| CifNetForImageClassification( | |
| (resnet): CifNetModel( | |
| (embedder): CifNetEmbeddings( | |
| (embedder): CifNetConvLayer( | |
| (convolution): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) | |
| (normalization): BatchNorm2d(64, 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(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() | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (1): 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() | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (2): CifNetStage( | |
| (layers): Sequential( | |
| (0): CifNetBasicLayer( | |
| (shortcut): CifNetShortCut( | |
| (convolution): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False) | |
| (normalization): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| ) | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (3): CifNetStage( | |
| (layers): Sequential( | |
| (0): CifNetBasicLayer( | |
| (shortcut): CifNetShortCut( | |
| (convolution): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False) | |
| (normalization): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| ) | |
| (layer): Sequential( | |
| (0): CifNetConvLayer( | |
| (convolution): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activation): SiLU() | |
| ) | |
| (1): CifNetConvLayer( | |
| (convolution): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (normalization): BatchNorm2d(512, 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=512, out_features=10, bias=True) | |
| ) | |
| ) | |
| ---------------------------------------------------------------- | |
| Layer (type) Output Shape Param # | |
| ================================================================ | |
| Conv2d-1 [4, 64, 112, 112] 9,408 | |
| BatchNorm2d-2 [4, 64, 112, 112] 128 | |
| SiLU-3 [4, 64, 112, 112] 0 | |
| CifNetConvLayer-4 [4, 64, 112, 112] 0 | |
| MaxPool2d-5 [4, 64, 56, 56] 0 | |
| CifNetEmbeddings-6 [4, 64, 56, 56] 0 | |
| Conv2d-7 [4, 64, 56, 56] 36,864 | |
| BatchNorm2d-8 [4, 64, 56, 56] 128 | |
| SiLU-9 [4, 64, 56, 56] 0 | |
| CifNetConvLayer-10 [4, 64, 56, 56] 0 | |
| Conv2d-11 [4, 64, 56, 56] 36,864 | |
| BatchNorm2d-12 [4, 64, 56, 56] 128 | |
| SiLU-13 [4, 64, 56, 56] 0 | |
| CifNetConvLayer-14 [4, 64, 56, 56] 0 | |
| Identity-15 [4, 64, 56, 56] 0 | |
| CifNetBasicLayer-16 [4, 64, 56, 56] 0 | |
| CifNetStage-17 [4, 64, 56, 56] 0 | |
| Conv2d-18 [4, 128, 28, 28] 73,728 | |
| BatchNorm2d-19 [4, 128, 28, 28] 256 | |
| SiLU-20 [4, 128, 28, 28] 0 | |
| CifNetConvLayer-21 [4, 128, 28, 28] 0 | |
| Conv2d-22 [4, 128, 28, 28] 147,456 | |
| BatchNorm2d-23 [4, 128, 28, 28] 256 | |
| SiLU-24 [4, 128, 28, 28] 0 | |
| CifNetConvLayer-25 [4, 128, 28, 28] 0 | |
| Conv2d-26 [4, 128, 28, 28] 8,192 | |
| BatchNorm2d-27 [4, 128, 28, 28] 256 | |
| CifNetShortCut-28 [4, 128, 28, 28] 0 | |
| CifNetBasicLayer-29 [4, 128, 28, 28] 0 | |
| CifNetStage-30 [4, 128, 28, 28] 0 | |
| Conv2d-31 [4, 256, 14, 14] 294,912 | |
| BatchNorm2d-32 [4, 256, 14, 14] 512 | |
| SiLU-33 [4, 256, 14, 14] 0 | |
| CifNetConvLayer-34 [4, 256, 14, 14] 0 | |
| Conv2d-35 [4, 256, 14, 14] 589,824 | |
| BatchNorm2d-36 [4, 256, 14, 14] 512 | |
| SiLU-37 [4, 256, 14, 14] 0 | |
| CifNetConvLayer-38 [4, 256, 14, 14] 0 | |
| Conv2d-39 [4, 256, 14, 14] 32,768 | |
| BatchNorm2d-40 [4, 256, 14, 14] 512 | |
| CifNetShortCut-41 [4, 256, 14, 14] 0 | |
| CifNetBasicLayer-42 [4, 256, 14, 14] 0 | |
| CifNetStage-43 [4, 256, 14, 14] 0 | |
| Conv2d-44 [4, 512, 7, 7] 1,179,648 | |
| BatchNorm2d-45 [4, 512, 7, 7] 1,024 | |
| SiLU-46 [4, 512, 7, 7] 0 | |
| CifNetConvLayer-47 [4, 512, 7, 7] 0 | |
| Conv2d-48 [4, 512, 7, 7] 2,359,296 | |
| BatchNorm2d-49 [4, 512, 7, 7] 1,024 | |
| SiLU-50 [4, 512, 7, 7] 0 | |
| CifNetConvLayer-51 [4, 512, 7, 7] 0 | |
| Conv2d-52 [4, 512, 7, 7] 131,072 | |
| BatchNorm2d-53 [4, 512, 7, 7] 1,024 | |
| CifNetShortCut-54 [4, 512, 7, 7] 0 | |
| CifNetBasicLayer-55 [4, 512, 7, 7] 0 | |
| CifNetStage-56 [4, 512, 7, 7] 0 | |
| CifNetEncoder-57 [[-1, 512, 7, 7]] 0 | |
| AdaptiveAvgPool2d-58 [4, 512, 1, 1] 0 | |
| CifNetModel-59 [[-1, 512, 7, 7], [-1, 512, 1, 1]] 0 | |
| Flatten-60 [4, 512] 0 | |
| Linear-61 [4, 10] 5,130 | |
| ================================================================ | |
| Total params: 4,910,922 | |
| Trainable params: 4,910,922 | |
| Non-trainable params: 0 | |
| ---------------------------------------------------------------- | |
| Input size (MB): 2.30 | |
| Forward/backward pass size (MB): 345.14 | |
| Params size (MB): 18.73 | |
| Estimated Total Size (MB): 366.17 | |
| ---------------------------------------------------------------- | |