mmdetection测试数据评估precision

需要修改mmdet/core/evaluation/mean_ap.py文件里的print_map_summary函数

 

def print_map_summary(mean_ap,
                      results,
                      dataset=None,
                      scale_ranges=None,
                      logger=None):
    """Print mAP and results of each class.

    A table will be printed to show the gts/dets/recall/AP of each class and
    the mAP.

    Args:
        mean_ap (float): Calculated from `eval_map()`.
        results (list[dict]): Calculated from `eval_map()`.
        dataset (list[str] | str | None): Dataset name or dataset classes.
        scale_ranges (list[tuple] | None): Range of scales to be evaluated.
        logger (logging.Logger | str | None): The way to print the mAP
            summary. See `mmcv.utils.print_log()` for details. Default: None.
    """

    if logger == 'silent':
        return

    if isinstance(results[0]['ap'], np.ndarray):
        num_scales = len(results[0]['ap'])
    else:
        num_scales = 1

    if scale_ranges is not None:
        assert len(scale_ranges) == num_scales

    num_classes = len(results)
    # TC添加下面一行于20220104
    precisions = np.zeros((num_scales, num_classes), dtype=np.float32)

    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']
        # TC添加下面两行于20220104
        if cls_result['recall'].size > 0:
            precisions[:, i] = np.array(cls_result['precision'], ndmin=2)[:, -1]

        num_gts[:, i] = cls_result['num_gts']

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

    if not isinstance(mean_ap, list):
        mean_ap = [mean_ap]
    # TC修改下面一行于20220104
    header = ['class', 'gts', 'dets', 'precision', 'recall', 'ap']
    # header = ['class', 'gts', 'dets', 'recall', 'ap']
    for i in range(num_scales):
        if scale_ranges is not None:
            print_log(f'Scale range {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'],
                # TC修改下面一行于20220104
                f'{precisions[i, j]:.3f}', f'{recalls[i, j]:.3f}', f'{aps[i, j]:.3f}'
                # f'{recalls[i, j]:.3f}', f'{aps[i, j]:.3f}'
            ]
            table_data.append(row_data)
        # TC修改下面一行于20220104
        table_data.append(['mAP', '', '', '', '', f'{mean_ap[i]:.3f}'])
        # table_data.append(['mAP', '', '', '', f'{mean_ap[i]:.3f}'])
        table = AsciiTable(table_data)
        table.inner_footing_row_border = True
        print_log('\n' + table.table, logger=logger)