onnx retina人脸检测

  

   

    

     

    
    

     

      import os
     
    
   

    

     

    
    

     

      import time
     
    
   

    

     

    
    

     

      from math import ceil
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import onnxruntime
     
    
   

    

     

    
    

     

      import numpy as np
     
    
   

    

     

    
    

     

      import cv2
     
    
   

    

     

    
    

     

      import argparse
     
    
   

    

     

    
    

     

      import argparse
     
    
   

    

     

    
    

     

      import numpy as np
     
    
   

    

     

    
    

     

      from data import cfg_mnet, cfg_peleenet
     
    
   

    

     

    
    

     

      from utils.nms.py_cpu_nms import py_cpu_nms
     
    
   

    

     

    
    

     

      from math import ceil
     
    
   

    

     

    
    

     

      from itertools import product as product
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      #sigmoid函数
     
    
   

    

     

    
    

     

      def sigmoid(x):
     
    
   

    

     

    
    

     

          s = 1 / (1 + np.exp(-1*x))
     
    
   

    

     

    
    

     
    return s
     
    
   

    

     

    
    

     

      def softmax(x, axis=1):
     
    
   

    

     

    
    

     
    # 计算每行的最大值
     
    
   

    

     

    
    

     

          row_max = x.max(axis=axis)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
    # 每行元素都需要减去对应的最大值，否则求exp(x)会溢出，导致inf情况
     
    
   

    

     

    
    

     

          row_max = row_max.reshape(-1, 1)
     
    
   

    

     

    
    

     

          x = x - row_max
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

          x_exp = np.exp(x)
     
    
   

    

     

    
    

     

          x_sum = np.sum(x_exp, axis=axis, keepdims=True)
     
    
   

    

     

    
    

     

          s = x_exp / x_sum
     
    
   

    

     

    
    

     
    return s
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      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,
     
    
   

    

     

    
    

     

       Shap
     
    
  
 

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/104508041