Datasets:
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fa8b928 | 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 | import os
"""PaddleOCR-VL-1.6 (default pipeline) on KDoc+Kolm pages -> score_external.py jsonl. Resumable.
Usage: run_paddlevl.py OUT_JSONL [LIMIT] [SHARD_INDEX SHARD_COUNT]"""
import json, os, sys, time
from pathlib import Path
from paddleocr import PaddleOCRVL
M = Path(os.environ.get("MANIFEST_DIR", "manifests"))
out = Path(sys.argv[1]); limit = int(sys.argv[2]) if len(sys.argv) > 2 else 0
si, sc = (int(sys.argv[3]), int(sys.argv[4])) if len(sys.argv) > 4 else (0, 1)
pages = [p for b in os.environ.get("BENCHES", "kdoc,kolm").split(",") for p in json.loads((M / f"{b}.json").read_text())["pages"]][si::sc]
if limit: pages = pages[:limit]
done = {json.loads(l)["id"] for l in out.open()} if out.exists() else set()
pipe = PaddleOCRVL(pipeline_version="v1.6", vl_rec_backend="vllm-server", vl_rec_server_url="http://127.0.0.1:8050/v1", vl_rec_api_model_name="PaddleOCR-VL-1.6-0.9B", vl_rec_max_concurrency=64)
with out.open("a") as f:
for p in pages:
if p["id"] in done: continue
t = time.time(); text, err, boxes = "", None, []
try:
for res in pipe.predict(p["image"]):
md = res.markdown
text += (md.get("markdown_texts", "") if isinstance(md, dict) else str(md))
r = res.json.get("res", res.json)
for b in r.get("parsing_res_list", []):
boxes.append({"label": b.get("block_label"), "bbox": b.get("block_bbox")})
boxes.append({"image_size": [r.get("width"), r.get("height")]})
except Exception as e:
err = repr(e)[:300]
f.write(json.dumps({"id": p["id"], "text": text, "boxes": boxes, "error": err, "seconds": round(time.time() - t, 2)}, ensure_ascii=False) + "\n"); f.flush()
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