File size: 5,012 Bytes
e17d175 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 | from __future__ import annotations
import argparse
import hashlib
import json
from pathlib import Path
from typing import Any
import pyarrow as pa
import pyarrow.parquet as pq
from perception_states import (
LOCK_PATH,
_load_episode,
discover_robot_episodes,
load_source_lock,
normalize_episode,
)
def state_type() -> pa.DataType:
return pa.struct(
[
("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 collect_states(lock_path: Path, cache_dir: Path | None) -> dict[str, dict[str, Any]]:
lock = load_source_lock(lock_path)
states: dict[str, dict[str, Any]] = {}
for episode in discover_robot_episodes(lock, cache_dir):
table, feature_info = _load_episode(episode, cache_dir)
row = normalize_episode(table, episode, feature_info)
bench_id = row.pop("bench_id")
states[bench_id] = row
return states
def embed_perception_states(
input_path: Path,
output_path: Path,
*,
lock_path: Path = LOCK_PATH,
cache_dir: Path | None = None,
) -> dict[str, Any]:
if input_path.resolve() == output_path.resolve():
raise ValueError("Input and output paths must differ")
states = collect_states(lock_path, cache_dir)
source = pq.ParquetFile(input_path)
if "perception_state" in source.schema_arrow.names:
raise ValueError("Input already contains perception_state")
if source.metadata.num_rows != 100:
raise ValueError(f"Expected 100 benchmark rows, found {source.metadata.num_rows}")
output_path.parent.mkdir(parents=True, exist_ok=True)
output_schema = source.schema_arrow.append(pa.field("perception_state", state_type()))
matched: set[str] = set()
video_only: list[str] = []
with pq.ParquetWriter(output_path, output_schema, compression="zstd") as writer:
for batch in source.iter_batches(batch_size=1):
table = pa.Table.from_batches([batch])
bench_id = str(table["id"][0].as_py())
state = states.get(bench_id)
if state is None:
video_only.append(bench_id)
else:
matched.add(bench_id)
enriched = table.append_column(
"perception_state", pa.array([state], type=state_type())
)
writer.write_table(enriched, row_group_size=1)
missing = set(states) - matched
if missing:
output_path.unlink(missing_ok=True)
raise ValueError(f"State IDs absent from benchmark: {sorted(missing)}")
if len(matched) != 75 or len(video_only) != 25:
output_path.unlink(missing_ok=True)
raise ValueError(
f"Expected 75 state rows and 25 video-only rows, got {len(matched)} and {len(video_only)}"
)
provenance = {
"schema_version": 1,
"artifact": output_path.name,
"input_sha256": _sha256(input_path),
"output_sha256": _sha256(output_path),
"source_lock": lock_path.name,
"source_lock_sha256": _sha256(lock_path),
"rows": source.metadata.num_rows,
"robot_state_rows": len(matched),
"video_only_rows": len(video_only),
"video_only_ids": video_only,
}
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 _sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def main() -> None:
parser = argparse.ArgumentParser(
description="Embed pinned robot proprioception into existing WGO-Bench rows."
)
parser.add_argument("input", type=Path)
parser.add_argument("output", type=Path)
parser.add_argument("--lock", type=Path, default=LOCK_PATH)
parser.add_argument("--cache-dir", type=Path)
args = parser.parse_args()
result = embed_perception_states(
args.input,
args.output,
lock_path=args.lock,
cache_dir=args.cache_dir,
)
print(json.dumps(result, indent=2))
if __name__ == "__main__":
main()
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