6fa5098c419e83e1c4d2d55b492eafdfe6a006ef,pro_gan_pytorch/CustomLayers.py,_equalized_conv2d,forward,#_equalized_conv2d#Any#,35

Before Change

        :param x: input
        :return: y => output
        
        try:
            dev_scale = self.scale.to(x.get_device())
        except RuntimeError:
            dev_scale = self.scale
        x = self.conv(x.mul(dev_scale))
        if self.use_bias:
            return x + self.bias.view(1, -1, 1, 1).expand_as(x)
        return x


class _equalized_deconv2d(th.nn.Module):
     Transpose convolution using the equalized learning rate 

After Change

        
        from torch.nn.functional import conv2d

        return conv2d(input=x,
                      weight=self.weight * self.scale,  // scale the weight on runtime
                      bias=self.bias if self.use_bias else None,
                      stride=self.stride,
                      padding=self.pad)

    def extra_repr(self):
        return ", ".join(map(str, self.weight.shape))

Instances