| 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 |