File size: 2,722 Bytes
ed46c5d | 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 | """Reference-free confidence of recognizer outputs: mean negative log-likelihood of the (loop-cut) predicted
transliteration under the recognizer that produced it, given the image and the recognition prompt.
Input: a prediction file with {"id", "image", "pred", ...}. Output: one line per row,
{"id", "nll", "n_tokens"}; nll is null for empty predictions. Reuses the training example builder
(train_lora.Examples) with the prediction as the target and without the end-of-turn token. Resumable.
"""
import argparse
import json
import math
import os
import sys
import torch
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from qwen_common import load_model # noqa: E402
from train_lora import Examples # noqa: E402
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--model", required=True)
ap.add_argument("--adapter", required=True)
ap.add_argument("--input", required=True)
ap.add_argument("--image_root", required=True)
ap.add_argument("--out", required=True)
ap.add_argument("--device", default="cuda")
ap.add_argument("--limit", type=int, default=0) # smoke tests
args = ap.parse_args()
rows = [json.loads(l) for l in open(args.input) if l.strip()]
done = set()
if os.path.exists(args.out):
done = {json.loads(l)["id"] for l in open(args.out) if l.strip()}
todo = [dict(r, target=r["pred"] or "") for r in rows if r["id"] not in done]
if args.limit:
todo = todo[:args.limit]
print(f"rows {len(rows)} done {len(done)} todo {len(todo)}", flush=True)
if not todo:
return
processor, model = load_model(args.model, adapter=args.adapter)
model.to(args.device).eval()
ds = Examples(todo, processor, "ocr", args.image_root)
ds.end = "" # score the transliteration only, not the end-of-turn token
fout = open(args.out, "a")
for i, r in enumerate(todo):
rec = {"id": r["id"], "nll": None, "n_tokens": 0}
if r["target"].strip():
try:
item = ds[i]
n = int((item["labels"] != -100).sum())
batch = {k: v.to(args.device) for k, v in item.items()}
with torch.no_grad(), torch.autocast("cuda", dtype=torch.bfloat16):
loss = model(**batch).loss.item()
rec.update(nll=loss if math.isfinite(loss) else None, n_tokens=n)
except Exception as e: # keep going; the row is reported as unscored
rec["error"] = repr(e)[:200]
fout.write(json.dumps(rec) + "\n")
fout.flush()
if (i + 1) % 50 == 0:
print(f"{i + 1}/{len(todo)}", flush=True)
print("DONE", flush=True)
if __name__ == "__main__":
main()
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