def __init__()

in ResNetBasic.py [0:0]

• 22 lines of code
• 6 McCabe index (conditional complexity)

    def __init__(self, indim, outdim, half_res):
        super(BottleneckBlock, self).__init__()
        bottleneckdim = int(outdim/4)
        self.indim = indim
        self.outdim = outdim
        self.C1 = nn.Conv2d(indim, bottleneckdim, kernel_size=1,  bias=False)
        self.relu = nn.ReLU()
        self.BN1 = nn.BatchNorm2d(bottleneckdim)
        self.C2 = nn.Conv2d(bottleneckdim, bottleneckdim, kernel_size=3, stride=2 if half_res else 1,padding=1)
        self.BN2 = nn.BatchNorm2d(bottleneckdim)
        self.C3 = nn.Conv2d(bottleneckdim, outdim, kernel_size=1, bias=False)
        self.BN3 = nn.BatchNorm2d(outdim)

        self.parametrized_layers = [self.C1, self.BN1, self.C2, self.BN2, self.C3, self.BN3]
        self.half_res = half_res


        # if the input number of channels is not equal to the output, then need a 1x1 convolution
        if indim!=outdim:
            self.shortcut = nn.Conv2d(indim, outdim, 1, stride=2 if half_res else 1, bias=False)
            self.parametrized_layers.append(self.shortcut)
            self.shortcut_type = '1x1'
        else:
            self.shortcut_type = 'identity'

        for layer in self.parametrized_layers:
            init_layer(layer)