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dataset.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5650d9fbc9f7cf9b94bb382e13bdbc03225ef0452c2e32c8b1db93a99c86b71e
+size 48219

images.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d7d1a3ae9c5a3ae0927bc99a25d62c7ed892a9447ff757d03c5c51e66d4112e3
+size 8394634

imagetrain.py

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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"],
+                }