File size: 3,334 Bytes
79017c8 | 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 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | #!/usr/bin/env python3
"""
regen_captions_v2.py — regenerate captions (v2, disease/lesion-centric) from an
existing *_images_v1.parquet, WITHOUT re-running extraction.
Works uniformly for the private / public-fundus / public-OCT manifests because
caption_v2.caption_rows_for() is a pure function of the manifest row.
Writes:
{out_dir}/{out_name} unified captions_v2 parquet
{out_dir}/{cohort}_captions_v2.parquet per-cohort (HF staging) if --per-cohort
"""
import argparse
import time
from pathlib import Path
import pandas as pd
import caption_v2
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--manifest", required=True, help="path to *_images_v1.parquet")
ap.add_argument("--out-dir", required=True)
ap.add_argument("--out-name", default="captions_v2.parquet")
ap.add_argument("--per-cohort", action="store_true",
help="also write one {cohort}_captions_v2.parquet per cohort")
ap.add_argument("--lesion-sidecar", default=None,
help="optional parquet (image_id, lesion_codes) to merge into "
"lesion_tags before captioning (from enrich_lesion_tags.py)")
args = ap.parse_args()
out_dir = Path(args.out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
print(f"loading manifest: {args.manifest}")
df = pd.read_parquet(args.manifest)
print(f" {len(df)} image rows, {df.cohort.nunique()} cohort(s)")
sidecar = {}
if args.lesion_sidecar:
sc = pd.read_parquet(args.lesion_sidecar)
sidecar = {r["image_id"]: list(r["lesion_codes"]) for _, r in sc.iterrows()}
print(f" merged lesion sidecar: {len(sidecar)} images with extra lesion tags")
t0 = time.time()
records = df.to_dict("records")
all_caps, per_cohort = [], {}
for i, row in enumerate(records):
if sidecar:
extra = sidecar.get(row.get("image_id"))
if extra:
cur = caption_v2._as_list(row.get("lesion_tags"))
row["lesion_tags"] = list(dict.fromkeys(cur + extra))
caps = caption_v2.caption_rows_for(row)
all_caps.extend(caps)
if args.per_cohort:
per_cohort.setdefault(row.get("cohort"), []).extend(caps)
if (i + 1) % 100000 == 0:
dt = time.time() - t0
print(f" ... {i+1}/{len(records)} ({dt:.0f}s, {(i+1)/dt:.0f} img/s)")
dt = time.time() - t0
print(f" done in {dt:.0f}s: {len(all_caps)} caption rows "
f"({len(all_caps)/max(len(records),1):.2f} per image)")
cap_df = pd.DataFrame(all_caps, columns=caption_v2.CAP_COLS)
uni = out_dir / args.out_name
cap_df.to_parquet(uni, index=False)
print(f"\n wrote unified: {uni} ({len(cap_df)} rows)")
print(" level distribution:")
print(cap_df["level"].value_counts().to_string())
if args.per_cohort:
for cohort, caps in per_cohort.items():
cp = out_dir / f"{cohort}_captions_v2.parquet"
pd.DataFrame(caps, columns=caption_v2.CAP_COLS).to_parquet(cp, index=False)
print(f" wrote {len(per_cohort)} per-cohort caption parquets")
# peek a few
print("\n sample captions:")
for t in cap_df["prompt_text"].drop_duplicates().head(12):
print(f" {t}")
if __name__ == "__main__":
main()
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