def main(unused_argv):
vocab, pretrained_matrix = load_glove(vocab_size=100000, embedding_size=300)
with tf.Graph().as_default():
image_id, image_features, indicator, word_ids, pointer_ids = import_mscoco(
mode="train", batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs, is_mini=FLAGS.is_mini)
lengths = tf.reduce_sum(indicator, [1])
show_and_tell_cell = ShowAndTellCell(300)
best_first_image_captioner = BestFirstImageCaptioner(show_and_tell_cell, vocab, pretrained_matrix)
word_logits, wids, pointer_logits, pids, ids, _lengths = best_first_image_captioner(
mean_image_features=image_features,
word_ids=word_ids, pointer_ids=pointer_ids, lengths=lengths)
tf.losses.sparse_softmax_cross_entropy(pointer_ids, pointer_logits)
tf.losses.sparse_softmax_cross_entropy(word_ids, word_logits)
loss = tf.losses.get_total_loss()
global_step = tf.train.get_or_create_global_step()
optimizer = tf.train.AdamOptimizer()
learning_step = optimizer.minimize(loss, var_list=best_first_image_captioner.variables,
global_step=global_step)
captioner_saver = tf.train.Saver(var_list=best_first_image_captioner.variables + [global_step])
captioner_ckpt, captioner_ckpt_name = get_best_first_checkpoint()
with tf.Session() as sess:
sess.run(tf.variables_initializer(optimizer.variables()))
if captioner_ckpt is not None:
captioner_saver.restore(sess, captioner_ckpt)
else:
sess.run(tf.variables_initializer(best_first_image_captioner.variables + [global_step]))
captioner_saver.save(sess, captioner_ckpt_name, global_step=global_step)
last_save = time.time()
for i in itertools.count():
time_start = time.time()
try:
twids, tpids, _ids, _lengths, _loss, _learning_step = sess.run([
word_ids, pointer_ids, ids, lengths, loss, learning_step])
except:
break
iteration = sess.run(global_step)
insertion_sequence = insertion_sequence_to_array(twids, tpids, _lengths, vocab)
print(PRINT_STRING.format(
iteration, _loss,
list_of_ids_to_string(insertion_sequence[0], vocab),
list_of_ids_to_string(twids[0, :].tolist(), vocab),
FLAGS.batch_size / (time.time() - time_start)))
new_save = time.time()
if new_save - last_save > 3600: # save the model every hour
captioner_saver.save(sess, captioner_ckpt_name, global_step=global_step)
last_save = new_save
captioner_saver.save(sess, captioner_ckpt_name, global_step=global_step)
print("Finishing training.")
def main(unused_argv):
vocab, pretrained_matrix = load_glove(vocab_size=100000, embedding_size=300)
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=100, is_mini=True))
best_first_module = BestFirstModule(pretrained_matrix)
pointer_logits, word_logits = best_first_module(
image_features, running_ids, previous_id, indicators=indicator, pointer_ids=pointer)
tf.losses.sparse_softmax_cross_entropy(pointer, pointer_logits)
tf.losses.sparse_softmax_cross_entropy(next_id, word_logits)
loss = tf.losses.get_total_loss()
ids = tf.argmax(word_logits, axis=-1, output_type=tf.int32)
global_step = tf.train.get_or_create_global_step()
learning_rate = LEARNING_RATE
learning_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,
var_list=best_first_module.variables, global_step=global_step)
captioner_saver = tf.train.Saver(var_list=best_first_module.variables + [global_step])
captioner_ckpt, captioner_ckpt_name = get_best_first_checkpoint()
with tf.Session() as sess:
if captioner_ckpt is not None:
captioner_saver.restore(sess, captioner_ckpt)
else:
sess.run(tf.variables_initializer(best_first_module.variables + [global_step]))
captioner_saver.save(sess, captioner_ckpt_name, global_step=global_step)
last_save = time.time()
_ids, _loss, _learning_step = sess.run([ids, loss, learning_step])
for i in itertools.count():
time_start = time.time()
try:
_ids, _loss, _learning_step = sess.run([ids, loss, learning_step])
except:
break
iteration = sess.run(global_step)
print(PRINT_STRING.format(
iteration, _loss, list_of_ids_to_string(_ids.tolist(), vocab),
BATCH_SIZE / (time.time() - time_start)))
new_save = time.time()
if new_save - last_save > 3600: # save the model every hour
captioner_saver.save(sess, captioner_ckpt_name, global_step=global_step)
last_save = new_save
captioner_saver.save(sess, captioner_ckpt_name, global_step=global_step)
print("Finishing training.")
def main(unused_argv):
vocab, pretrained_matrix = load_glove(vocab_size=100000,
embedding_size=300)
with tf.Graph().as_default():
image_id, spatial_features, input_seq, target_seq, indicator = (
import_mscoco(mode="train",
batch_size=BATCH_SIZE,
num_epochs=100,
is_mini=True))
visual_sentinel_cell = VisualSentinelCell(300)
image_captioner = ImageCaptioner(visual_sentinel_cell, vocab,
pretrained_matrix)
logits, ids = image_captioner(lengths=tf.reduce_sum(indicator, axis=1),
spatial_image_features=spatial_features,
seq_inputs=input_seq)
tf.losses.sparse_softmax_cross_entropy(target_seq,
logits,
weights=indicator)
loss = tf.losses.get_total_loss()
learning_step = tf.train.GradientDescentOptimizer(1.0).minimize(
loss, var_list=image_captioner.variables)
captioner_saver = tf.train.Saver(var_list=image_captioner.variables)
captioner_ckpt, captioner_ckpt_name = get_visual_sentinel_checkpoint()
with tf.Session() as sess:
sess.run(tf.variables_initializer(image_captioner.variables))
if captioner_ckpt is not None:
captioner_saver.restore(sess, captioner_ckpt)
captioner_saver.save(sess, captioner_ckpt_name)
last_save = time.time()
for i in itertools.count():
time_start = time.time()
try:
_ids, _loss, _learning_step = sess.run(
[ids, loss, learning_step])
except:
break
print(
PRINT_STRING.format(
i, _loss,
list_of_ids_to_string(_ids[0, :].tolist(), vocab),
BATCH_SIZE / (time.time() - time_start)))
new_save = time.time()
if new_save - last_save > 3600: # save the model every hour
captioner_saver.save(sess, captioner_ckpt_name)
last_save = new_save
captioner_saver.save(sess, captioner_ckpt_name)
print("Finishing training.")
def main(unused_argv):
vocab, pretrained_matrix = load_glove(vocab_size=100000,
embedding_size=300)
attribute_map = get_visual_attributes()
attribute_to_word_lookup_table = vocab.word_to_id(
attribute_map.reverse_vocab)
with tf.Graph().as_default():
(image_id, image_features, object_features, input_seq, target_seq,
indicator, attributes) = import_mscoco(mode="train",
batch_size=FLAGS.batch_size,
num_epochs=FLAGS.num_epochs,
is_mini=FLAGS.is_mini)
attribute_detector = AttributeDetector(1000)
_, image_attributes, object_attributes = attribute_detector(
image_features, object_features)
grounded_attribute_cell = GroundedAttributeCell(1024)
attribute_captioner = AttributeCaptioner(
grounded_attribute_cell, vocab, pretrained_matrix,
attribute_to_word_lookup_table)
logits, ids = attribute_captioner(lengths=tf.reduce_sum(indicator,
axis=1),
mean_image_features=image_features,
mean_object_features=object_features,
seq_inputs=input_seq,
image_attributes=image_attributes,
object_attributes=object_attributes)
tf.losses.sparse_softmax_cross_entropy(target_seq,
logits,
weights=indicator)
loss = tf.losses.get_total_loss()
global_step = tf.train.get_or_create_global_step()
learning_rate = tf.train.exponential_decay(5e-4,
global_step,
3 * 586363 //
FLAGS.batch_size,
0.8,
staircase=True)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
learning_step = optimizer.minimize(
loss,
var_list=attribute_captioner.variables,
global_step=global_step)
detector_saver = tf.train.Saver(var_list=attribute_detector.variables +
[global_step])
detector_ckpt, detector_ckpt_name = get_attribute_detector_checkpoint()
captioner_saver = tf.train.Saver(
var_list=attribute_captioner.variables + [global_step])
captioner_ckpt, captioner_ckpt_name = get_grounded_attribute_checkpoint(
)
with tf.Session() as sess:
sess.run(tf.variables_initializer(optimizer.variables()))
if detector_ckpt is not None:
detector_saver.restore(sess, detector_ckpt)
else:
sess.run(
tf.variables_initializer(attribute_detector.variables +
[global_step]))
if captioner_ckpt is not None:
captioner_saver.restore(sess, captioner_ckpt)
else:
sess.run(
tf.variables_initializer(attribute_captioner.variables +
[global_step]))
captioner_saver.save(sess,
captioner_ckpt_name,
global_step=global_step)
last_save = time.time()
for i in itertools.count():
time_start = time.time()
try:
_target, _ids, _loss, _learning_step = sess.run(
[target_seq, ids, loss, learning_step])
except:
break
iteration = sess.run(global_step)
print(
PRINT_STRING.format(
iteration, _loss,
list_of_ids_to_string(_ids[0, :].tolist(), vocab),
list_of_ids_to_string(_target[0, :].tolist(), vocab),
FLAGS.batch_size / (time.time() - time_start)))
new_save = time.time()
if new_save - last_save > 3600: # save the model every hour
captioner_saver.save(sess,
captioner_ckpt_name,
global_step=global_step)
last_save = new_save
captioner_saver.save(sess,
captioner_ckpt_name,
global_step=global_step)
print("Finishing training.")
def main(unused_argv):
vocab, pretrained_matrix = load_glove(vocab_size=100000,
embedding_size=300)
with tf.Graph().as_default():
image_id, spatial_features, input_seq, target_seq, indicator = import_mscoco(
mode="train",
batch_size=FLAGS.batch_size,
num_epochs=FLAGS.num_epochs,
is_mini=FLAGS.is_mini)
image_captioner = ImageCaptioner(SpatialAttentionCell(300), vocab,
pretrained_matrix)
logits, ids = image_captioner(lengths=tf.reduce_sum(indicator, axis=1),
spatial_image_features=spatial_features,
seq_inputs=input_seq)
tf.losses.sparse_softmax_cross_entropy(target_seq,
logits,
weights=indicator)
loss = tf.losses.get_total_loss()
global_step = tf.train.get_or_create_global_step()
optimizer = tf.train.AdamOptimizer()
learning_step = optimizer.minimize(loss,
var_list=image_captioner.variables,
global_step=global_step)
captioner_saver = tf.train.Saver(var_list=image_captioner.variables +
[global_step])
captioner_ckpt, captioner_ckpt_name = get_spatial_attention_checkpoint(
)
with tf.Session() as sess:
sess.run(tf.variables_initializer(optimizer.variables()))
if captioner_ckpt is not None:
captioner_saver.restore(sess, captioner_ckpt)
else:
sess.run(
tf.variables_initializer(image_captioner.variables +
[global_step]))
captioner_saver.save(sess,
captioner_ckpt_name,
global_step=global_step)
last_save = time.time()
for i in itertools.count():
time_start = time.time()
try:
_target, _ids, _loss, _learning_step = sess.run(
[target_seq, ids, loss, learning_step])
except:
break
iteration = sess.run(global_step)
print(
PRINT_STRING.format(
iteration, _loss,
list_of_ids_to_string(_ids[0, :].tolist(), vocab),
list_of_ids_to_string(_target[0, :].tolist(), vocab),
FLAGS.batch_size / (time.time() - time_start)))
new_save = time.time()
if new_save - last_save > 3600:
captioner_saver.save(sess,
captioner_ckpt_name,
global_step=global_step)
last_save = new_save
captioner_saver.save(sess,
captioner_ckpt_name,
global_step=global_step)
print("Finishing training.")
