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import argparse
import hashlib
import io
import json
import tarfile
from pathlib import Path
from typing import Any, Iterable
import pyarrow as pa
import pyarrow.parquet as pq
from huggingface_hub import hf_hub_download
LOCK_PATH = Path(__file__).with_name("perception_sources.lock.json")
PRIMARY_STATE_COLUMNS = ("observation.state", "state")
def load_source_lock(path: Path = LOCK_PATH) -> dict[str, Any]:
lock = json.loads(path.read_text(encoding="utf-8"))
if lock.get("schema_version") != 1:
raise ValueError(f"Unsupported perception-source lock schema: {lock.get('schema_version')!r}")
return lock
def discover_robot_episodes(lock: dict[str, Any], cache_dir: Path | None = None) -> list[dict[str, Any]]:
episodes: list[dict[str, Any]] = []
for source_name, source in lock["annotation_sources"].items():
parquet_path = _download(
source["repo_id"], source["revision"], source["parquet_path"], cache_dir
)
for row in pq.read_table(parquet_path).to_pylist():
if source_name == "droid":
repo_id = row["source_dataset"]
episode_index = int(row["episode_index"])
episodes.append(
{
"bench_id": row["bench_id"],
"robot_family": "droid",
"repo_id": repo_id,
"revision": lock["upstream_revisions"][repo_id],
"episode_index": episode_index,
"fps": float(row["fps"]),
"expected_num_frames": int(row["num_frames"]),
"source_subset": row["source_subset"],
"data_path": f"{row['source_subset']}/data/chunk-000.tar",
"tar_member": f"chunk-000/episode_{episode_index:06d}.parquet",
"info_path": f"{row['source_subset']}/meta/info.json",
}
)
elif source_name == "galaxea":
repo_id = row["source_dataset"]
episode_index = int(row["source_episode_index"])
episodes.append(
{
"bench_id": row["bench_id"],
"robot_family": "galaxea",
"repo_id": repo_id,
"revision": lock["upstream_revisions"][repo_id],
"episode_index": episode_index,
"fps": float(row["fps"]),
"expected_num_frames": int(row["num_frames"]),
"data_path": f"data/chunk-000/episode_{episode_index:06d}.parquet",
"info_path": "meta/info.json",
}
)
else:
raise ValueError(f"Unknown annotation source {source_name!r}")
episodes.sort(key=lambda item: item["bench_id"])
bench_ids = [item["bench_id"] for item in episodes]
if len(bench_ids) != len(set(bench_ids)):
raise ValueError("Duplicate bench_id in robotic source mappings")
expected = int(lock["coverage"]["robot_episodes"])
if len(episodes) != expected:
raise ValueError(f"Expected {expected} robotic episodes, found {len(episodes)}")
return episodes
def normalize_episode(
table: pa.Table,
episode: dict[str, Any],
feature_info: dict[str, Any],
) -> dict[str, Any]:
columns = set(table.column_names)
primary = next((name for name in PRIMARY_STATE_COLUMNS if name in columns), None)
if primary is None:
raise ValueError(f"{episode['bench_id']} has no supported proprioception column")
if "frame_index" not in columns or "timestamp" not in columns:
raise ValueError(f"{episode['bench_id']} is missing frame_index or timestamp")
frame_index = [int(_scalar(value)) for value in table["frame_index"].to_pylist()]
timestamps = [float(_scalar(value)) for value in table["timestamp"].to_pylist()]
if frame_index != sorted(frame_index) or timestamps != sorted(timestamps):
raise ValueError(f"{episode['bench_id']} state rows are not ordered")
if len(frame_index) != episode["expected_num_frames"]:
raise ValueError(
f"{episode['bench_id']} expected {episode['expected_num_frames']} frames, "
f"found {len(frame_index)} state rows"
)
channel_names = [primary]
channel_names.extend(
sorted(name for name in columns if name.endswith("_state") and name != primary)
)
channels = []
for name in channel_names:
values = [_vector(value) for value in table[name].to_pylist()]
names = list((feature_info.get(name) or {}).get("names") or [])
width = len(values[0]) if values else 0
if any(len(value) != width for value in values):
raise ValueError(f"{episode['bench_id']} channel {name!r} changes width")
if names and len(names) != width:
raise ValueError(f"{episode['bench_id']} channel {name!r} names do not match width")
if not names:
names = [f"value_{index}" for index in range(width)]
channels.append({"name": name, "value_names": names, "values": values})
return {
"bench_id": episode["bench_id"],
"robot_family": episode["robot_family"],
"source_dataset": episode["repo_id"],
"source_revision": episode["revision"],
"source_episode_index": episode["episode_index"],
"fps": episode["fps"],
"num_frames": len(frame_index),
"frame_index": frame_index,
"timestamp_sec": timestamps,
"primary_channel": primary,
"channels": channels,
}
def export_perception_states(
output_path: Path,
*,
lock_path: Path = LOCK_PATH,
cache_dir: Path | None = None,
bench_ids: Iterable[str] | None = None,
) -> dict[str, Any]:
lock = load_source_lock(lock_path)
episodes = discover_robot_episodes(lock, cache_dir)
selected = set(bench_ids or [])
if selected:
known = {episode["bench_id"] for episode in episodes}
unknown = selected - known
if unknown:
raise ValueError(f"Unknown robotic bench IDs: {sorted(unknown)}")
episodes = [episode for episode in episodes if episode["bench_id"] in selected]
rows = []
for episode in episodes:
table, feature_info = _load_episode(episode, cache_dir)
rows.append(normalize_episode(table, episode, feature_info))
output_path.parent.mkdir(parents=True, exist_ok=True)
table = pa.Table.from_pylist(rows, schema=perception_schema())
pq.write_table(table, output_path, compression="zstd")
digest = hashlib.sha256(output_path.read_bytes()).hexdigest()
provenance = {
"schema_version": 1,
"artifact": output_path.name,
"sha256": digest,
"episodes": len(rows),
"bench_ids": [row["bench_id"] for row in rows],
"source_lock": lock_path.name,
"source_lock_sha256": hashlib.sha256(lock_path.read_bytes()).hexdigest(),
"video_only_episodes": lock["coverage"]["video_only_episodes"],
}
provenance_path = output_path.with_suffix(output_path.suffix + ".provenance.json")
provenance_path.write_text(json.dumps(provenance, indent=2) + "\n", encoding="utf-8")
return provenance
def perception_schema() -> pa.Schema:
return pa.schema(
[
("bench_id", pa.string()),
("robot_family", pa.string()),
("source_dataset", pa.string()),
("source_revision", pa.string()),
("source_episode_index", pa.int64()),
("fps", pa.float64()),
("num_frames", pa.int64()),
("frame_index", pa.list_(pa.int64())),
("timestamp_sec", pa.list_(pa.float64())),
("primary_channel", pa.string()),
(
"channels",
pa.list_(
pa.struct(
[
("name", pa.string()),
("value_names", pa.list_(pa.string())),
("values", pa.list_(pa.list_(pa.float64()))),
]
)
),
),
]
)
def _load_episode(episode: dict[str, Any], cache_dir: Path | None) -> tuple[pa.Table, dict[str, Any]]:
info_path = _download(
episode["repo_id"], episode["revision"], episode["info_path"], cache_dir
)
feature_info = json.loads(info_path.read_text(encoding="utf-8")).get("features", {})
data_path = _download(
episode["repo_id"], episode["revision"], episode["data_path"], cache_dir
)
if episode.get("tar_member"):
with tarfile.open(data_path) as archive:
member = archive.extractfile(episode["tar_member"])
if member is None:
raise FileNotFoundError(episode["tar_member"])
table = pq.read_table(io.BytesIO(member.read()))
else:
table = pq.read_table(data_path)
return table, feature_info
def _download(repo_id: str, revision: str, filename: str, cache_dir: Path | None) -> Path:
return Path(
hf_hub_download(
repo_id=repo_id,
repo_type="dataset",
revision=revision,
filename=filename,
cache_dir=str(cache_dir) if cache_dir else None,
)
)
def _scalar(value: Any) -> Any:
if isinstance(value, (list, tuple)):
if len(value) != 1:
raise ValueError(f"Expected a scalar or one-element sequence, got {value!r}")
return value[0]
return value
def _vector(value: Any) -> list[float]:
if value is None:
return []
if not isinstance(value, (list, tuple)):
value = [value]
return [float(item) for item in value]
def main() -> None:
parser = argparse.ArgumentParser(
description="Export pinned proprioception sidecars for WGO-Bench robotic episodes."
)
parser.add_argument("output", type=Path)
parser.add_argument("--lock", type=Path, default=LOCK_PATH)
parser.add_argument("--cache-dir", type=Path)
parser.add_argument("--bench-id", action="append", default=[])
args = parser.parse_args()
result = export_perception_states(
args.output,
lock_path=args.lock,
cache_dir=args.cache_dir,
bench_ids=args.bench_id,
)
print(json.dumps(result, indent=2))
if __name__ == "__main__":
main()
|