def forward()

in models/layers/z_buffer_layers.py [0:0]

• 48 lines of code
• 7 McCabe index (conditional complexity)

    def forward(self, pts3D, src):
        bs = src.size(0)
        if len(src.size()) > 3:
            bs, c, w, _ = src.size()
            image_size = w

            pts3D = pts3D.permute(0, 2, 1)
            src = src.unsqueeze(2).repeat(1, 1, w, 1, 1).view(bs, c, -1)
        else:
            bs = src.size(0)
            image_size = self.size

        # Make sure these have been arranged in the same way
        assert pts3D.size(2) == 3
        assert pts3D.size(1) == src.size(2)  

        pts3D[:,:,1] = - pts3D[:,:,1]
        pts3D[:,:,0] = - pts3D[:,:,0]

        # Add on the default feature to the end of the src
        # src = torch.cat((src, self.default_feature.repeat(bs, 1, 1)), 2)

        radius = float(self.radius) / float(image_size) * 2.0

        pts3D = Pointclouds(points=pts3D, features=src.permute(0,2,1))
        points_idx, _, dist = rasterize_points(
            pts3D, image_size, radius, self.points_per_pixel
        )

        if os.environ["DEBUG"]:
            print("Max dist: ", dist.max(), pow(radius, self.opts.rad_pow))

        dist = dist / pow(radius, self.opts.rad_pow)

        if os.environ["DEBUG"]:
            print("Max dist: ", dist.max())

        alphas = (
            (1 - dist.clamp(max=1, min=1e-3).pow(0.5))
            .pow(self.opts.tau)
            .permute(0, 3, 1, 2)
        )

        if self.opts.accumulation == 'alphacomposite':
            transformed_src_alphas = compositing.alpha_composite(
                points_idx.permute(0, 3, 1, 2).long(),
                alphas,
                pts3D.features_packed().permute(1,0),
            )
        elif self.opts.accumulation == 'wsum':
            transformed_src_alphas = compositing.weighted_sum(
                points_idx.permute(0, 3, 1, 2).long(),
                alphas,
                pts3D.features_packed().permute(1,0),
            )
        elif self.opts.accumulation == 'wsumnorm':
            transformed_src_alphas = compositing.weighted_sum_norm(
                points_idx.permute(0, 3, 1, 2).long(),
                alphas,
                pts3D.features_packed().permute(1,0),
            )

        return transformed_src_alphas