pytorch focalloss

  

   

    

     

    
    

     
  import torch
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       gamma = torch.ones_like(focal_weight).cuda()
     
    
   

    

     

    
    

     

       gamma[focal_weight > 0.5] = 0.4
     
    
   

    

     

    
    

     

       gamma[focal_weight < 0.5] = 2.2
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       focal_weight = alpha_factor * torch.pow(focal_weight, gamma)
     
    
  
 

FocalLoss_cls 可以替代交叉熵 nn.CrossEntropyLoss()

  

   

    

     

    
    

     

      import torch
     
    
   

    

     

    
    

     

      import torch.nn as nn
     
    
   

    

     

    
    

     

      import torch.nn.functional as torch_F
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      class FocalLoss(nn.Module):
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 def __init__(self, gamma=0):
     
    
   

    

     

    
    

     

       super(FocalLoss, self).__init__()
     
    
   

    

     

    
    

     

       self.gamma = gamma
     
    
   

    

     

    
    

     
 # self.ce = torch.nn.CrossEntropyLoss(reduction='sum')
     
    
   

    

     

    
    

     

       self.ce = torch.nn.BCELoss(reduction='sum')
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 def forward(self, input, target):
     
    
   

    

     

    
    

     

       logp = self.ce(input, target)
     
    
   

    

     

    
    

     

       p = torch.exp(-logp)
     
    
   

    

     

    
    

     

       loss = (1 - p) ** self.gamma * logp
     
    
   

    

     

    
    

     

       re
     
    
  
 

文章来源: blog.csdn.net，作者：网奇，版权归原作者所有，如需转载，请联系作者。

原文链接：blog.csdn.net/jacke121/article/details/105578908