def forward()

in transforms.py [0:0]

• 15 lines of code
• 3 McCabe index (conditional complexity)

    def forward(self, image: torch.Tensor):
        C, H, W = image.shape
        if C != 4:
            err_msg = (
                f"This transform is for 4 channel RGBD input only; got {image.shape}"
            )
            raise ValueError(err_msg)
        color_img = image[:3, ...]  # (3, H, W)
        depth_img = image[3:4, ...]  # (1, H, W)

        # Clamp to 0.0 to prevent negative depth values
        depth_img = depth_img.clamp(min=self.min_depth)

        # divide by max_depth
        if self.clamp_max_before_scale:
            depth_img = depth_img.clamp(max=self.max_depth)

        depth_img /= self.max_depth

        img = torch.cat([color_img, depth_img], dim=0)
        return img