#!/usr/bin/env python """Stream the States view: a screenshot and its dense annotation. python examples/stream_states.py --root . --split train --limit 2000 The recommended training filter is `record["flags"]["state_train_eligible"]`, which drops near-duplicate scenes and no-op episode frames. Every publishable observation is in the payload, so an evaluation that wants them can have them. With `webdataset` installed the same thing is four lines: import webdataset as wds url = "data/train/part-{00000..00903}.tar" dataset = (wds.WebDataset(url, shardshuffle=True) .decode("pil") .to_tuple("png", "leaf.json", "screentag.txt", "record.json")) """ from __future__ import annotations import argparse import collections import sys import time from pathlib import Path sys.path.insert(0, str(Path(__file__).resolve().parent)) from _common import decode, iter_tar, shards_for # noqa: E402 def main() -> int: ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) ap.add_argument("--root", default=".") ap.add_argument("--split", default="train") ap.add_argument("--limit", type=int, default=2000) ap.add_argument("--eligible-only", action="store_true", help="the recommended training filter") ap.add_argument("--decode-images", action="store_true") args = ap.parse_args() shards = shards_for(args.root, args.split) if not shards: print("no shards under %s/data/%s" % (args.root, args.split), file=sys.stderr) return 1 seen = kept = 0 elements = 0 apps: collections.Counter = collections.Counter() started = time.time() for shard in shards: for sample in iter_tar(shard): seen += 1 item = decode(sample, with_image=args.decode_images) record = item["record"] if args.eligible_only and not record["flags"]["state_train_eligible"]: continue kept += 1 elements += len(item["elements"]) apps.update(record["apps"]) if kept >= args.limit: break if kept >= args.limit: break seconds = time.time() - started print("%s samples read, %s kept in %.1fs (%.0f samples/s)" % ("{:,}".format(seen), "{:,}".format(kept), seconds, kept / max(seconds, 1e-9))) print("mean elements per sample: %.1f" % (elements / max(kept, 1))) print("applications: %s" % ", ".join("%s %d" % pair for pair in apps.most_common(8))) return 0 if __name__ == "__main__": raise SystemExit(main())