def forward()

in models_all.py [0:0]

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

    def forward(self, x):
        bias = self.bias
        if bias is not None:
            bias = bias * self.b_mul

        have_convolution = False
        if self.upscale is not None and min(x.shape[2:]) * 2 >= 128:
            # this is the fused upscale + conv from StyleGAN, sadly this seems
            # incompatible with the non-fused way
            # this really needs to be cleaned up and go into the conv...
            w = self.weight * self.w_mul
            w = w.permute(1, 0, 2, 3)
            # probably applying a conv on w would be more efficient. also this
            # quadruples the weight (average)?!
            w = F.pad(w, (1, 1, 1, 1))
            w = (
                w[:, :, 1:, 1:]
                + w[:, :, :-1, 1:]
                + w[:, :, 1:, :-1]
                + w[:, :, :-1, :-1]
            )
            x = F.conv_transpose2d(x, w, stride=2, padding=(w.size(-1) - 1) // 2)
            have_convolution = True
        elif self.upscale is not None:
            x = self.upscale(x)

        if not have_convolution and self.intermediate is None:
            return F.conv2d(
                x, self.weight * self.w_mul, bias, padding=self.kernel_size // 2
            )
        elif not have_convolution:
            x = F.conv2d(
                x, self.weight * self.w_mul, None, padding=self.kernel_size // 2
            )

        if self.intermediate is not None:
            x = self.intermediate(x)
        if bias is not None:
            x = x + bias.view(1, -1, 1, 1)
        return x