| |
| from __future__ import annotations |
|
|
| import argparse |
| import json |
| import os |
| from pathlib import Path |
|
|
| REPO_ID = "Gyubeum/AndroidFlux_RM_Eval" |
| CONFIGS = ["ui_genie_pairwise", "android_flux_recovery_action_preference"] |
|
|
|
|
| def get_parquet(config: str, name: str, local_dir: str | None, token: str | None) -> str: |
| if local_dir: |
| return str(Path(local_dir) / config / name) |
| from huggingface_hub import hf_hub_download |
| return hf_hub_download( |
| repo_id=REPO_ID, repo_type="dataset", |
| filename=f"{config}/{name}", token=token, |
| ) |
|
|
|
|
| def main() -> None: |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--config", default="ui_genie_pairwise", choices=CONFIGS) |
| ap.add_argument("--subset", default="full", choices=["full", "reduced"]) |
| ap.add_argument("--n", type=int, default=2) |
| ap.add_argument("--local_dir", default=None, |
| help="Read the parquet from here instead of the Hub") |
| ap.add_argument("--image_dir", default="/tmp/rm_eval_images", |
| help="Where to write screenshots") |
| args = ap.parse_args() |
| token = os.environ.get("HF_TOKEN") |
|
|
| import pyarrow.parquet as pq |
|
|
| print("=" * 68) |
| print(f"1. {args.config}/samples.parquet") |
| print("=" * 68) |
| table = pq.read_table(get_parquet(args.config, "samples.parquet", args.local_dir, token)) |
| rows = table.to_pylist() |
| if args.subset == "reduced": |
| rows = [r for r in rows if r["in_reduced"]] |
| print(f" {len(rows)} rows ({args.subset}), {len(table.schema)} columns") |
| print(f" columns: {', '.join(table.schema.names)}") |
|
|
| rows = rows[: args.n] |
| for r in rows: |
| print(f"\n sample_id : {r['sample_id']}") |
| print(f" goal : {r['goal'][:90]}") |
| print(f" images : {r['n_images']} screen: {r['screen_width']}x{r['screen_height']}") |
| print(f" chosen : {r['chosen_response'][:80].replace(chr(10), ' ')}") |
| print(f" rejected : {r['rejected_response'][:80].replace(chr(10), ' ')}") |
| if args.config != "ui_genie_pairwise": |
| print(f" outcome : {r['outcome']} margin: {r['margin']} {r['model_name']}") |
|
|
| print("\n" + "=" * 68) |
| print("2. Resolving screenshots from images.parquet") |
| print("=" * 68) |
| wanted: set[str] = set() |
| for r in rows: |
| wanted |= set(r["image_ids"]) |
| images_path = get_parquet(args.config, "images.parquet", args.local_dir, token) |
| out_dir = Path(args.image_dir) / args.config |
| out_dir.mkdir(parents=True, exist_ok=True) |
|
|
| resolved: dict[str, Path] = {} |
| for batch in pq.ParquetFile(images_path).iter_batches(batch_size=32): |
| for row in batch.to_pylist(): |
| if row["image_id"] not in wanted or row["image_id"] in resolved: |
| continue |
| suffix = ".png" if row["mime_type"] == "image/png" else ".jpg" |
| path = out_dir / f"{row['image_id']}{suffix}" |
| if not path.is_file(): |
| path.write_bytes(row["encoded_bytes"]) |
| resolved[row["image_id"]] = path |
| if len(resolved) == len(wanted): |
| break |
| print(f" wrote {len(resolved)}/{len(wanted)} screenshots -> {out_dir}") |
|
|
| print("\n" + "=" * 68) |
| print("3. Rebuilding the reward-model prompt") |
| print("=" * 68) |
| r = rows[0] |
| prefix = json.loads(r["prefix_messages_json"]) |
| for candidate, label in ((r["chosen_response"], "chosen"), (r["rejected_response"], "rejected")): |
| messages = prefix + [{"role": "assistant", "content": candidate}] |
| print(f" {label:8} -> {len(messages)} turns, {len(r['image_ids'])} image(s)") |
| print(f" placeholders={r['user_content'].count('<image>')} " |
| f"n_images={r['n_images']} image_ids={len(r['image_ids'])}") |
| print(f" image order: {[str(resolved[i].name) for i in r['image_ids']]}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
