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18b1016 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 | #!/usr/bin/env python3
"""Validate cleaned SO-101 dataset structure and cross-modal alignment."""
from __future__ import annotations
import argparse
import csv
import hashlib
import json
import subprocess
from pathlib import Path
import numpy as np
import pyarrow.dataset as pads
import pyarrow.parquet as pq
FPS = 30
MODIFIED_FPV = {3, 5, 7, 8, 10}
EXPECTED_STREAM = {
"codec_name": "av1",
"width": 640,
"height": 480,
"pix_fmt": "yuv420p",
"r_frame_rate": "30/1",
"avg_frame_rate": "30/1",
}
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--source-root", type=Path, default=Path("data/so101_wm"))
parser.add_argument("--clean-root", type=Path, default=Path("data/so101_wm_clean"))
parser.add_argument(
"--blurred-staging",
type=Path,
default=Path("data/so101_wm/videos/observation.images.fpv/copied fixed and blured faces"),
)
parser.add_argument("--report", type=Path, default=Path("artifacts/dataset_qc/clean_release_validation.json"))
return parser.parse_args()
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for block in iter(lambda: handle.read(8 * 1024 * 1024), b""):
digest.update(block)
return digest.hexdigest()
def probe(path: Path) -> dict[str, object]:
command = [
"ffprobe", "-v", "error", "-select_streams", "v:0", "-show_entries",
"stream=codec_name,width,height,pix_fmt,r_frame_rate,avg_frame_rate,time_base,start_time,duration,nb_frames:format=duration",
"-of", "json", str(path),
]
payload = json.loads(subprocess.run(command, check=True, capture_output=True, text=True).stdout)
stream = payload["streams"][0]
stream["format_duration"] = payload["format"]["duration"]
return stream
def frame_pts(path: Path) -> np.ndarray:
command = [
"ffprobe", "-v", "error", "-select_streams", "v:0", "-show_entries",
"frame=best_effort_timestamp_time", "-of", "csv=p=0", str(path),
]
output = subprocess.run(command, check=True, capture_output=True, text=True).stdout
return np.asarray([float(line.strip().split(",")[0]) for line in output.splitlines() if line.strip()])
def same_tree_hashes(source: Path, clean: Path, pattern: str) -> tuple[int, list[str]]:
source_files = sorted(source.glob(pattern))
clean_files = sorted(clean.glob(pattern))
source_rel = [path.relative_to(source) for path in source_files]
clean_rel = [path.relative_to(clean) for path in clean_files]
problems = []
if source_rel != clean_rel:
problems.append(f"file lists differ for {pattern}")
return 0, problems
for relative in source_rel:
if sha256(source / relative) != sha256(clean / relative):
problems.append(f"unexpected byte difference: {relative}")
return len(source_rel), problems
def validate() -> tuple[dict[str, object], list[str]]:
args = parse_args()
source = args.source_root.resolve()
clean = args.clean_root.resolve()
staging = args.blurred_staging.resolve()
problems: list[str] = []
report: dict[str, object] = {}
# Every recorded numeric row and immutable metadata record must remain byte-identical.
immutable_checks = [
("data/**/*.parquet", "Parquet data"),
("meta/episodes/**/*.parquet", "episode metadata"),
("meta/tasks.parquet", "task metadata"),
("meta/info.json", "dataset schema"),
("videos/observation.images.left/**/*.mp4", "fixed-camera videos"),
]
immutable_counts = {}
for pattern, label in immutable_checks:
count, issues = same_tree_hashes(source, clean, pattern)
immutable_counts[label] = count
problems.extend(issues)
report["byte_identical_immutable_assets"] = immutable_counts
info = json.loads((clean / "meta/info.json").read_text(encoding="utf-8"))
numeric = pads.dataset(clean / "data", format="parquet").to_table()
total_rows = numeric.num_rows
if total_rows != int(info["total_frames"]):
problems.append(f"Parquet rows {total_rows} != info total_frames {info['total_frames']}")
index = numeric["index"].to_numpy(zero_copy_only=False)
if not np.array_equal(index, np.arange(total_rows, dtype=index.dtype)):
problems.append("global Parquet index is not consecutive")
frame_index = numeric["frame_index"].to_numpy(zero_copy_only=False)
timestamps = numeric["timestamp"].to_numpy(zero_copy_only=False)
if not np.allclose(timestamps, frame_index / FPS, atol=2e-5, rtol=0):
problems.append("timestamps do not equal frame_index / 30 within tolerance")
for feature in ("action", "observation.state"):
values = np.asarray(numeric[feature].to_pylist(), dtype=np.float64)
if not np.isfinite(values).all():
problems.append(f"{feature} contains NaN or infinite values")
# Episode metadata must point to matching Parquet and video spans.
episode_rows = pq.read_table(clean / "meta/episodes/chunk-000/file-000.parquet").to_pylist()
episode_ids = numeric["episode_index"].to_numpy(zero_copy_only=False)
task_ids = numeric["task_index"].to_numpy(zero_copy_only=False)
for row in episode_rows:
episode = int(row["episode_index"])
start = int(row["dataset_from_index"])
end = int(row["dataset_to_index"])
length = int(row["length"])
if end - start != length:
problems.append(f"episode {episode}: dataset range length mismatch")
if not np.all(episode_ids[start:end] == episode):
problems.append(f"episode {episode}: Parquet episode_index mismatch")
if len(set(task_ids[start:end].tolist())) != 1:
problems.append(f"episode {episode}: multiple task_index values")
for view in ("observation.images.left", "observation.images.fpv"):
video_duration = float(row[f"videos/{view}/to_timestamp"]) - float(row[f"videos/{view}/from_timestamp"])
if round(video_duration * FPS) != length:
problems.append(f"episode {episode}: {view} segment length mismatch")
# Every video stream must retain the declared geometry and total frame count.
video_totals = {}
for view in ("observation.images.left", "observation.images.fpv"):
total = 0
files = sorted((clean / "videos" / view / "chunk-000").glob("file-*.mp4"))
for path in files:
stream = probe(path)
for key, expected in EXPECTED_STREAM.items():
if stream.get(key) != expected:
problems.append(f"{path.name}: {key}={stream.get(key)!r}, expected {expected!r}")
total += int(stream["nb_frames"])
video_totals[view] = {"files": len(files), "frames": total}
if total != total_rows:
problems.append(f"{view}: {total} video frames != {total_rows} Parquet rows")
report["video_totals"] = video_totals
# Unmodified FPV files must be byte-identical. Modified files must match the
# validated staging versions and have exactly the same per-frame timestamps
# as the original source files.
pts_results = {}
for clean_path in sorted((clean / "videos/observation.images.fpv/chunk-000").glob("file-*.mp4")):
file_index = int(clean_path.stem.split("-")[-1])
original_path = source / "videos/observation.images.fpv/chunk-000" / clean_path.name
if file_index not in MODIFIED_FPV:
if sha256(clean_path) != sha256(original_path):
problems.append(f"unmodified FPV {clean_path.name} is not byte-identical")
continue
staged_path = staging / clean_path.name
if sha256(clean_path) != sha256(staged_path):
problems.append(f"clean FPV {clean_path.name} does not match blurred staging file")
original_pts = frame_pts(original_path)
clean_pts = frame_pts(clean_path)
if len(original_pts) != len(clean_pts):
problems.append(f"{clean_path.name}: per-frame timestamp count differs")
max_drift = None
else:
max_drift = float(np.max(np.abs(original_pts - clean_pts))) if len(original_pts) else 0.0
if max_drift > 1e-9:
problems.append(f"{clean_path.name}: maximum timestamp drift is {max_drift}s")
pts_results[clean_path.name] = {
"frames": len(clean_pts),
"maximum_pts_drift_s": max_drift,
"bytes_differ_from_original": sha256(clean_path) != sha256(original_path),
}
report["privacy_modified_fpv_timing"] = pts_results
# Supplied split must cover each episode once.
split_payload = json.loads((clean / "meta/cleaning/episode_splits.json").read_text(encoding="utf-8"))
split_sets = {name: set(values) for name, values in split_payload["splits"].items()}
union = set().union(*split_sets.values())
if union != set(range(int(info["total_episodes"]))):
problems.append("episode split does not cover all episodes")
names = list(split_sets)
for left_index, left in enumerate(names):
for right in names[left_index + 1 :]:
if split_sets[left] & split_sets[right]:
problems.append(f"episode split overlap: {left}/{right}")
report["split_sizes"] = {name: len(values) for name, values in split_sets.items()}
# Exclusions must be valid episode-local intervals and select synchronized rows.
with (clean / "meta/cleaning/intervention_exclusions.csv").open(encoding="utf-8") as handle:
exclusions = list(csv.DictReader(handle))
length_by_episode = {int(row["episode_index"]): int(row["length"]) for row in episode_rows}
active_rows = 0
for item in exclusions:
episode = int(item["episode_index"])
start = float(item["episode_start_s"])
end = float(item["episode_end_s"])
duration = length_by_episode[episode] / FPS
if not (0 <= start < end <= duration + 1e-5):
problems.append(f"invalid exclusion {item['review_id']} in episode {episode}")
if item["exclude_from_dynamics"] == "True":
selected = (episode_ids == episode) & (timestamps >= start) & (timestamps < end)
count = int(selected.sum())
active_rows += count
if count == 0:
problems.append(f"active exclusion {item['review_id']} selects no synchronized rows")
report["exclusions"] = {
"mapped_ranges": len(exclusions),
"active_mapped_ranges": sum(item["exclude_from_dynamics"] == "True" for item in exclusions),
"selected_rows_across_all_splits_before_overlap_deduplication": active_rows,
}
# Verify the exact train-only normalization row selection and moments.
train = np.asarray(sorted(split_sets["train"]), dtype=episode_ids.dtype)
keep = np.isin(episode_ids, train)
for item in exclusions:
if item["exclude_from_dynamics"] != "True":
continue
episode = int(item["episode_index"])
start = float(item["episode_start_s"])
end = float(item["episode_end_s"])
keep &= ~((episode_ids == episode) & (timestamps >= start) & (timestamps < end))
normalization = json.loads((clean / "meta/cleaning/normalization_stats.json").read_text(encoding="utf-8"))
if int(keep.sum()) != int(normalization["included_rows"]):
problems.append("normalization included_rows does not match split/exclusion mask")
for feature in ("action", "observation.state"):
values = np.asarray(numeric[feature].to_pylist(), dtype=np.float64)[keep]
stored = normalization["features"][feature]
if not np.allclose(values.mean(axis=0), stored["mean"], atol=1e-12, rtol=0):
problems.append(f"{feature} normalization mean mismatch")
if not np.allclose(values.std(axis=0), stored["std"], atol=1e-12, rtol=0):
problems.append(f"{feature} normalization std mismatch")
report["normalization_rows_verified"] = int(keep.sum())
report["parquet_rows"] = total_rows
report["status"] = "PASS" if not problems else "FAIL"
return report, problems
def main() -> None:
args = parse_args()
report, problems = validate()
args.report.parent.mkdir(parents=True, exist_ok=True)
args.report.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8")
print(json.dumps(report, indent=2))
if problems:
print("Problems:")
for problem in problems:
print(f"- {problem}")
raise SystemExit(1)
if __name__ == "__main__":
main()
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