src/data_audit.py

from __future__ import annotations

import argparse
import collections
import os

from .common import CAMERA_ORDER, load_rows, normalized_row


def main() -> None:
    parser = argparse.ArgumentParser(description="Audit flattened DriveLM data")
    parser.add_argument("--data-dir", required=True)
    parser.add_argument("--splits", nargs="+", default=["train", "val"])
    parser.add_argument("--num-views", type=int, default=6)
    parser.add_argument("--allow-missing-images", action="store_true")
    args = parser.parse_args()

    failed = False
    cameras = CAMERA_ORDER[: args.num_views]
    for split in args.splits:
        rows = [normalized_row(row) for row in load_rows(args.data_dir, split)]
        tasks = collections.Counter(row["task_type"] for row in rows)
        missing_text = sum(not row["question"] or not row["answer"] for row in rows)
        missing_images = collections.Counter()
        for row in rows:
            for camera in cameras:
                path = row["image_paths"].get(camera, "")
                if not path or not os.path.isfile(path):
                    missing_images[camera] += 1
        print(f"[{split}] rows={len(rows)} task_types={dict(tasks)}")
        print(f"[{split}] empty_question_or_answer={missing_text}")
        print(f"[{split}] missing_images={dict(missing_images)}")
        if rows:
            print(f"[{split}] first_question={rows[0]['question'][:160]!r}")
        failed |= missing_text > 0
        failed |= bool(missing_images) and not args.allow_missing_images
    if failed:
        raise SystemExit("Data audit failed; fix the reported issues before training")
    print("DATA_AUDIT_OK")


if __name__ == "__main__":
    main()