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+{"episode_hash": "69badde449d91f47d726c32a", "decision": "reject", "task": "folding_clothes", "lab": "mecka", "index": 450, "ts": 1781197233.6260743} +{"episode_hash": "69bae0f65d1a1f3632b7fc8f", "decision": "reject", "task": "folding_clothes", "lab": "mecka", "index": 451, "ts": 1781197239.3855052} +{"episode_hash": "69bae109828bb1912f3ef8c1", "decision": "reject", "task": "folding_clothes", "lab": "mecka", "index": 452, "ts": 1781197244.020985} +{"episode_hash": "69bae420f492c4f8a835a95c", "decision": "reject", "task": "folding_clothes", "lab": "mecka", "index": 453, "ts": 1781197251.9589348} +{"episode_hash": "69bae733b7af83de25a3812a", "decision": "reject", "task": "folding_clothes", "lab": "mecka", "index": 454, "ts": 1781197260.8201296} +{"episode_hash": "69bae9f54a1d1416e573806d", "decision": "reject", "task": "folding_clothes", "lab": "mecka", "index": 455, "ts": 1781197265.7826817} +{"episode_hash": "69bae9f54a1d1416e573806d", "decision": "reject", "task": "folding_clothes", "lab": "mecka", "index": 455, "ts": 1781197265.7830448} +{"episode_hash": "69baf4d8935a9aa010abb230", "decision": "accept", "task": "folding_clothes", "lab": "mecka", "index": 457, "ts": 1781197273.4255347} +{"episode_hash": "69bb00a60bb085884dbc50b7", "decision": "reject", "task": "folding_clothes", "lab": "mecka", "index": 458, "ts": 1781197275.3977556} +{"episode_hash": "69bb01bf11e9b1cd78d2945d", "decision": "accept", "task": "folding_clothes", "lab": "mecka", "index": 459, "ts": 1781197282.0095515} +{"episode_hash": "69bb040ffb4c94a31bd85cbe", "decision": "reject", "task": "folding_clothes", "lab": "mecka", "index": 460, "ts": 1781197337.4954076} +{"episode_hash": "69bb0e1f7fcc76cdadcc00bb", "decision": "accept", "task": "folding_clothes", "lab": "mecka", "index": 461, "ts": 1781197344.3267846} +{"episode_hash": "69bb0e1f7fcc76cdadcc00bb", "decision": "accept", "task": "folding_clothes", "lab": "mecka", "index": 461, "ts": 1781197347.3648772} +{"episode_hash": "69bb0e1f7fcc76cdadcc00bb", "decision": "accept", "task": "folding_clothes", "lab": "mecka", "index": 461, "ts": 1781197350.266412} +{"episode_hash": "69bb0e1f7fcc76cdadcc00bb", "decision": "accept", "task": "folding_clothes", "lab": "mecka", "index": 461, "ts": 1781197353.9567144} +{"episode_hash": "69baed114012b22f2ea5f48f", "decision": "reject", "task": "folding_clothes", "lab": "mecka", "index": 456, "ts": 1781197402.1831589} diff --git a/egomimic/scripts/data_quality/__init__.py b/egomimic/scripts/data_quality/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egomimic/scripts/data_quality/review_episodes.py b/egomimic/scripts/data_quality/review_episodes.py new file mode 100644 index 000000000..d66e4ac30 --- /dev/null +++ b/egomimic/scripts/data_quality/review_episodes.py @@ -0,0 +1,799 @@ +"""Interactive data-quality review tool for RLDB episodes. + +Eyeball each episode's raw front-camera video in a small Flask web app and +accept / reject / skip it. Every decision is appended to a JSONL file the +instant it is made, so the review is fully resumable and the accepted set can +later be wired into a filtered data config. + +This is a read-only consumer of the episode registry: it never writes to the +SQL table (``is_deleted`` is untouched) and never trains anything. It only +downloads the matched episodes' zarr stores and renders their ``images.front_1`` +frames to MP4 for viewing. + +---------------------------------------------------------------------------- +Usage +---------------------------------------------------------------------------- +Activate the venv first: ``source emimic/bin/activate`` + +1) Sanity-check the SQL filter (no downloads, no server): + + python -m egomimic.scripts.data_quality.review_episodes --list-only + +2) Review over an SSH port-forward (`ssh -L 5007:127.0.0.1:5007 `): + + python -m egomimic.scripts.data_quality.review_episodes \ + --lab mecka --task folding_clothes \ + --cache-dir /path/to/qc_cache + + then open http://127.0.0.1:5007 in your local browser. + Keys: [a]ccept [r]eject [s]kip [<-]/[->] prev/next. + +3) Export the accepted episode hashes as a ready-to-paste Python set: + + python -m egomimic.scripts.data_quality.review_episodes --print-accepted + + Paste the printed set into a copy of the data config, e.g. + ``egomimic/hydra_configs/data/mecka_pi_fold_clothes_freeform.yaml``: + + filter_lambdas: + - "lambda row: row['lab'] == 'mecka' and row['task'] == 'folding_clothes'" + - "lambda row: row['episode_hash'] in {'', '', ...}" + + (The ``episode_hash in {...}`` pattern is already used in + ``mecka_pi_eval.yaml``.) +""" + +from __future__ import annotations + +import argparse +import json +import math +import os +import threading +import time +from pathlib import Path + +import cv2 +import imageio +import imageio_ffmpeg # noqa: F401 (ensures a bundled ffmpeg is importable) +import numpy as np +import simplejpeg +from flask import Flask, Response, abort, jsonify, request, send_file + +from egomimic.rldb.filters import DatasetFilter +from egomimic.rldb.zarr.zarr_dataset_multi import S3EpisodeResolver, ZarrEpisode +from egomimic.utils.aws.aws_data_utils import load_env + +# Primary egocentric RGB key for all human/mecka embodiments (see CONTRIBUTING_DATA.md). +IMAGE_KEY = "images.front_1" +BUCKET_NAME = "rldb" +VALID_DECISIONS = ("accept", "reject", "uncertain", "skip") + +# Shared on-cluster store where episodes are already downloaded (= paths.dataset_dir +# in egomimic/hydra_configs/paths/default.yaml). Treated as read-only. +DEFAULT_DATASET_DIR = Path("/storage/project/r-dxu345-0/shared/egoverseS3ZarrDatasets") + + +# --------------------------------------------------------------------------- +# Episode discovery +# --------------------------------------------------------------------------- +def discover_episodes( + lab: str, task: str, extra_filters: list[str], debug: int | None +) -> tuple[list[str], dict[str, str], dict[str, str]]: + """Query the SQL registry and return matched episodes. + + Returns: + (ordered_hashes, hash_to_s3path, hash_to_task) + """ + lambdas = [f"lambda row: row['lab'] == {lab!r} and row['task'] == {task!r}"] + lambdas.extend(extra_filters or []) + filters = DatasetFilter(filter_lambdas=lambdas) + + paths, hash_to_task = S3EpisodeResolver._get_filtered_paths( + filters, debug=debug if debug else None + ) + hash_to_s3 = {h: p for (p, h) in paths} + ordered = sorted(hash_to_s3.keys()) + return ordered, hash_to_s3, hash_to_task + + +# --------------------------------------------------------------------------- +# Download + render +# --------------------------------------------------------------------------- +def resolve_episode_dir( + dataset_dir: Path, cache_dir: Path, episode_hash: str, s3_path: str +) -> Path: + """Return the local zarr store dir for an episode. + + Prefers the existing read-only shared store (``dataset_dir``); falls back to a + writable ``cache_dir`` for any episode not already present, downloading it + there only if needed. + """ + shared = dataset_dir / episode_hash + if shared.is_dir(): + return shared + cached = cache_dir / episode_hash + if not cached.is_dir(): + S3EpisodeResolver._sync_s3_to_local( + bucket_name=BUCKET_NAME, + s3_paths=[(s3_path, episode_hash)], + local_dir=cache_dir, + ) + if not cached.is_dir(): + raise FileNotFoundError( + f"Episode {episode_hash} not in {dataset_dir} and download failed" + ) + return cached + + +def effective_stride(total: int, stride: int, max_frames: int) -> int: + """Frame stride that keeps the clip within ``max_frames`` while spanning the + WHOLE episode. Uses ``stride`` for short episodes, but increases it for long + ones so the video always covers start-to-end (no truncated tail).""" + s = max(1, stride) + if max_frames and total > 0: + s = max(s, math.ceil(total / max_frames)) + return s + + +def _decode_json_entry(value): + """Decode one zarr JSON-encoded entry (mirrors ZarrDataset._decode_json_entry).""" + if isinstance(value, np.void): + value = value.item() + if isinstance(value, memoryview): + value = value.tobytes() + if isinstance(value, bytearray): + value = bytes(value) + if isinstance(value, bytes): + return json.loads(value.decode("utf-8")) + if isinstance(value, str): + return json.loads(value) + return value + + +def _episode_labels(ep: ZarrEpisode) -> list[dict]: + """Decoded language annotations for an open episode, sorted by start frame. + + Each entry is ``{"text": str, "start_idx": int, "end_idx": int}``. Returns an + empty list if the episode has no ``annotations`` array. + """ + if "annotations" not in ep._collect_keys(): + return [] + raw = ep._store["annotations"][:] + out: list[dict] = [] + for x in raw: + try: + d = _decode_json_entry(x) + except Exception: + continue + if isinstance(d, dict): + out.append( + { + "text": str(d.get("text", "")), + "start_idx": int(d.get("start_idx", -1)), + "end_idx": int(d.get("end_idx", -1)), + } + ) + out.sort(key=lambda a: a["start_idx"]) + return out + + +def read_annotations(zarr_dir: Path) -> list[dict]: + """Convenience wrapper: open an episode and return its language annotations.""" + return _episode_labels(ZarrEpisode(zarr_dir)) + + +def _placeholder_mp4(out_path: Path, text: str, fps: int) -> None: + """Write a tiny 1-second clip with an error message (unreadable episodes).""" + frame = np.zeros((360, 640, 3), dtype=np.uint8) + cv2.putText( + frame, + text[:60], + (20, 180), + cv2.FONT_HERSHEY_SIMPLEX, + 0.8, + (0, 0, 255), + 2, + cv2.LINE_AA, + ) + with imageio.get_writer( + str(out_path), + fps=fps, + codec="libx264", + pixelformat="yuv420p", + macro_block_size=16, + format="FFMPEG", + ) as w: + for _ in range(fps): + w.append_data(frame) + + +def render_video( + zarr_dir: Path, + out_path: Path, + fps: int | None, + stride: int, + max_frames: int, + max_width: int, + crf: int = 30, + preset: str = "veryfast", +) -> None: + """Decode ``images.front_1`` JPEGs from one episode into a browser-playable MP4. + + Tuned for fast review over an SSH tunnel: libjpeg decodes large frames at a + reduced scale (``min_width``) so decode is cheap, x264 uses a fast ``preset`` + and ``crf`` for small files, and ``+faststart`` moves the moov atom to the + front so the browser can begin playback before the file finishes downloading. + """ + try: + ep = ZarrEpisode(zarr_dir) + total = len(ep) + meta_fps = int(ep.metadata.get("fps", 30) or 30) + out_fps = fps if fps else max(1, min(meta_fps, 30)) + + if IMAGE_KEY not in ep._collect_keys(): + _placeholder_mp4(out_path, f"NO KEY {IMAGE_KEY}", out_fps) + return + + blobs = ep.read({IMAGE_KEY: (0, total)})[IMAGE_KEY] + # Stride chosen so the clip spans the whole episode within the frame budget. + eff_stride = effective_stride(total, stride, max_frames) + idxs = list(range(0, total, eff_stride)) + if not idxs: + _placeholder_mp4(out_path, "EMPTY EPISODE", out_fps) + return + + # libx264 output params: small files + fast encode + progressive playback. + out_params = [ + "-preset", + preset, + "-crf", + str(crf), + "-movflags", + "+faststart", + ] + tmp_path = out_path.with_suffix(".tmp.mp4") + wrote_any = False + with imageio.get_writer( + str(tmp_path), + fps=out_fps, + codec="libx264", + pixelformat="yuv420p", + macro_block_size=16, + format="FFMPEG", + output_params=out_params, + ) as writer: + for i in idxs: + try: + # min_width lets libjpeg downscale in the DCT domain (cheap) + # for large source frames; fast flags trade a little quality + # for speed (fine for QC playback). + frame = simplejpeg.decode_jpeg( + bytes(blobs[i]), + colorspace="RGB", + fastdct=True, + fastupsample=True, + min_width=max_width, + ) + except Exception: + continue + h, w = frame.shape[:2] + if w > max_width: + new_w = max_width + new_h = int(round(h * (max_width / w))) + frame = cv2.resize( + frame, (new_w, new_h), interpolation=cv2.INTER_AREA + ) + writer.append_data(np.ascontiguousarray(frame)) + wrote_any = True + + if not wrote_any: + tmp_path.unlink(missing_ok=True) + _placeholder_mp4(out_path, "ALL FRAMES UNREADABLE", out_fps) + return + os.replace(tmp_path, out_path) + except Exception as exc: # noqa: BLE001 - surface any failure as a viewable clip + try: + _placeholder_mp4(out_path, f"RENDER ERROR: {exc}", 10) + except Exception: + raise + + +# --------------------------------------------------------------------------- +# Decisions persistence (JSONL, last-line-wins per hash) +# --------------------------------------------------------------------------- +class DecisionLog: + def __init__(self, path: Path): + self.path = path + self.path.parent.mkdir(parents=True, exist_ok=True) + self._lock = threading.Lock() + self.decisions: dict[str, str] = {} + self._load() + + def _load(self) -> None: + if not self.path.exists(): + return + with self.path.open("r") as f: + for line in f: + line = line.strip() + if not line: + continue + try: + rec = json.loads(line) + except json.JSONDecodeError: + continue + h, d = rec.get("episode_hash"), rec.get("decision") + if h and d in VALID_DECISIONS: + self.decisions[h] = d + + def record( + self, episode_hash: str, decision: str, task: str, lab: str, index: int + ) -> None: + rec = { + "episode_hash": episode_hash, + "decision": decision, + "task": task, + "lab": lab, + "index": index, + "ts": time.time(), + } + line = json.dumps(rec) + with self._lock: + with self.path.open("a") as f: + f.write(line + "\n") + f.flush() + os.fsync(f.fileno()) + self.decisions[episode_hash] = decision + + def counts(self) -> dict[str, int]: + c = {k: 0 for k in VALID_DECISIONS} + for d in self.decisions.values(): + c[d] = c.get(d, 0) + 1 + return c + + +# --------------------------------------------------------------------------- +# Flask app +# --------------------------------------------------------------------------- +INDEX_HTML = """ +Episode QC + + +
+ + accept 0 + reject 0 + uncertain 0 + skip 0 + + a accept   r reject   u uncertain +   s skip   ←/→ nav +
+
+ +
hash:  |  task: +  |  current decision: -
+
+ + + + + + + +
+
+
+ + +""" + + +def build_app(cfg, ordered, hash_to_s3, hash_to_task, log) -> Flask: + app = Flask(__name__) + render_locks: dict[str, threading.Lock] = {} + locks_guard = threading.Lock() + + def _lock_for(h: str) -> threading.Lock: + with locks_guard: + return render_locks.setdefault(h, threading.Lock()) + + def _ensure_video(h: str) -> Path: + out = cfg.video_dir / f"{h}.mp4" + if out.exists(): + return out + with _lock_for(h): + if out.exists(): + return out + zarr_dir = resolve_episode_dir( + cfg.dataset_dir, cfg.cache_dir, h, hash_to_s3[h] + ) + render_video( + zarr_dir, + out, + cfg.fps, + cfg.stride, + cfg.max_frames, + cfg.max_width, + crf=cfg.crf, + preset=cfg.preset, + ) + return out + + def _start_prewarm(n_workers: int) -> None: + """Render undecided episodes ahead of time in the background so they're + already cached (only the transfer cost remains) by the time the user + reaches them. Bounded concurrency; skips already-rendered clips.""" + cursor = {"i": 0} + cur_lock = threading.Lock() + + def worker(): + while True: + with cur_lock: + i = cursor["i"] + cursor["i"] += 1 + if i >= len(ordered): + return + h = ordered[i] + if (cfg.video_dir / f"{h}.mp4").exists() or h in log.decisions: + continue + try: + _ensure_video(h) + except Exception: # noqa: BLE001 - keep warming the rest + continue + + for _ in range(n_workers): + threading.Thread(target=worker, daemon=True).start() + + if getattr(cfg, "prewarm", 0): + _start_prewarm(cfg.prewarm) + + @app.get("/") + def index() -> Response: + return Response(INDEX_HTML, mimetype="text/html") + + @app.get("/state") + def state(): + eps = [ + { + "hash": h, + "task": hash_to_task.get(h, cfg.task), + "decision": log.decisions.get(h), + } + for h in ordered + ] + return jsonify(total=len(ordered), counts=log.counts(), episodes=eps) + + @app.get("/video/") + def video(h: str): + if h not in hash_to_s3: + abort(404) + out = _ensure_video(h) + return send_file(str(out), mimetype="video/mp4", conditional=True) + + ann_cache: dict[str, dict] = {} + + @app.get("/annotations/") + def annotations(h: str): + if h not in hash_to_s3: + abort(404) + if h not in ann_cache: + try: + zarr_dir = resolve_episode_dir( + cfg.dataset_dir, cfg.cache_dir, h, hash_to_s3[h] + ) + ep = ZarrEpisode(zarr_dir) + meta_fps = int(ep.metadata.get("fps", 30) or 30) + out_fps = cfg.fps if cfg.fps else max(1, min(meta_fps, 30)) + total = len(ep) + eff_stride = effective_stride(total, cfg.stride, cfg.max_frames) + # The video samples every `eff_stride`-th original frame and plays + # at `out_fps`, so one second of video advances this many ORIGINAL + # frames — used client-side to map currentTime -> annotation span. + ann_cache[h] = { + "labels": _episode_labels(ep), + "orig_per_sec": out_fps * eff_stride, + "total_frames": total, + } + except Exception: # noqa: BLE001 - missing/unreadable annotations -> empty + ann_cache[h] = {"labels": [], "orig_per_sec": 1, "total_frames": 0} + return jsonify(ann_cache[h]) + + @app.get("/prefetch/") + def prefetch(h: str): + if h in hash_to_s3: + threading.Thread(target=_ensure_video, args=(h,), daemon=True).start() + return ("", 204) + + @app.post("/decide") + def decide(): + data = request.get_json(force=True) + h, d = data.get("hash"), data.get("decision") + if h not in hash_to_s3 or d not in VALID_DECISIONS: + abort(400) + log.record( + h, d, hash_to_task.get(h, cfg.task), cfg.lab, int(data.get("index", -1)) + ) + return jsonify(ok=True, counts=log.counts()) + + return app + + +# --------------------------------------------------------------------------- +# CLI +# --------------------------------------------------------------------------- +def parse_args(): + p = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter + ) + p.add_argument("--lab", default="mecka") + p.add_argument("--task", default="folding_clothes") + p.add_argument( + "--dataset-dir", + type=Path, + default=DEFAULT_DATASET_DIR, + help="Existing (read-only) shared store of downloaded episodes " + "(= paths.dataset_dir). Episodes found here are never re-downloaded.", + ) + p.add_argument( + "--cache-dir", + type=Path, + default=Path("./qc_cache"), + help="Writable fallback cache for episodes NOT in --dataset-dir.", + ) + p.add_argument( + "--video-dir", + type=Path, + default=Path("./qc_videos"), + help="Local cache for rendered MP4s.", + ) + p.add_argument( + "--out", + type=Path, + default=None, + help="Decisions JSONL (default ./data_quality_review/__decisions.jsonl).", + ) + p.add_argument("--host", default="127.0.0.1") + p.add_argument("--port", type=int, default=5007) + p.add_argument( + "--fps", + type=int, + default=None, + help="Playback fps (default: episode fps, capped 30).", + ) + p.add_argument( + "--stride", + type=int, + default=3, + help="Use every Nth frame (higher = smaller/faster).", + ) + p.add_argument("--max-frames", type=int, default=450, help="Cap frames per clip.") + p.add_argument( + "--max-width", + type=int, + default=480, + help="Resize cap in px (lower = smaller/faster).", + ) + p.add_argument( + "--crf", + type=int, + default=30, + help="x264 quality (higher = smaller file, 28-32 good for QC).", + ) + p.add_argument( + "--preset", default="veryfast", help="x264 speed preset (ultrafast..medium)." + ) + p.add_argument( + "--prewarm", + type=int, + default=2, + help="Background workers that pre-render upcoming episodes so they " + "load instantly. 0 disables. Use more CPU headroom (e.g. on a " + "compute node) to raise this.", + ) + p.add_argument( + "--debug", type=int, default=None, help="Limit to first N matched episodes." + ) + p.add_argument( + "--extra-filter", + action="append", + default=[], + help="Extra filter lambda string (repeatable).", + ) + p.add_argument( + "--list-only", + action="store_true", + help="Print matched episodes and exit (no downloads, no server).", + ) + p.add_argument( + "--print-accepted", + action="store_true", + help="Print accepted hashes from the decisions file as a Python set and exit.", + ) + p.add_argument( + "--print-uncertain", + action="store_true", + help="Print uncertain (flagged-to-revisit) hashes from the decisions file and exit.", + ) + args = p.parse_args() + if args.out is None: + args.out = ( + Path("./data_quality_review") / f"{args.lab}_{args.task}_decisions.jsonl" + ) + # Resolve to absolute paths: Flask's send_file interprets relative paths + # against the app root_path (the package dir), not the process cwd. + args.dataset_dir = args.dataset_dir.resolve() + args.cache_dir = args.cache_dir.resolve() + args.video_dir = args.video_dir.resolve() + args.out = args.out.resolve() + return args + + +def main(): + cfg = parse_args() + load_env() + + if cfg.print_accepted or cfg.print_uncertain: + log = DecisionLog(cfg.out) + want = "accept" if cfg.print_accepted else "uncertain" + hashes = sorted(h for h, d in log.decisions.items() if d == want) + print(f"# {len(hashes)} {want} episodes from {cfg.out}") + inner = ", ".join(repr(h) for h in hashes) + print("lambda row: row['episode_hash'] in {" + inner + "}") + return + + ordered, hash_to_s3, hash_to_task = discover_episodes( + cfg.lab, cfg.task, cfg.extra_filter, cfg.debug + ) + print(f"Matched {len(ordered)} episodes for lab={cfg.lab!r} task={cfg.task!r}") + + if cfg.list_only: + for h in ordered: + print(f"{h}\t{hash_to_s3[h]}") + return + + if not ordered: + raise SystemExit("No episodes matched; nothing to review.") + + cfg.cache_dir.mkdir(parents=True, exist_ok=True) + cfg.video_dir.mkdir(parents=True, exist_ok=True) + log = DecisionLog(cfg.out) + + present = sum(1 for h in ordered if (cfg.dataset_dir / h).is_dir()) + missing = len(ordered) - present + print( + f"Episodes already in {cfg.dataset_dir}: {present}/{len(ordered)}" + + ( + f" ({missing} will be fetched to {cfg.cache_dir} on demand)" + if missing + else "" + ) + ) + done = sum(1 for h in ordered if h in log.decisions) + print(f"Resuming: {done}/{len(ordered)} already decided. Decisions -> {cfg.out}") + print( + f"Open http://{cfg.host}:{cfg.port} (SSH: ssh -L {cfg.port}:127.0.0.1:{cfg.port} )" + ) + + app = build_app(cfg, ordered, hash_to_s3, hash_to_task, log) + app.run(host=cfg.host, port=cfg.port, threaded=True) + + +if __name__ == "__main__": + main()