增大感受野

  

   

    

     

    
    

     

       self.stage1 = nn.Sequential(
     
    
   

    

     

    
    

     

       conv_bn(3, 64, 2, leaky = 0.1), # 3
     
    
   

    

     

    
    

     

       conv_dw(64, 96, 1),   # 7
     
    
   

    

     

    
    

     

       conv_dw(96, 96, 2),  # 11
     
    
   

    

     

    
    

     

       conv_dw(96, 128, 1),  # 19
     
    
   

    

     

    
    

     

       conv_dw(128, 128, 2),  # 27
     
    
   

    

     

    
    

     

       conv_dw(128, 144, 1),  # 43
     
    
   

    

     

    
    

     

       )
     
    
  
 

  

   

    

     

    
    

     

      # import mish
     
    
   

    

     

    
    

     

      def conv_bn(inp, oup, stride = 1, leaky = 0):
     
    
   

    

     

    
    

     
 return nn.Sequential(
     
    
   

    

     

    
    

     

       nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
     
    
   

    

     

    
    

     

       nn.BatchNorm2d(oup),
     
    
   

    

     

    
    

     
 # mish.Mish()
     
    
   

    

     

    
    

     

       nn.ReLU6()
     
    
   

    

     

    
    

     

       )
     
    
  
 

1.

  

   

    

     

    
    

     

      x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      self.conv2 = nn.Conv2d(96, 96, kernel_size=3, stride=2, padding=1)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      self.conv4_0 = nn.Conv2d(128, 128, kernel_size=5, stride=2, padding=2)
     
    
  
 

增大感受野的方法
主要的方法是从增加网络的深度出发（这也

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/104764646