dschlueter
4638a12571
Neue Standardregel p2t (GLM/Cloud wird nicht mehr benutzt):
- pix2text liest zuerst; übernommen wird immer seine Lesart, wenn Qwen sie
bestätigt oder – nur bei Uneinigkeit gefragt – Gemma. Qwen = Gemma gegen
pix2text geht an einen Menschen. Schlusspunkt muss bestätigt sein; Bild-Veto
und Pandoc auf pix2texts Lesart. Alte Regel als consensus_rule=vlm3 wählbar.
- pix2text auf der GPU (onnxruntime-gpu, FORMULA_P2T_DEVICE=auto/cpu/cuda:N)
- Ausgabe bereinigt: post_process aus pix2tex, Fettschreibweisen \boldmath/\bf
-> \boldsymbol/\mathbf; an 400 Lesarten gleiches gesetztes Bild
- Normalform: \mathop, \begin{array}[t]
- calibrate: --engines, Regel-Tabelle, --reuse; wahre Formeln aus dem alt-Text
bereinigt (aligned, \mathop{}\!, upgreek)
- tools/zeittest.py, tools/regeltest.py (--reuse, --exclude)
Messung: Göllmann Band 1 (2 x 150) 86 % / 85 % automatisch, 0 falsch
(bisher 78 % / 70 %); Band 2, zwei Stichproben zu 50: 78 % und 86 %, 0 falsch.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
218 lines
12 KiB
Python
218 lines
12 KiB
Python
#!/usr/bin/env python3
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"""Run the standard consensus rule on formulas whose TeX is known and show every case for review.
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python3 tools/regeltest.py EPUB [--n 50] [--name regeltest] [--reuse vergleich/regeltest/ergebnis.json]
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[--exclude vergleich/regeltest/ergebnis.json ...]
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Like `run`: pix2text reads every formula first, then Qwen (local); Gemma (local2) only where they
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disagree; then the rough image check on pix2text's reading and decide_pair_tiebreak. Writes
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vergleich/<name>/index.html (not versioned): summary, then per formula the original, the true
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formula and every reading set with pdflatex, the decision and whether the result is right.
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--reuse takes the readings (and their times) of an earlier run instead of asking the models again;
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--exclude leaves out the formulas of earlier runs, so a new sample has none of them.
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"""
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from __future__ import annotations
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import argparse
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import html
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import json
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import random
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import statistics
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import sys
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import time
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from pathlib import Path
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import numpy as np
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from PIL import Image
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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from konsens_bericht import orig_img, png_uri # noqa: E402
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from formula_img2latex.calibrate import _best_preset, corpus # noqa: E402
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from formula_img2latex.checks import pandoc_mathml_ok # noqa: E402
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from formula_img2latex.consensus import same_reading # noqa: E402
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from formula_img2latex.decide import consensus_thresholds, decide_pair_tiebreak # noqa: E402
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from formula_img2latex.engines import make_engine # noqa: E402
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from formula_img2latex.engines.p2t import tidy_p2t # noqa: E402
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from formula_img2latex.imageio import load_ink # noqa: E402
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from formula_img2latex.render import render_many # noqa: E402
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from formula_img2latex.tex import strip_delimiters # noqa: E402
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from formula_img2latex.verify import verify # noqa: E402
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TOOLS = [("local", "Qwen"), ("p2t", "pix2text"), ("local2", "Gemma")]
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def agreements(texes: list[str | None], display: bool, preset: str) -> list[tuple[int, int, str]]:
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"""Pairs (Qwen, pix2text), (pix2text, Gemma), (Qwen, Gemma) as in pipeline.decide_all_consensus (strict)."""
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out = []
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for i, j in ((0, 1), (1, 2), (0, 2)):
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a, b = texes[i], texes[j]
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if a is None or b is None:
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continue
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how = same_reading(a, b, display, preset)
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if how:
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out.append((i, j, how))
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return out
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def main() -> int:
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ap = argparse.ArgumentParser()
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ap.add_argument("epub", type=Path)
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ap.add_argument("--n", type=int, default=50)
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ap.add_argument("--name", default="regeltest")
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ap.add_argument("--reuse", type=Path, default=None, help="Lesarten aus einem früheren ergebnis.json")
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ap.add_argument("--exclude", type=Path, action="append", default=[],
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help="Formeln aus diesem früheren ergebnis.json auslassen (mehrfach möglich)")
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args = ap.parse_args()
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cached = {}
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if args.reuse:
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cached = {c["name"]: c["readings"] for c in json.loads(args.reuse.read_text(encoding="utf-8"))["cases"]}
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for rs in cached.values(): # earlier runs stored pix2text's raw spaced output
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if rs[1] and rs[1].get("tex"):
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rs[1]["tex"] = tidy_p2t(rs[1].get("raw") or rs[1]["tex"])
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excluded = {c["name"] for x in args.exclude for c in json.loads(x.read_text(encoding="utf-8"))["cases"]}
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items = [it for it in corpus(args.epub) if it[0] not in excluded]
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smp = random.Random(42).sample(items, min(args.n + 1, len(items)))
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formulas, warm = smp[:args.n], smp[args.n]
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preset = _best_preset(smp[:12])
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th = consensus_thresholds()
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eng = {}
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for key, label in ([] if cached else TOOLS):
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e = make_engine(key)
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t0 = time.time()
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e.check()
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e.recognize(warm[1], warm[0], "", warm[3]) # warm-up, not counted
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eng[key] = e
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print(f"{label}: {e.model}, bereit in {time.time() - t0:.1f} s", file=sys.stderr, flush=True)
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cases = []
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for k, (name, data, truth, disp) in enumerate(formulas, 1):
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old = cached.get(name)
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if cached and old is None:
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raise SystemExit(f"{name} fehlt in {args.reuse} (andere Stichprobe?)")
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reads: list[dict | None] = [None, None, None]
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for pos in (1, 0): # pix2text first, then Qwen
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reads[pos] = old[pos] if old else eng[TOOLS[pos][0]].recognize(data, name, "", disp).to_json()
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texes = [r and r.get("tex") for r in reads]
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agree = agreements(texes, disp, preset)
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if not any((i, j) == (0, 1) for i, j, _ in agree): # disputed: ask Gemma
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reads[2] = old[2] if old else eng["local2"].recognize(data, name, "", disp).to_json()
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texes = [r and r.get("tex") for r in reads]
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agree = agreements(texes, disp, preset)
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_st, _why, use = decide_pair_tiebreak(reads, agree, None, True, None, th)
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veto = None
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pandoc = True
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if use is not None and texes[use]:
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v = verify(load_ink(data, name), texes[use], disp, preset, check_variants=False)
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veto = {"ok": v.ok, "reason": v.reason, "metrics": v.metrics}
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pandoc = pandoc_mathml_ok([(texes[use], disp)])[0]
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status, reason, use = decide_pair_tiebreak(reads, agree, veto, pandoc, None, th)
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ok = [bool(t) and bool(same_reading(t, truth, disp, preset)) for t in texes]
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accepted = status == "auto_accepted"
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cases.append({"name": name, "truth": truth, "display": disp, "readings": reads, "ok": ok, "agree": agree,
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"status": status, "reason": reason, "use": use, "veto": veto,
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"right": bool(accepted and ok[use])})
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mark = ("✓" if ok[use] else "✗ FALSCH") if accepted else "Review"
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print(f"{k:3d} {status:13s} {mark:8s} {reason[:110]}", file=sys.stderr, flush=True)
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n = len(cases)
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acc = [c for c in cases if c["status"] == "auto_accepted"]
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wrong = [c for c in acc if not c["right"]]
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review = [c for c in cases if c["status"] == "needs_review"]
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gemma = sum(c["readings"][2] is not None for c in cases)
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secs = {key: [c["readings"][p]["seconds"] for c in cases if c["readings"][p]] for p, (key, _l) in enumerate(TOOLS)}
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per_formula = [sum(r["seconds"] for r in c["readings"] if r) for c in cases]
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summary = {"formulas": n, "accepted": len(acc), "wrong": len(wrong), "review": len(review),
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"other": n - len(acc) - len(review), "gemma_asked": gemma,
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"mean_s_per_formula": statistics.mean(per_formula),
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"mean_s": {k: statistics.mean(v) if v else None for k, v in secs.items()},
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"correct_alone": {key: sum(c["ok"][p] for c in cases if c["readings"][p])
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for p, (key, _l) in enumerate(TOOLS)}}
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out = Path(__file__).resolve().parents[1] / "vergleich" / args.name
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out.mkdir(parents=True, exist_ok=True)
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(out / "ergebnis.json").write_text(json.dumps({"epub": args.epub.name, "preset": preset, "summary": summary,
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"cases": cases}, ensure_ascii=False, indent=1, default=str),
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encoding="utf-8")
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def order(c: dict) -> int:
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return 0 if c["status"] == "auto_accepted" and not c["right"] else 1 if c["status"] != "auto_accepted" else 2
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shown = sorted(cases, key=order)
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texes = []
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for c in shown:
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texes.append((strip_delimiters(c["truth"]), c["display"]))
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texes += [((r or {}).get("tex") or "\\text{–}", c["display"]) for r in c["readings"]]
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rendered = render_many(texes, preset)
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by_name = {f[0]: f[1] for f in formulas}
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lab = dict(TOOLS)
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head = (f"<h1>Regeltest: {html.escape(args.epub.name[:70])}</h1>"
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"<p>Regel: pix2text liest zuerst, Qwen bestätigt → annehmen. Sonst fragt Gemma; Gemma = pix2text → annehmen; "
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"Qwen = Gemma gegen pix2text → Mensch. Übernommen wird immer pix2texts Lesart (Leerzeichen bereinigt). "
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"Annahme nur mit Pandoc und Bild-Veto. "
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"✓/✗ = gleiche Zeichen an gleicher Stelle wie die wahre Formel (Abstände, kursiv/aufrecht, Fettdruck egal).</p>"
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"<table class=sum>"
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f"<tr><th>Formeln</th><td>{n}</td></tr>"
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f"<tr><th>automatisch angenommen</th><td>{len(acc)} ({len(acc) / n:.0%})</td></tr>"
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f"<tr><th>davon FALSCH</th><td><b>{len(wrong)}</b></td></tr>"
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f"<tr><th>Review (Mensch)</th><td>{len(review)} ({len(review) / n:.0%})</td></tr>"
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f"<tr><th>Gemma gefragt</th><td>{gemma} ({gemma / n:.0%})</td></tr>"
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f"<tr><th>Ø Zeit pro Formel (alle Tools nacheinander)</th><td>{summary['mean_s_per_formula']:.2f} s</td></tr>"
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+ "".join(f"<tr><th>Ø {lab[k]}</th><td>{v:.2f} s</td></tr>" for k, v in summary["mean_s"].items() if v)
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+ "".join(f"<tr><th>{lab[k]} allein richtig</th><td>{v}/{len(secs[k])}</td></tr>"
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for k, v in summary["correct_alone"].items())
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+ "</table>")
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parts = [head]
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per = 4
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for i, c in enumerate(shown):
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imgs = []
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for rr in rendered[per * i: per * (i + 1)]:
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if rr.ok:
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g = (rr.rasterize(2.0) * 255).astype(np.uint8)
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imgs.append(f"<img src='{png_uri(Image.fromarray(255 - g))}'>")
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else:
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imgs.append("<i>nicht setzbar</i>")
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if c["status"] == "auto_accepted":
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cls, verdict = ("good", "angenommen, richtig") if c["right"] else ("bad", "angenommen, FALSCH")
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elif c["status"] == "needs_review":
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cls, verdict = "rev", "Review"
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else:
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cls, verdict = "rev", c["status"]
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ncc = (c["veto"] or {}).get("metrics", {}).get("ncc")
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rows = [f"<tr><th>Original</th><td><img src='{png_uri(orig_img(by_name[c['name']], 2))}'></td><td></td></tr>",
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f"<tr><th>richtig</th><td>{imgs[0]}</td><td><code>{html.escape(c['truth'])}</code></td></tr>"]
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for p, (key, label) in enumerate(TOOLS):
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r = c["readings"][p]
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if r is None:
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rows.append(f"<tr><th>{label}</th><td><i>nicht gefragt</i></td><td></td></tr>")
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continue
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mark = "<b class=ok>✓</b>" if c["ok"][p] else "<b class=no>✗</b>"
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used = " <b>← verwendet</b>" if c["use"] == p and c["status"] == "auto_accepted" else ""
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rows.append(f"<tr><th>{mark} {label}{used}<br><small>{r['seconds']:.2f} s</small></th><td>{imgs[p + 1]}</td>"
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f"<td><code>{html.escape(r.get('tex') or r.get('error') or '–')}</code>"
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+ (f"<br><small>roh: <code>{html.escape(r.get('raw') or '')}</code></small>"
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if key == "p2t" and r.get("raw") and r.get("raw") != r.get("tex") else "")
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+ "</td></tr>")
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parts.append(f"<section class={cls}><div class=v>{verdict} · {html.escape(c['reason'])}"
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f"{f' · Bild-Ähnlichkeit {ncc:.2f}' if ncc is not None else ''}</div>"
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f"<small>{html.escape(c['name'])}</small><table>{''.join(rows)}</table></section>")
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(out / "index.html").write_text(
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"<!doctype html><meta charset=utf-8><title>Regeltest</title><style>"
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"body{font:14px system-ui,sans-serif;margin:16px;background:#f6f6f3}section{background:#fff;border:1px solid #ddd;"
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"border-left:6px solid #999;border-radius:6px;padding:6px 10px;margin:10px 0}section.good{border-left-color:#1f7a3a}"
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"section.bad{border-left-color:#a32020;background:#fff5f5}section.rev{border-left-color:#c88a00}"
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".v{font-weight:600;font-size:13px}table.sum{border-collapse:collapse;background:#fff}"
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"table.sum td,table.sum th{border:1px solid #ccc;padding:4px 10px}"
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"th{text-align:left;font-weight:400;color:#555;width:190px;vertical-align:middle}td{padding:3px 8px;vertical-align:middle}"
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".ok{color:#1f7a3a}.no{color:#a32020}code{font-size:12px}img{max-width:640px}</style>" + "".join(parts),
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encoding="utf-8")
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print(json.dumps(summary, ensure_ascii=False, indent=1))
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print(out / "index.html")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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