c in metrics:
        if metric == 'mIoU':
            iou = total_area_intersect / total_area_union
            acc = total_area_intersect / total_area_label
            ret_metrics['IoU'] = iou
            ret_metrics['Acc'] = acc
        elif metric == 'mDice':
            dice = 2 * total_area_intersect / (
                total_area_pred_label + total_area_label)
            acc = total_area_intersect / total_area_label
            ret_metrics['Dice'] = dice
            ret_metrics['Acc'] = acc
        elif metric == 'mFscore':
            precision = total_area_intersect / total_area_pred_label
            recall = total_area_intersect / total_area_label
            f_value = torch.tensor(
                [f_score(x[0], x[1], beta) for x in zip(precision, recall)])
            ret_metrics['Fscore'] = f_value
            ret_metrics['Precision'] = precision
            ret_metrics['Recall'] = recall

    ret_metrics = {
        metric: value.numpy()
        for metric, value in ret_metrics.items()
    }
    if nan_to_num is not None:
        ret_metrics = OrderedDict({
            metric: np.nan_to_num(metric_value, nan=nan_to_num)
            for metric, metric_value in ret_metrics.items()
        })
    return ret_metrics