def METHOD_NAME(self):
    """Test using the metric in the same way as Evaluator."""
    dummy_pred_1 = self._create_dummy_results(track_id=1)
    dummy_pred_2 = self._create_dummy_results(track_id=1)
    dummy_pred_3 = self._create_dummy_results(track_id=2)
    dummy_mask = np.zeros((720, 1280), order='F', dtype=np.uint8)
    dummy_mask[100:150, 100:150] = 1
    rle_mask = mask_util.encode(dummy_mask)
    rle_mask['counts'] = rle_mask['counts'].decode('utf-8')
    instances_1 = [{
        'bbox_label': 0,
        'bbox': [100, 100, 150, 150],
        'ignore_flag': 0,
        'instance_id': 1,
        'mask': rle_mask,
    }]
    instances_2 = [{
        'bbox_label': 0,
        'bbox': [100, 100, 150, 150],
        'ignore_flag': 0,
        'instance_id': 2,
        'mask': rle_mask,
    }]
    vis_metric = METRICS.build(
        dict(
            type='YouTubeVISMetric',
            outfile_prefix=f'{self.tmp_dir.name}/test',
        ))
    vis_metric.dataset_meta = dict(classes=['car', 'train'])
    data_batch = dict(inputs=None, data_samples=None)
    gt_insatnce = InstanceData(**dummy_pred_1)
    img_data_sample = DetDataSample()
    img_data_sample.pred_track_instances = gt_insatnce
    img_data_sample.set_metainfo(
        dict(
            img_id=1,
            video_id=1,
            ori_video_length=2,
            ori_shape=(720, 1280),
            instances=instances_1))
    gt_insatnce_2 = InstanceData(**dummy_pred_2)
    img_data_sample_2 = DetDataSample()
    img_data_sample_2.pred_track_instances = gt_insatnce_2
    img_data_sample_2.set_metainfo(
        dict(
            img_id=2,
            video_id=1,
            ori_video_length=2,
            ori_shape=(720, 1280),
            instances=instances_1))
    track_data_sample = TrackDataSample()
    track_data_sample.video_data_samples = [
        img_data_sample, img_data_sample_2
    ]
    predictions = []
    if isinstance(track_data_sample, BaseDataElement):
        predictions.append(track_data_sample.to_dict())
    vis_metric.process(data_batch, predictions)
    gt_insatnce = InstanceData(**dummy_pred_3)
    img_data_sample = DetDataSample()
    img_data_sample.pred_track_instances = gt_insatnce
    img_data_sample.set_metainfo(
        dict(
            img_id=3,
            video_id=2,
            ori_video_length=1,
            ori_shape=(720, 1280),
            instances=instances_2))
    track_data_sample = TrackDataSample()
    track_data_sample.video_data_samples = [img_data_sample]
    predictions = []
    if isinstance(track_data_sample, BaseDataElement):
        predictions.append(track_data_sample.to_dict())
    vis_metric.process(data_batch, predictions)
    eval_results = vis_metric.evaluate(size=3)
    target = {
        'youtube_vis/segm_mAP': 1.0,
        'youtube_vis/segm_mAP_50': 1.0,
        'youtube_vis/segm_mAP_75': 1.0,
        'youtube_vis/segm_mAP_s': 1.0,
        'youtube_vis/segm_mAP_m': -1.0,
        'youtube_vis/segm_mAP_l': -1.0,
    }
    self.assertDictEqual(eval_results, target)