with open(args.vocabulary, 'r') as f:
for line in f:
vocab.append(line.strip())
# get the image paths
im_paths = glob.glob('./data/demo/*.jpg')
print(im_paths)
# read checkpoint file
if args.ckpt:
ckpt = tf.train.get_checkpoint_state(args.ckpt)
else:
raise ValueError
# set config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
saver = tf.train.Saver()
with tf.Session(config=tfconfig) as sess:
print('Restored from {}'.format(ckpt.model_checkpoint_path))
saver.restore(sess, ckpt.model_checkpoint_path)
# for n in tf.get_default_graph().as_graph_def().node:
# if 'input_feed' in n.name:
# print(n.name)
for path in im_paths:
test_im(sess, net, path, vocab)
im_paths = glob.glob('./data/demo/*.jpg')
print(im_paths)
# read checkpoint file
if args.ckpt:
ckpt = tf.train.get_checkpoint_state(args.ckpt)
else:
raise ValueError
# set config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
saver = tf.train.Saver()
with tf.Session(config=tfconfig) as sess:
print('Restored from {}'.format(ckpt.model_checkpoint_path))
saver.restore(sess, ckpt.model_checkpoint_path)
# for n in tf.get_default_graph().as_graph_def().node:
# if 'input_feed' in n.name:
# print(n.name)
# for html visualization
pre_results = {}
save_path = './vis/data'
for path in im_paths:
test_im(sess, net, path, vocab, pre_results)
# with open(save_path + '/results.json', 'w') as f:
# json.dump(pre_results, f)