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import datasets
import json
from os import listdir
from os.path import isfile, join
_CITATION = ""
_DESCRIPTION = "Dataset for training agents with Maze-v0 environment."
_HOMEPAGE = "https://huggingface.co/datasets/NathanGavenski/imagetrain"
_LICENSE = ""
_REPO = "https://huggingface.co/datasets/NathanGavenski/imagetrain"
class ImageSet(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
"obs": datasets.Value("string"),
"actions": datasets.Value("int32"),
"rewards": datasets.Value("float32"),
"episode_starts": datasets.Value("bool"),
"maze": datasets.Value("string"),
}),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
image_path = dl_manager.download_and_extract(f"{_REPO}/resolve/main/images.tar.gz")
info_path = dl_manager.download_and_extract(f"{_REPO}/resolve/main/dataset.tar.gz")
return [
datasets.SplitGenerator(
name="all_routes",
gen_kwargs={
"images": image_path,
"infos": f"{info_path}/all_routes.jsonl"
}
),
datasets.SplitGenerator(
name="single_route",
gen_kwargs={
"images": image_path,
"infos": f"{info_path}/single_route.jsonl"
}
),
datasets.SplitGenerator(
name="shortest_route",
gen_kwargs={
"images": image_path,
"infos": f"{info_path}/shortest_route.jsonl"
}
),
]
def _generate_examples(self, images, infos):
images_paths = f"{images}/images"
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"],
"maze": record["maze"],
}
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