Method forward

modules/networks.py:97–126

(self, x)

        self.padding_mode=padding_mode

    def forward(self, x):
        w= torch._weight_norm(self.weight, self.g, 0)



        if self.padding_mode != 'zeros':
            x= F.pad(x, (self.padding_amount,self.padding_amount), mode=self.padding_mode)
        
            x= F.conv1d(
                    x,
                    w,
                    bias=self.bias,
                    stride=self.stride,
                    padding=0,
                    dilation=self.dilation,
                    groups=self.groups,
                )
            # print("x after conv", x.shape)
            return x
        else:
        
            return F.conv1d(
                x,
                w,
                bias=self.bias,
                stride=self.stride,
                padding=self.padding,
                dilation=self.dilation,
                groups=self.groups,
            )


def kaiming_init(m, is_linear, nonlinearity="silu"):