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OpenHumnoidDataset.py

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+# TestNew.py
+import json
+import random
+from datasets import (
+    BuilderConfig,
+    DatasetInfo,
+    DownloadManager,
+    GeneratorBasedBuilder,
+    SplitGenerator,
+    Split,
+    Features,
+    Image,
+    Sequence,
+    Value,
+)
+from huggingface_hub import hf_hub_url
+
+
+_REPO_ID = "iamirulofficial/OpenHumnoidDataset"          # change if you ever fork the repo
+
+
+class ImageSubsetConfig(BuilderConfig):
+    """BuilderConfig for full dataset vs. small random sample."""
+    def __init__(self, name, sample_size=None, **kwargs):
+        super().__init__(
+            name=name,
+            version="1.0.1",
+            description=kwargs.get("description", "")
+        )
+        self.sample_size = sample_size
+
+
+class MyImageDataset(GeneratorBasedBuilder):
+    """Images + 2‑D actuated_angle labels stored in metadata.json."""
+    BUILDER_CONFIGS = [
+        ImageSubsetConfig(
+            name="full",
+            sample_size=None,                     # all images
+            description="Entire dataset (≈100 GB)"
+        ),
+        ImageSubsetConfig(
+            name="small",
+            sample_size=2,                        # tiny sample for quick tests
+            description="Two random images"
+        ),
+    ]
+    DEFAULT_CONFIG_NAME = "small"
+
+    def _info(self) -> DatasetInfo:
+        return DatasetInfo(
+            description="Images with a 2‑D actuated_angle from metadata.json",
+            features=Features(
+                {
+                    "image": Image(),                    # PIL.Image will be returned
+                    "actuated_angle": {
+                    "0": Value("int32"),
+                    "1": Value("int32"),
+                },  # [angle0, angle1]
+                }
+            ),
+            supervised_keys=None,
+        )
+
+    # --------------------------------------------------------------------- #
+    # Download phase                                                         #
+    # --------------------------------------------------------------------- #
+    def _split_generators(self, dl_manager: DownloadManager):
+        # 1️⃣  Download metadata.json (tiny text file)
+        meta_path = dl_manager.download(
+            hf_hub_url(_REPO_ID, "metadata.json", repo_type="dataset")
+        )
+
+        # 2️⃣  Decide which filenames we need
+        with open(meta_path, encoding="utf-8") as f:
+            metadata = json.load(f)                      # {"frame_000.png": {"0":…, …}, …}
+
+        all_fnames = list(metadata)
+        if self.config.sample_size:                      # small‑config branch
+            random.seed(42)
+            selected = sorted(random.sample(all_fnames, self.config.sample_size))
+        else:
+            selected = sorted(all_fnames)                # full dataset
+
+        # 3️⃣  Build URLs → dl_manager.download() → local paths
+        url_dict = {
+            fname: hf_hub_url(
+                _REPO_ID, f"images/{fname}", repo_type="dataset"
+            )
+            for fname in selected
+        }
+        img_paths = dl_manager.download(url_dict)        # same keys, but local files
+
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={
+                    "img_paths": img_paths,
+                    "metadata": metadata,
+                },
+            )
+        ]
+
+    # --------------------------------------------------------------------- #
+    # Generate examples                                                     #
+    # --------------------------------------------------------------------- #
+    def _generate_examples(self, img_paths: dict, metadata: dict):
+        """
+        Yields (key, example) where example =
+        { "image": <local‑file‑path>, "actuated_angle": [int, int] }
+        """
+        for idx, (fname, local_path) in enumerate(img_paths.items()):
+            meta = metadata.get(fname, {})
+            angles = [int(meta.get("0", 0)), int(meta.get("1", 0))]
+            yield idx, {"image": local_path, "actuated_angle": {               # 👈  dict, not list
+                "0": int(meta.get("0", 0)),
+                "1": int(meta.get("1", 0)),
+            }}

metadata.json

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+{
+    "frame_0a0ab072-9729-4e16-88c5-a3ea9ed959d0_0036.png": {
+        "0": 90,
+        "1": 20
+    },
+    "frame_0a1d5ccc-e1c7-4c66-8fbc-f87080c3f408_0046.png": {
+        "0": 90,
+        "1": 20
+    },
+    "frame_0a1d30e1-d358-4533-a192-75c5c86a2d56_0049.png": {
+        "0": 90,
+        "1": 20
+    },
+    "frame_0a0bf5c3-9ee4-41aa-9298-ccfebdb1b5ea_0029.png": {
+        "0": 90,
+        "1": 20
+    },
+    "frame_0a9d926d-9372-47d6-b54f-514996ed504c_0017.png":{
+        "0":90,
+        "2":20
+    }
+}