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CifNetForImageClassification(
(resnet): CifNetModel(
(embedder): CifNetEmbeddings(
(embedder): CifNetConvLayer(
(convolution): Conv2d(3, 32, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(normalization): BatchNorm2d(32, 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(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): 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): 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, 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()
)
)
)
)
)
(3): 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()
)
)
)
(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()
)
)
)
)
)
)
)
(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, 32, 112, 112] 4,704
BatchNorm2d-2 [4, 32, 112, 112] 64
SiLU-3 [4, 32, 112, 112] 0
CifNetConvLayer-4 [4, 32, 112, 112] 0
MaxPool2d-5 [4, 32, 56, 56] 0
CifNetEmbeddings-6 [4, 32, 56, 56] 0
Conv2d-7 [4, 64, 28, 28] 18,432
BatchNorm2d-8 [4, 64, 28, 28] 128
SiLU-9 [4, 64, 28, 28] 0
CifNetConvLayer-10 [4, 64, 28, 28] 0
Conv2d-11 [4, 64, 28, 28] 36,864
BatchNorm2d-12 [4, 64, 28, 28] 128
SiLU-13 [4, 64, 28, 28] 0
CifNetConvLayer-14 [4, 64, 28, 28] 0
Conv2d-15 [4, 64, 28, 28] 2,048
BatchNorm2d-16 [4, 64, 28, 28] 128
CifNetShortCut-17 [4, 64, 28, 28] 0
CifNetBasicLayer-18 [4, 64, 28, 28] 0
CifNetStage-19 [4, 64, 28, 28] 0
Conv2d-20 [4, 64, 28, 28] 36,864
BatchNorm2d-21 [4, 64, 28, 28] 128
SiLU-22 [4, 64, 28, 28] 0
CifNetConvLayer-23 [4, 64, 28, 28] 0
Conv2d-24 [4, 64, 28, 28] 36,864
BatchNorm2d-25 [4, 64, 28, 28] 128
SiLU-26 [4, 64, 28, 28] 0
CifNetConvLayer-27 [4, 64, 28, 28] 0
Identity-28 [4, 64, 28, 28] 0
CifNetBasicLayer-29 [4, 64, 28, 28] 0
Conv2d-30 [4, 64, 28, 28] 36,864
BatchNorm2d-31 [4, 64, 28, 28] 128
SiLU-32 [4, 64, 28, 28] 0
CifNetConvLayer-33 [4, 64, 28, 28] 0
Conv2d-34 [4, 64, 28, 28] 36,864
BatchNorm2d-35 [4, 64, 28, 28] 128
SiLU-36 [4, 64, 28, 28] 0
CifNetConvLayer-37 [4, 64, 28, 28] 0
Identity-38 [4, 64, 28, 28] 0
CifNetBasicLayer-39 [4, 64, 28, 28] 0
CifNetStage-40 [4, 64, 28, 28] 0
Conv2d-41 [4, 128, 14, 14] 73,728
BatchNorm2d-42 [4, 128, 14, 14] 256
SiLU-43 [4, 128, 14, 14] 0
CifNetConvLayer-44 [4, 128, 14, 14] 0
Conv2d-45 [4, 128, 14, 14] 147,456
BatchNorm2d-46 [4, 128, 14, 14] 256
SiLU-47 [4, 128, 14, 14] 0
CifNetConvLayer-48 [4, 128, 14, 14] 0
Conv2d-49 [4, 128, 14, 14] 8,192
BatchNorm2d-50 [4, 128, 14, 14] 256
CifNetShortCut-51 [4, 128, 14, 14] 0
CifNetBasicLayer-52 [4, 128, 14, 14] 0
Conv2d-53 [4, 128, 14, 14] 147,456
BatchNorm2d-54 [4, 128, 14, 14] 256
SiLU-55 [4, 128, 14, 14] 0
CifNetConvLayer-56 [4, 128, 14, 14] 0
Conv2d-57 [4, 128, 14, 14] 147,456
BatchNorm2d-58 [4, 128, 14, 14] 256
SiLU-59 [4, 128, 14, 14] 0
CifNetConvLayer-60 [4, 128, 14, 14] 0
Identity-61 [4, 128, 14, 14] 0
CifNetBasicLayer-62 [4, 128, 14, 14] 0
Conv2d-63 [4, 128, 14, 14] 147,456
BatchNorm2d-64 [4, 128, 14, 14] 256
SiLU-65 [4, 128, 14, 14] 0
CifNetConvLayer-66 [4, 128, 14, 14] 0
Conv2d-67 [4, 128, 14, 14] 147,456
BatchNorm2d-68 [4, 128, 14, 14] 256
SiLU-69 [4, 128, 14, 14] 0
CifNetConvLayer-70 [4, 128, 14, 14] 0
Identity-71 [4, 128, 14, 14] 0
CifNetBasicLayer-72 [4, 128, 14, 14] 0
Conv2d-73 [4, 128, 14, 14] 147,456
BatchNorm2d-74 [4, 128, 14, 14] 256
SiLU-75 [4, 128, 14, 14] 0
CifNetConvLayer-76 [4, 128, 14, 14] 0
Conv2d-77 [4, 128, 14, 14] 147,456
BatchNorm2d-78 [4, 128, 14, 14] 256
SiLU-79 [4, 128, 14, 14] 0
CifNetConvLayer-80 [4, 128, 14, 14] 0
Identity-81 [4, 128, 14, 14] 0
CifNetBasicLayer-82 [4, 128, 14, 14] 0
CifNetStage-83 [4, 128, 14, 14] 0
Conv2d-84 [4, 256, 7, 7] 294,912
BatchNorm2d-85 [4, 256, 7, 7] 512
SiLU-86 [4, 256, 7, 7] 0
CifNetConvLayer-87 [4, 256, 7, 7] 0
Conv2d-88 [4, 256, 7, 7] 589,824
BatchNorm2d-89 [4, 256, 7, 7] 512
SiLU-90 [4, 256, 7, 7] 0
CifNetConvLayer-91 [4, 256, 7, 7] 0
Conv2d-92 [4, 256, 7, 7] 32,768
BatchNorm2d-93 [4, 256, 7, 7] 512
CifNetShortCut-94 [4, 256, 7, 7] 0
CifNetBasicLayer-95 [4, 256, 7, 7] 0
Conv2d-96 [4, 256, 7, 7] 589,824
BatchNorm2d-97 [4, 256, 7, 7] 512
SiLU-98 [4, 256, 7, 7] 0
CifNetConvLayer-99 [4, 256, 7, 7] 0
Conv2d-100 [4, 256, 7, 7] 589,824
BatchNorm2d-101 [4, 256, 7, 7] 512
SiLU-102 [4, 256, 7, 7] 0
CifNetConvLayer-103 [4, 256, 7, 7] 0
Identity-104 [4, 256, 7, 7] 0
CifNetBasicLayer-105 [4, 256, 7, 7] 0
Conv2d-106 [4, 256, 7, 7] 589,824
BatchNorm2d-107 [4, 256, 7, 7] 512
SiLU-108 [4, 256, 7, 7] 0
CifNetConvLayer-109 [4, 256, 7, 7] 0
Conv2d-110 [4, 256, 7, 7] 589,824
BatchNorm2d-111 [4, 256, 7, 7] 512
SiLU-112 [4, 256, 7, 7] 0
CifNetConvLayer-113 [4, 256, 7, 7] 0
Identity-114 [4, 256, 7, 7] 0
CifNetBasicLayer-115 [4, 256, 7, 7] 0
CifNetStage-116 [4, 256, 7, 7] 0
CifNetEncoder-117 [[-1, 256, 7, 7]] 0
AdaptiveAvgPool2d-118 [4, 256, 1, 1] 0
CifNetModel-119 [[-1, 256, 7, 7], [-1, 256, 1, 1]] 0
Flatten-120 [4, 256] 0
Linear-121 [4, 10] 2,570
================================================================
Total params: 4,609,834
Trainable params: 4,609,834
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 2.30
Forward/backward pass size (MB): 177.16
Params size (MB): 17.59
Estimated Total Size (MB): 197.04
----------------------------------------------------------------
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