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156e7ce | 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 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | """Self-contained scorer for nutrientdocs/form-field-vlm-benchmark.
Predictions are a JSON list of {page_id, box:[x,y,w,h], type, label?, group_id?} in original pixels.
No checkout of the training repository is required.
"""
from __future__ import annotations
import argparse
import json
from collections import Counter
from pathlib import Path
def iou(a, b):
x0, y0 = max(a[0], b[0]), max(a[1], b[1])
x1, y1 = min(a[0] + a[2], b[0] + b[2]), min(a[1] + a[3], b[1] + b[3])
inter = max(0.0, x1 - x0) * max(0.0, y1 - y0)
union = a[2] * a[3] + b[2] * b[3] - inter
return inter / union if union else 0.0
def match(preds, golds, threshold):
pb, gb = {}, {}
for p in preds:
pb.setdefault(p["page_id"], []).append(p)
for g in golds:
gb.setdefault(g["page_id"], []).append(g)
matched, unp, ung = [], [], []
for page_id in sorted(set(pb) | set(gb)):
ps, gs, candidates = pb.get(page_id, []), gb.get(page_id, []), []
for pi, p in enumerate(ps):
for gi, g in enumerate(gs):
overlap = iou(p["box"], g["box"])
if overlap >= threshold:
candidates.append((overlap, pi, gi))
candidates.sort(key=lambda row: (-row[0], row[1], row[2]))
used_p, used_g = set(), set()
for overlap, pi, gi in candidates:
if pi not in used_p and gi not in used_g:
used_p.add(pi); used_g.add(gi); matched.append((ps[pi], gs[gi], overlap))
unp.extend(p for i, p in enumerate(ps) if i not in used_p)
ung.extend(g for i, g in enumerate(gs) if i not in used_g)
return matched, unp, ung
def metric(matched, unp, ung, page_ids):
n_pred, n_gold = len(matched) + len(unp), len(matched) + len(ung)
correct = sum(p.get("type") == g.get("type") for p, g, _ in matched)
precision = correct / n_pred if n_pred else 0.0
recall = correct / n_gold if n_gold else 0.0
f1 = 2 * precision * recall / (precision + recall) if precision + recall else 0.0
pc, gc = Counter(), Counter()
for p, g, _ in matched: pc[p["page_id"]] += 1; gc[g["page_id"]] += 1
for p in unp: pc[p["page_id"]] += 1
for g in ung: gc[g["page_id"]] += 1
mae = sum(abs(pc[p] - gc[p]) for p in page_ids) / len(page_ids) if page_ids else 0.0
return {"correct": correct, "precision": precision, "recall": recall, "f1": f1,
"n_pred": n_pred, "n_gold": n_gold,
"fp_per_page": len(unp) / len(page_ids) if page_ids else 0.0, "count_mae": mae}
def score_slice(matched, unp, ung, page_ids, predicate):
return metric([m for m in matched if predicate(m[1])],
[p for p in unp if predicate(p)], [g for g in ung if predicate(g)], page_ids)
def score(preds, golds, page_ids, threshold):
matched, unp, ung = match(preds, golds, threshold)
gold_counts = Counter(g["page_id"] for g in golds)
edges = ((1, 5), (6, 15), (16, 40), (41, 10**9))
density = lambda pid: next((f"{lo}-{hi}" if hi < 10**9 else f"{lo}+"
for lo, hi in edges if lo <= gold_counts[pid] <= hi), "0")
types = sorted({g["type"] for g in golds})
labels = [f"{lo}-{hi}" if hi < 10**9 else f"{lo}+" for lo, hi in edges]
by_type = {t: score_slice(matched, unp, ung, page_ids, lambda x, t=t: x.get("type") == t)
for t in types}
by_density = {d: score_slice(matched, unp, ung, page_ids,
lambda x, d=d: density(x["page_id"]) == d) for d in labels}
by_type_density = {t: {d: score_slice(matched, unp, ung, page_ids,
lambda x, t=t, d=d: x.get("type") == t and density(x["page_id"]) == d) for d in labels}
for t in types}
n_match = len(matched)
label_hits = sum(str(p.get("label") or "").strip().casefold() == str(g.get("label") or "").strip().casefold()
for p, g, _ in matched)
group_pairs = [(p, g) for p, g, _ in matched if g.get("group_id") is not None]
group_hits = sum(p.get("group_id") == g.get("group_id") for p, g in group_pairs)
box_precision = n_match / len(preds) if preds else 0.0
box_recall = n_match / len(golds) if golds else 0.0
return {"overall": metric(matched, unp, ung, page_ids), "by_type": by_type,
"by_density": by_density, "by_type_density": by_type_density,
"box_precision": box_precision,
"box_recall": box_recall,
"box_f1": 2 * box_precision * box_recall / (box_precision + box_recall)
if box_precision + box_recall else 0.0,
"matched_label_exact": label_hits / n_match if n_match else None,
"matched_radio_group_exact": group_hits / len(group_pairs) if group_pairs else None,
"n_matched_radio_widgets": len(group_pairs)}
def load_benchmark(repo, local):
from datasets import load_dataset, load_from_disk
if local:
loaded = load_from_disk(local)
return loaded["test"] if hasattr(loaded, "keys") else loaded
return load_dataset(repo, split="test")
def main():
ap = argparse.ArgumentParser(description=__doc__)
ap.add_argument("--benchmark-repo", default="nutrientdocs/form-field-vlm-benchmark")
ap.add_argument("--benchmark", help="optional local datasets.save_to_disk directory")
ap.add_argument("--predictions", required=True)
ap.add_argument("--out", default="result.json")
args = ap.parse_args()
ds = load_benchmark(args.benchmark_repo, args.benchmark)
page_ids, golds = [], []
for row in ds:
page_ids.append(row["page_id"])
for field in row["fields"]:
golds.append({"page_id": row["page_id"], "box": list(field["box"]), "type": field["type"],
"label": field.get("label"), "group_id": field.get("group_id")})
preds = json.loads(Path(args.predictions).read_text(encoding="utf-8"))
required = {"page_id", "box", "type"}
if not isinstance(preds, list) or any(not isinstance(p, dict) or not required <= p.keys() for p in preds):
raise SystemExit("predictions must be a JSON list with page_id, box, and type on every item")
report = {"benchmark": args.benchmark_repo, "n_pages": len(page_ids), "n_gold": len(golds),
"headline": "field-level exact F1 at IoU 0.5",
"by_iou": {str(t): score(preds, golds, page_ids, t) for t in (0.5, 0.2)}}
Path(args.out).write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8")
print(f"F1@0.5={report['by_iou']['0.5']['overall']['f1']:.3f} "
f"F1@0.2={report['by_iou']['0.2']['overall']['f1']:.3f} -> {args.out}")
if __name__ == "__main__":
main()
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