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