def print_map_summary()

in models/vision/detection/awsdet/core/evaluation/mean_ap.py [0:0]

• 45 lines of code
• 13 McCabe index (conditional complexity)

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 `awsdet.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)

    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 isinstance(dataset, str):
        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])
            ]
            table_data.append(row_data)
        table_data.append(['mAP', '', '', '', '{:.3f}'.format(mean_ap[i])])
        table = AsciiTable(table_data)
        table.inner_footing_row_border = True
        print_log('\n' + table.table, logger=logger)