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混淆矩阵、准确率、查准率、查全率、DSC、IoU、敏感度的计算

news 2025/6/28 1:04:24

1.背景介绍

在训练的模型的时候，需要评价模型的好坏，就涉及到混淆矩阵、准确率、查准率、查全率、DSC、IoU、敏感度的计算。

2、混淆矩阵的概念

所谓的混淆矩阵如下表所示：

TP:真正类，真的正例被预测为正例

FN:假负类，样本为正例，被预测为负类

FP:假正类，原本实际为负，但是被预测为正例

TN:真负类，真的负样本被预测为负类。

从混淆矩阵当中，可以得到更高级的分类指标：Accuracy（准确率），Precision（查准率），Recall（查全率），Specificity（特异性），Sensitivity（灵敏度）。

3. 常用的分类指标

3.1 Accuracy（准确率）

不管是哪个类别，只要预测正确，其数量都放在分子上，而分母是全部数据量。常用于表示模型的精度，当数据类别不平衡时，不能用于模型的评价。

$Accuracy=\frac{TP+TN}{TP+FN+FN+TN}$

3.2 Precision（查准率）

即所有预测为正的样本中，预测正确的样本的所占的比重。

$Precision = \frac{TP}{TP+FP}$

3.3 Recall（查全率）

真实的为正的样本，被正确检测出来的比重。

$Recall=\frac{TP}{TP+FN}$

3.4 Specificity（特异性）

特异性指标，也称负正类率（False Positive Rate, FPR），计算的是模型错识别为正类的负类样本占所有负类样本的比例，一般越低越好。

$FPR = \frac{FP}{TN+FP}$

3.5 DSC（Dice coefficient）

Dice系数，是一种相似性度量，度量二进制图像分割的准确性。

如图所示红色的框的区域时Groudtruth，而蓝色的框为预测值Prediction。

$DSC=\frac{2\left | G\sqcap P \right |}{\left | p \right |+\left | G \right |}$

3.6 IoU（交并比）

$IoU=\frac{p\sqcap G}{p\bigsqcup G}$

3.7 Sensitivity（灵敏度）

反应的时预测正确的区域在Groundtruth中所占的比重。

$SEN=\frac{\left | p \left | \sqcap \right |g\right | }{\left | G \right | }$

4. 计算程序

ConfusionMatrix 这个类可以直接计算出混淆矩阵

from collections import defaultdict, deque
import datetime
import time
import torch
import torch.nn.functional as F
import torch.distributed as dist
import errno
import osclass SmoothedValue(object):"""Track a series of values and provide access to smoothed values over awindow or the global series average."""def __init__(self, window_size=20, fmt=None):if fmt is None:fmt = "{value:.4f} ({global_avg:.4f})"self.deque = deque(maxlen=window_size)self.total = 0.0self.count = 0self.fmt = fmtdef update(self, value, n=1):self.deque.append(value)self.count += nself.total += value * ndef synchronize_between_processes(self):"""Warning: does not synchronize the deque!"""if not is_dist_avail_and_initialized():returnt = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')dist.barrier()dist.all_reduce(t)t = t.tolist()self.count = int(t[0])self.total = t[1]@propertydef median(self):d = torch.tensor(list(self.deque))return d.median().item()@propertydef avg(self):d = torch.tensor(list(self.deque), dtype=torch.float32)return d.mean().item()@propertydef global_avg(self):return self.total / self.count@propertydef max(self):return max(self.deque)@propertydef value(self):return self.deque[-1]def __str__(self):return self.fmt.format(median=self.median,avg=self.avg,global_avg=self.global_avg,max=self.max,value=self.value)class ConfusionMatrix(object):def __init__(self, num_classes):self.num_classes = num_classesself.mat = Nonedef update(self, a, b):n = self.num_classesif self.mat is None:# 创建混淆矩阵self.mat = torch.zeros((n, n), dtype=torch.int64, device=a.device)with torch.no_grad():# 寻找GT中为目标的像素索引k = (a >= 0) & (a < n)# 统计像素真实类别a[k]被预测成类别b[k]的个数(这里的做法很巧妙)inds = n * a[k].to(torch.int64) + b[k]self.mat += torch.bincount(inds, minlength=n**2).reshape(n, n)def reset(self):if self.mat is not None:self.mat.zero_()def compute(self):h = self.mat.float()# 计算全局预测准确率(混淆矩阵的对角线为预测正确的个数)acc_global = torch.diag(h).sum() / h.sum()# 计算每个类别的准确率acc = torch.diag(h) / h.sum(1)# 计算每个类别预测与真实目标的iouiu = torch.diag(h) / (h.sum(1) + h.sum(0) - torch.diag(h))return acc_global, acc, iudef reduce_from_all_processes(self):if not torch.distributed.is_available():returnif not torch.distributed.is_initialized():returntorch.distributed.barrier()torch.distributed.all_reduce(self.mat)def __str__(self):acc_global, acc, iu = self.compute()return ('global correct: {:.1f}\n''average row correct: {}\n''IoU: {}\n''mean IoU: {:.1f}').format(acc_global.item() * 100,['{:.1f}'.format(i) for i in (acc * 100).tolist()],['{:.1f}'.format(i) for i in (iu * 100).tolist()],iu.mean().item() * 100)class DiceCoefficient(object):def __init__(self, num_classes: int = 2, ignore_index: int = -100):self.cumulative_dice = Noneself.num_classes = num_classesself.ignore_index = ignore_indexself.count = Nonedef update(self, pred, target):if self.cumulative_dice is None:self.cumulative_dice = torch.zeros(1, dtype=pred.dtype, device=pred.device)if self.count is None:self.count = torch.zeros(1, dtype=pred.dtype, device=pred.device)# compute the Dice score, ignoring backgroundpred = F.one_hot(pred.argmax(dim=1), self.num_classes).permute(0, 3, 1, 2).float()dice_target = build_target(target, self.num_classes, self.ignore_index)self.cumulative_dice += multiclass_dice_coeff(pred[:, 1:], dice_target[:, 1:], ignore_index=self.ignore_index)self.count += 1@propertydef value(self):if self.count == 0:return 0else:return self.cumulative_dice / self.countdef reset(self):if self.cumulative_dice is not None:self.cumulative_dice.zero_()if self.count is not None:self.count.zeros_()def reduce_from_all_processes(self):if not torch.distributed.is_available():returnif not torch.distributed.is_initialized():returntorch.distributed.barrier()torch.distributed.all_reduce(self.cumulative_dice)torch.distributed.all_reduce(self.count)

分类指标的计算

import torch# SR : Segmentation Result
# GT : Ground Truthdef get_accuracy(SR,GT,threshold=0.5):SR = SR > thresholdGT = GT == torch.max(GT)corr = torch.sum(SR==GT)tensor_size = SR.size(0)*SR.size(1)*SR.size(2)*SR.size(3)acc = float(corr)/float(tensor_size)return accdef get_sensitivity(SR,GT,threshold=0.5):# Sensitivity == RecallSR = SR > thresholdGT = GT == torch.max(GT)# TP : True Positive# FN : False NegativeTP = ((SR==1)+(GT==1))==2FN = ((SR==0)+(GT==1))==2SE = float(torch.sum(TP))/(float(torch.sum(TP+FN)) + 1e-6)     return SEdef get_specificity(SR,GT,threshold=0.5):SR = SR > thresholdGT = GT == torch.max(GT)# TN : True Negative# FP : False PositiveTN = ((SR==0)+(GT==0))==2FP = ((SR==1)+(GT==0))==2SP = float(torch.sum(TN))/(float(torch.sum(TN+FP)) + 1e-6)return SPdef get_precision(SR,GT,threshold=0.5):SR = SR > thresholdGT = GT == torch.max(GT)# TP : True Positive# FP : False PositiveTP = ((SR==1)+(GT==1))==2FP = ((SR==1)+(GT==0))==2PC = float(torch.sum(TP))/(float(torch.sum(TP+FP)) + 1e-6)return PCdef get_F1(SR,GT,threshold=0.5):# Sensitivity == RecallSE = get_sensitivity(SR,GT,threshold=threshold)PC = get_precision(SR,GT,threshold=threshold)F1 = 2*SE*PC/(SE+PC + 1e-6)return F1def get_JS(SR,GT,threshold=0.5):# JS : Jaccard similaritySR = SR > thresholdGT = GT == torch.max(GT)Inter = torch.sum((SR+GT)==2)Union = torch.sum((SR+GT)>=1)JS = float(Inter)/(float(Union) + 1e-6)return JSdef get_DC(SR,GT,threshold=0.5):# DC : Dice CoefficientSR = SR > thresholdGT = GT == torch.max(GT)Inter = torch.sum((SR+GT)==2)DC = float(2*Inter)/(float(torch.sum(SR)+torch.sum(GT)) + 1e-6)return DC

参考文献：

混淆矩阵的概念-CSDN博客

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