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(