def init_weights()

in models/networks/discriminators.py [0:0]

• 34 lines of code
• 19 McCabe index (conditional complexity)

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

        self.apply(init_func)

        # propagate to children
        for m in self.children():
            if hasattr(m, "init_weights"):
                m.init_weights(init_type, gain)