def predict_func_tem():
if True:
test_feeder = utils.DataIterator3(data_dir=data_dir)
print(test_feeder.image_num)
for cur_batch in range(int(test_feeder.size / FLAGS.batch_size) + 1):
indexs = []
cur_batch_num = FLAGS.batch_size
if cur_batch == int(test_feeder.size / FLAGS.batch_size):
cur_batch_num = test_feeder.size - cur_batch * FLAGS.batch_size
for i in range(cur_batch_num):
indexs.append(cur_batch * FLAGS.batch_size + i)
def predict_func():
g = model.Graph()
with tf.Session(graph=g.graph) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
saver.restore(sess, ckpt)
print('restore from ckpt{}'.format(ckpt))
else:
print('cannot restore')
test_feeder = utils.DataIterator3(data_dir=data_dir)
f = open(save_dir, mode="a")
print("total data:", test_feeder.size)
print("total image in folder", test_feeder.total_pic_read)
total_epoch = int(test_feeder.size / FLAGS.batch_size) + 1
for cur_batch in range(total_epoch):
print("cur_epoch/total_epoch", cur_batch, "/", total_epoch)
indexs = []
cur_batch_num = FLAGS.batch_size
if cur_batch == int(test_feeder.size / FLAGS.batch_size):
cur_batch_num = test_feeder.size - cur_batch * FLAGS.batch_size
for i in range(cur_batch_num):
indexs.append(cur_batch * FLAGS.batch_size + i)
test_inputs, num_batch = test_feeder.input_index_generate_batch(
indexs)
test_feed = {
g.inputs: test_inputs,
g.seq_len: np.array([g.cnn_time] * test_inputs.shape[0]),
g.keep_prob_fc: 1,
g.keep_prob_cv1: 1,
g.keep_prob_cv2: 1,
g.keep_prob_cv3: 1,
g.keep_prob_cv4: 1
}
dense_decoded, logits_before_ctc = sess.run(
[g.dense_decoded, g.logits_before_ctc], test_feed)
for encode_list, dense_list, e_num in zip(logits_before_ctc,
dense_decoded,
num_batch):
decode_string = utils.decode_function1(encode_list)
decode_string1 = utils.decode_function1(dense_list)
f.write(e_num + "," + decode_string + "," + decode_string1 +
"," + str(encode_list) + "\n")
f.close()
print("saved prediction")
def predict_func():
g = model_transfer.Graph(pb_file_path=pb_file_path)
with tf.Session(graph=g.graph) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
saver.restore(sess, ckpt)
print('restore from ckpt{}'.format(ckpt))
else:
print('cannot restore')
test_feeder = utils.DataIterator3(data_dir=data_dir)
f = open(save_dir, mode="a")
print("total data:", test_feeder.size)
print("total image in folder", test_feeder.total_pic_read)
total_epoch = int(test_feeder.size / FLAGS.batch_size) + 1
for cur_batch in range(total_epoch):
print("cur_epoch/total_epoch", cur_batch, "/", total_epoch)
indexs = []
cur_batch_num = FLAGS.batch_size
if cur_batch == int(test_feeder.size / FLAGS.batch_size):
cur_batch_num = test_feeder.size - cur_batch * FLAGS.batch_size
for i in range(cur_batch_num):
indexs.append(cur_batch * FLAGS.batch_size + i)
test_inputs, num_batch = test_feeder.input_index_generate_batch(
indexs)
test_feed = {
g.original_pic: test_inputs,
g.seq_len: np.array([Flage_width] * test_inputs.shape[0])
}
dense_decoded = sess.run(g.dense_decoded, test_feed)
for encode_list, e_num in zip(dense_decoded, num_batch):
decode_string = utils.decode_function(encode_list)
# print(e_num,decode_string)
f.write(e_num + "," + decode_string + "\n")
f.close()
print("saved prediction")
