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？
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])