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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
----------------------------------------------------------------
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