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
n_classes = len(CLASSES)
# create the structure of the net having a certain shape (which depends on the number of classes)
net.create_architecture(sess,
"TEST",
2,
tag='default',
anchor_scales=[8, 16, 32])
saver = tf.train.Saver()
saver.restore(sess, tfmodel)
# if has model, get the name from it
# if does not, then just use the initialization weights
filename = 'default/' + args.network_name
imdb = get_imdb(test_dataset_name)
#配置Session参数
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
sess = tf.Session(config=tfconfig)
# load network
if args.network_name == 'vgg16':
net = vgg16()
elif args.network_name == 'res50':
net = resnetv1(num_layers=50)
elif args.network_name == 'res101':
net = resnetv1(num_layers=101)
elif args.network_name == 'res152':
net = resnetv1(num_layers=152)
elif args.network_name == 'mobile':
net = mobilenetv1()
else:
raise NotImplementedError
# load model
net.create_architecture("TEST",
imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
