| 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): 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() | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (1): 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() | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (2): CifNetStage( | |
| (layers): Sequential( | |
| (0): CifNetBasicLayer( | |
| (shortcut): CifNetShortCut( | |
| (convolution): Conv2d(64, 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(64, 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() | |
| ) | |
| ) | |
| ) | |
| (1): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): 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() | |
| ) | |
| (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() | |
| ) | |
| ) | |
| ) | |
| (2): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): 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() | |
| ) | |
| (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): CifNetBasicLayer( | |
| (shortcut): Identity() | |
| (layer): Sequential( | |
| (0): 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() | |
| ) | |
| (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() | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (pooler): AdaptiveAvgPool2d(output_size=(1, 1)) | |
| ) | |
| (classifier): Sequential( | |
| (0): Flatten(start_dim=1, end_dim=-1) | |
| (1): Linear(in_features=256, 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, 32, 28, 28] 4,608 | |
| BatchNorm2d-8 [4, 32, 28, 28] 64 | |
| SiLU-9 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-10 [4, 32, 28, 28] 0 | |
| Conv2d-11 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-12 [4, 32, 28, 28] 64 | |
| SiLU-13 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-14 [4, 32, 28, 28] 0 | |
| Conv2d-15 [4, 32, 28, 28] 512 | |
| BatchNorm2d-16 [4, 32, 28, 28] 64 | |
| CifNetShortCut-17 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-18 [4, 32, 28, 28] 0 | |
| Conv2d-19 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-20 [4, 32, 28, 28] 64 | |
| SiLU-21 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-22 [4, 32, 28, 28] 0 | |
| Conv2d-23 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-24 [4, 32, 28, 28] 64 | |
| SiLU-25 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-26 [4, 32, 28, 28] 0 | |
| Identity-27 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-28 [4, 32, 28, 28] 0 | |
| Conv2d-29 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-30 [4, 32, 28, 28] 64 | |
| SiLU-31 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-32 [4, 32, 28, 28] 0 | |
| Conv2d-33 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-34 [4, 32, 28, 28] 64 | |
| SiLU-35 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-36 [4, 32, 28, 28] 0 | |
| Identity-37 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-38 [4, 32, 28, 28] 0 | |
| Conv2d-39 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-40 [4, 32, 28, 28] 64 | |
| SiLU-41 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-42 [4, 32, 28, 28] 0 | |
| Conv2d-43 [4, 32, 28, 28] 9,216 | |
| BatchNorm2d-44 [4, 32, 28, 28] 64 | |
| SiLU-45 [4, 32, 28, 28] 0 | |
| CifNetConvLayer-46 [4, 32, 28, 28] 0 | |
| Identity-47 [4, 32, 28, 28] 0 | |
| CifNetBasicLayer-48 [4, 32, 28, 28] 0 | |
| CifNetStage-49 [4, 32, 28, 28] 0 | |
| Conv2d-50 [4, 64, 14, 14] 18,432 | |
| BatchNorm2d-51 [4, 64, 14, 14] 128 | |
| SiLU-52 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-53 [4, 64, 14, 14] 0 | |
| Conv2d-54 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-55 [4, 64, 14, 14] 128 | |
| SiLU-56 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-57 [4, 64, 14, 14] 0 | |
| Conv2d-58 [4, 64, 14, 14] 2,048 | |
| BatchNorm2d-59 [4, 64, 14, 14] 128 | |
| CifNetShortCut-60 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-61 [4, 64, 14, 14] 0 | |
| Conv2d-62 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-63 [4, 64, 14, 14] 128 | |
| SiLU-64 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-65 [4, 64, 14, 14] 0 | |
| Conv2d-66 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-67 [4, 64, 14, 14] 128 | |
| SiLU-68 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-69 [4, 64, 14, 14] 0 | |
| Identity-70 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-71 [4, 64, 14, 14] 0 | |
| Conv2d-72 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-73 [4, 64, 14, 14] 128 | |
| SiLU-74 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-75 [4, 64, 14, 14] 0 | |
| Conv2d-76 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-77 [4, 64, 14, 14] 128 | |
| SiLU-78 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-79 [4, 64, 14, 14] 0 | |
| Identity-80 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-81 [4, 64, 14, 14] 0 | |
| Conv2d-82 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-83 [4, 64, 14, 14] 128 | |
| SiLU-84 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-85 [4, 64, 14, 14] 0 | |
| Conv2d-86 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-87 [4, 64, 14, 14] 128 | |
| SiLU-88 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-89 [4, 64, 14, 14] 0 | |
| Identity-90 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-91 [4, 64, 14, 14] 0 | |
| Conv2d-92 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-93 [4, 64, 14, 14] 128 | |
| SiLU-94 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-95 [4, 64, 14, 14] 0 | |
| Conv2d-96 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-97 [4, 64, 14, 14] 128 | |
| SiLU-98 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-99 [4, 64, 14, 14] 0 | |
| Identity-100 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-101 [4, 64, 14, 14] 0 | |
| Conv2d-102 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-103 [4, 64, 14, 14] 128 | |
| SiLU-104 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-105 [4, 64, 14, 14] 0 | |
| Conv2d-106 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-107 [4, 64, 14, 14] 128 | |
| SiLU-108 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-109 [4, 64, 14, 14] 0 | |
| Identity-110 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-111 [4, 64, 14, 14] 0 | |
| Conv2d-112 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-113 [4, 64, 14, 14] 128 | |
| SiLU-114 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-115 [4, 64, 14, 14] 0 | |
| Conv2d-116 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-117 [4, 64, 14, 14] 128 | |
| SiLU-118 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-119 [4, 64, 14, 14] 0 | |
| Identity-120 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-121 [4, 64, 14, 14] 0 | |
| Conv2d-122 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-123 [4, 64, 14, 14] 128 | |
| SiLU-124 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-125 [4, 64, 14, 14] 0 | |
| Conv2d-126 [4, 64, 14, 14] 36,864 | |
| BatchNorm2d-127 [4, 64, 14, 14] 128 | |
| SiLU-128 [4, 64, 14, 14] 0 | |
| CifNetConvLayer-129 [4, 64, 14, 14] 0 | |
| Identity-130 [4, 64, 14, 14] 0 | |
| CifNetBasicLayer-131 [4, 64, 14, 14] 0 | |
| CifNetStage-132 [4, 64, 14, 14] 0 | |
| Conv2d-133 [4, 256, 7, 7] 147,456 | |
| BatchNorm2d-134 [4, 256, 7, 7] 512 | |
| SiLU-135 [4, 256, 7, 7] 0 | |
| CifNetConvLayer-136 [4, 256, 7, 7] 0 | |
| Conv2d-137 [4, 256, 7, 7] 589,824 | |
| BatchNorm2d-138 [4, 256, 7, 7] 512 | |
| SiLU-139 [4, 256, 7, 7] 0 | |
| CifNetConvLayer-140 [4, 256, 7, 7] 0 | |
| Conv2d-141 [4, 256, 7, 7] 16,384 | |
| BatchNorm2d-142 [4, 256, 7, 7] 512 | |
| CifNetShortCut-143 [4, 256, 7, 7] 0 | |
| CifNetBasicLayer-144 [4, 256, 7, 7] 0 | |
| Conv2d-145 [4, 256, 7, 7] 589,824 | |
| BatchNorm2d-146 [4, 256, 7, 7] 512 | |
| SiLU-147 [4, 256, 7, 7] 0 | |
| CifNetConvLayer-148 [4, 256, 7, 7] 0 | |
| Conv2d-149 [4, 256, 7, 7] 589,824 | |
| BatchNorm2d-150 [4, 256, 7, 7] 512 | |
| SiLU-151 [4, 256, 7, 7] 0 | |
| CifNetConvLayer-152 [4, 256, 7, 7] 0 | |
| Identity-153 [4, 256, 7, 7] 0 | |
| CifNetBasicLayer-154 [4, 256, 7, 7] 0 | |
| Conv2d-155 [4, 256, 7, 7] 589,824 | |
| BatchNorm2d-156 [4, 256, 7, 7] 512 | |
| SiLU-157 [4, 256, 7, 7] 0 | |
| CifNetConvLayer-158 [4, 256, 7, 7] 0 | |
| Conv2d-159 [4, 256, 7, 7] 589,824 | |
| BatchNorm2d-160 [4, 256, 7, 7] 512 | |
| SiLU-161 [4, 256, 7, 7] 0 | |
| CifNetConvLayer-162 [4, 256, 7, 7] 0 | |
| Identity-163 [4, 256, 7, 7] 0 | |
| CifNetBasicLayer-164 [4, 256, 7, 7] 0 | |
| Conv2d-165 [4, 256, 7, 7] 589,824 | |
| BatchNorm2d-166 [4, 256, 7, 7] 512 | |
| SiLU-167 [4, 256, 7, 7] 0 | |
| CifNetConvLayer-168 [4, 256, 7, 7] 0 | |
| Conv2d-169 [4, 256, 7, 7] 589,824 | |
| BatchNorm2d-170 [4, 256, 7, 7] 512 | |
| SiLU-171 [4, 256, 7, 7] 0 | |
| CifNetConvLayer-172 [4, 256, 7, 7] 0 | |
| Identity-173 [4, 256, 7, 7] 0 | |
| CifNetBasicLayer-174 [4, 256, 7, 7] 0 | |
| CifNetStage-175 [4, 256, 7, 7] 0 | |
| CifNetEncoder-176 [[-1, 256, 7, 7]] 0 | |
| AdaptiveAvgPool2d-177 [4, 256, 1, 1] 0 | |
| CifNetModel-178 [[-1, 256, 7, 7], [-1, 256, 1, 1]] 0 | |
| Flatten-179 [4, 256] 0 | |
| Linear-180 [4, 10] 2,570 | |
| ================================================================ | |
| Total params: 4,947,994 | |
| Trainable params: 4,947,994 | |
| Non-trainable params: 0 | |
| ---------------------------------------------------------------- | |
| Input size (MB): 2.30 | |
| Forward/backward pass size (MB): 133.14 | |
| Params size (MB): 18.88 | |
| Estimated Total Size (MB): 154.31 | |
| ---------------------------------------------------------------- | |