def __init__()

in models/alexnet.py [0:0]

• 28 lines of code
• 3 McCabe index (conditional complexity)

    def __init__(self, features, num_classes, sobel):
        super(AlexNet, self).__init__()
        self.features = features
        self.classifier = nn.Sequential(nn.Dropout(0.5),
                            nn.Linear(256 * 6 * 6, 4096),
                            nn.ReLU(inplace=True),
                            nn.Dropout(0.5),
                            nn.Linear(4096, 4096),
                            nn.ReLU(inplace=True))

        self.top_layer = nn.Linear(4096, num_classes)
        self._initialize_weights()

        if sobel:
            grayscale = nn.Conv2d(3, 1, kernel_size=1, stride=1, padding=0)
            grayscale.weight.data.fill_(1.0 / 3.0)
            grayscale.bias.data.zero_()
            sobel_filter = nn.Conv2d(1, 2, kernel_size=3, stride=1, padding=1)
            sobel_filter.weight.data[0, 0].copy_(
                torch.FloatTensor([[1, 0, -1], [2, 0, -2], [1, 0, -1]])
            )
            sobel_filter.weight.data[1, 0].copy_(
                torch.FloatTensor([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])
            )
            sobel_filter.bias.data.zero_()
            self.sobel = nn.Sequential(grayscale, sobel_filter)
            for p in self.sobel.parameters():
                p.requires_grad = False
        else:
            self.sobel = None