IoUMetric中CPA准确率计算错误

[cs-xiyan]

准确率应该是TP/TP+FP，而CPA是每类的准确率。所以 原代码应该改为

 # 总准确率 TP+TN/TP+TN+FP+FN
        all_acc = total_area_intersect.sum() / total_area_pred_label.sum()
        ret_metrics = OrderedDict({'aAcc': all_acc})
        # 计算指标
        for metric in metrics:
            # mIoU
            if metric == 'mIoU':
                # 交集比并集
                iou = total_area_intersect / total_area_union
                # 准确率 该类交集比该类
                acc = total_area_intersect / total_area_pred_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_pred_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

[cs-xiyan]

具体问题在mmseg/evaluation/metrics/iou_metric.py

[ssxfc]

practically, you are wrong!