Group Normalization

https://github.com/kuangliu/pytorch-groupnorm/blob/master/groupnorm.py

Group Normalization

  

   

    

     

    
    

     

      import torch
     
    
   

    

     

    
    

     

      import torch.nn as nn
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      class GroupNorm(nn.Module):
     
    
   

    

     

    
    

     
 def __init__(self, num_features, num_groups=32, eps=1e-5):
     
    
   

    

     

    
    

     

       super(GroupNorm, self).__init__()
     
    
   

    

     

    
    

     

       self.weight = nn.Parameter(torch.ones(1,num_features,1,1))
     
    
   

    

     

    
    

     

       self.bias = nn.Parameter(torch.zeros(1,num_features,1,1))
     
    
   

    

     

    
    

     

       self.num_groups = num_groups
     
    
   

    

     

    
    

     

       self.eps = eps
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 def forward(self, x):
     
    
   

    

     

    
    

     

       N,C,H,W = x.size()
     
    
   

    

     

    
    

     

       G = self.num_groups
     
    
   

    

     

    
    

     
 assert C % G == 0
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       x = x.view(N,G,-1)
     
    
   

    

     

    
    

     

       mean = x.mean(-1, keepdim=True)
     
    
   

    

     

    
    

     

       var = x.var(-1, keepdim=True)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       x = (x-mean) / (var+self.eps).sqrt()
     
    
   

    

     

    
    

     

       x = x.view(N,C,H,W)
     
    
   

    

     

    
    

     
 return x * self.weight + self.bias
     
    
  
 

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/103638131