DeepGaze/DG1_arch.txt

DeepGazeI(
  (features): FeatureExtractor(
    (features): RGBalexnet(
      (0): Normalizer()
      (1): AlexNet(
        (features): Sequential(
          (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
          (1): ReLU(inplace=True)
          (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
          (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
          (4): ReLU(inplace=True)
          (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
          (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (7): ReLU(inplace=True)
          (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (9): ReLU(inplace=True)
          (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (11): ReLU(inplace=True)
          (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
        )
        (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))
        (classifier): Sequential(
          (0): Dropout(p=0.5, inplace=False)
          (1): Linear(in_features=9216, out_features=4096, bias=True)
          (2): ReLU(inplace=True)
          (3): Dropout(p=0.5, inplace=False)
          (4): Linear(in_features=4096, out_features=4096, bias=True)
          (5): ReLU(inplace=True)
          (6): Linear(in_features=4096, out_features=1000, bias=True)
        )
      )
    )
  )
  (readout_network): Sequential(
    (conv0): Conv2d(256, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
  )
  (finalizer): Finalizer(
    (gauss): GaussianFilterNd()
  )
)