加权CE_loss和BCE_loss稍有不同
1.标签为long类型，BCE标签为float类型
2.当reduction为mean时计算每个像素点的损失的平均，BCE除以像素数得到平均值，CE除以像素对应的权重之和得到平均值。
参数配置torch.nn.CrossEntropyLoss(weight=None,size_average=None,ignore_index=-100,reduce=None,reduction=‘mean’,label_smoothing=0.0)
增加加权的CE_loss代码实现
# 总之, CrossEntropyLoss() = softmax + log + NLLLoss() = log_softmax + NLLLoss(), 具体等价应用如下:
import torch
import torch.nn as nn
import torch.nn.functional as F
import random
import numpy as npclass CrossEntropyLoss2d(nn.Module):def __init__(self, weight=None):super(CrossEntropyLoss2d, self).__init__()self.nll_loss = nn.CrossEntropyLoss(weight, reduction='mean')def forward(self, preds, targets):return self.nll_loss(preds, targets)
语义分割类别计算
class CE_w_loss(nn.Module):def __init__(self,ignore_index=255):super(CE_w_loss, self).__init__()self.ignore_index = ignore_index# self.CE = nn.CrossEntropyLoss(ignore_index=self.ignore_index)def forward(self, outputs, targets):class_num = outputs.shape[1]# print("class_num :",class_num )# # 计算每个类别在整个 batch 中的像素数占比class_pixel_counts = torch.bincount(targets.flatten(), minlength=class_num) # 假设有class_num个类别class_pixel_proportions = class_pixel_counts.float() / torch.numel(targets)# # 根据类别占比计算权重class_weights = 1.0 / (torch.log(1.02 + class_pixel_proportions)).double() # 使用对数变换平衡权重# # print("class_weights :",class_weights)## 定义交叉熵损失函数，并使用动态计算的类别权重criterion = nn.CrossEntropyLoss(ignore_index=self.ignore_index,weight= class_weights)# 计算损失loss = criterion(outputs, targets)print(loss.item()) # 打印损失值return lossnp.random.seed(666)pred = np.ones((2, 5, 256,256))seg = np.ones((2, 5, 256, 256)) # 灰度label = np.ones((2, 256, 256)) # 灰度pred = torch.from_numpy(pred)seg = torch.from_numpy(seg).int() # 灰度label = torch.from_numpy(label).long()ce = CE_w_loss()loss = ce(pred, label)print("loss:",loss.item())
报错
Weight=torch.from_numpy(np.array([0.1, 0.8, 1.0, 1.0])).float() 报错
Weight=torch.from_numpy(np.array([0.1, 0.8, 1.0, 1.0])).double() 正确
参考：[1]https://blog.csdn.net/CSDN_of_ding/article/details/111515226
[2] https://blog.csdn.net/qq_40306845/article/details/137651442
[3] https://www.zhihu.com/question/400443029/answer/2477658229
