PangJian123
commented
5 years ago I have solved the test problem,it can evaluate my own dataset under windows10 . Following next steps to evaluate your own dataset: First: make sure you can demo your own dataset Second:add following code to a new test_net.py Third:Input python test_net.py (the following code that i modified is not standard,i make this in order to evaluate AP and MAP.you can contact me If you have any questions)
# --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Zheqi he, Xinlei Chen, based on code from Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# import _init_paths
from lib.utils.test import test_net
from lib.config import config as cfg
# from lib.config import cfg, cfg_from_file, cfg_from_list
from lib.datasets.factory import get_imdb
import argparse
import pprint
import time, os, sys
import tensorflow as tf
from lib.nets.vgg16 import vgg16
# from nets.resnet_v1 import resnetv1
# from nets.mobilenet_v1 import mobilenetv1
demonet = 'vgg16'
dataset = 'pascal_voc'
NETS = {'vgg16': ('vgg16.ckpt',)}
DATASETS = {'pascal_voc': ('voc_2007_trainval',)}
tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0])
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='Test a Fast R-CNN network')
parser.add_argument('--cfg', dest='cfg_file',
help='optional config file', default=None, type=str)
parser.add_argument('--model', dest='model',
help='model to test',
default=None, type=str)
parser.add_argument('--imdb', dest='imdb_name',
help='dataset to test',
default='voc_2007_test', type=str)
parser.add_argument('--comp', dest='comp_mode', help='competition mode',
action='store_true')
parser.add_argument('--num_dets', dest='max_per_image',
help='max number of detections per image',
default=100, type=int)
parser.add_argument('--tag', dest='tag',
help='tag of the model',
default='', type=str)
# parser.add_argument('--net', dest='net',
# help='vgg16, res50, res101, res152, mobile',
# default='res50', type=str)
parser.add_argument('--set', dest='set_cfgs',
help='set config keys', default=None,
nargs=argparse.REMAINDER)
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_0712')
# if len(sys.argv) == 1:
# parser.print_help()
# sys.exit(1)
args = parser.parse_args() # 解析参数
return args
if __name__ == '__main__':
args = parse_args()
print('Called with args:')
print(args)
# if args.cfg_file is not None:
# cfg_from_file(args.cfg_file)
# if args.set_cfgs is not None:
# cfg_from_list(args.set_cfgs)
print('Using config:')
pprint.pprint(cfg)
# if has model, get the name from it
# if does not, then just use the initialization weights
if tfmodel:
filename = os.path.splitext(os.path.basename(tfmodel))[0]
else:
filename = os.path.splitext(os.path.basename(args.weight))[0]
tag = args.tag
tag = tag if tag else 'default'
filename = tag + '/' + filename
imdb = get_imdb(args.imdb_name)
imdb.competition_mode(args.comp_mode)
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()
# elif args.net == 'res50':
# net = resnetv1(num_layers=50)
# elif args.net == 'res101':
# net = resnetv1(num_layers=101)
# elif args.net == 'res152':
# net = resnetv1(num_layers=152)
# elif args.net == 'mobile':
# net = mobilenetv1()
else:
raise NotImplementedError
# load model
net.create_architecture(sess, mode="TEST",num_classes=6, tag='default',
anchor_scales=[8, 16, 32],
anchor_ratios=[0.5, 1, 2])
if tfmodel:
print(('Loading model check point from {:s}').format(tfmodel))
saver = tf.train.Saver()
saver.restore(sess, tfmodel)
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(args.weight))
sess.run(tf.global_variables_initializer())
print('Loaded.')
test_net(sess, net, imdb, filename, max_per_image=args.max_per_image)
sess.close()
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forwardxu0129
AriadneZhao
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i want to evaluate my dataset.who can help me ,thank you very much!!!