def __init__()

in Dassl.pytorch/dassl/modeling/backbone/wide_resnet.py [0:0]

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

    def __init__(self, depth, widen_factor, dropRate=0.0):
        super().__init__()
        nChannels = [
            16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor
        ]
        assert (depth-4) % 6 == 0
        n = (depth-4) / 6
        block = BasicBlock
        # 1st conv before any network block
        self.conv1 = nn.Conv2d(
            3, nChannels[0], kernel_size=3, stride=1, padding=1, bias=False
        )
        # 1st block
        self.block1 = NetworkBlock(
            n, nChannels[0], nChannels[1], block, 1, dropRate
        )
        # 2nd block
        self.block2 = NetworkBlock(
            n, nChannels[1], nChannels[2], block, 2, dropRate
        )
        # 3rd block
        self.block3 = NetworkBlock(
            n, nChannels[2], nChannels[3], block, 2, dropRate
        )
        # global average pooling and classifier
        self.bn1 = nn.BatchNorm2d(nChannels[3])
        self.relu = nn.LeakyReLU(0.01, inplace=True)

        self._out_features = nChannels[3]

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(
                    m.weight, mode="fan_out", nonlinearity="relu"
                )
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.bias.data.zero_()