How to use the mmcv.load function in mmcv

def voc_eval_with_return(result_file,
                         dataset,
                         iou_thr=0.5,
                         print_summary=True,
                         only_ap=True):
    det_results = mmcv.load(result_file)
    gt_bboxes = []
    gt_labels = []
    gt_ignore = []
    for i in range(len(dataset)):
        ann = dataset.get_ann_info(i)
        bboxes = ann['bboxes']
        labels = ann['labels']
        if 'bboxes_ignore' in ann:
            ignore = np.concatenate([
                np.zeros(bboxes.shape[0], dtype=np.bool),
                np.ones(ann['bboxes_ignore'].shape[0], dtype=np.bool)
            ])
            gt_ignore.append(ignore)
            bboxes = np.vstack([bboxes, ann['bboxes_ignore']])
            labels = np.concatenate([labels, ann['labels_ignore']])
        gt_bboxes.append(bboxes)

# json load/dump with a file-like object
    with tempfile.NamedTemporaryFile(mode, delete=False) as f:
        tmp_filename = f.name
        mmcv.dump(test_obj, f, file_format=file_format)
    assert osp.isfile(tmp_filename)
    with open(tmp_filename, mode) as f:
        load_obj = mmcv.load(f, file_format=file_format)
    assert load_obj == test_obj
    os.remove(tmp_filename)

    # automatically inference the file format from the given filename
    tmp_filename = osp.join(tempfile.gettempdir(),
                            'mmcv_test_dump.' + file_format)
    mmcv.dump(test_obj, tmp_filename)
    assert osp.isfile(tmp_filename)
    load_obj = mmcv.load(tmp_filename)
    assert load_obj == test_obj
    os.remove(tmp_filename)

def voc_eval(result_file, dataset, iou_thr=0.5):
    det_results = mmcv.load(result_file)
    gt_bboxes = []
    gt_labels = []
    gt_ignore = []
    for i in range(len(dataset)):
        ann = dataset.get_ann_info(i)
        bboxes = ann['bboxes']
        labels = ann['labels']
        if 'bboxes_ignore' in ann:
            ignore = np.concatenate([
                np.zeros(bboxes.shape[0], dtype=np.bool),
                np.ones(ann['bboxes_ignore'].shape[0], dtype=np.bool)
            ])
            gt_ignore.append(ignore)
            bboxes = np.vstack([bboxes, ann['bboxes_ignore']])
            labels = np.concatenate([labels, ann['labels_ignore']])
        gt_bboxes.append(bboxes)

def main():
    args = parse_args()

    cfg = mmcv.Config.fromfile(args.config)

    dataset = obj_from_dict(cfg.data.test, datasets, dict(test_mode=True))

    output_list = []
    for out in args.outputs:
        output_list.append(mmcv.load(out))

    if args.score_weights:
        weights = np.array(args.score_weights) / sum(args.score_weights)
    else:
        weights = [1. / len(output_list) for _ in output_list]

    def merge_scores(idx):
        def merge_part(arrs, index, weights):
            if arrs[0][index] is not None:
                return np.sum([a[index] * w for a, w in zip(arrs, weights)],
                              axis=0)
            else:
                return None

        results = [output[idx] for output in output_list]
        rel_props = output_list[0][idx][0]

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 = []
        for ann in ann_info:
            if ann.get('ignore', False) or ann['iscrowd']:

metrics = [
            'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10', 'AR100',
            'ARs', 'ARm', 'ARl'
        ]
    elif isinstance(metric, list):
        metrics = metric
    else:
        metrics = [metric]

    for metric_name in metrics:
        assert metric_name in [
            'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10', 'AR100',
            'ARs', 'ARm', 'ARl'
        ]

    eval_output = mmcv.load(filename)

    num_distortions = len(list(eval_output.keys()))
    results = np.zeros((num_distortions, 6, len(metrics)), dtype='float32')

    for corr_i, distortion in enumerate(eval_output):
        for severity in eval_output[distortion]:
            for metric_j, metric_name in enumerate(metrics):
                mAP = eval_output[distortion][severity][task][metric_name]
                results[corr_i, severity, metric_j] = mAP

    P = results[0, 0, :]
    if aggregate == 'benchmark':
        mPC = np.mean(results[:15, 1:, :], axis=(0, 1))
    else:
        mPC = np.mean(results[:, 1:, :], axis=(0, 1))
    rPC = mPC / P

def load_annotations(self, ann_file):
        return mmcv.load(ann_file)