| |
| """Re-score the stored per-item outputs of VeriSpoof-Base / -Plus (reference_results/<model>/<subset>.parquet) with |
| scripts/score.py and compare with the metrics reported by the research evaluation (reported_metrics.json). |
| |
| python scripts/check_reference.py [--bench <dataset dir>] |
| |
| Metrics that need model log-probabilities not stored per item (ad_reasoning eer_loc, flip_rate, head_* token-head |
| metrics, held-out faithfulness rates) are listed as not re-scorable. |
| """ |
| import argparse |
| import json |
| import os |
| import sys |
|
|
| sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) |
| import score as SC |
|
|
| HERE = os.path.dirname(os.path.abspath(__file__)) |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--bench", default=os.path.join(HERE, "..")) |
| a = ap.parse_args() |
| ok = True |
| for model in ("verispoof_base", "verispoof_plus"): |
| d = f"{a.bench}/reference_results/{model}" |
| rep = json.load(open(f"{d}/reported_metrics.json")) |
| n_cmp, worst, skipped = 0, (0.0, None), set() |
| for s, r in rep.items(): |
| items = SC.load_items(a.bench, s) |
| m = SC.score_subset(items, SC.read_table(f"{d}/{s}"), None, s) |
| for k, v in r.items(): |
| if not isinstance(v, (int, float)) or isinstance(v, bool): |
| continue |
| if k not in m or m[k] is None: |
| skipped.add(k) |
| continue |
| diff = abs(m[k] - v) |
| n_cmp += 1 |
| if diff > worst[0]: |
| worst = (diff, f"{s}:{k} rescored {m[k]} reported {v}") |
| if diff > 1e-3: |
| ok = False |
| print(f"MISMATCH {model} {s} {k}: rescored {m[k]} reported {v}") |
| print(f"{model}: {len(rep)} subsets, {n_cmp} metric values compared, max |diff| {worst[0]:.2g} ({worst[1]}); " |
| f"not re-scorable from stored outputs: {sorted(skipped)}") |
| print("ALL MATCH" if ok else "SOME MISMATCHES") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
