ResNet(
  1.67 M, 111.801% Params, 92.48 MMac, 100.000% MACs, 
  (conv1): Conv2d(448, 0.030% Params, 458.75 KMac, 0.496% MACs, 3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (bn1): BatchNorm2d(32, 0.002% Params, 32.77 KMac, 0.035% MACs, 16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (relu): ReLU(0, 0.000% Params, 16.38 KMac, 0.018% MACs, )
  (layer1): MakeLayer(
    15.58 k, 1.045% Params, 16.25 MMac, 17.575% MACs, 
    (downsample): Sequential(
      1.22 k, 0.082% Params, 1.25 MMac, 1.346% MACs, 
      (0): Conv2d(1.09 k, 0.073% Params, 1.11 MMac, 1.205% MACs, 16, 64, kernel_size=(1, 1), stride=(1, 1))
      (1): BatchNorm2d(128, 0.009% Params, 131.07 KMac, 0.142% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (blockdict): ModuleDict(
      14.37 k, 0.964% Params, 15.01 MMac, 16.229% MACs, 
      (block1): Bottleneck(
        5.09 k, 0.341% Params, 5.31 MMac, 5.740% MACs, 
        (conv1): Conv2d(272, 0.018% Params, 278.53 KMac, 0.301% MACs, 16, 16, kernel_size=(1, 1), stride=(1, 1))
        (bn1): BatchNorm2d(32, 0.002% Params, 32.77 KMac, 0.035% MACs, 16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(2.32 k, 0.156% Params, 2.38 MMac, 2.569% MACs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(32, 0.002% Params, 32.77 KMac, 0.035% MACs, 16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(1.09 k, 0.073% Params, 1.11 MMac, 1.205% MACs, 16, 64, kernel_size=(1, 1), stride=(1, 1))
        (bn3): BatchNorm2d(128, 0.009% Params, 131.07 KMac, 0.142% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 98.3 KMac, 0.106% MACs, )
        
      )
      (block2): Bottleneck(
        4.64 k, 0.311% Params, 4.85 MMac, 5.244% MACs, 
        (conv1): Conv2d(1.04 k, 0.070% Params, 1.06 MMac, 1.152% MACs, 64, 16, kernel_size=(1, 1), stride=(1, 1))
        (bn1): BatchNorm2d(32, 0.002% Params, 32.77 KMac, 0.035% MACs, 16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(2.32 k, 0.156% Params, 2.38 MMac, 2.569% MACs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(32, 0.002% Params, 32.77 KMac, 0.035% MACs, 16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(1.09 k, 0.073% Params, 1.11 MMac, 1.205% MACs, 16, 64, kernel_size=(1, 1), stride=(1, 1))
        (bn3): BatchNorm2d(128, 0.009% Params, 131.07 KMac, 0.142% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 98.3 KMac, 0.106% MACs, )
      )
      (block3): Bottleneck(
        4.64 k, 0.311% Params, 4.85 MMac, 5.244% MACs, 
        (conv1): Conv2d(1.04 k, 0.070% Params, 1.06 MMac, 1.152% MACs, 64, 16, kernel_size=(1, 1), stride=(1, 1))
        (bn1): BatchNorm2d(32, 0.002% Params, 32.77 KMac, 0.035% MACs, 16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(2.32 k, 0.156% Params, 2.38 MMac, 2.569% MACs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(32, 0.002% Params, 32.77 KMac, 0.035% MACs, 16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(1.09 k, 0.073% Params, 1.11 MMac, 1.205% MACs, 16, 64, kernel_size=(1, 1), stride=(1, 1))
        (bn3): BatchNorm2d(128, 0.009% Params, 131.07 KMac, 0.142% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 98.3 KMac, 0.106% MACs, )
      )
    )
  )
  (layer2): MakeLayer(
    87.04 k, 5.838% Params, 24.15 MMac, 26.115% MACs, 
    (downsample): Sequential(
      8.58 k, 0.575% Params, 2.2 MMac, 2.374% MACs, 
      (0): Conv2d(8.32 k, 0.558% Params, 2.13 MMac, 2.303% MACs, 64, 128, kernel_size=(1, 1), stride=(2, 2))
      (1): BatchNorm2d(256, 0.017% Params, 65.54 KMac, 0.071% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (blockdict): ModuleDict(
      78.46 k, 5.263% Params, 21.95 MMac, 23.741% MACs, 
      (block1): Bottleneck(
        24.51 k, 1.644% Params, 8.0 MMac, 8.646% MACs, 
        (conv1): Conv2d(2.08 k, 0.140% Params, 2.13 MMac, 2.303% MACs, 64, 32, kernel_size=(1, 1), stride=(1, 1))
        (bn1): BatchNorm2d(64, 0.004% Params, 65.54 KMac, 0.071% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(9.25 k, 0.620% Params, 2.37 MMac, 2.560% MACs, 32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
        (bn2): BatchNorm2d(64, 0.004% Params, 16.38 KMac, 0.018% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(4.22 k, 0.283% Params, 1.08 MMac, 1.169% MACs, 32, 128, kernel_size=(1, 1), stride=(1, 1))
        (bn3): BatchNorm2d(256, 0.017% Params, 65.54 KMac, 0.071% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 73.73 KMac, 0.080% MACs, )
        
      )
      (block2): Bottleneck(
        17.98 k, 1.206% Params, 4.65 MMac, 5.032% MACs, 
        (conv1): Conv2d(4.13 k, 0.277% Params, 1.06 MMac, 1.143% MACs, 128, 32, kernel_size=(1, 1), stride=(1, 1))
        (bn1): BatchNorm2d(64, 0.004% Params, 16.38 KMac, 0.018% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(9.25 k, 0.620% Params, 2.37 MMac, 2.560% MACs, 32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(64, 0.004% Params, 16.38 KMac, 0.018% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(4.22 k, 0.283% Params, 1.08 MMac, 1.169% MACs, 32, 128, kernel_size=(1, 1), stride=(1, 1))
        (bn3): BatchNorm2d(256, 0.017% Params, 65.54 KMac, 0.071% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 49.15 KMac, 0.053% MACs, )
      )
      (block3): Bottleneck(
        17.98 k, 1.206% Params, 4.65 MMac, 5.032% MACs, 
        (conv1): Conv2d(4.13 k, 0.277% Params, 1.06 MMac, 1.143% MACs, 128, 32, kernel_size=(1, 1), stride=(1, 1))
        (bn1): BatchNorm2d(64, 0.004% Params, 16.38 KMac, 0.018% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(9.25 k, 0.620% Params, 2.37 MMac, 2.560% MACs, 32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(64, 0.004% Params, 16.38 KMac, 0.018% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(4.22 k, 0.283% Params, 1.08 MMac, 1.169% MACs, 32, 128, kernel_size=(1, 1), stride=(1, 1))
        (bn3): BatchNorm2d(256, 0.017% Params, 65.54 KMac, 0.071% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 49.15 KMac, 0.053% MACs, )
      )
      (block4): Bottleneck(
        17.98 k, 1.206% Params, 4.65 MMac, 5.032% MACs, 
        (conv1): Conv2d(4.13 k, 0.277% Params, 1.06 MMac, 1.143% MACs, 128, 32, kernel_size=(1, 1), stride=(1, 1))
        (bn1): BatchNorm2d(64, 0.004% Params, 16.38 KMac, 0.018% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(9.25 k, 0.620% Params, 2.37 MMac, 2.560% MACs, 32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(64, 0.004% Params, 16.38 KMac, 0.018% MACs, 32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(4.22 k, 0.283% Params, 1.08 MMac, 1.169% MACs, 32, 128, kernel_size=(1, 1), stride=(1, 1))
        (bn3): BatchNorm2d(256, 0.017% Params, 65.54 KMac, 0.071% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 49.15 KMac, 0.053% MACs, )
      )
    )
  )
  (layer3): MakeLayer(
    483.58 k, 32.437% Params, 32.72 MMac, 35.381% MACs, 
    (downsample): Sequential(
      33.54 k, 2.249% Params, 2.15 MMac, 2.321% MACs, 
      (0): Conv2d(33.02 k, 2.215% Params, 2.11 MMac, 2.285% MACs, 128, 256, kernel_size=(1, 1), stride=(2, 2))
      (1): BatchNorm2d(512, 0.034% Params, 32.77 KMac, 0.035% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (blockdict): ModuleDict(
      450.05 k, 30.188% Params, 30.57 MMac, 33.060% MACs, 
      (block1): Bottleneck(
        96.13 k, 6.448% Params, 7.8 MMac, 8.433% MACs, 
        (conv1): Conv2d(8.26 k, 0.554% Params, 2.11 MMac, 2.285% MACs, 128, 64, kernel_size=(1, 1), stride=(1, 1))
        (bn1): BatchNorm2d(128, 0.009% Params, 32.77 KMac, 0.035% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(36.93 k, 2.477% Params, 2.36 MMac, 2.556% MACs, 64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
        (bn2): BatchNorm2d(128, 0.009% Params, 8.19 KMac, 0.009% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(16.64 k, 1.116% Params, 1.06 MMac, 1.152% MACs, 64, 256, kernel_size=(1, 1), stride=(1, 1))
        (bn3): BatchNorm2d(512, 0.034% Params, 32.77 KMac, 0.035% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 36.86 KMac, 0.040% MACs, )
       
      )
      (block2): Bottleneck(
        70.78 k, 4.748% Params, 4.55 MMac, 4.925% MACs, 
        (conv1): Conv2d(16.45 k, 1.103% Params, 1.05 MMac, 1.138% MACs, 256, 64, kernel_size=(1, 1), stride=(1, 1))
        (bn1): BatchNorm2d(128, 0.009% Params, 8.19 KMac, 0.009% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(36.93 k, 2.477% Params, 2.36 MMac, 2.556% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(128, 0.009% Params, 8.19 KMac, 0.009% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(16.64 k, 1.116% Params, 1.06 MMac, 1.152% MACs, 64, 256, kernel_size=(1, 1), stride=(1, 1))
        (bn3): BatchNorm2d(512, 0.034% Params, 32.77 KMac, 0.035% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 24.58 KMac, 0.027% MACs, )
      )
      (block3): Bottleneck(
        70.78 k, 4.748% Params, 4.55 MMac, 4.925% MACs, 
        (conv1): Conv2d(16.45 k, 1.103% Params, 1.05 MMac, 1.138% MACs, 256, 64, kernel_size=(1, 1), stride=(1, 1))
        (bn1): BatchNorm2d(128, 0.009% Params, 8.19 KMac, 0.009% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(36.93 k, 2.477% Params, 2.36 MMac, 2.556% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(128, 0.009% Params, 8.19 KMac, 0.009% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(16.64 k, 1.116% Params, 1.06 MMac, 1.152% MACs, 64, 256, kernel_size=(1, 1), stride=(1, 1))
        (bn3): BatchNorm2d(512, 0.034% Params, 32.77 KMac, 0.035% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 24.58 KMac, 0.027% MACs, )
      )
      (block4): Bottleneck(
        70.78 k, 4.748% Params, 4.55 MMac, 4.925% MACs, 
        (conv1): Conv2d(16.45 k, 1.103% Params, 1.05 MMac, 1.138% MACs, 256, 64, kernel_size=(1, 1), stride=(1, 1))
        (bn1): BatchNorm2d(128, 0.009% Params, 8.19 KMac, 0.009% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(36.93 k, 2.477% Params, 2.36 MMac, 2.556% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(128, 0.009% Params, 8.19 KMac, 0.009% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(16.64 k, 1.116% Params, 1.06 MMac, 1.152% MACs, 64, 256, kernel_size=(1, 1), stride=(1, 1))
        (bn3): BatchNorm2d(512, 0.034% Params, 32.77 KMac, 0.035% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 24.58 KMac, 0.027% MACs, )
      )
      (block5): Bottleneck(
        70.78 k, 4.748% Params, 4.55 MMac, 4.925% MACs, 
        (conv1): Conv2d(16.45 k, 1.103% Params, 1.05 MMac, 1.138% MACs, 256, 64, kernel_size=(1, 1), stride=(1, 1))
        (bn1): BatchNorm2d(128, 0.009% Params, 8.19 KMac, 0.009% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(36.93 k, 2.477% Params, 2.36 MMac, 2.556% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(128, 0.009% Params, 8.19 KMac, 0.009% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(16.64 k, 1.116% Params, 1.06 MMac, 1.152% MACs, 64, 256, kernel_size=(1, 1), stride=(1, 1))
        (bn3): BatchNorm2d(512, 0.034% Params, 32.77 KMac, 0.035% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 24.58 KMac, 0.027% MACs, )
      )
      (block6): Bottleneck(
        70.78 k, 4.748% Params, 4.55 MMac, 4.925% MACs, 
        (conv1): Conv2d(16.45 k, 1.103% Params, 1.05 MMac, 1.138% MACs, 256, 64, kernel_size=(1, 1), stride=(1, 1))
        (bn1): BatchNorm2d(128, 0.009% Params, 8.19 KMac, 0.009% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(36.93 k, 2.477% Params, 2.36 MMac, 2.556% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(128, 0.009% Params, 8.19 KMac, 0.009% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(16.64 k, 1.116% Params, 1.06 MMac, 1.152% MACs, 64, 256, kernel_size=(1, 1), stride=(1, 1))
        (bn3): BatchNorm2d(512, 0.034% Params, 32.77 KMac, 0.035% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 24.58 KMac, 0.027% MACs, )
      )
    )
  )
  (layer4): MakeLayer(
    1.07 M, 72.104% Params, 18.83 MMac, 20.366% MACs, 
    (downsample): Sequential(
      132.61 k, 8.895% Params, 2.12 MMac, 2.294% MACs, 
      (0): Conv2d(131.58 k, 8.826% Params, 2.11 MMac, 2.277% MACs, 256, 512, kernel_size=(1, 1), stride=(2, 2))
      (1): BatchNorm2d(1.02 k, 0.069% Params, 16.38 KMac, 0.018% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (blockdict): ModuleDict(
      942.34 k, 63.209% Params, 16.71 MMac, 18.071% MACs, 
      (block1): Bottleneck(
        380.67 k, 25.534% Params, 7.7 MMac, 8.327% MACs, 
        (conv1): Conv2d(32.9 k, 2.207% Params, 2.11 MMac, 2.277% MACs, 256, 128, kernel_size=(1, 1), stride=(1, 1))
        (bn1): BatchNorm2d(256, 0.017% Params, 16.38 KMac, 0.018% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(147.58 k, 9.899% Params, 2.36 MMac, 2.553% MACs, 128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
        (bn2): BatchNorm2d(256, 0.017% Params, 4.1 KMac, 0.004% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(66.05 k, 4.430% Params, 1.06 MMac, 1.143% MACs, 128, 512, kernel_size=(1, 1), stride=(1, 1))
        (bn3): BatchNorm2d(1.02 k, 0.069% Params, 16.38 KMac, 0.018% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 18.43 KMac, 0.020% MACs, )
      
      )
      (block2): Bottleneck(
        280.83 k, 18.837% Params, 4.51 MMac, 4.872% MACs, 
        (conv1): Conv2d(65.66 k, 4.405% Params, 1.05 MMac, 1.136% MACs, 512, 128, kernel_size=(1, 1), stride=(1, 1))
        (bn1): BatchNorm2d(256, 0.017% Params, 4.1 KMac, 0.004% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(147.58 k, 9.899% Params, 2.36 MMac, 2.553% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(256, 0.017% Params, 4.1 KMac, 0.004% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(66.05 k, 4.430% Params, 1.06 MMac, 1.143% MACs, 128, 512, kernel_size=(1, 1), stride=(1, 1))
        (bn3): BatchNorm2d(1.02 k, 0.069% Params, 16.38 KMac, 0.018% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 12.29 KMac, 0.013% MACs, )
      )
      (block3): Bottleneck(
        280.83 k, 18.837% Params, 4.51 MMac, 4.872% MACs, 
        (conv1): Conv2d(65.66 k, 4.405% Params, 1.05 MMac, 1.136% MACs, 512, 128, kernel_size=(1, 1), stride=(1, 1))
        (bn1): BatchNorm2d(256, 0.017% Params, 4.1 KMac, 0.004% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(147.58 k, 9.899% Params, 2.36 MMac, 2.553% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (bn2): BatchNorm2d(256, 0.017% Params, 4.1 KMac, 0.004% MACs, 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(66.05 k, 4.430% Params, 1.06 MMac, 1.143% MACs, 128, 512, kernel_size=(1, 1), stride=(1, 1))
        (bn3): BatchNorm2d(1.02 k, 0.069% Params, 16.38 KMac, 0.018% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(0, 0.000% Params, 12.29 KMac, 0.013% MACs, )
      )
    )
  )
  (avgpool): AdaptiveAvgPool2d(0, 0.000% Params, 8.19 KMac, 0.009% MACs, output_size=(1, 1))
  (fc): Linear(5.13 k, 0.344% Params, 5.13 KMac, 0.006% MACs, in_features=512, out_features=10, bias=True)
)