| 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): CifNetSelfAttentionLayer( | |
| (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) | |
| ) | |
| (in_conv): 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() | |
| ) | |
| (attention): CifNetSelfAttention( | |
| (q_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1)) | |
| (k_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1)) | |
| (v_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1)) | |
| (o_proj): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| ) | |
| (activation): SiLU() | |
| (attention_norm): CifNetRMSNorm() | |
| (out_conv): 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() | |
| ) | |
| ) | |
| ) | |
| ) | |
| (1-3): 3 x CifNetStage( | |
| (layers): Sequential( | |
| (0): CifNetSelfAttentionLayer( | |
| (shortcut): Identity() | |
| (in_conv): 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() | |
| ) | |
| (attention): CifNetSelfAttention( | |
| (q_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1)) | |
| (k_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1)) | |
| (v_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1)) | |
| (o_proj): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| ) | |
| (activation): SiLU() | |
| (attention_norm): CifNetRMSNorm() | |
| (out_conv): 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): CifNetSelfAttentionLayer( | |
| (shortcut): Identity() | |
| (in_conv): 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() | |
| ) | |
| (attention): CifNetSelfAttention( | |
| (q_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1)) | |
| (k_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1)) | |
| (v_proj): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1)) | |
| (o_proj): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1)) | |
| ) | |
| (activation): SiLU() | |
| (attention_norm): CifNetRMSNorm() | |
| (out_conv): 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() | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (pooler): AdaptiveAvgPool2d(output_size=(1, 1)) | |
| ) | |
| (classifier): Sequential( | |
| (0): Flatten(start_dim=1, end_dim=-1) | |
| (1): Linear(in_features=128, 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, 128, 28, 28] 73,728 | |
| BatchNorm2d-8 [4, 128, 28, 28] 256 | |
| SiLU-9 [4, 128, 28, 28] 0 | |
| CifNetConvLayer-10 [4, 128, 28, 28] 0 | |
| CifNetRMSNorm-11 [4, 28, 28, 128] 128 | |
| Conv2d-12 [4, 32, 28, 28] 4,128 | |
| Conv2d-13 [4, 32, 28, 28] 4,128 | |
| Conv2d-14 [4, 32, 28, 28] 4,128 | |
| Conv2d-15 [4, 128, 28, 28] 4,224 | |
| CifNetSelfAttention-16 [4, 128, 28, 28] 0 | |
| SiLU-17 [4, 128, 28, 28] 0 | |
| Conv2d-18 [4, 128, 28, 28] 147,456 | |
| 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] 8,192 | |
| BatchNorm2d-23 [4, 128, 28, 28] 256 | |
| CifNetShortCut-24 [4, 128, 28, 28] 0 | |
| CifNetSelfAttentionLayer-25 [4, 128, 28, 28] 0 | |
| CifNetStage-26 [4, 128, 28, 28] 0 | |
| Conv2d-27 [4, 128, 28, 28] 147,456 | |
| BatchNorm2d-28 [4, 128, 28, 28] 256 | |
| SiLU-29 [4, 128, 28, 28] 0 | |
| CifNetConvLayer-30 [4, 128, 28, 28] 0 | |
| CifNetRMSNorm-31 [4, 28, 28, 128] 128 | |
| Conv2d-32 [4, 32, 28, 28] 4,128 | |
| Conv2d-33 [4, 32, 28, 28] 4,128 | |
| Conv2d-34 [4, 32, 28, 28] 4,128 | |
| Conv2d-35 [4, 128, 28, 28] 4,224 | |
| CifNetSelfAttention-36 [4, 128, 28, 28] 0 | |
| SiLU-37 [4, 128, 28, 28] 0 | |
| Conv2d-38 [4, 128, 28, 28] 147,456 | |
| BatchNorm2d-39 [4, 128, 28, 28] 256 | |
| SiLU-40 [4, 128, 28, 28] 0 | |
| CifNetConvLayer-41 [4, 128, 28, 28] 0 | |
| Identity-42 [4, 128, 28, 28] 0 | |
| CifNetSelfAttentionLayer-43 [4, 128, 28, 28] 0 | |
| Conv2d-44 [4, 128, 28, 28] 147,456 | |
| BatchNorm2d-45 [4, 128, 28, 28] 256 | |
| SiLU-46 [4, 128, 28, 28] 0 | |
| CifNetConvLayer-47 [4, 128, 28, 28] 0 | |
| CifNetRMSNorm-48 [4, 28, 28, 128] 128 | |
| Conv2d-49 [4, 32, 28, 28] 4,128 | |
| Conv2d-50 [4, 32, 28, 28] 4,128 | |
| Conv2d-51 [4, 32, 28, 28] 4,128 | |
| Conv2d-52 [4, 128, 28, 28] 4,224 | |
| CifNetSelfAttention-53 [4, 128, 28, 28] 0 | |
| SiLU-54 [4, 128, 28, 28] 0 | |
| Conv2d-55 [4, 128, 28, 28] 147,456 | |
| BatchNorm2d-56 [4, 128, 28, 28] 256 | |
| SiLU-57 [4, 128, 28, 28] 0 | |
| CifNetConvLayer-58 [4, 128, 28, 28] 0 | |
| Identity-59 [4, 128, 28, 28] 0 | |
| CifNetSelfAttentionLayer-60 [4, 128, 28, 28] 0 | |
| CifNetStage-61 [4, 128, 28, 28] 0 | |
| Conv2d-62 [4, 128, 28, 28] 147,456 | |
| BatchNorm2d-63 [4, 128, 28, 28] 256 | |
| SiLU-64 [4, 128, 28, 28] 0 | |
| CifNetConvLayer-65 [4, 128, 28, 28] 0 | |
| CifNetRMSNorm-66 [4, 28, 28, 128] 128 | |
| Conv2d-67 [4, 32, 28, 28] 4,128 | |
| Conv2d-68 [4, 32, 28, 28] 4,128 | |
| Conv2d-69 [4, 32, 28, 28] 4,128 | |
| Conv2d-70 [4, 128, 28, 28] 4,224 | |
| CifNetSelfAttention-71 [4, 128, 28, 28] 0 | |
| SiLU-72 [4, 128, 28, 28] 0 | |
| Conv2d-73 [4, 128, 28, 28] 147,456 | |
| BatchNorm2d-74 [4, 128, 28, 28] 256 | |
| SiLU-75 [4, 128, 28, 28] 0 | |
| CifNetConvLayer-76 [4, 128, 28, 28] 0 | |
| Identity-77 [4, 128, 28, 28] 0 | |
| CifNetSelfAttentionLayer-78 [4, 128, 28, 28] 0 | |
| Conv2d-79 [4, 128, 28, 28] 147,456 | |
| BatchNorm2d-80 [4, 128, 28, 28] 256 | |
| SiLU-81 [4, 128, 28, 28] 0 | |
| CifNetConvLayer-82 [4, 128, 28, 28] 0 | |
| CifNetRMSNorm-83 [4, 28, 28, 128] 128 | |
| Conv2d-84 [4, 32, 28, 28] 4,128 | |
| Conv2d-85 [4, 32, 28, 28] 4,128 | |
| Conv2d-86 [4, 32, 28, 28] 4,128 | |
| Conv2d-87 [4, 128, 28, 28] 4,224 | |
| CifNetSelfAttention-88 [4, 128, 28, 28] 0 | |
| SiLU-89 [4, 128, 28, 28] 0 | |
| Conv2d-90 [4, 128, 28, 28] 147,456 | |
| BatchNorm2d-91 [4, 128, 28, 28] 256 | |
| SiLU-92 [4, 128, 28, 28] 0 | |
| CifNetConvLayer-93 [4, 128, 28, 28] 0 | |
| Identity-94 [4, 128, 28, 28] 0 | |
| CifNetSelfAttentionLayer-95 [4, 128, 28, 28] 0 | |
| CifNetStage-96 [4, 128, 28, 28] 0 | |
| Conv2d-97 [4, 128, 28, 28] 147,456 | |
| BatchNorm2d-98 [4, 128, 28, 28] 256 | |
| SiLU-99 [4, 128, 28, 28] 0 | |
| CifNetConvLayer-100 [4, 128, 28, 28] 0 | |
| CifNetRMSNorm-101 [4, 28, 28, 128] 128 | |
| Conv2d-102 [4, 32, 28, 28] 4,128 | |
| Conv2d-103 [4, 32, 28, 28] 4,128 | |
| Conv2d-104 [4, 32, 28, 28] 4,128 | |
| Conv2d-105 [4, 128, 28, 28] 4,224 | |
| CifNetSelfAttention-106 [4, 128, 28, 28] 0 | |
| SiLU-107 [4, 128, 28, 28] 0 | |
| Conv2d-108 [4, 128, 28, 28] 147,456 | |
| BatchNorm2d-109 [4, 128, 28, 28] 256 | |
| SiLU-110 [4, 128, 28, 28] 0 | |
| CifNetConvLayer-111 [4, 128, 28, 28] 0 | |
| Identity-112 [4, 128, 28, 28] 0 | |
| CifNetSelfAttentionLayer-113 [4, 128, 28, 28] 0 | |
| Conv2d-114 [4, 128, 28, 28] 147,456 | |
| BatchNorm2d-115 [4, 128, 28, 28] 256 | |
| SiLU-116 [4, 128, 28, 28] 0 | |
| CifNetConvLayer-117 [4, 128, 28, 28] 0 | |
| CifNetRMSNorm-118 [4, 28, 28, 128] 128 | |
| Conv2d-119 [4, 32, 28, 28] 4,128 | |
| Conv2d-120 [4, 32, 28, 28] 4,128 | |
| Conv2d-121 [4, 32, 28, 28] 4,128 | |
| Conv2d-122 [4, 128, 28, 28] 4,224 | |
| CifNetSelfAttention-123 [4, 128, 28, 28] 0 | |
| SiLU-124 [4, 128, 28, 28] 0 | |
| Conv2d-125 [4, 128, 28, 28] 147,456 | |
| BatchNorm2d-126 [4, 128, 28, 28] 256 | |
| SiLU-127 [4, 128, 28, 28] 0 | |
| CifNetConvLayer-128 [4, 128, 28, 28] 0 | |
| Identity-129 [4, 128, 28, 28] 0 | |
| CifNetSelfAttentionLayer-130 [4, 128, 28, 28] 0 | |
| CifNetStage-131 [4, 128, 28, 28] 0 | |
| CifNetEncoder-132 [[-1, 128, 28, 28]] 0 | |
| AdaptiveAvgPool2d-133 [4, 128, 1, 1] 0 | |
| CifNetModel-134 [[-1, 128, 28, 28], [-1, 128, 1, 1]] 0 | |
| Flatten-135 [4, 128] 0 | |
| Linear-136 [4, 10] 1,290 | |
| ================================================================ | |
| Total params: 2,130,666 | |
| Trainable params: 2,130,666 | |
| Non-trainable params: 0 | |
| ---------------------------------------------------------------- | |
| Input size (MB): 2.30 | |
| Forward/backward pass size (MB): 542.07 | |
| Params size (MB): 8.13 | |
| Estimated Total Size (MB): 552.50 | |
| ---------------------------------------------------------------- | |