captioner_saver.restore(sess, captioner_ckpt)
used_ids = set()
json_dump = []
for i in itertools.count():
time_start = time.time()
try:
_ids, _target_seq, _image_id = sess.run(
[ids, target_seq, image_id])
except:
break
the_captions = recursive_ids_to_string(_ids[:, 0, :].tolist(),
vocab)
the_labels = recursive_ids_to_string(
_target_seq[:, :].tolist(), vocab)
the_image_ids = _image_id.tolist()
for j, x, y in zip(the_image_ids, the_captions, the_labels):
if not j in used_ids:
used_ids.add(j)
json_dump.append({"image_id": j, "caption": x})
print(
PRINT_STRING.format(
i, the_captions[0], the_labels[0],
FLAGS.batch_size / (time.time() - time_start)))
print("Finishing evaluating.")
evaluate(FLAGS.mode, json_dump,
captioner_ckpt_name.replace("model.ckpt", ""),
(get_train_annotations_file() if FLAGS.mode
in ["train", "eval"] else get_val_annotations_file()))
assert (captioner_ckpt is not None)
captioner_saver.restore(sess, captioner_ckpt)
used_ids = set()
json_dump = []
for i in itertools.count():
time_start = time.time()
try:
_ids, _target_seq, _image_id = sess.run(
[ids, target_seq, image_id])
except:
break
the_captions = recursive_ids_to_string(_ids[:, 0, :].tolist(),
vocab)
the_labels = recursive_ids_to_string(
_target_seq[:, :].tolist(), vocab)
the_image_ids = _image_id.tolist()
for j, x, y in zip(the_image_ids, the_captions, the_labels):
if not j in used_ids:
used_ids.add(j)
json_dump.append({"image_id": j, "caption": x})
print(
PRINT_STRING.format(
i, the_captions[0], the_labels[0],
BATCH_SIZE / (time.time() - time_start)))
print("Finishing evaluating.")
coco_get_metrics(json_dump, "ckpts/show_and_tell/",
get_train_annotations_file())
with tf.Session() as sess:
assert(captioner_ckpt is not None)
captioner_saver.restore(sess, captioner_ckpt)
used_ids = set()
json_dump = []
for i in itertools.count():
time_start = time.time()
try:
_ids, _target_seq, _image_id = sess.run([word_ids, target_seq, image_id])
except:
break
the_captions = recursive_ids_to_string(_ids[:, 0, :].tolist(), vocab)
the_labels = recursive_ids_to_string(_target_seq[:, :].tolist(), vocab)
the_image_ids = _image_id.tolist()
for j, x, y in zip(the_image_ids, the_captions, the_labels):
if not j in used_ids:
used_ids.add(j)
json_dump.append({"image_id": j, "caption": x})
print(PRINT_STRING.format(i, the_captions[0], the_labels[0],
FLAGS.batch_size / (time.time() - time_start)))
print("Finishing evaluating.")
evaluate(
FLAGS.mode,
json_dump,
captioner_ckpt_name.replace("model.ckpt", ""),
(get_train_annotations_file() if FLAGS.mode in ["train", "eval"]
else get_val_annotations_file()))