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"""Modifies TFDS dataset with a map function, updates the feature definition and stores new dataset."""
from functools import partial
from absl import app, flags
import tensorflow as tf
import tensorflow_datasets as tfds
from rlds_dataset_mod.mod_functions import TFDS_MOD_FUNCTIONS
from rlds_dataset_mod.multithreaded_adhoc_tfds_builder import (
MultiThreadedAdhocDatasetBuilder,
)
FLAGS = flags.FLAGS
flags.DEFINE_string("dataset", None, "Dataset name.")
flags.DEFINE_string("data_dir", None, "Directory where source data is stored.")
flags.DEFINE_string("target_dir", None, "Directory where modified data is stored.")
flags.DEFINE_list("mods", None, "List of modification functions, applied in order.")
flags.DEFINE_integer("n_workers", 10, "Number of parallel workers for data conversion.")
flags.DEFINE_integer(
"max_episodes_in_memory",
100,
"Number of episodes converted & stored in memory before writing to disk.",
)
def mod_features(features):
"""Modifies feature dict."""
for mod in FLAGS.mods:
features = TFDS_MOD_FUNCTIONS[mod].mod_features(features)
return features
def mod_dataset_generator(builder, split, mods):
"""Modifies dataset features."""
ds = builder.as_dataset(split=split)
for mod in mods:
ds = TFDS_MOD_FUNCTIONS[mod].mod_dataset(ds)
for episode in tfds.core.dataset_utils.as_numpy(ds):
yield episode
def main(_):
builder = tfds.builder(FLAGS.dataset, data_dir=FLAGS.data_dir)
features = mod_features(builder.info.features)
print("############# Target features: ###############")
print(features)
print("##############################################")
assert FLAGS.data_dir != FLAGS.target_dir # prevent overwriting original dataset
mod_dataset_builder = MultiThreadedAdhocDatasetBuilder(
name=FLAGS.dataset,
version=builder.version,
features=features,
split_datasets={split: builder.info.splits[split] for split in builder.info.splits},
config=builder.builder_config,
data_dir=FLAGS.target_dir,
description=builder.info.description,
generator_fcn=partial(mod_dataset_generator, builder=builder, mods=FLAGS.mods),
n_workers=FLAGS.n_workers,
max_episodes_in_memory=FLAGS.max_episodes_in_memory,
)
mod_dataset_builder.download_and_prepare()
if __name__ == "__main__":
app.run(main)
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