
| SiLU-216 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-217 [4, 32, 28, 28] 0 | |
| Identity-218 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-219 [4, 32, 28, 28] 0 | |
| Conv2d-220 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-221 [4, 32, 28, 28] 64 | |
| SiLU-222 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-223 [4, 32, 28, 28] 0 | |
| Conv2d-224 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-225 [4, 32, 28, 28] 64 | |
| SiLU-226 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-227 [4, 32, 28, 28] 0 | |
| Identity-228 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-229 [4, 32, 28, 28] 0 | |
| Conv2d-230 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-231 [4, 32, 28, 28] 64 | |
| SiLU-232 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-233 [4, 32, 28, 28] 0 | |
| Conv2d-234 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-235 [4, 32, 28, 28] 64 | |
| SiLU-236 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-237 [4, 32, 28, 28] 0 | |
| Identity-238 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-239 [4, 32, 28, 28] 0 | |
| Conv2d-240 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-241 [4, 32, 28, 28] 64 | |
| SiLU-242 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-243 [4, 32, 28, 28] 0 | |
| Conv2d-244 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-245 [4, 32, 28, 28] 64 | |
| SiLU-246 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-247 [4, 32, 28, 28] 0 | |
| Identity-248 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-249 [4, 32, 28, 28] 0 | |
| Conv2d-250 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-251 [4, 32, 28, 28] 64 | |
| SiLU-252 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-253 [4, 32, 28, 28] 0 | |
| Conv2d-254 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-255 [4, 32, 28, 28] 64 | |
| SiLU-256 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-257 [4, 32, 28, 28] 0 | |
| Identity-258 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-259 [4, 32, 28, 28] 0 | |
| Conv2d-260 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-261 [4, 32, 28, 28] 64 | |
| SiLU-262 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-263 [4, 32, 28, 28] 0 | |
| Conv2d-264 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-265 [4, 32, 28, 28] 64 | |
| SiLU-266 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-267 [4, 32, 28, 28] 0 | |
| Identity-268 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-269 [4, 32, 28, 28] 0 | |
| Conv2d-270 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-271 [4, 32, 28, 28] 64 | |
| SiLU-272 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-273 [4, 32, 28, 28] 0 | |
| Conv2d-274 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-275 [4, 32, 28, 28] 64 | |
| SiLU-276 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-277 [4, 32, 28, 28] 0 | |
| Identity-278 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-279 [4, 32, 28, 28] 0 | |
| Conv2d-280 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-281 [4, 32, 28, 28] 64 | |
| SiLU-282 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-283 [4, 32, 28, 28] 0 | |
| Conv2d-284 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-285 [4, 32, 28, 28] 64 | |
| SiLU-286 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-287 [4, 32, 28, 28] 0 | |
| Identity-288 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-289 [4, 32, 28, 28] 0 | |
| Conv2d-290 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-291 [4, 32, 28, 28] 64 | |
| SiLU-292 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-293 [4, 32, 28, 28] 0 | |
| Conv2d-294 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-295 [4, 32, 28, 28] 64 | |
| SiLU-296 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-297 [4, 32, 28, 28] 0 | |
| Identity-298 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-299 [4, 32, 28, 28] 0 | |
| Conv2d-300 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-301 [4, 32, 28, 28] 64 | |
| SiLU-302 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-303 [4, 32, 28, 28] 0 | |
| Conv2d-304 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-305 [4, 32, 28, 28] 64 | |
| SiLU-306 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-307 [4, 32, 28, 28] 0 | |
| Identity-308 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-309 [4, 32, 28, 28] 0 | |
| Conv2d-310 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-311 [4, 32, 28, 28] 64 | |
| SiLU-312 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-313 [4, 32, 28, 28] 0 | |
| Conv2d-314 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-315 [4, 32, 28, 28] 64 | |
| SiLU-316 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-317 [4, 32, 28, 28] 0 | |
| Identity-318 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-319 [4, 32, 28, 28] 0 | |
| Conv2d-320 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-321 [4, 32, 28, 28] 64 | |
| SiLU-322 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-323 [4, 32, 28, 28] 0 | |
| Conv2d-324 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-325 [4, 32, 28, 28] 64 | |
| SiLU-326 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-327 [4, 32, 28, 28] 0 | |
| Identity-328 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-329 [4, 32, 28, 28] 0 | |
| Conv2d-330 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-331 [4, 32, 28, 28] 64 | |
| SiLU-332 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-333 [4, 32, 28, 28] 0 | |
| Conv2d-334 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-335 [4, 32, 28, 28] 64 | |
| SiLU-336 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-337 [4, 32, 28, 28] 0 | |
| Identity-338 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-339 [4, 32, 28, 28] 0 | |
| Conv2d-340 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-341 [4, 32, 28, 28] 64 | |
| SiLU-342 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-343 [4, 32, 28, 28] 0 | |
| Conv2d-344 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-345 [4, 32, 28, 28] 64 | |
| SiLU-346 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-347 [4, 32, 28, 28] 0 | |
| Identity-348 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-349 [4, 32, 28, 28] 0 | |
| Conv2d-350 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-351 [4, 32, 28, 28] 64 | |
| SiLU-352 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-353 [4, 32, 28, 28] 0 | |
| Conv2d-354 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-355 [4, 32, 28, 28] 64 | |
| SiLU-356 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-357 [4, 32, 28, 28] 0 | |
| Identity-358 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-359 [4, 32, 28, 28] 0 | |
| Conv2d-360 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-361 [4, 32, 28, 28] 64 | |
| SiLU-362 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-363 [4, 32, 28, 28] 0 | |
| Conv2d-364 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-365 [4, 32, 28, 28] 64 | |
| SiLU-366 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-367 [4, 32, 28, 28] 0 | |
| Identity-368 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-369 [4, 32, 28, 28] 0 | |
| CifNetStage-370 [4, 32, 28, 28] 0 | |
| Conv2d-371 [4, 64, 14, 14] 18,432 | |
| BatchNorm2d-372 [4, 64, 14, 14] 128 | |
| SiLU-373 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-374 [4, 64, 14, 14] 0 | |
| Conv2d-375 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-376 [4, 64, 14, 14] 128 | |
| SiLU-377 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-378 [4, 64, 14, 14] 0 | |
| Conv2d-379 [4, 64, 14, 14] 2,048 | |
| BatchNorm2d-380 [4, 64, 14, 14] 128 | |
| CifNetShortCut-381 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-382 [4, 64, 14, 14] 0 | |
| Conv2d-383 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-384 [4, 64, 14, 14] 128 | |
| SiLU-385 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-386 [4, 64, 14, 14] 0 | |
| Conv2d-387 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-388 [4, 64, 14, 14] 128 | |
| SiLU-389 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-390 [4, 64, 14, 14] 0 | |
| Identity-391 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-392 [4, 64, 14, 14] 0 | |
| Conv2d-393 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-394 [4, 64, 14, 14] 128 | |
| SiLU-395 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-396 [4, 64, 14, 14] 0 | |
| Conv2d-397 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-398 [4, 64, 14, 14] 128 | |
| SiLU-399 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-400 [4, 64, 14, 14] 0 | |
| Identity-401 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-402 [4, 64, 14, 14] 0 | |
| Conv2d-403 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-404 [4, 64, 14, 14] 128 | |
| SiLU-405 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-406 [4, 64, 14, 14] 0 | |
| Conv2d-407 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-408 [4, 64, 14, 14] 128 | |
| SiLU-409 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-410 [4, 64, 14, 14] 0 | |
| Identity-411 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-412 [4, 64, 14, 14] 0 | |
| Conv2d-413 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-414 [4, 64, 14, 14] 128 | |
| SiLU-415 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-416 [4, 64, 14, 14] 0 | |
| Conv2d-417 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-418 [4, 64, 14, 14] 128 | |
| SiLU-419 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-420 [4, 64, 14, 14] 0 | |
| Identity-421 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-422 [4, 64, 14, 14] 0 | |
| Conv2d-423 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-424 [4, 64, 14, 14] 128 | |
| SiLU-425 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-426 [4, 64, 14, 14] 0 | |
| Conv2d-427 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-428 [4, 64, 14, 14] 128 | |
| SiLU-429 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-430 [4, 64, 14, 14] 0 | |
| Identity-431 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-432 [4, 64, 14, 14] 0 | |
| Conv2d-433 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-434 [4, 64, 14, 14] 128 | |
| SiLU-435 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-436 [4, 64, 14, 14] 0 | |
| Conv2d-437 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-438 [4, 64, 14, 14] 128 | |
| SiLU-439 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-440 [4, 64, 14, 14] 0 | |
| Identity-441 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-442 [4, 64, 14, 14] 0 | |
| Conv2d-443 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-444 [4, 64, 14, 14] 128 | |
| SiLU-445 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-446 [4, 64, 14, 14] 0 | |
| Conv2d-447 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-448 [4, 64, 14, 14] 128 | |
| SiLU-449 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-450 [4, 64, 14, 14] 0 | |
| Identity-451 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-452 [4, 64, 14, 14] 0 | |
| Conv2d-453 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-454 [4, 64, 14, 14] 128 | |
| SiLU-455 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-456 [4, 64, 14, 14] 0 | |
| Conv2d-457 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-458 [4, 64, 14, 14] 128 | |
| SiLU-459 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-460 [4, 64, 14, 14] 0 | |
| Identity-461 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-462 [4, 64, 14, 14] 0 | |
| Conv2d-463 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-464 [4, 64, 14, 14] 128 | |
| SiLU-465 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-466 [4, 64, 14, 14] 0 | |
| Conv2d-467 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-468 [4, 64, 14, 14] 128 | |
| SiLU-469 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-470 [4, 64, 14, 14] 0 | |
| Identity-471 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-472 [4, 64, 14, 14] 0 | |
| Conv2d-473 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-474 [4, 64, 14, 14] 128 | |
| SiLU-475 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-476 [4, 64, 14, 14] 0 | |
| Conv2d-477 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-478 [4, 64, 14, 14] 128 | |
| SiLU-479 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-480 [4, 64, 14, 14] 0 | |
| Identity-481 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-482 [4, 64, 14, 14] 0 | |
| Conv2d-483 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-484 [4, 64, 14, 14] 128 | |
| SiLU-485 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-486 [4, 64, 14, 14] 0 | |
| Conv2d-487 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-488 [4, 64, 14, 14] 128 | |
| SiLU-489 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-490 [4, 64, 14, 14] 0 | |
| Identity-491 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-492 [4, 64, 14, 14] 0 | |
| Conv2d-493 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-494 [4, 64, 14, 14] 128 | |
| SiLU-495 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-496 [4, 64, 14, 14] 0 | |
| Conv2d-497 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-498 [4, 64, 14, 14] 128 | |
| SiLU-499 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-500 [4, 64, 14, 14] 0 | |
| Identity-501 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-502 [4, 64, 14, 14] 0 | |
| Conv2d-503 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-504 [4, 64, 14, 14] 128 | |
| SiLU-505 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-506 [4, 64, 14, 14] 0 | |
| Conv2d-507 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-508 [4, 64, 14, 14] 128 | |
| SiLU-509 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-510 [4, 64, 14, 14] 0 | |
| Identity-511 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-512 [4, 64, 14, 14] 0 | |
| Conv2d-513 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-514 [4, 64, 14, 14] 128 | |
| SiLU-515 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-516 [4, 64, 14, 14] 0 | |
| Conv2d-517 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-518 [4, 64, 14, 14] 128 | |
| SiLU-519 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-520 [4, 64, 14, 14] 0 | |
| Identity-521 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-522 [4, 64, 14, 14] 0 | |
| Conv2d-523 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-524 [4, 64, 14, 14] 128 | |
| SiLU-525 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-526 [4, 64, 14, 14] 0 | |
| Conv2d-527 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-528 [4, 64, 14, 14] 128 | |
| SiLU-529 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-530 [4, 64, 14, 14] 0 | |
| Identity-531 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-532 [4, 64, 14, 14] 0 | |
| Conv2d-533 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-534 [4, 64, 14, 14] 128 | |
| SiLU-535 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-536 [4, 64, 14, 14] 0 | |
| Conv2d-537 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-538 [4, 64, 14, 14] 128 | |
| SiLU-539 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-540 [4, 64, 14, 14] 0 | |
| Identity-541 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-542 [4, 64, 14, 14] 0 | |
| Conv2d-543 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-544 [4, 64, 14, 14] 128 | |
| SiLU-545 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-546 [4, 64, 14, 14] 0 | |
| Conv2d-547 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-548 [4, 64, 14, 14] 128 | |
| SiLU-549 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-550 [4, 64, 14, 14] 0 | |
| Identity-551 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-552 [4, 64, 14, 14] 0 | |
| CifNetStage-553 [4, 64, 14, 14] 0 | |
| CifNetEncoder-554 [[-1, 64, 14, 14]] 0 | |
| AdaptiveAvgPool2d-555 [4, 64, 1, 1] 0 | |
| CifNetModel-556 [[-1, 64, 14, 14], [-1, 64, 1, 1]] 0 | |
| Flatten-557 [4, 64] 0 | |
| Linear-558 [4, 10] 650 | |
| ================================================================ | |
| Total params: 1,732,634 | |
| Trainable params: 1,732,634 | |
| Non-trainable params: 0 | |
| ---------------------------------------------------------------- | |
| Input size (MB): 2.30 | |
| Forward/backward pass size (MB): 520.92 | |
| Params size (MB): 6.61 | |
| Estimated Total Size (MB): 529.82 | |
| ---------------------------------------------------------------- | |