def init_func()

in activemri/experimental/cvpr19_models/models/reconstruction.py [0:0]

• 24 lines of code
• 11 McCabe index (conditional complexity)

def init_func(m):
    init_type = "normal"
    gain = 0.02
    classname = m.__class__.__name__
    if hasattr(m, "weight") and (
        classname.find("Conv") != -1 or classname.find("Linear") != -1
    ):
        if init_type == "normal":
            torch.nn.init.normal_(m.weight.data, 0.0, gain)
        elif init_type == "xavier":
            torch.nn.init.xavier_normal_(m.weight.data, gain=gain)
        elif init_type == "kaiming":
            torch.nn.init.kaiming_normal_(m.weight.data, a=0, mode="fan_in")
        elif init_type == "orthogonal":
            torch.nn.init.orthogonal_(m.weight.data, gain=gain)
        else:
            raise NotImplementedError(
                "initialization method [%s] is not implemented" % init_type
            )
        if hasattr(m, "bias") and m.bias is not None:
            torch.nn.init.constant_(m.bias.data, 0.0)
    elif classname.find("BatchNorm2d") != -1:
        torch.nn.init.normal_(m.weight.data, 1.0, gain)
        torch.nn.init.constant_(m.bias.data, 0.0)