def __call__(self, panoptic, segments):
        """Generates the training target.
        reference: https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/createPanopticImgs.py
        reference: https://github.com/facebookresearch/detectron2/blob/master/datasets/prepare_panoptic_fpn.py#L18

        Args:
            panoptic (np.ndarray): Colored image encoding panoptic label.
            segments (list): A list of dictionary containing information of every segment, it has fields:
                - id: panoptic id, after decoding `panoptic`.
                - category_id: semantic class id.
                - area: segment area.
                - bbox: segment bounding box.
                - iscrowd: crowd region.

        Returns:
            A dictionary with fields:
                - semantic: Tensor, semantic label, shape=(H, W).
                - foreground: Tensor, foreground mask label, shape=(H, W).
                - center: Tensor, center heatmap, shape=(1, H, W).
                - center_points: List, center coordinates, with tuple (y-coord, x-coord).
                - offset: Tensor, offset, shape=(2, H, W), first dim is (offset_y, offset_x).
                - semantic_weights: Tensor, loss weight for semantic prediction, shape=(H, W).
                - center_weights: Tensor, ignore region of center prediction, shape=(H, W), used as weights for center
                    regression 0 is ignore, 1 is has instance. Multiply this mask to loss.
                - offset_weights: Tensor, ignore region of offset prediction, shape=(H, W), used as weights for offset
                    regression 0 is ignore, 1 is has instance. Multiply this mask to loss.
        """
        panoptic = self.rgb2id(panoptic)
        height, width = panoptic.shape[0], panoptic.shape[1]
        semantic = np.zeros_like(panoptic, dtype=np.uint8) + self.ignore_index
        foreground = np.zeros_like(panoptic, dtype=np.uint8)
        center = np.zeros((1, height, width), dtype=np.float32)
        center_pts = []
        offset = np.zeros((2, height, width), dtype=np.float32)
        y_coord = np.ones_like(panoptic, dtype=np.float32)
        x_coord = np.ones_like(panoptic, dtype=np.float32)
        y_coord = np.cumsum(y_coord, axis=0) - 1
        x_coord = np.cumsum(x_coord, axis=1) - 1
        # Generate pixel-wise loss weights
        semantic_weights = np.ones_like(panoptic, dtype=np.uint8)
        # 0: ignore, 1: has instance
        # three conditions for a region to be ignored for instance branches:
        # (1) It is labeled as `ignore_index`
        # (2) It is crowd region (iscrowd=1)
        # (3) (Optional) It is stuff region (for offset branch)
        center_weights = np.zeros_like(panoptic, dtype=np.uint8)
        offset_weights = np.zeros_like(panoptic, dtype=np.uint8)
        for seg in segments:
            cat_id = seg["category_id"]
            if self.ignore_crowd_in_semantic:
                if not seg['iscrowd']:
                    semantic[panoptic == seg["id"]] = cat_id
            else:
                semantic[panoptic == seg["id"]] = cat_id
            if cat_id in self.thing_list:
                foreground[panoptic == seg["id"]] = 1
            if not seg['iscrowd']:
                # Ignored regions are not in `segments`.
                # Handle crowd region.
                center_weights[panoptic == seg["id"]] = 1
                if self.ignore_stuff_in_offset:
                    # Handle stuff region.
                    if cat_id in self.thing_list:
                        offset_weights[panoptic == seg["id"]] = 1
                else:
                    offset_weights[panoptic == seg["id"]] = 1
            if cat_id in self.thing_list:
                # find instance center
                mask_index = np.where(panoptic == seg["id"])
                if len(mask_index[0]) == 0:
                    # the instance is completely cropped
                    continue

                # Find instance area
                ins_area = len(mask_index[0])
                if ins_area < self.small_instance_area:
                    semantic_weights[panoptic ==
                                     seg["id"]] = self.small_instance_weight

                center_y, center_x = np.mean(mask_index[0]), np.mean(mask_index[
                    1])
                center_pts.append([center_y, center_x])

                # generate center heatmap
                y, x = int(center_y), int(center_x)
                # outside image boundary
                if x < 0 or y < 0 or \
                        x >= width or y >= height:
                    continue
                sigma = self.sigma
                # upper left
                ul = int(np.round(x - 3 * sigma - 1)), int(
                    np.round(y - 3 * sigma - 1))
                # bottom right
                br = int(np.round(x + 3 * sigma + 2)), int(
                    np.round(y + 3 * sigma + 2))

                c, d = max(0, -ul[0]), min(br[0], width) - ul[0]
                a, b = max(0, -ul[1]), min(br[1], height) - ul[1]

                cc, dd = max(0, ul[0]), min(br[0], width)
                aa, bb = max(0, ul[1]), min(br[1], height)
                center[0, aa:bb, cc:dd] = np.maximum(center[0, aa:bb, cc:dd],
                                                     self.g[a:b, c:d])

                # generate offset (2, h, w) -> (y-dir, x-dir)
                offset_y_index = (np.zeros_like(mask_index[0]), mask_index[0],
                                  mask_index[1])
                offset_x_index = (np.ones_like(mask_index[0]), mask_index[0],
                                  mask_index[1])
                offset[offset_y_index] = center_y - y_coord[mask_index]
                offset[offset_x_index] = center_x - x_coord[mask_index]

        return dict(
            semantic=semantic.astype('long'),
            foreground=foreground.astype('long'),
            center=center.astype(np.float32),
            center_points=center_pts,
            offset=offset.astype(np.float32),
            semantic_weights=semantic_weights.astype(np.float32),
            center_weights=center_weights.astype(np.float32),
            offset_weights=offset_weights.astype(np.float32))
Generates the training target.
        reference: https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/createPanopticImgs.py
        reference: https://github.com/facebookresearch/detectron2/blob/master/datasets/prepare_panoptic_fpn.py#L18

        Args:
            panoptic (np.ndarray): Colored image encoding panoptic label.
            segments (list): A list of dictionary containing information of every segment, it has fields:
                - id: panoptic id, after decoding `panoptic`.
                - category_id: semantic class id.
                - area: segment area.
                - bbox: segment bounding box.
                - iscrowd: crowd region.

        Returns:
            A dictionary with fields:
                - semantic: Tensor, semantic label, shape=(H, W).
                - foreground: Tensor, foreground mask label, shape=(H, W).
                - center: Tensor, center heatmap, shape=(1, H, W).
                - center_points: List, center coordinates, with tuple (y-coord, x-coord).
                - offset: Tensor, offset, shape=(2, H, W), first dim is (offset_y, offset_x).
                - semantic_weights: Tensor, loss weight for semantic prediction, shape=(H, W).
                - center_weights: Tensor, ignore region of center prediction, shape=(H, W), used as weights for center
                    regression 0 is ignore, 1 is has instance. Multiply this mask to loss.
                - offset_weights: Tensor, ignore region of offset prediction, shape=(H, W), used as weights for offset
                    regression 0 is ignore, 1 is has instance. Multiply this mask to loss.
        
contrib/PanopticDeepLab/transforms/target_transforms.py