torch.nn.Linear 笔记

最多支持两维，

我准备用这个代替1*1的卷积核

  

   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import torch
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      x = torch.randn(128, 20)  # 输入的维度是（128，20）
     
    
   

    

     

    
    

     

      m = torch.nn.Linear(20, 50)  # 20,30是指维度
     
    
   

    

     

    
    

     

      output = m(x)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      print('m.weight.shape: ', m.weight.shape)
     
    
   

    

     

    
    

     

      print('m.bias.shape:', m.bias.shape)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      print('output.shape:', output.shape)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # ans = torch.mm(input,torch.t(m.weight))+m.bias 等价于下面的
     
    
   

    

     

    
    

     

      ans = torch.mm(x, m.weight.t()) + m.bias
     
    
   

    

     

    
    

     

      print('ans.shape:', ans.shape)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      print(torch.equal(ans, output))
     
    
  
 

  

   

    

     

    
    

     

      class ClassHead(nn.Module):
     
    
   

    

     

    
    

     
 def __init__(self, inchannels=512, num_classes=2):
     
    
   

    

     

    
    

     

       super(ClassHead, self).__init__()
     
    
   

    

     

    
    

     

       self.num_classes = num_classes
     
    
   

    

     

    
    

     

       self.conv1x1 = nn.Conv2d(inchannels, self.num_classes, kernel_size=(1, 1), stride=1, padding=0)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 def forward(self, x):
     
    
   

    

     

    
    

     

       out = self.conv1x1(x)
     
    
   

    

     

    
    

     

       out = out.permute(0, 2, 3, 1).contiguous()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 return out.view(out.shape[0], -1, self.num_classes)
     
    
  
 

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/104598453