| import datasets |
| import json |
| import tarfile |
| from os import listdir, makedirs |
| from os.path import isfile, join |
|
|
| from datasets import DatasetInfo, GeneratorBasedBuilder, Split, SplitGenerator |
|
|
|
|
| _CITATION = "" |
|
|
| _DESCRIPTION = "Expert Dataset for the Frozen Lake Custom Environment. See paper for more details." |
|
|
| _HOMEPAGE = "https://huggingface.co/datasets/Ptisni/frozen_lake" |
|
|
| _LICENSE = "" |
|
|
| _REPO = "https://huggingface.co/datasets/Ptisni/frozen_lake" |
|
|
|
|
| class ImageSet(GeneratorBasedBuilder): |
|
|
| def _info(self): |
| return DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features({ |
| "obs": datasets.Value("string"), |
| "actions": datasets.Value("int32"), |
| "rewards": datasets.Value("float32"), |
| "episode_starts": datasets.Value("bool"), |
| }), |
| supervised_keys=None, |
| homepage=_HOMEPAGE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| info_path = dl_manager.download_and_extract(f"{_REPO}/resolve/main/fl_dataset.tar.gz") |
| image_path = dl_manager.download_and_extract(f"{_REPO}/resolve/main/images.tar.gz") |
|
|
| return [ |
| SplitGenerator( |
| name=Split.TRAIN, |
| gen_kwargs={ |
| "images_paths": f"{image_path}/images", |
| "infos": f"{info_path}/dataset/FrozenLakeExpert/teacher.jsonl" |
| } |
| ) |
| ] |
|
|
| def _generate_examples(self, images_paths, infos): |
| images = [join(images_paths, f) for f in listdir(images_paths) if isfile(join(images_paths, f))] |
|
|
| images_dict = {} |
| for image in images: |
| images_dict[image.split("/")[-1].split(".")[0]] = image |
|
|
| with open(infos, encoding="utf-8") as data: |
| for idx, line in enumerate(data): |
| record = json.loads(line) |
| index = record["obs"].split(".")[0] |
| yield idx, { |
| "obs": images_dict[index], |
| "actions": record["actions"], |
| "rewards": record["rewards"], |
| "episode_starts": record["episode_starts"], |
| } |
