ResNet(
  714.09 k, 101.626% Params, 36.09 MMac, 100.000% MACs, 
  (conv1): Conv2d(448, 0.064% Params, 458.75 KMac, 1.271% MACs, 3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (bn1): BatchNorm2d(32, 0.005% Params, 32.77 KMac, 0.091% MACs, 16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (relu): ReLU(0, 0.000% Params, 16.38 KMac, 0.045% MACs, )
  (layer1): MakeLayer(
    9.41 k, 1.339% Params, 9.7 MMac, 26.879% MACs, 
    (blockdict): ModuleDict(
      9.41 k, 1.339% Params, 9.7 MMac, 26.879% MACs, 
      (block1): BasicBlock(
        4.7 k, 0.669% Params, 4.85 MMac, 13.440% MACs, 
        (conv1): Conv2d(2.32 k, 0.330% Params, 2.38 MMac, 6.584% MACs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn1): BatchNorm2d(32, 0.005% Params, 32.77 KMac, 0.091% MACs, 16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(2.32 k, 0.330% Params, 2.38 MMac, 6.584% MACs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(32, 0.005% Params, 32.77 KMac, 0.091% MACs, 16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 32.77 KMac, 0.091% MACs, )
      )
      (block2): BasicBlock(
        4.7 k, 0.669% Params, 4.85 MMac, 13.440% MACs, 
        (conv1): Conv2d(2.32 k, 0.330% Params, 2.38 MMac, 6.584% MACs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn1): BatchNorm2d(32, 0.005% Params, 32.77 KMac, 0.091% MACs, 16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(2.32 k, 0.330% Params, 2.38 MMac, 6.584% MACs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(32, 0.005% Params, 32.77 KMac, 0.091% MACs, 16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 32.77 KMac, 0.091% MACs, )
      )
    )
  )
  (layer2): MakeLayer(
    33.86 k, 4.818% Params, 8.7 MMac, 24.109% MACs, 
    (downsample): Sequential(
      608, 0.087% Params, 155.65 KMac, 0.431% MACs, 
      (0): Conv2d(544, 0.077% Params, 139.26 KMac, 0.386% MACs, 16, 32, kernel_size=(1, 1), stride=(2, 2))
      (1): BatchNorm2d(64, 0.009% Params, 16.38 KMac, 0.045% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (blockdict): ModuleDict(
      33.25 k, 4.732% Params, 8.54 MMac, 23.678% MACs, 
      (block1): BasicBlock(
        14.62 k, 2.081% Params, 3.76 MMac, 10.420% MACs, 
        (conv1): Conv2d(4.64 k, 0.660% Params, 1.19 MMac, 3.292% MACs, 16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
        (bn1): BatchNorm2d(64, 0.009% Params, 16.38 KMac, 0.045% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(9.25 k, 1.316% Params, 2.37 MMac, 6.561% MACs, 32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(64, 0.009% Params, 16.38 KMac, 0.045% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 16.38 KMac, 0.045% MACs, )
       
      )
      (block2): BasicBlock(
        18.62 k, 2.650% Params, 4.78 MMac, 13.258% MACs, 
        (conv1): Conv2d(9.25 k, 1.316% Params, 2.37 MMac, 6.561% MACs, 32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn1): BatchNorm2d(64, 0.009% Params, 16.38 KMac, 0.045% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(9.25 k, 1.316% Params, 2.37 MMac, 6.561% MACs, 32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(64, 0.009% Params, 16.38 KMac, 0.045% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 16.38 KMac, 0.045% MACs, )
      )
    )
  )
  (layer3): MakeLayer(
    134.27 k, 19.109% Params, 8.61 MMac, 23.860% MACs, 
    (downsample): Sequential(
      2.24 k, 0.319% Params, 143.36 KMac, 0.397% MACs, 
      (0): Conv2d(2.11 k, 0.301% Params, 135.17 KMac, 0.375% MACs, 32, 64, kernel_size=(1, 1), stride=(2, 2))
      (1): BatchNorm2d(128, 0.018% Params, 8.19 KMac, 0.023% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (blockdict): ModuleDict(
      132.03 k, 18.790% Params, 8.47 MMac, 23.462% MACs, 
      (block1): BasicBlock(
        57.92 k, 8.243% Params, 3.72 MMac, 10.295% MACs, 
        (conv1): Conv2d(18.5 k, 2.632% Params, 1.18 MMac, 3.280% MACs, 32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
        (bn1): BatchNorm2d(128, 0.018% Params, 8.19 KMac, 0.023% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(36.93 k, 5.255% Params, 2.36 MMac, 6.550% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(128, 0.018% Params, 8.19 KMac, 0.023% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 8.19 KMac, 0.023% MACs, )
       
      )
      (block2): BasicBlock(
        74.11 k, 10.547% Params, 4.75 MMac, 13.167% MACs, 
        (conv1): Conv2d(36.93 k, 5.255% Params, 2.36 MMac, 6.550% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn1): BatchNorm2d(128, 0.018% Params, 8.19 KMac, 0.023% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(36.93 k, 5.255% Params, 2.36 MMac, 6.550% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(128, 0.018% Params, 8.19 KMac, 0.023% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 8.19 KMac, 0.023% MACs, )
      )
    )
  )
  (layer4): MakeLayer(
    534.78 k, 76.108% Params, 8.56 MMac, 23.735% MACs, 
    (downsample): Sequential(
      8.58 k, 1.220% Params, 137.22 KMac, 0.380% MACs, 
      (0): Conv2d(8.32 k, 1.184% Params, 133.12 KMac, 0.369% MACs, 64, 128, kernel_size=(1, 1), stride=(2, 2))
      (1): BatchNorm2d(256, 0.036% Params, 4.1 KMac, 0.011% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (blockdict): ModuleDict(
      526.21 k, 74.887% Params, 8.43 MMac, 23.355% MACs, 
      (block1): BasicBlock(
        230.53 k, 32.808% Params, 3.69 MMac, 10.233% MACs, 
        (conv1): Conv2d(73.86 k, 10.511% Params, 1.18 MMac, 3.275% MACs, 64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
        (bn1): BatchNorm2d(256, 0.036% Params, 4.1 KMac, 0.011% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(147.58 k, 21.003% Params, 2.36 MMac, 6.544% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(256, 0.036% Params, 4.1 KMac, 0.011% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 4.1 KMac, 0.011% MACs, )
      
      )
      (block2): BasicBlock(
        295.68 k, 42.080% Params, 4.73 MMac, 13.122% MACs, 
        (conv1): Conv2d(147.58 k, 21.003% Params, 2.36 MMac, 6.544% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn1): BatchNorm2d(256, 0.036% Params, 4.1 KMac, 0.011% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(147.58 k, 21.003% Params, 2.36 MMac, 6.544% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(256, 0.036% Params, 4.1 KMac, 0.011% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 4.1 KMac, 0.011% MACs, )
      )
    )
  )
  (avgpool): AdaptiveAvgPool2d(0, 0.000% Params, 2.05 KMac, 0.006% MACs, output_size=(1, 1))
  (fc): Linear(1.29 k, 0.184% Params, 1.29 KMac, 0.004% MACs, in_features=128, out_features=10, bias=True)
)