image_id, spatial_features, input_seq, target_seq, indicator = import_mscoco(
mode=FLAGS.mode,
batch_size=FLAGS.batch_size,
num_epochs=1,
is_mini=FLAGS.is_mini)
visual_sentinel_cell = VisualSentinelCell(300, num_image_features=2048)
attribute_image_captioner = AttributeImageCaptioner(
visual_sentinel_cell, vocab, pretrained_matrix, attribute_map,
attribute_embeddings_map)
attribute_detector = AttributeDetector(1000)
_, top_k_attributes = attribute_detector(
tf.reduce_mean(spatial_features, [1, 2]))
logits, ids = attribute_image_captioner(
top_k_attributes, spatial_image_features=spatial_features)
captioner_saver = tf.train.Saver(var_list=remap_decoder_name_scope(
attribute_image_captioner.variables))
attribute_detector_saver = tf.train.Saver(
var_list=attribute_detector.variables)
captioner_ckpt, captioner_ckpt_name = get_visual_sentinel_attribute_checkpoint(
)
attribute_detector_ckpt, attribute_detector_ckpt_name = get_attribute_detector_checkpoint(
)
with tf.Session() as sess:
assert (captioner_ckpt is not None
and attribute_detector_ckpt is not None)
captioner_saver.restore(sess, captioner_ckpt)
attribute_detector_saver.restore(sess, attribute_detector_ckpt)
used_ids = set()
json_dump = []
embedding_size=300)
with tf.Graph().as_default():
image_id, mean_features, input_seq, target_seq, indicator = (
import_mscoco(mode="train",
batch_size=BATCH_SIZE,
num_epochs=1,
is_mini=True))
image_captioner = ImageCaptioner(ShowAndTellCell(300),
vocab,
pretrained_matrix,
trainable=False,
beam_size=BEAM_SIZE)
logits, ids = image_captioner(mean_image_features=mean_features)
captioner_saver = tf.train.Saver(
var_list=remap_decoder_name_scope(image_captioner.variables))
captioner_ckpt, captioner_ckpt_name = get_show_and_tell_checkpoint()
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(
[ids, target_seq, image_id])
except:
with tf.Graph().as_default():
image_id, running_ids, indicator, previous_id, next_id, pointer, image_features = (
import_mscoco(mode="train",
batch_size=BATCH_SIZE,
num_epochs=1,
is_mini=True))
best_first_module = BestFirstModule(pretrained_matrix)
pointer_logits, word_logits = best_first_module(image_features,
running_ids,
previous_id,
indicators=indicator)
ids = tf.argmax(word_logits, axis=-1, output_type=tf.int32)
pointer_ids = tf.argmax(pointer_logits, axis=-1, output_type=tf.int32)
captioner_saver = tf.train.Saver(
var_list=remap_decoder_name_scope(best_first_module.variables))
captioner_ckpt, captioner_ckpt_name = get_best_first_checkpoint()
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:
_caption, _ids, _next_id, _model_pointer, _label_pointer, _image_id = sess.run(
[
running_ids, ids, next_id, pointer_ids, pointer,