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d5049a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | 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()
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