aspp

用法：pc上20ms

  

   

    

     

    
    

     

       aspp = ASPP(320, [3, 6, 9])
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       input = torch.randn(2, 320, 10, 10)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # torch.onnx.export(pelee_net, input, "pelee_net.onnx", verbose=True)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 for i in range(10):
     
    
   

    

     

    
    

     

       start=time.time()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # x, *shortcuts = net(input)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # print(time.time()-start,x.shape)
     
    
   

    

     

    
    

     

       start = time.time()
     
    
   

    

     

    
    

     

       x=aspp(input)
     
    
   

    

     

    
    

     

       print(2,time.time() - start, x.shape)
     
    
  
 

  

   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      from torch.nn import functional as F
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      class ASPPPooling(nn.Sequential):
     
    
   

    

     

    
    

     
 def __init__(self, in_channels, out_channels):
     
    
   

    

     

    
    

     

       super(ASPPPooling, self).__init__(
     
    
   

    

     

    
    

     

       nn.AdaptiveAvgPool2d(1),
     
    
   

    

     

    
    

     

       nn.Conv2d(in_channels, out_channels, 1, bias=False),
     
    
   

    

     

    
    

     

       nn.BatchNorm2d(out_channels),
     
    
   

    

     

    
    

     

       nn.ReLU())
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 def forward(self, x):
     
    
   

    

     

    
    

     

       size = x.shape[-2:]
     
    
   

    

     

    
    

     
 for mod in self:
     
    
   

    

     

    
    

     

       x = mod(x)
     
    
   

    

     

    
    

     
 return F.interpola
     
    
  
 

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/111306323