text: # encoding: utf-8
  """
  @author:  liaoxingyu
  @contact: sherlockliao01@gmail.com
  """
  
  import torch
  from torch import nn
  
  
  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)
  
      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 = torch.zeros(log_probs.size()).scatter_(1, targets.unsqueeze(1).data.cpu(), 1)
          targets = targets.to(inputs.device)
          targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes
          loss = (-targets * log_probs).mean(0).sum()
          return loss
  