text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from torch.nn import *
  
  
  class CrossEntropyLabelSmooth(nn.Module):
      """Cross entropy loss with label smoothing regularizer.
  
      Reference:
      Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
      Equation: y = (1 - epsilon) * y + epsilon / K.
  
      Args:
          num_classes (int): number of classes.
          epsilon (float): weight.
      """
  
      def __init__(self, num_classes, epsilon=0.1):
          super(CrossEntropyLabelSmooth, self).__init__()
          self.num_classes = num_classes
          self.epsilon = epsilon
          self.logsoftmax = nn.LogSoftmax(dim=1).cuda()
  
      def forward(self, inputs, targets):
          """
          Args:
              inputs: prediction matrix (before softmax) with shape (batch_size, num_classes)
              targets: ground truth labels with shape (num_classes)
          """
          log_probs = self.logsoftmax(inputs)
          # targets.unsqueeze(1)在1维处，增加一维，如[2,3]->[2,1,3],这样每一行都是一个[1,3]的数组
          # scatter_(1,labels,1),对了labels做one-hot编码
          targets = torch.zeros_like(log_probs).scatter_(1, targets.unsqueeze(1), 1)
          targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes
          loss = (- targets * log_probs).mean(0).sum()
          return loss
  
  
  class SoftEntropy(nn.Module):
      def __init__(self):
          super(SoftEntropy, self).__init__()
          self.logsoftmax = nn.LogSoftmax(dim=1).cuda()
  
      def forward(self, inputs, targets):
          log_probs = self.logsoftmax(inputs)
          loss = (- F.softmax(targets, dim=1).detach() * log_probs).mean(0).sum()
          return loss
  