#!/usr/bin/env python3 """Export the Hugging Face VCR dataset back to the original r2c layout.""" from __future__ import annotations import argparse import json import os import shutil from itertools import islice from pathlib import Path from datasets import load_dataset, load_from_disk from huggingface_hub import hf_hub_download from tqdm import tqdm def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("repo_id", help="Hugging Face dataset repo id, e.g. rowanz/vcr") parser.add_argument("output_dir", type=Path, help="Directory to create in r2c-compatible layout") parser.add_argument("--token", default=None, help="HF token for private/gated repos") parser.add_argument("--limit", type=int, default=None, help="Optional per-split row limit for smoke tests") parser.add_argument("--local-stage-dir", type=Path, default=None, help="Use a local vcr_hf.py stage directory instead of the Hub") args = parser.parse_args() args.output_dir.mkdir(parents=True, exist_ok=True) images_dir = args.output_dir / "vcr1images" images_dir.mkdir(parents=True, exist_ok=True) for split in ("train", "val", "test"): if args.local_stage_dir is None: src = hf_hub_download( repo_id=args.repo_id, repo_type="dataset", filename=f"original_annotations/{split}.jsonl", token=args.token, ) else: src = args.local_stage_dir / "original_annotations" / f"{split}.jsonl" dst = args.output_dir / f"{split}.jsonl" if args.limit is None: shutil.copyfile(src, dst) else: with open(src, "r", encoding="utf-8") as f_in, open(dst, "w", encoding="utf-8") as f_out: for i, line in enumerate(f_in): if i >= args.limit: break f_out.write(line) if args.local_stage_dir is None: if args.limit is None: split_expr = {s: s for s in ("train", "validation", "test")} ds = load_dataset(args.repo_id, "image_examples", split=split_expr, token=args.token) if not isinstance(ds, dict): ds = {"train": ds} else: ds = { split_name: load_dataset( args.repo_id, "image_examples", split=split_name, streaming=True, token=args.token, ) for split_name in ("train", "validation", "test") } else: local_ds = load_from_disk(args.local_stage_dir / "image_examples.dataset") ds = {} for split_name in ("train", "validation", "test"): ds[split_name] = local_ds[split_name].select(range(min(args.limit, len(local_ds[split_name])))) if args.limit else local_ds[split_name] seen: set[str] = set() for split_name, split_ds in ds.items(): rows = islice(split_ds, args.limit) if args.limit is not None else split_ds for row in tqdm(rows, desc=f"export {split_name} images", total=args.limit): img_fn = row["img_fn"] metadata_fn = row["metadata_fn"] if img_fn not in seen: image = row["image"] dst_img = images_dir / img_fn dst_img.parent.mkdir(parents=True, exist_ok=True) image.save(dst_img) seen.add(img_fn) if metadata_fn not in seen: metadata = { "boxes": row["boxes"], "segms": json.loads(row["segms_json"]), "names": row["objects"], "width": row["width"], "height": row["height"], } dst_meta = images_dir / metadata_fn dst_meta.parent.mkdir(parents=True, exist_ok=True) with open(dst_meta, "w", encoding="utf-8") as f: json.dump(metadata, f, separators=(",", ":")) seen.add(metadata_fn) print(f"Wrote r2c-compatible VCR layout to {args.output_dir}", flush=True) os._exit(0) if __name__ == "__main__": main()