def get_D_norm_layer()

in models/layers/normalization.py [0:0]

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

def get_D_norm_layer(opt, norm_type="instance"):
    # helper function to get # output channels of the previous layer
    def get_out_channel(layer):
        if hasattr(layer, "out_channels"):
            return getattr(layer, "out_channels")
        return layer.weight.size(0)

    # this function will be returned
    def add_norm_layer(layer):
        nonlocal norm_type
        if norm_type.startswith("spectral"):
            layer = spectral_norm(layer)
            subnorm_type = norm_type[len("spectral") :]

        if subnorm_type == "none" or len(subnorm_type) == 0:
            return layer

        # remove bias in the previous layer, which is meaningless
        # since it has no effect after normalization
        if getattr(layer, "bias", None) is not None:
            delattr(layer, "bias")
            layer.register_parameter("bias", None)

        if subnorm_type == "batch":
            norm_layer = nn.BatchNorm2d(get_out_channel(layer), affine=True)

        elif subnorm_type == "instance":
            norm_layer = nn.InstanceNorm2d(get_out_channel(layer), affine=False)
        else:
            raise ValueError(
                "normalization layer %s is not recognized" % subnorm_type
            )

        return nn.Sequential(layer, norm_layer)

    return add_norm_layer