def main(unused_argv):
vocab, pretrained_matrix = load_glove(vocab_size=100000,
embedding_size=300)
attribute_map, attribute_embeddings_map = get_visual_attributes(
), np.random.normal(0, 0.1, [1000, 2048])
with tf.Graph().as_default():
image_id, mean_features, object_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)
up_down_cell = UpDownCell(300, num_image_features=4096)
attribute_image_captioner = AttributeImageCaptioner(
up_down_cell, vocab, pretrained_matrix, attribute_map,
attribute_embeddings_map)
attribute_detector = AttributeDetector(1000)
_, top_k_attributes = attribute_detector(mean_features)
logits, ids = attribute_image_captioner(
top_k_attributes,
lengths=tf.reduce_sum(indicator, axis=1),
mean_image_features=mean_features,
mean_object_features=object_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=attribute_image_captioner.variables,
global_step=global_step)
captioner_saver = tf.train.Saver(
var_list=attribute_image_captioner.variables + [global_step])
attribute_detector_saver = tf.train.Saver(
var_list=attribute_detector.variables)
captioner_ckpt, captioner_ckpt_name = get_up_down_attribute_checkpoint(
)
attribute_detector_ckpt, attribute_detector_ckpt_name = get_attribute_detector_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(
attribute_image_captioner.variables + [global_step]))
if attribute_detector_ckpt is not None:
attribute_detector_saver.restore(sess, attribute_detector_ckpt)
else:
sess.run(tf.variables_initializer(
attribute_detector.variables))
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.")
if __name__ == "__main__":
vocab, pretrained_matrix = load_glove(vocab_size=100000,
embedding_size=300)
attribute_map, attribute_embeddings_map = get_visual_attributes(
), np.random.normal(0, 0.1, [1000, 2048])
with tf.Graph().as_default():
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(
)
vocab, pretrained_matrix = load_glove(vocab_size=100000,
embedding_size=300)
attribute_map, attribute_embeddings_map = get_visual_attributes(
), np.random.normal(0, 0.1, [1000, 2048])
with tf.Graph().as_default():
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)
spatial_attention_cell = SpatialAttentionCell(300,
num_image_features=2048)
attribute_image_captioner = AttributeImageCaptioner(
spatial_attention_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_spatial_attention_attribute_checkpoint(
)
attribute_detector_ckpt, attribute_detector_ckpt_name = get_attribute_detector_checkpoint(
)
vocab, pretrained_matrix = load_glove(vocab_size=100000,
embedding_size=300)
attribute_map, attribute_embeddings_map = get_visual_attributes(
), np.random.normal(0, 0.1, [1000, 2048])
with tf.Graph().as_default():
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)
show_attend_and_tell_cell = ShowAttendAndTellCell(
300, num_image_features=2048)
attribute_image_captioner = AttributeImageCaptioner(
show_attend_and_tell_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_show_attend_and_tell_attribute_checkpoint(
)
attribute_detector_ckpt, attribute_detector_ckpt_name = get_attribute_detector_checkpoint(
)
if __name__ == "__main__":
vocab, pretrained_matrix = load_glove(vocab_size=100000,
embedding_size=300)
attribute_map, attribute_embeddings_map = get_visual_attributes(
), np.random.normal(0, 0.1, [1000, 2048])
with tf.Graph().as_default():
image_id, mean_features, object_features, input_seq, target_seq, indicator = import_mscoco(
mode=FLAGS.mode,
batch_size=FLAGS.batch_size,
num_epochs=1,
is_mini=FLAGS.is_mini)
up_down_cell = UpDownCell(300, num_image_features=2048)
attribute_image_captioner = AttributeImageCaptioner(
up_down_cell, vocab, pretrained_matrix, attribute_map,
attribute_embeddings_map)
attribute_detector = AttributeDetector(1000)
_, top_k_attributes = attribute_detector(mean_features)
logits, ids = attribute_image_captioner(
top_k_attributes,
mean_image_features=mean_features,
mean_object_features=object_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_up_down_attribute_checkpoint(
)
attribute_detector_ckpt, attribute_detector_ckpt_name = get_attribute_detector_checkpoint(