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",
2, # 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))
print(filename)
test_net(sess, net, imdb, filename, max_per_image=100)
sess.close()
from lib.config import config as cfg
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
if __name__ == '__main__':
# Create session
tfconfig = tf.ConfigProto(allow_soft_placement=True)
# tfconfig.gpu_options.allow_growth = True
sess = tf.Session(config=tfconfig)
# init net
net = vgg16(batch_size=1)
net.create_architecture(sess, "TEST", 2, tag='default', anchor_scales=[8, 16, 32], anchor_ratios=(1, 2))
for i in range(80001) :
if i==0 : continue
if (i%1000)!=0 : continue
weights_filename = "default\\voc_2007_trainval\\default\\vgg16_faster_rcnn_iter_{0}.ckpt".format(i)
print("Load weights : {0}".format(weights_filename))
saver = tf.train.Saver()
saver.restore(sess, weights_filename)
#set imdb
imdb_name = "voc_2007_test"
imdb = get_imdb(imdb_name)
accuracy, recall, precision, tpr, fpr = test_net(sess, net, imdb, weights_filename, max_per_image=100, thresh=0.0)
record_file = open("TestNet_Record_iter_{0}.csv".format(i), "w")
record_file.write("accuracy,recall,precision,tpr,fpr\n")
num = len(accuracy[0])
print("Data Number = {0} :: {1}, {2}, {3}, {4}".format(num, len(recall[0]), len(precision[0]), len(tpr[0]), len(fpr[0])))
for idx in range(num) :
record_file.write("{0:3f},{1:3f},{2:3f},{3:3f},{4:3f}\n".format(accuracy[0][idx], recall[0][idx], precision[0][idx], tpr[0][idx], fpr[0][idx]))
record_file.close()
# 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",
19,
tag='default',
anchor_scales=[8, 16, 32])
saver = tf.train.Saver()
saver.restore(sess, tfmodel)
print('Loaded network {:s}'.format(tfmodel))
im_names = os.listdir(cfg.FLAGS2["data_dir"] + '/demo') # 测试图片所在位置
for im_name in im_names:
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print('Demo for data/demo/{}'.format(im_name))
demo(sess, net, im_name)
imdb = get_imdb("voc_2007_trainval")
test_net(sess, net, imdb, 'default')
plt.show()
# 保存测试图片所在位置，并设置输出格式
#plt.savefig(cfg.FLAGS2["data_dir"] + '/test_result/'+ im_name, format='png', transparent=True, pad_inches=0,
#dpi=300, bbox_inches='tight')
# plt.show()
sess = tf.Session(config=tfconfig)
# load network
if demonet == 'vgg16':
net = vgg16(batch_size=1)
net.create_architecture(sess,
"TEST",
7,
tag='default',
anchor_scales=[8, 16, 32])
print("loading model...")
saver = tf.train.Saver()
saver.restore(sess, tfmodel)
print('Loaded network {:s}'.format(tfmodel))
imdb = get_imdb(args.imdb_name)
test_net(sess,
net,
imdb,
weights_filename=None,
max_per_image=100,
thresh=0.5)
'''
im_names = open(os.path.join(TEST_DIR, "test.txt")).readlines()
m = len(im_names)
for i in range(m):
im_name = im_names[i].strip("\n")
im_name = im_name + ".jpg"
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print('Demo for data/demo/{}'.format(im_name))
demo(sess, net, im_name)
'''
raise IOError(
('{:s} not found.\nDid you download the proper networks from '
'our server and place them properly?').format(tfmodel + '.meta'))
imdb = combined_roidb(DATASETS[dataset][0])
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' or 'voc_2007_trainval+test':
net = vgg16(batch_size=1)
# elif demonet == 'res101':
# net = resnetv1(batch_size=1, num_layers=101)
#else:
# raise NotImplementedError
net.create_architecture(sess,
"TEST",
cfg.FLAGS2["CLASSES"].__len__(),
tag='default',
anchor_scales=[8, 16, 32])
# start a session
saver = tf.train.Saver()
saver.restore(sess, tfmodel)
#print ('Loading model weights from {:s}').format(args.model)
test_net(sess, net, imdb, demonet)
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",
imdb.num_classes,
tag='default',
anchor_scales=[8, 16, 32])
saver = tf.train.Saver()
saver.restore(sess, tfmodel)
print('Loaded network {:s}'.format(tfmodel))
# for file in os.listdir("./lib/layer_utils"):
# if file.endswith(".jpg"):
# print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
# print('Demo for lib/layer_utils/{}'.format(file))
# demo(sess, net, file)
# plt.show()
test_net(sess, net, imdb, demonet, max_per_image=100)
sess.close()