负样本回归loss

从decode看，如果系数(loc[2:] 为宽高)是1，那么就是priors[:,2:]，就是候选框。

  

   

    

     

    
    

     

      def decode(loc, priors, variances):
     
    
   

    

     

    
    

     
    """Decode locations from predictions using priors to undo
     
    
   

    

     

    
    

     

       the encoding we did for offset regression at train time.
     
    
   

    

     

    
    

     

       Args:
     
    
   

    

     

    
    

     

       loc (tensor): location predictions for loc layers,
     
    
   

    

     

    
    

     

       Shape: [num_priors,4]
     
    
   

    

     

    
    

     

       priors (tensor): Prior boxes in center-offset form.
     
    
   

    

     

    
    

     

       Shape: [num_priors,4].
     
    
   

    

     

    
    

     

       variances: (list[float]) Variances of priorboxes
     
    
   

    

     

    
    

     

       Return:
     
    
   

    

     

    
    

     

       decoded bounding box predictions
     
    
   

    

     

    
    

     

       """
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

          boxes = torch.cat((
     
    
   

    

     

    
    

     

              priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
     
    
   

    

     

    
    

     

              priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
     
    
   

    

     

    
    

     

          boxes[:, :2] -= boxes[:, 2:] / 2
     
    
   

    

     

    
    

     

          boxes[:, 2:] += boxes[:, :2]
     
    
   

    

     

    
    

     
    return boxes
     
    
  
 

def encode(matched, priors, variances
 

文章来源: blog.csdn.net，作者：AI视觉网奇，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/103876459