def __init__()

in models/vgg16.py [0:0]

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

    def __init__(self, features, num_classes, sobel):
        super(VGG, self).__init__()
        self.features = features
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(True),
            nn.Dropout(0.5),
            nn.Linear(4096, 4096),
            nn.ReLU(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