mxnet测试网络速度

  

   

    

     

    
    

     

      # -*- coding: utf-8 -*-
     
    
   

    

     

    
    

     

      """
     
    
   

    

     

    
    

     

      File Name: calculate_flops.py
     
    
   

    

     

    
    

     

      Author: liangdepeng
     
    
   

    

     

    
    

     

      mail: liangdepeng@gmail.com
     
    
   

    

     

    
    

     

      """
     
    
   

    

     

    
    

     

      import time
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import cv2
     
    
   

    

     

    
    

     

      import mxnet as mx
     
    
   

    

     

    
    

     

      import argparse
     
    
   

    

     

    
    

     

      import numpy as np
     
    
   

    

     

    
    

     

      import json
     
    
   

    

     

    
    

     

      import re
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      def parse_args():
     
    
   

    

     

    
    

     

       parser = argparse.ArgumentParser(description='')
     
    
   

    

     

    
    

     

       parser.add_argument('-ds', '--data_shapes', default=["data,1,3,112,112"], type=str, nargs='+',
     
    
   

    

     

    
    

     

       help='data_shapes, format: arg_name,s1,s2,...,sn, example: data,1,3,224,224')
     
    
   

    

     

    
    

     

       parser.add_argument('-ls', '--label_shapes', default=["label,1,512"], type=str, nargs='+',
     
    
   

    

     

    
    

     

       help='label_shapes, format: arg_name,s1,s2,...,sn, example: label,1,1,224,224')
     
    
   

    

     

    
    

     

       parser.add_argument('-s', '--symbol_path', type=str, default=r'softmax_label-symbol.json', help='')
     
    
   

    

     

    
    

     
 #
     
    
   

    

     

    
    

     
 return parser.parse_args()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      def single_input(path):
     
    
   

    

     

    
    

     

       img = cv2.imread(path)
     
    
   

    

     

    
    

     
 # mxnet三通道输入是严格的RGB格式，而cv2.imread的默认是BGR格式，因此需要做一个转换
     
    
   

    

     

    
    

     

       img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     
    
   

    

     

    
    

     

       img = cv2.resize(img, (112, 112))
     
    
   

    

     

    
    

     

       img = img.transpose(2, 0, 1)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # 添加一个第四维度并构建NDArray
     
    
   

    

     

    
    

     

       img = img[np.newaxis, :]
     
    
   

    

     

    
    

     

       array = mx.nd.array(img)
     
    
   

    

     

    
    

     
 return array
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      if __name__ == '__main__':
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 from collections import namedtuple
     
    
   

    

     

    
    

     

       args = parse_args()
     
    
   

    

     

    
    

     

       sym = mx.sym.load(args.symbol_path)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       data_shapes = list()
     
    
   

    

     

    
    

     

       data_names = list()
     
    
   

    

     

    
    

     
 if args.data_shapes is not None and len(args.data_shapes) > 0:
     
    
   

    

     

    
    

     
 for shape in args.data_shapes:
     
    
   

    

     

    
    

     

       items = shape.replace('\'', '').replace('"', '').split(',')
     
    
   

    

     

    
    

     

       data_shapes.append((items[0], tuple([int(s) for s in items[1:]])))
     
    
   

    

     

    
    

     

       data_names.append(items[0])
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       label_shapes = None
     
    
   

    

     

    
    

     

       label_names = list()
     
    
   

    

     

    
    

     
 if args.label_shapes is not None and len(args.label_shapes) > 0:
     
    
   

    

     

    
    

     

       label_shapes = list()
     
    
   

    

     

    
    

     
 for shape in args.label_shapes:
     
    
   

    

     

    
    

     

       items = shape.replace('\'', '').replace('"', '').split(',')
     
    
   

    

     

    
    

     

       label_shapes.append((items[0], tuple([int(s) for s in items[1:]])))
     
    
   

    

     

    
    

     

       label_names.append(items[0])
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       devs = [mx.cpu()]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 if len(label_names) == 0:
     
    
   

    

     

    
    

     

       label_names = None
     
    
   

    

     

    
    

     

       model = mx.mod.Module(context=devs, symbol=sym, data_names=data_names, label_names=None)
     
    
   

    

     

    
    

     

       model.bind(data_shapes=data_shapes,  for_training=False)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       model._params_dirty = True
     
    
   

    

     

    
    

     

       model.params_initialized = True
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       model.save_checkpoint("0412", 1)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       time1 = time.time()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # print("模型加载和重建时间：{0}".format(time1 - time0))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       Batch = namedtuple("batch", ['data'])
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       img1_path = r'1054086.jpg'
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       array1 = single_input(img1_path)
     
    
   

    

     

    
    

     

       start1 = time.time()
     
    
   

    

     

    
    

     
 for i in range(40):
     
    
   

    

     

    
    

     

       start = time.time()
     
    
   

    

     

    
    

     

       model.forward(Batch([array1]))
     
    
   

    

     

    
    

     

       vector1 = model.get_outputs()[0].asnumpy()
     
    
   

    

     

    
    

     

       print("time", time.time() - start, vector1.shape)
     
    
   

    

     

    
    

     

       print("total time", time.time() - start1)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
  
 

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/115648268