def _eval_once(saver, summary_writer, argmaxs, answer_ids, vocab, rev_vocab,
num_examples_per_epoch, b_global_step):
"""Run Eval once.
"""
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.95)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False, gpu_options=gpu_options)) as sess:
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
print('model path:', ckpt.model_checkpoint_path)
saver.restore(sess, ckpt.model_checkpoint_path)
# extract global_step from it.
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
if global_step == b_global_step:
return global_step
else:
print('No checkpoint file found')
return
# Start the queue runners.
coord = tf.train.Coordinator()
try:
threads = []
for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
threads.extend(qr.create_threads(sess, coord=coord, daemon=True,
start=True))
num_iter = 1 + int(
num_examples_per_epoch / FLAGS.batch_size / FLAGS.num_gpus
)
desc_list = []
answer_list = []
desc_token_list = []
answer_token_list = []
step = 0
while step < num_iter and not coord.should_stop():
results = sess.run([argmaxs, answer_ids])
desc = results[0].tolist()
answer = results[1].tolist()
desc_list += desc
answer_list += answer
step += 1
for i in xrange(len(desc_list)):
desc = []
answer = []
for k in xrange(len(desc_list[i])):
token_id = desc_list[i][k]
if token_id == EOS_ID:
break
desc.append(rev_vocab[token_id])
for k in xrange(len(answer_list[i])):
token_id = answer_list[i][k]
if token_id == EOS_ID:
break
answer.append(rev_vocab[token_id])
desc_token_list.append(desc)
answer_token_list.append(answer)
colorlog.info(
colored("Validation Output Example (%s)" % global_step, 'green')
)
for i, (desc, answer) in enumerate(
zip(desc_token_list[:15], answer_token_list[:15])
):
print("%d." % (i))
print(' '.join(answer))
print(' '.join(desc) + "\n")
evaluator = Evaluator()
result = evaluator.evaluation(
desc_token_list, answer_token_list, "coco"
)
summary = _inject_summary(result)
summary_writer.add_summary(summary, global_step)
except Exception as e: # pylint: disable=broad-except
coord.request_stop(e)
coord.request_stop()
coord.join(threads, stop_grace_period_secs=10)
return global_step
def eval_once(saver, summary_writer, argmaxs, ans_ids, vocab, rev_vocab,
num_data, b_global_step):
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.80)
sess_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,
gpu_options=gpu_options)
with tf.Session(config=sess_config) as sess:
print('Hello')
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
if ckpt and ckpt.model_checkpoint_path:
print(ckpt.model_checkpoint_path)
saver.restore(sess, ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
if global_step == b_global_step:
return global_step
else:
print('No checkpoint file found.')
return
step = 0
max_iter = num_data // FLAGS.BATCH_SIZE
res = []
ans = []
res_tokens_list = []
ans_tokens_list = []
while (step < max_iter):
results = sess.run([argmaxs, ans_ids])
res += results[0].tolist()
ans += results[1].tolist()
step += 1
for i in range(len(res)):
caption = []
answer = []
for k in range(len(res[i])):
token_id = res[i][k]
if token_id == EOS_ID:
break
caption.append(rev_vocab[token_id])
for k in range(len(ans[i])):
token_id = ans[i][k]
if token_id == EOS_ID:
break
answer.append(rev_vocab[token_id])
res_tokens_list.append(caption)
ans_tokens_list.append(answer)
colorlog.info(
colored("Validation output example: (%s)" % global_step, 'green'))
for i, (res,
ans) in enumerate(zip(res_tokens_list[:5],
ans_tokens_list[:5])):
print('%d' % i)
print(' '.join(ans))
print(' '.join(res))
evaluator = Evaluator()
result = evaluator.evaluation(res_tokens_list, ans_tokens_list, 'coco')
summary = inject_summary(result)
summary_writer.add_summary(summary, global_step)
return global_step
