How to use the mmcv.is_str function in mmcv

def coco_eval_with_return(result_files,
                          result_types,
                          coco,
                          max_dets=(100, 300, 1000)):
    for res_type in result_types:
        assert res_type in [
            'proposal', 'proposal_fast', 'bbox', 'segm', 'keypoints'
        ]

    if mmcv.is_str(coco):
        coco = COCO(coco)
    assert isinstance(coco, COCO)

    if result_types == ['proposal_fast']:
        ar = fast_eval_recall(result_files, coco, np.array(max_dets))
        for i, num in enumerate(max_dets):
            print('AR@{}\t= {:.4f}'.format(num, ar[i]))
        return

    eval_results = {}
    for res_type in result_types:
        result_file = result_files[res_type]
        assert result_file.endswith('.json')

        coco_dets = coco.loadRes(result_file)
        img_ids = coco.getImgIds()

def to_tensor(data):
    """Convert objects of various python types to :obj:`torch.Tensor`.

    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
    :class:`Sequence`, :class:`int` and :class:`float`.
    """
    if isinstance(data, torch.Tensor):
        return data
    elif isinstance(data, np.ndarray):
        return torch.from_numpy(data)
    elif isinstance(data, Sequence) and not mmcv.is_str(data):
        return torch.tensor(data)
    elif isinstance(data, int):
        return torch.LongTensor([data])
    elif isinstance(data, float):
        return torch.FloatTensor([data])
    else:
        raise TypeError('type {} cannot be converted to tensor.'.format(
            type(data)))

def get_classes(dataset):
    """Get class names of a dataset."""
    alias2name = {}
    for name, aliases in dataset_aliases.items():
        for alias in aliases:
            alias2name[alias] = name

    if mmcv.is_str(dataset):
        if dataset in alias2name:
            labels = eval(alias2name[dataset] + '_classes()')
        else:
            raise ValueError('Unrecognized dataset: {}'.format(dataset))
    else:
        raise TypeError('dataset must a str, but got {}'.format(type(dataset)))
    return labels

model,
                 batch_processor,
                 optimizer=None,
                 work_dir=None,
                 log_level=logging.INFO,
                 logger=None):
        assert callable(batch_processor)
        self.model = model
        if optimizer is not None:
            self.optimizer = self.init_optimizer(optimizer)
        else:
            self.optimizer = None
        self.batch_processor = batch_processor

        # create work_dir
        if mmcv.is_str(work_dir):
            self.work_dir = osp.abspath(work_dir)
            mmcv.mkdir_or_exist(self.work_dir)
        elif work_dir is None:
            self.work_dir = None
        else:
            raise TypeError('"work_dir" must be a str or None')

        # get model name from the model class
        if hasattr(self.model, 'module'):
            self._model_name = self.model.module.__class__.__name__
        else:
            self._model_name = self.model.__class__.__name__

        self._rank, self._world_size = get_dist_info()
        self.timestamp = get_time_str()
        if logger is None:

assert len(scale_ranges) == num_scales

    num_classes = len(results)

    recalls = np.zeros((num_scales, num_classes), dtype=np.float32)
    aps = np.zeros((num_scales, num_classes), dtype=np.float32)
    num_gts = np.zeros((num_scales, num_classes), dtype=int)
    for i, cls_result in enumerate(results):
        if cls_result['recall'].size > 0:
            recalls[:, i] = np.array(cls_result['recall'], ndmin=2)[:, -1]
        aps[:, i] = cls_result['ap']
        num_gts[:, i] = cls_result['num_gts']

    if dataset is None:
        label_names = [str(i) for i in range(1, num_classes + 1)]
    elif mmcv.is_str(dataset):
        label_names = get_classes(dataset)
    else:
        label_names = dataset

    if not isinstance(mean_ap, list):
        mean_ap = [mean_ap]

    header = ['class', 'gts', 'dets', 'recall', 'ap']
    for i in range(num_scales):
        if scale_ranges is not None:
            print_log('Scale range {}'.format(scale_ranges[i]), logger=logger)
        table_data = [header]
        for j in range(num_classes):
            row_data = [
                label_names[j], num_gts[i, j], results[j]['num_dets'],
                '{:.3f}'.format(recalls[i, j]), '{:.3f}'.format(aps[i, j])

def build_from_cfg(cfg, registry, default_args=None):
    """Build a module from config dict.

    Args:
        cfg (dict): Config dict. It should at least contain the key "type".
        registry (:obj:`Registry`): The registry to search the type from.
        default_args (dict, optional): Default initialization arguments.

    Returns:
        obj: The constructed object.
    """
    assert isinstance(cfg, dict) and 'type' in cfg
    assert isinstance(default_args, dict) or default_args is None
    args = cfg.copy()
    obj_type = args.pop('type')
    if mmcv.is_str(obj_type):
        obj_type = registry.get(obj_type)
        if obj_type is None:
            raise KeyError('{} is not in the {} registry'.format(
                obj_type, registry.name))
    elif not inspect.isclass(obj_type):
        raise TypeError('type must be a str or valid type, but got {}'.format(
            type(obj_type)))
    if default_args is not None:
        for name, value in default_args.items():
            args.setdefault(name, value)
    return obj_type(**args)

def fast_eval_recall(results,
                     coco,
                     max_dets,
                     iou_thrs=np.arange(0.5, 0.96, 0.05)):
    if mmcv.is_str(results):
        assert results.endswith('.pkl')
        results = mmcv.load(results)
    elif not isinstance(results, list):
        raise TypeError(
            'results must be a list of numpy arrays or a filename, not {}'.
            format(type(results)))

    gt_bboxes = []
    img_ids = coco.getImgIds()
    for i in range(len(img_ids)):
        ann_ids = coco.getAnnIds(imgIds=img_ids[i])
        ann_info = coco.loadAnns(ann_ids)
        if len(ann_info) == 0:
            gt_bboxes.append(np.zeros((0, 4)))
            continue
        bboxes = []

Args:
        info (dict): Object types and arguments.
        parent (:class:`module`): Module which may containing expected object
            classes.
        default_args (dict, optional): Default arguments for initializing the
            object.

    Returns:
        any type: Object built from the dict.
    """
    assert isinstance(info, dict) and 'type' in info
    assert isinstance(default_args, dict) or default_args is None
    args = info.copy()
    obj_type = args.pop('type')
    if mmcv.is_str(obj_type):
        if parent is not None:
            obj_type = getattr(parent, obj_type)
        else:
            obj_type = sys.modules[obj_type]
    elif not isinstance(obj_type, type):
        raise TypeError('type must be a str or valid type, but got {}'.format(
            type(obj_type)))
    if default_args is not None:
        for name, value in default_args.items():
            args.setdefault(name, value)
    return obj_type(**args)

def _build_module(cfg, registry, default_args):
    assert isinstance(cfg, dict) and 'type' in cfg
    assert isinstance(default_args, dict) or default_args is None
    args = cfg.copy()
    obj_type = args.pop('type')
    if mmcv.is_str(obj_type):
        if obj_type not in registry.module_dict:
            raise KeyError('{} is not in the {} registry'.format(
                obj_type, registry.name))
        obj_type = registry.module_dict[obj_type]
    elif not isinstance(obj_type, type):
        raise TypeError('type must be a str or valid type, but got {}'.format(
            type(obj_type)))
    if default_args is not None:
        for name, value in default_args.items():
            args.setdefault(name, value)
    return obj_type(**args)