# Path: settings.py
# FLAGS = flags.FLAGS


import os
import tensorflow as tf
from tensorflow.python.platform import gfile
from settings import FLAGS
        h: height of image
        w: width of image
        c: depth of image
    """
    path = os.path.abspath(directory)
    if mode == 'train':
      data_filter = FLAGS.train_files
    elif mode == 'valid':
      data_filter = FLAGS.valid_files
    elif mode == 'test':
      data_filter = FLAGS.test_files

    filenames = gfile.Glob(os.path.join(path, data_filter))

    if not filenames:
        raise RuntimeError('No data files found.')

    with tf.name_scope('input'):
        filename_queue = tf.train.string_input_producer(
          filenames, num_epochs=num_epochs)

        # sharing the same file even when multiple reader threads used
        image_seq_tensor, video_id, features = read_and_decode(filename_queue)


        if mode == 'valid' or mode == 'test':
          if not batch_size:
            batch_size = get_number_of_records(filenames)
          assert batch_size > 0
          image_seq_batch, video_id_batch, metadata_batch = tf.train.batch(

# Path: settings.py
# FLAGS = flags.FLAGS


# Path: data_prep/model_input.py
import os
import tensorflow as tf
from tensorflow.python.platform import gfile
from settings import FLAGS

        h: height of image
        w: width of image
        c: depth of image
    """
    path = os.path.abspath(directory)
    if mode == 'train':
      data_filter = FLAGS.train_files
    elif mode == 'valid':
      data_filter = FLAGS.valid_files
    elif mode == 'test':
      data_filter = FLAGS.test_files

    filenames = gfile.Glob(os.path.join(path, data_filter))

    if not filenames:
        raise RuntimeError('No data files found.')

    with tf.name_scope('input'):
        filename_queue = tf.train.string_input_producer(
          filenames, num_epochs=num_epochs)

        # sharing the same file even when multiple reader threads used
        image_seq_tensor, video_id, features = read_and_decode(filename_queue)


        if mode == 'valid' or mode == 'test':
          if not batch_size:
            batch_size = get_number_of_records(filenames)
          assert batch_size > 0
          image_seq_batch, video_id_batch, metadata_batch = tf.train.batch(

              [image_seq_tensor, video_id, features['metadata']], batch_size=batch_size, num_threads=FLAGS.num_threads,