File size: 21,625 Bytes
9371cfb fc9e8cf 9371cfb 04c0bde 9371cfb 04c0bde fc9e8cf 9371cfb | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 | #!/usr/bin/env python3
"""Analyze the gated Xperience-10M HF repo without downloading dataset files."""
from __future__ import annotations
import argparse
import getpass
import json
import os
from collections import Counter, defaultdict
from datetime import datetime, timezone
from pathlib import Path
from statistics import median
from typing import Any
from huggingface_hub import HfApi
REQUIRED_EPISODE_FILES = [
"annotation.hdf5",
"fisheye_cam0.mp4",
"fisheye_cam1.mp4",
"fisheye_cam2.mp4",
"fisheye_cam3.mp4",
"stereo_left.mp4",
"stereo_right.mp4",
]
TRAINING_EXCLUDE = {"visualization.rrd"}
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--repo-id", default="ropedia-ai/xperience-10m")
parser.add_argument("--output", type=Path, default=Path("results/omni_finetune/full_dataset_metadata_audit.json"))
parser.add_argument("--report-output", type=Path, default=Path("results/omni_finetune/FULL_DATASET_METADATA_AUDIT.md"))
parser.add_argument("--token", default=os.environ.get("HF_TOKEN", "").strip())
parser.add_argument("--top-n", type=int, default=20)
return parser.parse_args()
def file_size(sibling: Any) -> int:
value = getattr(sibling, "size", None)
if isinstance(value, int):
return value
lfs = getattr(sibling, "lfs", None)
if isinstance(lfs, dict) and isinstance(lfs.get("size"), int):
return int(lfs["size"])
return 0
def human_bytes(num: float | int) -> str:
value = float(num)
for unit in ["B", "KiB", "MiB", "GiB", "TiB", "PiB"]:
if abs(value) < 1024.0 or unit == "PiB":
return f"{value:.2f} {unit}"
value /= 1024.0
return f"{value:.2f} PiB"
def pct(part: int, whole: int) -> float:
return round((part / whole * 100.0), 4) if whole else 0.0
def episode_parent(path: str) -> str:
return str(Path(path).parent).replace("\\", "/")
def summarize_sizes(values: list[int]) -> dict[str, Any]:
if not values:
return {"count": 0}
ordered = sorted(values)
q1 = ordered[len(ordered) // 4]
q3 = ordered[(len(ordered) * 3) // 4]
return {
"count": len(values),
"min_bytes": ordered[0],
"p25_bytes": q1,
"median_bytes": int(median(ordered)),
"p75_bytes": q3,
"max_bytes": ordered[-1],
"mean_bytes": int(sum(values) / len(values)),
"min_human": human_bytes(ordered[0]),
"median_human": human_bytes(median(ordered)),
"mean_human": human_bytes(sum(values) / len(values)),
"max_human": human_bytes(ordered[-1]),
}
def near_size_files(files: list[dict[str, Any]], target: int, count: int) -> list[dict[str, Any]]:
ranked = sorted(files, key=lambda item: abs(int(item["bytes"]) - target))
return ranked[:count]
def summarize_numbers(values: list[int]) -> dict[str, Any]:
if not values:
return {"count": 0}
ordered = sorted(values)
return {
"count": len(values),
"min": ordered[0],
"p25": ordered[len(ordered) // 4],
"median": int(median(ordered)),
"p75": ordered[(len(ordered) * 3) // 4],
"max": ordered[-1],
"mean": round(sum(values) / len(values), 2),
}
def md_table(headers: list[str], rows: list[list[Any]]) -> list[str]:
lines = [
"| " + " | ".join(headers) + " |",
"| " + " | ".join("---" for _ in headers) + " |",
]
lines.extend("| " + " | ".join(str(cell) for cell in row) + " |" for row in rows)
return lines
def main() -> int:
args = parse_args()
token = args.token or getpass.getpass("HF token: ").strip()
if not token:
raise SystemExit("HF token is required for gated dataset metadata.")
api = HfApi(token=token)
info = api.repo_info(
repo_id=args.repo_id,
repo_type="dataset",
files_metadata=True,
token=token,
)
siblings = list(info.siblings or [])
files = []
total_bytes = 0
ext_counter: Counter[str] = Counter()
basename_counter: Counter[str] = Counter()
top_level_counter: Counter[str] = Counter()
by_parent: dict[str, dict[str, Any]] = defaultdict(lambda: {"files": {}, "bytes": 0})
by_top_level_bytes: Counter[str] = Counter()
for sibling in siblings:
path = str(getattr(sibling, "rfilename", ""))
if not path or path == ".gitattributes":
continue
size = file_size(sibling)
total_bytes += size
ext = Path(path).suffix.lower() or "<no_ext>"
name = Path(path).name
top = path.split("/", 1)[0]
ext_counter[ext] += 1
basename_counter[name] += 1
top_level_counter[top] += 1
by_top_level_bytes[top] += size
parent = episode_parent(path)
bucket = by_parent[parent]
bucket["files"][name] = {"path": path, "bytes": size}
bucket["bytes"] += size
files.append({"path": path, "bytes": size, "extension": ext, "basename": name, "top_level": top})
episode_records = []
for parent, bucket in by_parent.items():
present = set(bucket["files"])
if not (present & set(REQUIRED_EPISODE_FILES)):
continue
has_annotation = "annotation.hdf5" in present
has_fisheye_cam0 = "fisheye_cam0.mp4" in present
video_count = sum(1 for name in REQUIRED_EPISODE_FILES[1:] if name in present)
missing_required = [name for name in REQUIRED_EPISODE_FILES if name not in present]
training_bytes = sum(
meta["bytes"]
for name, meta in bucket["files"].items()
if name not in TRAINING_EXCLUDE
)
episode_records.append(
{
"episode_path": parent,
"episode_id": Path(parent).name,
"top_level_session": parent.split("/", 1)[0],
"file_count": len(present),
"total_bytes": int(bucket["bytes"]),
"training_bytes_excluding_visualization_rrd": int(training_bytes),
"has_annotation": has_annotation,
"has_fisheye_cam0": has_fisheye_cam0,
"video_count": video_count,
"has_all_six_videos": video_count == 6,
"is_degraded_valid": has_annotation and has_fisheye_cam0,
"is_complete": has_annotation and video_count == 6,
"has_visualization_rrd": "visualization.rrd" in present,
"missing_required_files": missing_required,
}
)
complete = [ep for ep in episode_records if ep["is_complete"]]
degraded = [ep for ep in episode_records if ep["is_degraded_valid"]]
incomplete = [ep for ep in episode_records if not ep["is_complete"]]
training_sizes = [ep["training_bytes_excluding_visualization_rrd"] for ep in complete]
episode_sizes = [ep["total_bytes"] for ep in episode_records]
complete_by_session: Counter[str] = Counter(ep["top_level_session"] for ep in complete)
degraded_by_session: Counter[str] = Counter(ep["top_level_session"] for ep in degraded)
episode_count_by_session: Counter[str] = Counter(ep["top_level_session"] for ep in episode_records)
video_count_hist = Counter(str(ep["video_count"]) for ep in episode_records)
rrd_bytes = sum(item["bytes"] for item in files if item["basename"] == "visualization.rrd")
all_complete_training_bytes = sum(ep["training_bytes_excluding_visualization_rrd"] for ep in complete)
median_32_bytes = int(median(training_sizes)) * 32 if training_sizes else 0
mean_32_bytes = int(sum(training_sizes) / len(training_sizes)) * 32 if training_sizes else 0
largest_files = sorted(files, key=lambda item: item["bytes"], reverse=True)[: args.top_n]
annotation_files = [item for item in files if item["basename"] == "annotation.hdf5"]
annotation_sizes = [item["bytes"] for item in annotation_files]
annotation_size_summary = summarize_sizes(annotation_sizes)
annotation_median = int(annotation_size_summary.get("median_bytes", 0))
largest_episodes = sorted(episode_records, key=lambda item: item["total_bytes"], reverse=True)[: args.top_n]
smallest_complete = sorted(complete, key=lambda item: item["training_bytes_excluding_visualization_rrd"])[: args.top_n]
selected_32 = []
for session, _count in sorted(complete_by_session.items()):
candidates = [ep for ep in complete if ep["top_level_session"] == session]
candidates.sort(key=lambda ep: ep["training_bytes_excluding_visualization_rrd"])
selected_32.append(candidates[0])
if len(selected_32) == 32:
break
payload = {
"status": "pass",
"generated_at_utc": datetime.now(timezone.utc).isoformat(timespec="seconds"),
"repo_id": args.repo_id,
"repo_sha": getattr(info, "sha", None),
"gated": getattr(info, "gated", None),
"last_modified": getattr(info, "last_modified", None).isoformat() if getattr(info, "last_modified", None) else None,
"card_data": getattr(info, "card_data", None).to_dict() if getattr(info, "card_data", None) and hasattr(getattr(info, "card_data", None), "to_dict") else None,
"summary": {
"sibling_count": len(siblings),
"file_count_excluding_gitattributes": len(files),
"total_bytes_from_file_metadata": total_bytes,
"total_human_from_file_metadata": human_bytes(total_bytes),
"training_bytes_excluding_visualization_rrd": total_bytes - rrd_bytes,
"training_human_excluding_visualization_rrd": human_bytes(total_bytes - rrd_bytes),
"visualization_rrd_bytes": rrd_bytes,
"visualization_rrd_human": human_bytes(rrd_bytes),
"top_level_session_count": len(top_level_counter),
"episode_like_folder_count": len(episode_records),
"annotation_hdf5_count": basename_counter["annotation.hdf5"],
"mp4_count": sum(count for name, count in basename_counter.items() if name.endswith(".mp4")),
"visualization_rrd_count": basename_counter["visualization.rrd"],
"complete_episode_count": len(complete),
"degraded_valid_episode_count": len(degraded),
"complete_episode_pct": pct(len(complete), len(episode_records)),
"degraded_valid_episode_pct": pct(len(degraded), len(episode_records)),
"complete_sessions": len(complete_by_session),
"degraded_valid_sessions": len(degraded_by_session),
"all_complete_episode_training_bytes_excluding_visualization_rrd": all_complete_training_bytes,
"all_complete_episode_training_human_excluding_visualization_rrd": human_bytes(all_complete_training_bytes),
},
"file_type_counts": dict(sorted(ext_counter.items())),
"basename_counts": dict(sorted(basename_counter.items())),
"video_count_histogram": dict(sorted(video_count_hist.items())),
"episode_count_per_session_summary": summarize_numbers(list(episode_count_by_session.values())),
"episode_size_summary": summarize_sizes(episode_sizes),
"annotation_file_size_summary": annotation_size_summary,
"complete_episode_training_size_summary": summarize_sizes(training_sizes),
"incomplete_episode_records": incomplete,
"pilot_scale_estimates": {
"windows_per_episode": 256,
"all_complete_episodes_windows_at_256_each": len(complete) * 256,
"episode_32_windows_at_256_each": 32 * 256,
"episode_100_windows_at_256_each": 100 * 256,
"episode_500_windows_at_256_each": 500 * 256,
"median_based_32_episode_training_bytes": median_32_bytes,
"median_based_32_episode_training_human": human_bytes(median_32_bytes),
"mean_based_32_episode_training_bytes": mean_32_bytes,
"mean_based_32_episode_training_human": human_bytes(mean_32_bytes),
},
"selected_32_smallest_one_per_session_estimate": {
"episode_count": len(selected_32),
"estimated_training_bytes_excluding_visualization_rrd": sum(
ep["training_bytes_excluding_visualization_rrd"] for ep in selected_32
),
"estimated_training_human": human_bytes(
sum(ep["training_bytes_excluding_visualization_rrd"] for ep in selected_32)
),
"episodes": selected_32,
},
"top_level_sessions_by_file_count_top_n": top_level_counter.most_common(args.top_n),
"top_level_sessions_by_bytes_top_n": [
{"session": session, "bytes": bytes_, "human": human_bytes(bytes_)}
for session, bytes_ in by_top_level_bytes.most_common(args.top_n)
],
"largest_files_top_n": [
{**item, "human": human_bytes(item["bytes"])} for item in largest_files
],
"smallest_annotation_files_top_n": [
{**item, "human": human_bytes(item["bytes"])}
for item in sorted(annotation_files, key=lambda item: item["bytes"])[: args.top_n]
],
"median_annotation_files_top_n": [
{**item, "human": human_bytes(item["bytes"])}
for item in near_size_files(annotation_files, annotation_median, args.top_n)
],
"largest_annotation_files_top_n": [
{**item, "human": human_bytes(item["bytes"])}
for item in sorted(annotation_files, key=lambda item: item["bytes"], reverse=True)[: args.top_n]
],
"largest_episode_folders_top_n": [
{**item, "total_human": human_bytes(item["total_bytes"]), "training_human": human_bytes(item["training_bytes_excluding_visualization_rrd"])}
for item in largest_episodes
],
"smallest_complete_episode_training_folders_top_n": [
{**item, "total_human": human_bytes(item["total_bytes"]), "training_human": human_bytes(item["training_bytes_excluding_visualization_rrd"])}
for item in smallest_complete
],
"download_recommendation": {
"metadata_only_audit_requires_training_host": False,
"recommended_download_host": "Any HF-reachable download machine with enough scratch storage; transfer prepared episodes to the training host if that host cannot access Hugging Face.",
"training_host_role": "training and local manifest validation after data is prepared",
"exclude_files": sorted(TRAINING_EXCLUDE),
"minimum_pilot": "32 complete episodes from different top-level sessions if storage permits; degraded-valid episodes only for loader checks.",
},
}
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
summary = payload["summary"]
complete_sizes = payload["complete_episode_training_size_summary"]
annotation_sizes_report = payload["annotation_file_size_summary"]
pilot = payload["pilot_scale_estimates"]
selected_32_estimate = payload["selected_32_smallest_one_per_session_estimate"]
card_data = payload["card_data"] or {}
report = [
"# Xperience-10M HF Metadata Audit",
"",
"Metadata-only analysis of the gated Hugging Face dataset. No MP4, HDF5, RRD, or model files were downloaded.",
"",
"## Access and Source",
"",
f"- Repo: `{args.repo_id}`",
f"- Repo SHA: `{payload['repo_sha']}`",
f"- Last modified: `{payload['last_modified']}`",
f"- Gated mode: `{payload['gated']}`",
f"- Pretty name: `{card_data.get('pretty_name', 'Xperience-10M')}`",
f"- License field: `{card_data.get('license', 'unknown')}`",
f"- HF size category: `{', '.join(card_data.get('size_categories', [])) or 'unknown'}`",
f"- Tags: `{', '.join(card_data.get('tags', []))}`",
"",
"## Current Hub File Metadata",
"",
*md_table(
["Measure", "Value"],
[
["Files listed by API", f"{summary['file_count_excluding_gitattributes']:,}"],
["Total bytes from file metadata", f"{summary['total_human_from_file_metadata']} ({summary['total_bytes_from_file_metadata']:,} bytes)"],
["Bytes excluding visualization.rrd", f"{summary['training_human_excluding_visualization_rrd']} ({summary['training_bytes_excluding_visualization_rrd']:,} bytes)"],
["visualization.rrd bytes", f"{summary['visualization_rrd_human']} ({summary['visualization_rrd_bytes']:,} bytes)"],
["Top-level session folders", f"{summary['top_level_session_count']:,}"],
["Episode-like folders", f"{summary['episode_like_folder_count']:,}"],
],
),
"",
"## File Composition",
"",
*md_table(
["File type", "Count"],
[[key, f"{value:,}"] for key, value in payload["file_type_counts"].items()],
),
"",
"## Episode Completeness",
"",
*md_table(
["Measure", "Value"],
[
["annotation.hdf5 files", f"{summary['annotation_hdf5_count']:,}"],
["MP4 files", f"{summary['mp4_count']:,}"],
["visualization.rrd files", f"{summary['visualization_rrd_count']:,}"],
["Complete episodes: annotation + all six MP4 views", f"{summary['complete_episode_count']:,} ({summary['complete_episode_pct']}%)"],
["Degraded-valid episodes: annotation + fisheye_cam0", f"{summary['degraded_valid_episode_count']:,} ({summary['degraded_valid_episode_pct']}%)"],
["Sessions with complete episodes", f"{summary['complete_sessions']:,}"],
["Video-count histogram per episode", json.dumps(payload["video_count_histogram"], sort_keys=True)],
],
),
"",
"## Episode Size Distribution",
"",
*md_table(
["Statistic", "Training bytes per complete episode, excluding visualization.rrd"],
[
["Min", complete_sizes.get("min_human")],
["P25", human_bytes(complete_sizes.get("p25_bytes", 0))],
["Median", complete_sizes.get("median_human")],
["P75", human_bytes(complete_sizes.get("p75_bytes", 0))],
["Mean", complete_sizes.get("mean_human")],
["Max", complete_sizes.get("max_human")],
],
),
"",
"## Annotation File Size Distribution",
"",
*md_table(
["Statistic", "annotation.hdf5 size"],
[
["Min", annotation_sizes_report.get("min_human")],
["P25", human_bytes(annotation_sizes_report.get("p25_bytes", 0))],
["Median", annotation_sizes_report.get("median_human")],
["P75", human_bytes(annotation_sizes_report.get("p75_bytes", 0))],
["Mean", annotation_sizes_report.get("mean_human")],
["Max", annotation_sizes_report.get("max_human")],
],
),
"",
"## Pilot Scale Estimates",
"",
*md_table(
["Pilot", "Episodes", "Max windows at 256/episode", "Storage estimate"],
[
["32-episode smallest one-per-session", selected_32_estimate["episode_count"], pilot["episode_32_windows_at_256_each"], selected_32_estimate["estimated_training_human"]],
["32-episode median-sized estimate", 32, pilot["episode_32_windows_at_256_each"], pilot["median_based_32_episode_training_human"]],
["32-episode mean-sized estimate", 32, pilot["episode_32_windows_at_256_each"], pilot["mean_based_32_episode_training_human"]],
["100-episode pilot", 100, pilot["episode_100_windows_at_256_each"], f"roughly {human_bytes(complete_sizes.get('median_bytes', 0) * 100)} at median episode size"],
["500-episode pilot", 500, pilot["episode_500_windows_at_256_each"], f"roughly {human_bytes(complete_sizes.get('median_bytes', 0) * 500)} at median episode size"],
["All complete visible HF episodes", summary["complete_episode_count"], pilot["all_complete_episodes_windows_at_256_each"], summary["all_complete_episode_training_human_excluding_visualization_rrd"]],
],
),
"",
"## Incomplete Episode Records",
"",
json.dumps(incomplete, indent=2) if incomplete else "None found.",
"",
"## Download and Compute Recommendation",
"",
"- This metadata listing check can run on any machine with Hugging Face access.",
"- If the training host cannot reach Hugging Face, download on an HF-reachable machine, then transfer prepared episode folders to the training host.",
"- For training downloads, include `annotation.hdf5` plus the six MP4 streams; exclude `visualization.rrd` unless Rerun visualization is specifically needed.",
"- For the first real training pilot, prefer 32 complete episodes from different top-level sessions and avoid selecting only the tiny outlier episodes.",
"- The training host is used after staged data exists: manifest validation, preprocessing, LoRA training, and held-out evaluation.",
]
args.report_output.write_text("\n".join(report) + "\n", encoding="utf-8")
print(f"PASS: wrote {args.output}")
print(f"PASS: wrote {args.report_output}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
|