import tensorflow as tf

    parser.add_argument('--symbolic', action='store_true')
    args = parser.parse_args()

    if not args.symbolic:
        augs = fbresnet_augmentor(args.aug == 'train')
        df = get_imagenet_dataflow(
            args.data, 'train', args.batch, augs)
        # For val augmentor, Should get >100 it/s (i.e. 3k im/s) here on a decent E5 server.
        TestDataSpeed(df).start()
    else:
        assert args.aug == 'train'
        data = get_imagenet_tfdata(args.data, 'train', args.batch)

        itr = data.make_initializable_iterator()
        dp = itr.get_next()
        dpop = tf.group(*dp)
        with tf.Session(config=get_default_sess_config()) as sess:
            sess.run(itr.initializer)
            for _ in tqdm.trange(200):
                sess.run(dpop)
            for _ in tqdm.trange(5000, smoothing=0.1):
                sess.run(dpop)
