def load_cityscapes_panoptic(image_dir, gt_dir, gt_json, meta):
    """
    Args:
        image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train".
        gt_dir (str): path to the raw annotations. e.g.,
            "~/cityscapes/gtFine/cityscapes_panoptic_train".
        gt_json (str): path to the json file. e.g.,
            "~/cityscapes/gtFine/cityscapes_panoptic_train.json".
        meta (dict): dictionary containing "thing_dataset_id_to_contiguous_id"
            and "stuff_dataset_id_to_contiguous_id" to map category ids to
            contiguous ids for training.

    Returns:
        list[dict]: a list of dicts in Detectron2 standard format. (See
        `Using Custom Datasets </tutorials/datasets.html>`_ )
    """

    def _convert_category_id(segment_info, meta):
        if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
            segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
                segment_info["category_id"]
            ]
        else:
            segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
                segment_info["category_id"]
            ]
        return segment_info

    assert os.path.exists(
        gt_json
    ), "Please run `python cityscapesscripts/preparation/createPanopticImgs.py` to generate label files."  # noqa
    with open(gt_json) as f:
        json_info = json.load(f)
    files = get_cityscapes_panoptic_files(image_dir, gt_dir, json_info)
    ret = []
    for image_file, label_file, segments_info in files:
        sem_label_file = (
            image_file.replace("leftImg8bit", "gtFine").split(".")[0] + "_labelTrainIds.png"
        )
        segments_info = [_convert_category_id(x, meta) for x in segments_info]
        ret.append(
            {
                "file_name": image_file,
                "image_id": "_".join(
                    os.path.splitext(os.path.basename(image_file))[0].split("_")[:3]
                ),
                "sem_seg_file_name": sem_label_file,
                "pan_seg_file_name": label_file,
                "segments_info": segments_info,
            }
        )
    assert len(ret), f"No images found in {image_dir}!"
    assert PathManager.isfile(
        ret[0]["sem_seg_file_name"]
    ), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py"  # noqa
    assert PathManager.isfile(
        ret[0]["pan_seg_file_name"]
    ), "Please generate panoptic annotation with python cityscapesscripts/preparation/createPanopticImgs.py"  # noqa
    return ret

    Args:
        image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train".
        gt_dir (str): path to the raw annotations. e.g.,
            "~/cityscapes/gtFine/cityscapes_panoptic_train".
        gt_json (str): path to the json file. e.g.,
            "~/cityscapes/gtFine/cityscapes_panoptic_train.json".
        meta (dict): dictionary containing "thing_dataset_id_to_contiguous_id"
            and "stuff_dataset_id_to_contiguous_id" to map category ids to
            contiguous ids for training.

    Returns:
        list[dict]: a list of dicts in Detectron2 standard format. (See
        `Using Custom Datasets </tutorials/datasets.html>`_ )
    
video_chat_with_MOSS/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/cityscapes_panoptic.py