Faster-RCNN-TensorFlow-Python3-master训练后，如何得到AP，mAP的结果

Question

查了很多资料，tf-faster-rcnn和caffe-faster-rcnn里都是用test__net.py
来评估训练结果。但是我用的是Faster-RCNN-TensorFlow-Python3-master，里面没有test_net.py。那要怎么获得AP和mAP的结果呢？

Answer 1

不知道你这个问题是否已经解决, 如果还没有解决的话:

• 给你找了一篇非常好的博客，你可以看看是否有帮助，链接：tensorflow版本faster rcnn的demo.py代码详解
• 除此之外, 这篇博客: 对Faster-RCNN-TensorFlow-Python3.5-master训练模型的评价mAP中的 一、新建test_net.py文件 部分也许能够解决你的问题, 你可以仔细阅读以下内容或者直接跳转源博客中阅读:
  

  放Faster-RCNN-TensorFlow-Python3.5-master 根文件夹。

  #!/usr/bin/env python
  
  # --------------------------------------------------------
  # Tensorflow Faster R-CNN
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Xinlei Chen, based on code from Ross Girshick
  # --------------------------------------------------------
  
  """
  Demo script showing detections in sample images.
  See README.md for installation instructions before running.
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import argparse
  import os
  
  import tensorflow as tf
  from lib.nets.vgg16 import vgg16
  from lib.datasets.factory import get_imdb
  from lib.utils.test import test_net
  
  # NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',), 'res101': ('res101_faster_rcnn_iter_110000.ckpt',)}
  NETS = {'vgg16': ('vgg16_faster_rcnn_iter_40000.ckpt',)}   #训练输出模型
  DATASETS = {'pascal_voc': ('voc_2007_trainval',), 'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)}
  
  
  
  def parse_args():
      """Parse input arguments."""
      parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN test')
      parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]',
                          choices=NETS.keys(), default='vgg16')
      parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]',
                          choices=DATASETS.keys(), default='pascal_voc')
      args = parser.parse_args()
  
      return args
  
  
  if __name__ == '__main__':
      args = parse_args()
  
      # model path
      demonet = args.demo_net
      dataset = args.dataset
      tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0])  #模型路径
      # 获得模型文件名称
      filename = (os.path.splitext(tfmodel)[0]).split('\\')[-1]
      filename = 'default' + '/' + filename
      imdb = get_imdb("voc_2007_test")  # 得到
      imdb.competition_mode('competition mode')
      if not os.path.isfile(tfmodel + '.meta'):
          print(tfmodel)
          raise IOError(('{:s} not found.\nDid you download the proper networks from '
                         'our server and place them properly?').format(tfmodel + '.meta'))
  
      # set config
      tfconfig = tf.ConfigProto(allow_soft_placement=True)
      tfconfig.gpu_options.allow_growth = True
  
      # init session
      sess = tf.Session(config=tfconfig)
      # load network
      if demonet == 'vgg16':
          net = vgg16(batch_size=1)
      # elif demonet == 'res101':
      # net = resnetv1(batch_size=1, num_layers=101)
      else:
          raise NotImplementedError
      net.create_architecture(sess, "TEST", 9,  #  记得修改第3个参数为：类别数量+1
                              tag='default', anchor_scales=[8, 16, 32])
      saver = tf.train.Saver()
      saver.restore(sess, tfmodel)
  
      print('Loaded network {:s}'.format(tfmodel))
      test_net(sess, net, imdb, filename, max_per_image=100)
      sess.close()
  

  需要自行修改的地方：

  1.改为自己训练输出对ckpt文件名

  NETS = {'vgg16': ('vgg16_faster_rcnn_iter_40000.ckpt',)}   #训练输出模型

  2.修改第3个参数为：类别数量+1

   net.create_architecture(sess, "TEST", 9,  #  记得修改第3个参数为：类别数量+1
                              tag='default', anchor_scales=[8, 16, 32])

如果你已经解决了该问题, 非常希望你能够分享一下解决方案, 写成博客, 将相关链接放在评论区, 以帮助更多的人 ^-^