def __init__()

in common/nets/resnet.py [0:0]

• 25 lines of code
• 4 McCabe index (conditional complexity)

    def __init__(self, resnet_type):
	
        resnet_spec = {18: (BasicBlock, [2, 2, 2, 2], [64, 64, 128, 256, 512], 'resnet18'),
		       34: (BasicBlock, [3, 4, 6, 3], [64, 64, 128, 256, 512], 'resnet34'),
		       50: (Bottleneck, [3, 4, 6, 3], [64, 256, 512, 1024, 2048], 'resnet50'),
		       101: (Bottleneck, [3, 4, 23, 3], [64, 256, 512, 1024, 2048], 'resnet101'),
		       152: (Bottleneck, [3, 8, 36, 3], [64, 256, 512, 1024, 2048], 'resnet152')}
        block, layers, channels, name = resnet_spec[resnet_type]
        
        self.name = name
        self.inplanes = 64
        super(ResNetBackbone, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                nn.init.normal_(m.weight, mean=0, std=0.001)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)