cambw_lmms_eval/scripts/prepare_data.py

#!/usr/bin/env python3
"""
从 Cambrian-W benchmark_single 的 part1 / part2 / part3 JSON 生成 lmms-eval 用的 doc 列表（JSONL）。
- part1_long: part1_long_videos_-_dual_format_appearance.json
- part2_3_short: part2_short_videos_-_place_&_motion.json + part3_short_videos_-_objects_with_dual_format_fixed_choices.json
每个 doc = 一个 checkpoint 级别的评估项（含 video_path, task_type, question, answer, frame_indices 等）。
"""
from __future__ import annotations

import argparse
import json
import os
from pathlib import Path
from typing import Any, Dict, List, Optional

SOURCE_FOLDER_TO_DATA_SUBDIR = {
    "new_long_video/corrected_json_2": "new_long_video_persp",
    "top20merge/corrected_json": "top20merge_0207_persp",
    "long_video/corrected_json_2": "long_video_persp",
    "top20merge_full/corrected_json_2": "top20merge_0207_persp",
}


def resolve_video_path(video: Dict[str, Any], data_root: str) -> Optional[str]:
    video_path = video.get("video_path") or video.get("video")
    if video_path:
        video_path = str(video_path).strip()
        if os.path.isabs(video_path):
            for old_prefix in [
                "/lustre/fs12/portfolios/nvr/projects/nvr_av_end2endav/users/ymingli/projects/xty/cambw/data",
                "/lustre/fsw/portfolios/nvr/users/ymingli/projects/xty/cambw/data",
                "/data",
                "/path/to/data",
            ]:
                if video_path.startswith(old_prefix):
                    video_path = os.path.join(data_root, video_path[len(old_prefix):].lstrip("/"))
                    break
        else:
            video_path = os.path.join(data_root, video_path)
        return video_path
    video_name = video.get("video_name")
    source_folder = video.get("source_folder")
    if not video_name or not source_folder:
        return None
    subdir = SOURCE_FOLDER_TO_DATA_SUBDIR.get(source_folder)
    if not subdir:
        return None
    base = video_name if video_name.endswith(".mp4") else f"{video_name}.mp4"
    return os.path.join(data_root, subdir, base)


def task_has_valid_checkpoints(task: Dict[str, Any]) -> bool:
    return any(cp.get("answer") is not None for cp in task.get("checkpoints", []))


def flatten_part1(bench_path: Path, data_root: str) -> List[Dict[str, Any]]:
    with bench_path.open("r") as f:
        data = json.load(f)
    docs = []
    for v in data.get("videos") or []:
        video_path = resolve_video_path(v, data_root)
        if not video_path:
            continue
        video_name = v.get("video_name", "")
        tasks = v.get("tasks") or []
        for ti, t in enumerate(tasks):
            if not task_has_valid_checkpoints(t):
                continue
            ttype = t.get("task_type", "")
            if t.get("variant"):
                ttype = f"{ttype}_{t['variant']}"
            for cpi, cp in enumerate(t.get("checkpoints", [])):
                if cp.get("answer") is None:
                    continue
                doc = {
                    "doc_id": f"{video_name}|{ti}|{ttype}|{cpi}",
                    "video_name": video_name,
                    "video_path": video_path,
                    "task_type": ttype,
                    "question": t.get("question", ""),
                    "answer": cp["answer"],
                }
                if ttype == "frame_recall" and cp.get("frames"):
                    doc["frame_indices"] = [int(f["frame_idx"]) for f in cp["frames"]]
                else:
                    doc["frame_indices"] = None
                if cp.get("options") is not None:
                    doc["options"] = cp["options"]
                else:
                    doc["options"] = None
                if t.get("subset_concepts") is not None:
                    doc["subset_concepts"] = t["subset_concepts"]
                else:
                    doc["subset_concepts"] = None
                docs.append(doc)
    return docs


def flatten_part2_or_part3(bench_path: Path, data_root: str) -> List[Dict[str, Any]]:
    return flatten_part1(bench_path, data_root)


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--benchmark-dir", type=str, required=True)
    parser.add_argument("--data-root", type=str, required=True)
    parser.add_argument("--out-dir", type=str, default="data")
    args = parser.parse_args()
    bench_dir = Path(args.benchmark_dir)
    out_dir = Path(args.out_dir)
    out_dir.mkdir(parents=True, exist_ok=True)

    part1_file = bench_dir / "part1_long_videos_-_dual_format_appearance.json"
    if part1_file.exists():
        docs1 = flatten_part1(part1_file, args.data_root)
        out1 = out_dir / "part1_long.jsonl"
        with out1.open("w") as f:
            for d in docs1:
                f.write(json.dumps(d, ensure_ascii=False) + "\n")
        print(f"part1_long: {len(docs1)} docs -> {out1}")
    else:
        print(f"Skip part1: not found {part1_file}")

    part2_file = bench_dir / "part2_short_videos_-_place_&_motion.json"
    part3_file = bench_dir / "part3_short_videos_-_objects_with_dual_format_fixed_choices.json"
    docs2_3 = []
    if part2_file.exists():
        docs2_3.extend(flatten_part2_or_part3(part2_file, args.data_root))
        print(f"part2: {len(docs2_3)} docs")
    if part3_file.exists():
        n_before = len(docs2_3)
        docs2_3.extend(flatten_part2_or_part3(part3_file, args.data_root))
        print(f"part3: +{len(docs2_3) - n_before} docs")
    if docs2_3:
        out2_3 = out_dir / "part2_3_short.jsonl"
        with out2_3.open("w") as f:
            for d in docs2_3:
                f.write(json.dumps(d, ensure_ascii=False) + "\n")
        print(f"part2_3_short: {len(docs2_3)} docs -> {out2_3}")


if __name__ == "__main__":
    main()