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Offline + optional live audit of taxonomy routing and tagger outputs.
Usage (from backend/):
../.venv/Scripts/python.exe scripts/audit_classify_accuracy.py
../.venv/Scripts/python.exe scripts/audit_classify_accuracy.py --images DIR
"""
from __future__ import annotations
import argparse
import json
import sys
import time
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from app.taxonomy import choose_best_destination, reload_taxonomy # noqa: E402
SELECTED = {
"fertilization",
"NTR",
"incest",
"nakadashi",
"fellatio",
"loli",
"shota",
"monster_girl",
"furry",
"Pokemon",
}
FIXTURES: list[tuple[str, dict[str, float], str | None]] = [
("loli_hard", {"loli": 0.92, "flat_chest": 0.99}, "loli"),
("fashion_fp", {"lolita_fashion": 0.99, "gothic_lolita": 0.95}, None),
("shota_hard", {"shota": 0.88, "1boy": 0.99}, "shota"),
("ntr_hard", {"netorare": 0.8}, "NTR"),
("incest_hard", {"incest": 0.8, "siblings": 0.99}, "incest"),
("siblings_fp", {"siblings": 0.99}, None),
("nakadashi", {"internal_cumshot": 0.9}, "nakadashi"),
("fert_over_creampie", {"fertilization": 0.8, "cum_in_pussy": 0.99}, "fertilization"),
("fellatio_impl", {"deepthroat": 0.9}, "fellatio"),
("monster_girl", {"monster_girl": 0.9, "horns": 0.99}, "monster_girl"),
("parts_fp", {"horns": 0.99, "wings": 0.98}, None),
("furry", {"furry_female": 0.9, "animal_ears": 0.99}, "furry"),
("pokemon", {"pokemon_(creature)": 0.9}, "Pokemon"),
]
def audit_fixtures() -> int:
reload_taxonomy()
failed = 0
print("=== TAXONOMY FIXTURE AUDIT ===")
for name, scores, expected in FIXTURES:
folder, score, _ = choose_best_destination(scores, SELECTED)
ok = folder == expected
mark = "PASS" if ok else "FAIL"
if not ok:
failed += 1
print(f"{mark} {name}: got={folder}({score}) expected={expected}")
return failed
def audit_images(image_dir: Path, models: list[str], limit: int) -> int:
from app.services import extract_scores
paths = sorted(
p
for p in image_dir.rglob("*")
if p.suffix.lower() in {".jpg", ".jpeg", ".png", ".webp", ".bmp"}
)[:limit]
if not paths:
print(f"ERROR: no images under {image_dir}", file=sys.stderr)
return 1
print(f"\n=== MODEL IMAGE AUDIT ({len(paths)} images) ===")
failed = 0
for model in models:
empty = 0
routed = 0
errors = 0
latencies: list[float] = []
samples: list[dict] = []
for path in paths:
t0 = time.perf_counter()
try:
scores = extract_scores(path, tagger_model=model, wd_general_threshold=0.35)
except Exception as err:
errors += 1
print(f" ERR {model} {path.name}: {err}")
continue
latencies.append((time.perf_counter() - t0) * 1000)
if not scores:
empty += 1
folder, score, _ = choose_best_destination(scores, SELECTED)
if folder is not None:
routed += 1
if len(samples) < 3:
top = sorted(scores.items(), key=lambda kv: kv[1], reverse=True)[:5]
samples.append(
{
"file": path.name,
"folder": folder,
"folder_score": score,
"top": [f"{t}={s:.3f}" for t, s in top],
}
)
avg = sum(latencies) / len(latencies) if latencies else None
ok = errors == 0 and empty == 0
if not ok:
failed += 1
print(
json.dumps(
{
"model": model,
"ok": ok,
"images": len(paths),
"empty_scores": empty,
"errors": errors,
"taxonomy_routed": routed,
"avg_ms": round(avg, 1) if avg is not None else None,
"samples": samples,
},
indent=2,
)
)
return failed
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--images", type=Path, default=None)
parser.add_argument("--limit", type=int, default=12)
parser.add_argument(
"--models",
nargs="+",
default=["ml_danbooru", "wd_swinv2_v3", "wd_eva02_large"],
)
args = parser.parse_args()
failed = audit_fixtures()
if args.images:
failed += audit_images(args.images, args.models, args.limit)
else:
print("\n(skip image audit: pass --images DIR for live tagger accuracy smoke)")
print(f"\n=== DONE failed={failed} ===")
return 1 if failed else 0
if __name__ == "__main__":
raise SystemExit(main())
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