def get_input(hparams, data_files):
"""Get the input."""
if FLAGS.problem == "pixel_help":
data_source = input_utils.DataSource.PIXEL_HELP
elif FLAGS.problem == "android_howto":
data_source = input_utils.DataSource.ANDROID_HOWTO
elif FLAGS.problem == "rico_sca":
data_source = input_utils.DataSource.RICO_SCA
else:
raise ValueError("Unrecognized test: %s" % FLAGS.problem)
tf.logging.info("Testing data_source=%s data_files=%s" %
(FLAGS.problem, data_files))
dataset = input_utils.input_fn(
data_files,
FLAGS.decode_batch_size,
repeat=1,
data_source=data_source,
max_range=hparams.max_span,
max_dom_pos=hparams.max_dom_pos,
max_pixel_pos=(hparams.max_pixel_pos),
load_extra=True,
load_dom_dist=(hparams.screen_encoder == "gcn"))
iterator = tf.data.make_one_shot_iterator(dataset)
features = iterator.get_next()
return features
def input_fn():
return input_utils.input_fn(data_files=files,
batch_size=batch_size,
repeat=repeat,
required_agreement=required_agreement,
data_source=data_source,
max_range=max_range,
max_dom_pos=max_dom_pos,
max_pixel_pos=max_pixel_pos,
mean_synthetic_length=mean_synthetic_length,
stddev_synthetic_length=stddev_synthetic_length,
load_extra=load_extra,
buffer_size=buffer_size,
load_screen=load_screen,
shuffle_size=shuffle_size,
load_dom_dist=load_dom_dist)
