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,