"""Single entry point for independent crop and native-fullpage checkpoint evaluation.""" import argparse import hashlib import json import os from pathlib import Path import subprocess import sys EXP = Path(__file__).resolve().parent V5 = EXP.parent.parent PAGE = Path('/autodl-tmp/official_table_pages_20261031') PACK = PAGE / 'hf_sync/canonical_rebalanced' CROP_PY = '/root/autodl-tmp/venv_train/bin/python' PAGE_PY = '/root/autodl-tmp/venv_paddleocrvl_eval/bin/python' def dump(path, obj): path.parent.mkdir(parents=True, exist_ok=True) tmp = path.with_suffix('.tmp') tmp.write_text(json.dumps(obj, ensure_ascii=False, indent=2) + '\n') tmp.replace(path) def digest(path): return hashlib.sha256(path.read_bytes()).hexdigest() def plan(model, tag, track): crop_env = dict(os.environ, PYTHONPATH='/root/autodl-tmp/vllm_local/site-packages:/root/autodl-tmp/venv_train/lib/python3.12/site-packages:/autodl-fs/data/vllm_env/lib/python3.12/site-packages', POVL_VLLM='/root/autodl-tmp/vllm_local/bin/vllm', POVL_PARALLEL_ENGINE_BOOT='1', TOKENIZERS_PARALLELISM='false', OMP_NUM_THREADS='4', MKL_NUM_THREADS='4') crop_env.pop('POVL_RUN_DIR', None) page_env = dict(crop_env) page_env.pop('PYTHONPATH', None) jobs = [] if track in ('crop', 'both'): jobs.append(('crop', [CROP_PY, str(V5 / 'src/eval_mix.py'), '--model', model, '--tag', tag + '_crop', '--packs', str(PACK), '--gpus', '0', '1', '2', '3', '--per-gpu', '3', '--conc-per-worker', '16', '--score-workers', '12', '--score-batch', '64', '--max-tokens', '16384'], crop_env)) if track in ('fullpage', 'both'): jobs.append(('fullpage', [PAGE_PY, str(EXP / 'whole_page_eval.py'), '--model', model, '--tag', tag + '_fullpage'], page_env)) return jobs def crop_complete(directory): scores, summary = directory / 'scores.jsonl', directory / 'summary.json' if not scores.exists() or not summary.exists(): return False keys = set() for line in scores.open(): r = json.loads(line) key = r['cohort'], r['sample_id'] if key in keys: raise RuntimeError('Duplicate crop score identity: ' + str(key)) keys.add(key) return len(keys) == 176810 def check_free_gpus(): free = subprocess.check_output(['nvidia-smi', '--query-gpu=memory.free', '--format=csv,noheader,nounits'], text=True).splitlines() if len(free) != 4 or min(map(int, free)) < 40000: raise RuntimeError('Crop evaluation needs four free GPUs with >=40000 MiB each; existing training is left running.') def main(): ap = argparse.ArgumentParser(description=__doc__) ap.add_argument('--model', required=True, help='Complete PaddleOCR-VL merged checkpoint directory') ap.add_argument('--tag', required=True, help='Unique checkpoint/run name') ap.add_argument('--track', choices=['both', 'crop', 'fullpage'], default='both') ap.add_argument('--output-dir', type=Path, default=Path('/autodl-tmp/table_evaluation')) ap.add_argument('--dry-run', action='store_true', help='Print commands only; no writes or GPU work') args = ap.parse_args() if not args.tag or Path(args.tag).name != args.tag or args.tag in ('.', '..'): ap.error('--tag must be one directory name') model_path = Path(args.model).resolve() config = model_path / 'config.json' if not config.exists() or json.loads(config.read_text()).get('model_type') != 'paddleocr_vl': ap.error('--model must contain a PaddleOCR-VL config.json') weights = sorted(model_path.glob('*.safetensors')) if not weights or (model_path / 'adapter_config.json').exists(): ap.error('Merge the adapter with the base first; a complete model is required') jobs = plan(str(model_path), args.tag, args.track) if args.dry_run: print(json.dumps({'model': str(model_path), 'track': args.track, 'gpus': 4, 'vllm_instances_per_gpu': 3, 'commands': {name: cmd for name, cmd, env in jobs}, 'inherited_training_run_removed': all('POVL_RUN_DIR' not in env for _, _, env in jobs), 'fullpage_pythonpath_removed': all('PYTHONPATH' not in env for name, _, env in jobs if name == 'fullpage'), 'summary': str(args.output_dir / args.tag / 'results.json')}, indent=2)) return root = args.output_dir / args.tag identity = {'model': str(model_path), 'config_sha256': digest(config), 'weights': [{'name': p.name, 'bytes': p.stat().st_size, 'mtime_ns': p.stat().st_mtime_ns} for p in weights], 'crop_release_sha256': digest(PACK / 'release_audit.json')} binding = root / 'identity.json' if binding.exists() and json.loads(binding.read_text()) != identity: raise RuntimeError('Model or evaluation pack changed; use a new --tag') # An unbound old crop cache cannot safely be associated with this model. crop_dir = V5 / 'eval/runs' / (args.tag + '_crop') if not binding.exists() and args.track in ('crop', 'both') and crop_dir.exists(): raise RuntimeError('Existing crop tag has no unified model binding; use a new --tag') dump(binding, identity) report_path = root / 'results.json' report = json.loads(report_path.read_text()) if report_path.exists() else {'model': str(model_path), 'tracks': {}, 'combined_metric': None, 'note': 'Crop/fullpage metrics remain independent.'} for name, cmd, env in jobs: dump(root / 'status.json', {'phase': name, 'command': cmd}) if name == 'crop' and crop_complete(crop_dir): print('Reusing completed, identity-bound crop scores', flush=True) else: if name == 'crop': check_free_gpus() print('Evaluating ' + name, flush=True) with (root / (name + '.log')).open('a') as log: subprocess.run(cmd, cwd=V5, env=env, stdout=log, stderr=subprocess.STDOUT, check=True) summary = crop_dir / 'summary.json' if name == 'crop' else PAGE / (args.tag + '_fullpage') / 'SUMMARY.json' if name == 'crop' and not crop_complete(crop_dir): raise RuntimeError('Crop evaluation incomplete; inspect ' + str(root / 'crop.log')) result = json.loads(summary.read_text()) if name == 'fullpage' and not result.get('complete'): raise RuntimeError('Fullpage evaluation incomplete; inspect ' + str(root / 'fullpage.log')) report['tracks'][name] = {'summary_file': str(summary), 'summary': result} dump(report_path, report) dump(root / 'status.json', {'phase': 'complete', 'tracks': [name for name, _, _ in jobs], 'results': str(report_path)}) print(str(report_path), flush=True) if __name__ == '__main__': main()