ResNet(
  712.67 k, 101.597% Params, 35.92 MMac, 100.000% MACs, 
  (conv1): Conv2d(432, 0.062% Params, 442.37 KMac, 1.232% MACs, 3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
  (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, 0.046% MACs, )
  (layer1): MakeLayer(
    9.34 k, 1.332% Params, 9.63 MMac, 26.822% MACs, 
    (blockdict): ModuleDict(
      9.34 k, 1.332% Params, 9.63 MMac, 26.822% MACs, 
      (block1): BasicBlock(
        4.67 k, 0.666% Params, 4.82 MMac, 13.411% MACs, 
        (conv1): Conv2d(2.3 k, 0.328% Params, 2.36 MMac, 6.569% MACs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (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(2.3 k, 0.328% Params, 2.36 MMac, 6.569% MACs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (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(
        4.67 k, 0.666% Params, 4.82 MMac, 13.411% MACs, 
        (conv1): Conv2d(2.3 k, 0.328% Params, 2.36 MMac, 6.569% MACs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (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(2.3 k, 0.328% Params, 2.36 MMac, 6.569% MACs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (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(
    33.66 k, 4.799% Params, 8.65 MMac, 24.085% MACs, 
    (downsample): Sequential(
      576, 0.082% Params, 147.46 KMac, 0.411% MACs, 
      (0): Conv2d(512, 0.073% Params, 131.07 KMac, 0.365% MACs, 16, 32, kernel_size=(1, 1), stride=(2, 2), bias=False)
      (1): BatchNorm2d(64, 0.009% Params, 16.38 KMac, 0.046% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (blockdict): ModuleDict(
      33.09 k, 4.717% Params, 8.5 MMac, 23.675% MACs, 
      (block1): BasicBlock(
        14.53 k, 2.071% Params, 3.74 MMac, 10.400% MACs, 
        (conv1): Conv2d(4.61 k, 0.657% Params, 1.18 MMac, 3.284% MACs, 16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, 0.009% Params, 16.38 KMac, 0.046% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(9.22 k, 1.314% Params, 2.36 MMac, 6.569% MACs, 32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, 0.009% Params, 16.38 KMac, 0.046% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 16.38 KMac, 0.046% MACs, )
        (downsample): Sequential(
          576, 0.082% Params, 147.46 KMac, 0.411% MACs, 
          (0): Conv2d(512, 0.073% Params, 131.07 KMac, 0.365% MACs, 16, 32, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(64, 0.009% Params, 16.38 KMac, 0.046% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (block2): BasicBlock(
        18.56 k, 2.646% Params, 4.77 MMac, 13.274% MACs, 
        (conv1): Conv2d(9.22 k, 1.314% Params, 2.36 MMac, 6.569% MACs, 32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, 0.009% Params, 16.38 KMac, 0.046% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(9.22 k, 1.314% Params, 2.36 MMac, 6.569% MACs, 32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, 0.009% Params, 16.38 KMac, 0.046% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 16.38 KMac, 0.046% MACs, )
      )
    )
  )
  (layer3): MakeLayer(
    133.89 k, 19.087% Params, 8.59 MMac, 23.903% MACs, 
    (downsample): Sequential(
      2.18 k, 0.310% Params, 139.26 KMac, 0.388% MACs, 
      (0): Conv2d(2.05 k, 0.292% Params, 131.07 KMac, 0.365% MACs, 32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)
      (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(
      131.71 k, 18.777% Params, 8.45 MMac, 23.515% MACs, 
      (block1): BasicBlock(
        57.73 k, 8.230% Params, 3.7 MMac, 10.309% MACs, 
        (conv1): Conv2d(18.43 k, 2.628% Params, 1.18 MMac, 3.284% MACs, 32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (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(36.86 k, 5.255% Params, 2.36 MMac, 6.569% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (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, )
        (downsample): Sequential(
          2.18 k, 0.310% Params, 139.26 KMac, 0.388% MACs, 
          (0): Conv2d(2.05 k, 0.292% Params, 131.07 KMac, 0.365% MACs, 32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (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)
        )
      )
      (block2): BasicBlock(
        73.98 k, 10.547% Params, 4.74 MMac, 13.206% MACs, 
        (conv1): Conv2d(36.86 k, 5.255% Params, 2.36 MMac, 6.569% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (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(36.86 k, 5.255% Params, 2.36 MMac, 6.569% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (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(
    534.02 k, 76.129% Params, 8.55 MMac, 23.812% MACs, 
    (downsample): Sequential(
      8.45 k, 1.204% Params, 135.17 KMac, 0.376% MACs, 
      (0): Conv2d(8.19 k, 1.168% Params, 131.07 KMac, 0.365% MACs, 64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
      (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(
      525.57 k, 74.924% Params, 8.42 MMac, 23.435% MACs, 
      (block1): BasicBlock(
        230.14 k, 32.809% Params, 3.69 MMac, 10.264% MACs, 
        (conv1): Conv2d(73.73 k, 10.511% Params, 1.18 MMac, 3.284% MACs, 64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (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(147.46 k, 21.021% Params, 2.36 MMac, 6.569% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (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, )
        (downsample): Sequential(
          8.45 k, 1.204% Params, 135.17 KMac, 0.376% MACs, 
          (0): Conv2d(8.19 k, 1.168% Params, 131.07 KMac, 0.365% MACs, 64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (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)
        )
      )
      (block2): BasicBlock(
        295.42 k, 42.115% Params, 4.73 MMac, 13.172% MACs, 
        (conv1): Conv2d(147.46 k, 21.021% Params, 2.36 MMac, 6.569% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (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(147.46 k, 21.021% Params, 2.36 MMac, 6.569% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (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)
)