def __init__()

in models/densenet3.py [0:0]

• 35 lines of code
• 7 McCabe index (conditional complexity)

    def __init__(self, depth, num_classes, growth_rate=12,
                 reduction=0.5, bottleneck=True, dropRate=0.0):
        super(DenseNet3, self).__init__()
        in_planes = 2 * growth_rate
        n = (depth - 4) // 3
        if bottleneck == True:
            n = n//2
            block = BottleneckBlock
        else:
            block = BasicBlock
        # 1st conv before any dense block
        self.conv1 = nn.Conv2d(3, in_planes, kernel_size=3, stride=1,
                               padding=1, bias=False)
        # 1st block
        self.block1 = DenseBlock(n, in_planes, growth_rate, block, dropRate)
        in_planes = int(in_planes+n*growth_rate)
        self.trans1 = TransitionBlock(in_planes, int(math.floor(in_planes*reduction)), dropRate=dropRate)
        in_planes = int(math.floor(in_planes*reduction))
        # 2nd block
        self.block2 = DenseBlock(n, in_planes, growth_rate, block, dropRate)
        in_planes = int(in_planes+n*growth_rate)
        self.trans2 = TransitionBlock(in_planes, int(math.floor(in_planes*reduction)), dropRate=dropRate)
        in_planes = int(math.floor(in_planes*reduction))
        # 3rd block
        self.block3 = DenseBlock(n, in_planes, growth_rate, block, dropRate)
        in_planes = int(in_planes+n*growth_rate)
        # global average pooling and classifier
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.relu = nn.ReLU(inplace=True)
        self.fc = nn.Linear(in_planes, num_classes)
        self.in_planes = in_planes

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.bias.data.zero_()