check_video_id.py 文件源码

def check_video_id():
  tf.set_random_seed(0)  # for reproducibility
  with tf.Graph().as_default():
    # convert feature_names and feature_sizes to lists of values
    feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
        FLAGS.feature_names, FLAGS.feature_sizes)

    # prepare a reader for each single model prediction result
    all_readers = []

    all_patterns = FLAGS.eval_data_patterns
    all_patterns = map(lambda x: x.strip(), all_patterns.strip().strip(",").split(","))
    for i in xrange(len(all_patterns)):
      reader = readers.EnsembleReader(
          feature_names=feature_names, feature_sizes=feature_sizes)
      all_readers.append(reader)

    input_reader = None
    input_data_pattern = None
    if FLAGS.input_data_pattern is not None:
      input_reader = readers.EnsembleReader(
          feature_names=["mean_rgb","mean_audio"], feature_sizes=[1024,128])
      input_data_pattern = FLAGS.input_data_pattern

    if FLAGS.eval_data_patterns is "":
      raise IOError("'eval_data_patterns' was not specified. " +
                     "Nothing to evaluate.")

    build_graph(
        all_readers=all_readers,
        input_reader=input_reader,
        input_data_pattern=input_data_pattern,
        all_eval_data_patterns=all_patterns,
        batch_size=FLAGS.batch_size)

    logging.info("built evaluation graph")
    video_id_equal = tf.get_collection("video_id_equal")[0]
    input_distance = tf.get_collection("input_distance")[0]

    check_loop(video_id_equal, input_distance, all_patterns)