pairs/pair_13/input.txt

on
            maps or list of prediction result filenames.
        gt_seg_maps (list[ndarray] | list[str]): list of ground truth
            segmentation maps or list of label filenames.
        num_classes (int): Number of categories.
        ignore_index (int): Index that will be ignored in evaluation.
        nan_to_num (int, optional): If specified, NaN values will be replaced
            by the numbers defined by the user. Default: None.
        label_map (dict): Mapping old labels to new labels. Default: dict().
        reduce_zero_label (bool): Wether ignore zero label. Default: False.
        beta (int): Determines the weight of recall in the combined score.
            Default: False.


     Returns:
        dict[str, float | ndarray]: Default metrics.
            <aAcc> float: Overall accuracy on all images.
            <Fscore> ndarray: Per category recall, shape (num_classes, ).
            <Precision> ndarray: Per category precision, shape (num_classes, ).
            <Recall> ndarray: Per category f-score, shape (num_classes, ).
    """
    fscore_result = eval_metrics(
        results=results,
        gt_seg_maps=gt_seg_maps,
        num_classes=num_classes,
        ignore_index=ignore_index,
        metrics=['mFscore'],
        nan_to_num=nan_to_num,
        label_map=label_map,
        reduce_zero_label=reduce_zero_label,
        beta=beta)
    return fscore_result


def eval_metrics(results,
                 gt_seg_maps,
                 num_classes,
                 ignore_index,
                 metrics=['mIoU'],
                 nan_to_num=None,
                 label_map=dict(),
                 reduce_zero_label=False,
                 beta=1):
    """Calculate evaluation metrics
    Args:
        results (list[ndarray] | list[str]): List of prediction segmentation
            maps or list of prediction result filenames.
        gt_seg_maps (list[ndarray] | list[str]): list of ground truth
            segmentation maps or list of label filenames.
        num_classes (int): Number of categories.
        ignore_index (int): Index that will be ignored in evaluation.
        metrics (list[str] | str): Metrics to be evaluated, 'mIoU' and 'mDice'.
        nan_to_num (int, optional): If specified, NaN values will be replaced
            by the numbers defined by the user. Default: None.
        label_map (dict): Mapping old labels to new labels. Default: dict().
        reduce_zero_label (bool): Wether ignore zero label. Default: False.
     Returns:
        float: Overall accuracy on all images.
        ndarray: Per category accuracy, shape (num_classes, ).
        ndarray: Per category evaluation metrics, shape (num_classes, ).
    """
    if isinstance(metrics, str):
        metrics = [metrics]
    allowed_metrics = ['mIoU', 'mDice', 'mFscore']
    if not set(metrics).issubset(set(allowed_metrics)):
        raise KeyError('metrics {} is not supported'.format(metrics))

    total_area_intersect, total_area_union, total_area_pred_label, \
        total_area_label = total_intersect_and_union(
            results, gt_seg_maps, num_classes, ignore_index, label_map,
            reduce_zero_label)
    all_acc = total_area_intersect.sum() / total_area_label.sum()
    ret_metrics = OrderedDict({'aAcc': all_acc})
    for metri