How to use the fvcore.common.file_io.PathManager.get_local_path function in fvcore

with self.assertRaises(ValueError):
            PathManager.ls(self._tmpfile, foo="foo")  # type: ignore
        with self.assertRaises(ValueError):
            PathManager.mkdirs(self._tmpfile, foo="foo")  # type: ignore
        with self.assertRaises(ValueError):
            PathManager.open(self._tmpfile, foo="foo")  # type: ignore
        with self.assertRaises(ValueError):
            PathManager.rm(self._tmpfile, foo="foo")  # type: ignore

        PathManager.set_strict_kwargs_checking(False)

        PathManager.copy(
            self._tmpfile, self._tmpfile, foo="foo"  # type: ignore
        )
        PathManager.exists(self._tmpfile, foo="foo")  # type: ignore
        PathManager.get_local_path(self._tmpfile, foo="foo")  # type: ignore
        PathManager.isdir(self._tmpfile, foo="foo")  # type: ignore
        PathManager.isfile(self._tmpfile, foo="foo")  # type: ignore
        PathManager.ls(self._tmpdir, foo="foo")  # type: ignore
        PathManager.mkdirs(self._tmpdir, foo="foo")  # type: ignore
        f = PathManager.open(self._tmpfile, foo="foo")  # type: ignore
        f.close()

        with open(os.path.join(self._tmpdir, "test_rm.txt"), "w") as f:
            rm_file = f.name
            f.write(self._tmpfile_contents)
            f.flush()
        PathManager.rm(rm_file, foo="foo")  # type: ignore

for p in self._predictions:
                with open(os.path.join(pred_dir, p["file_name"]), "wb") as f:
                    f.write(p.pop("png_string"))

            with open(gt_json, "r") as f:
                json_data = json.load(f)
            json_data["annotations"] = self._predictions
            with PathManager.open(self._predictions_json, "w") as f:
                f.write(json.dumps(json_data))

            from panopticapi.evaluation import pq_compute

            with contextlib.redirect_stdout(io.StringIO()):
                pq_res = pq_compute(
                    gt_json,
                    PathManager.get_local_path(self._predictions_json),
                    gt_folder=gt_folder,
                    pred_folder=pred_dir,
                )

        res = {}
        res["PQ"] = 100 * pq_res["All"]["pq"]
        res["SQ"] = 100 * pq_res["All"]["sq"]
        res["RQ"] = 100 * pq_res["All"]["rq"]
        res["PQ_th"] = 100 * pq_res["Things"]["pq"]
        res["SQ_th"] = 100 * pq_res["Things"]["sq"]
        res["RQ_th"] = 100 * pq_res["Things"]["rq"]
        res["PQ_st"] = 100 * pq_res["Stuff"]["pq"]
        res["SQ_st"] = 100 * pq_res["Stuff"]["sq"]
        res["RQ_st"] = 100 * pq_res["Stuff"]["rq"]

        results = OrderedDict({"panoptic_seg": res})

image_root (str): the directory where the images in this json file exists.
        dataset_name (str): the name of the dataset (e.g., "lvis_v0.5_train").
            If provided, this function will put "thing_classes" into the metadata
            associated with this dataset.

    Returns:
        list[dict]: a list of dicts in Detectron2 standard format. (See
        `Using Custom Datasets `_ )

    Notes:
        1. This function does not read the image files.
           The results do not have the "image" field.
    """
    from lvis import LVIS

    json_file = PathManager.get_local_path(json_file)

    timer = Timer()
    lvis_api = LVIS(json_file)
    if timer.seconds() > 1:
        logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))

    if dataset_name is not None:
        meta = get_lvis_instances_meta(dataset_name)
        MetadataCatalog.get(dataset_name).set(**meta)

    # sort indices for reproducible results
    img_ids = sorted(list(lvis_api.imgs.keys()))
    # imgs is a list of dicts, each looks something like:
    # {'license': 4,
    #  'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',
    #  'file_name': 'COCO_val2014_000000001268.jpg',

def _loadGEval(self):
        smpl_subdiv_fpath = PathManager.get_local_path("detectron2://densepose/SMPL_subdiv.mat")
        pdist_transform_fpath = PathManager.get_local_path(
            "detectron2://densepose/SMPL_SUBDIV_TRANSFORM.mat"
        )
        pdist_matrix_fpath = PathManager.get_local_path("detectron2://densepose/Pdist_matrix.pkl")
        SMPL_subdiv = loadmat(smpl_subdiv_fpath)
        self.PDIST_transform = loadmat(pdist_transform_fpath)
        self.PDIST_transform = self.PDIST_transform["index"].squeeze()
        UV = np.array([SMPL_subdiv["U_subdiv"], SMPL_subdiv["V_subdiv"]]).squeeze()
        ClosestVertInds = np.arange(UV.shape[1]) + 1
        self.Part_UVs = []
        self.Part_ClosestVertInds = []
        for i in np.arange(24):
            self.Part_UVs.append(UV[:, SMPL_subdiv["Part_ID_subdiv"].squeeze() == (i + 1)])
            self.Part_ClosestVertInds.append(
                ClosestVertInds[SMPL_subdiv["Part_ID_subdiv"].squeeze() == (i + 1)]
            )

def evaluate(self):
        comm.synchronize()

        self._predictions = comm.gather(self._predictions)
        self._predictions = list(itertools.chain(*self._predictions))
        if not comm.is_main_process():
            return

        gt_json = PathManager.get_local_path(self._metadata.panoptic_json)
        gt_folder = self._metadata.panoptic_root

        with tempfile.TemporaryDirectory(prefix="panoptic_eval") as pred_dir:
            logger.info("Writing all panoptic predictions to {} ...".format(pred_dir))
            for p in self._predictions:
                with open(os.path.join(pred_dir, p["file_name"]), "wb") as f:
                    f.write(p.pop("png_string"))

            with open(gt_json, "r") as f:
                json_data = json.load(f)
            json_data["annotations"] = self._predictions
            with PathManager.open(self._predictions_json, "w") as f:
                f.write(json.dumps(json_data))

            from panopticapi.evaluation import pq_compute

def _get_local_path(self, path):
        logger = logging.getLogger(__name__)
        catalog_path = ModelCatalog.get(path[len(self.PREFIX) :])
        logger.info("Catalog entry {} points to {}".format(path, catalog_path))
        return PathManager.get_local_path(catalog_path)

self.keypoint_hflip_indices = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN)
        else:
            self.keypoint_hflip_indices = None

        if self.densepose_on:
            densepose_transform_srcs = [
                MetadataCatalog.get(ds).densepose_transform_src
                for ds in cfg.DATASETS.TRAIN + cfg.DATASETS.TEST
            ]
            assert len(densepose_transform_srcs) > 0
            # TODO: check that DensePose transformation data is the same for
            # all the datasets. Otherwise one would have to pass DB ID with
            # each entry to select proper transformation data. For now, since
            # all DensePose annotated data uses the same data semantics, we
            # omit this check.
            densepose_transform_data_fpath = PathManager.get_local_path(densepose_transform_srcs[0])
            self.densepose_transform_data = DensePoseTransformData.load(
                densepose_transform_data_fpath
            )

        self.is_train = is_train

def _get_local_path(self, path):
        name = path[len(self.PREFIX) :]
        return PathManager.get_local_path(self.S3_DETECTRON2_PREFIX + name)

loaded into the dataset dict (besides "iscrowd", "bbox", "keypoints",
            "category_id", "segmentation"). The values for these keys will be returned as-is.
            For example, the densepose annotations are loaded in this way.

    Returns:
        list[dict]: a list of dicts in Detectron2 standard format. (See
        `Using Custom Datasets `_ )

    Notes:
        1. This function does not read the image files.
           The results do not have the "image" field.
    """
    from pycocotools.coco import COCO

    timer = Timer()
    json_file = PathManager.get_local_path(json_file)
    with contextlib.redirect_stdout(io.StringIO()):
        coco_api = COCO(json_file)
    if timer.seconds() > 1:
        logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))

    id_map = None
    if dataset_name is not None:
        meta = MetadataCatalog.get(dataset_name)
        cat_ids = sorted(coco_api.getCatIds())
        cats = coco_api.loadCats(cat_ids)
        # The categories in a custom json file may not be sorted.
        thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])]
        meta.thing_classes = thing_classes

        # In COCO, certain category ids are artificially removed,
        # and by convention they are always ignored.