main.py

#!/usr/bin/python
import tensorflow as tf

from config import Config
from model import CaptionGenerator
from dataset import prepare_train_data, prepare_eval_data, prepare_test_data

FLAGS = tf.app.flags.FLAGS

tf.flags.DEFINE_string('phase', 'train',
                       'The phase can be train, eval or test')

tf.flags.DEFINE_boolean('load', False,
                        'Turn on to load a pretrained model from either \
                        the latest checkpoint or a specified file')

tf.flags.DEFINE_string('model_file', None,
                       'If sepcified, load a pretrained model from this file')

tf.flags.DEFINE_boolean('load_cnn', False,
                        'Turn on to load a pretrained CNN model')

tf.flags.DEFINE_string('cnn_model_file', './vgg16_no_fc.npy',
                       'The file containing a pretrained CNN model')

tf.flags.DEFINE_boolean('train_cnn', False,
                        'Turn on to train both CNN and RNN. \
                         Otherwise, only RNN is trained')

tf.flags.DEFINE_integer('beam_size', 3,
                        'The size of beam search for caption generation')

tf.logging.set_verbosity(tf.logging.INFO)

def main(argv):
    config = Config()
    config.phase = FLAGS.phase
    config.train_cnn = FLAGS.train_cnn
    config.beam_size = FLAGS.beam_size

    model = CaptionGenerator(config)
    # model.train()

    with tf.Session() as sess:
    #     sess.run(tf.global_variables_initializer())
        sess.run(tf.local_variables_initializer())
    #     if model.init_fn:
    #         model.init_fn(sess)
        
    

        # Start populating the filename queue.
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)

        for i in range(1):
            # Retrieve a single instance:
            example = sess.run(model.images)
            print(example,type(example),example.shape)

        coord.request_stop()
        coord.join(threads)

    # with tf.Session() as sess:
    #     if FLAGS.phase == 'train':
    #         # training phase
    #         # data = prepare_train_data(config)
    #         model = CaptionGenerator(config)
    #         sess.run(tf.global_variables_initializer())
    #         # if FLAGS.load:
    #         #     model.load(sess, FLAGS.model_file)
    #         # if FLAGS.load_cnn:
    #         #     model.load_cnn(sess, FLAGS.cnn_model_file)
    #         # tf.get_default_graph().finalize()
    #         # model.train(sess)

    #     elif FLAGS.phase == 'eval':
    #         # evaluation phase
    #         coco, data, vocabulary = prepare_eval_data(config)
    #         model = CaptionGenerator(config)
    #         model.load(sess, FLAGS.model_file)
    #         tf.get_default_graph().finalize()
    #         model.eval(sess, coco, data, vocabulary)

    #     else:
    #         # testing phase
    #         data, vocabulary = prepare_test_data(config)
    #         model = CaptionGenerator(config)
    #         model.load(sess, FLAGS.model_file)
    #         tf.get_default_graph().finalize()
    #         model.test(sess, data, vocabulary)

if __name__ == '__main__':
    tf.app.run()