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import argparse
import json
import os
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
from typing import Dict, Iterable, List, Sequence, Set
from psq_rag.llm.rewrite_local_t5 import local_t5_rewrite_prompt
from psq_rag.pipeline.preproc import extract_user_provided_tags_upto_3_words
REPO_ROOT = Path(__file__).resolve().parents[1]
DEFAULT_SAMPLES = (
REPO_ROOT
/ "data"
/ "eval_samples"
/ "e621_sfw_sample_1000_seed123_buffer10000_caption_evident_n30.jsonl"
)
def _canon_tag(tag: str) -> str:
t = " ".join(str(tag or "").strip().split()).lower()
return t.replace(" ", "_").replace("\\(", "(").replace("\\)", ")")
def _parse_tag_set(text: str) -> Set[str]:
out: Set[str] = set()
for raw in (text or "").split(","):
t = _canon_tag(raw)
if t:
out.add(t)
return out
def _set_metrics(pred_sets: Sequence[Set[str]], gold_sets: Sequence[Set[str]]) -> Dict[str, float]:
n = len(pred_sets)
if n == 0:
return {
"n": 0,
"set_precision": 0.0,
"set_recall": 0.0,
"set_f1": 0.0,
"exact_set_match": 0.0,
"avg_pred_tags": 0.0,
"avg_gold_tags": 0.0,
}
p_vals: List[float] = []
r_vals: List[float] = []
f_vals: List[float] = []
exact = 0
pred_sizes: List[int] = []
gold_sizes: List[int] = []
for pset, gset in zip(pred_sets, gold_sets):
pred_sizes.append(len(pset))
gold_sizes.append(len(gset))
if pset == gset:
exact += 1
if not pset and not gset:
p_vals.append(1.0)
r_vals.append(1.0)
f_vals.append(1.0)
continue
if not pset or not gset:
p_vals.append(0.0)
r_vals.append(0.0)
f_vals.append(0.0)
continue
tp = len(pset & gset)
p = tp / len(pset)
r = tp / len(gset)
f = (2 * p * r / (p + r)) if (p + r) > 0 else 0.0
p_vals.append(p)
r_vals.append(r)
f_vals.append(f)
return {
"n": float(n),
"set_precision": sum(p_vals) / n,
"set_recall": sum(r_vals) / n,
"set_f1": sum(f_vals) / n,
"exact_set_match": exact / n,
"avg_pred_tags": sum(pred_sizes) / n,
"avg_gold_tags": sum(gold_sizes) / n,
}
@dataclass
class SampleRow:
sample_id: int
caption: str
gold_tags: Set[str]
def _load_rows(samples_path: Path, caption_field: str) -> List[SampleRow]:
rows: List[SampleRow] = []
with samples_path.open("r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line:
continue
obj = json.loads(line)
if obj.get("_meta"):
continue
cap = str(obj.get(caption_field, "") or "").strip()
if not cap:
continue
gt: Set[str] = set()
expanded = obj.get("tags_ground_truth_expanded")
if isinstance(expanded, list):
for t in expanded:
c = _canon_tag(str(t))
if c:
gt.add(c)
if not gt:
cat = obj.get("tags_ground_truth_categorized")
if isinstance(cat, str):
try:
cat = json.loads(cat)
except Exception:
cat = {}
if isinstance(cat, dict):
for vals in cat.values():
if isinstance(vals, list):
for t in vals:
c = _canon_tag(str(t))
if c:
gt.add(c)
rows.append(
SampleRow(
sample_id=int(obj.get("id", -1)),
caption=cap,
gold_tags=gt,
)
)
return rows
def _eval_model(
model_dir: Path,
rows: Sequence[SampleRow],
num_beams: int,
max_new_tokens: int,
max_source_length: int,
) -> Dict[str, Dict[str, float]]:
pred_no_heur: List[Set[str]] = []
pred_with_heur: List[Set[str]] = []
gold_sets: List[Set[str]] = []
for r in rows:
out = local_t5_rewrite_prompt(
r.caption,
log=lambda _msg: None,
model_dir=str(model_dir),
num_beams=num_beams,
max_new_tokens=max_new_tokens,
max_source_length=max_source_length,
)
p0 = _parse_tag_set(out)
p1 = set(p0)
if out.strip():
for term in extract_user_provided_tags_upto_3_words(r.caption):
c = _canon_tag(term)
if c:
p1.add(c)
pred_no_heur.append(p0)
pred_with_heur.append(p1)
gold_sets.append(r.gold_tags)
return {
"t5_local_rewrite_no_heur": _set_metrics(pred_no_heur, gold_sets),
"t5_local_rewrite_with_heur": _set_metrics(pred_with_heur, gold_sets),
}
def _iter_model_dirs(raw_models: Iterable[str]) -> List[Path]:
dirs: List[Path] = []
for raw in raw_models:
p = Path(raw)
if not p.is_absolute():
p = (REPO_ROOT / p).resolve()
dirs.append(p)
return dirs
def main() -> int:
ap = argparse.ArgumentParser(description="Rewrite-only T5 evaluation on caption-evident n30 set.")
ap.add_argument("--samples", type=Path, default=DEFAULT_SAMPLES)
ap.add_argument("--caption-field", type=str, default="caption_cogvlm")
ap.add_argument("--limit", type=int, default=30)
ap.add_argument("--num-beams", type=int, default=4)
ap.add_argument("--max-new-tokens", type=int, default=128)
ap.add_argument("--max-source-length", type=int, default=160)
ap.add_argument("--model-dir", action="append", required=True,
help="Model directory; repeat for multiple models.")
ap.add_argument("--out-json", type=Path, default=REPO_ROOT / "data" / "analysis")
args = ap.parse_args()
samples_path = args.samples if args.samples.is_absolute() else (REPO_ROOT / args.samples).resolve()
if not samples_path.is_file():
raise FileNotFoundError(f"Samples file not found: {samples_path}")
model_dirs = _iter_model_dirs(args.model_dir)
for d in model_dirs:
if not d.is_dir():
raise FileNotFoundError(f"Model directory not found: {d}")
if not (d / "model.safetensors").is_file():
raise FileNotFoundError(f"model.safetensors missing in: {d}")
rows = _load_rows(samples_path, args.caption_field)
if args.limit > 0:
rows = rows[: min(args.limit, len(rows))]
result_rows = []
for d in model_dirs:
metrics = _eval_model(
d,
rows=rows,
num_beams=max(1, args.num_beams),
max_new_tokens=max(8, args.max_new_tokens),
max_source_length=max(16, args.max_source_length),
)
result_rows.append(
{
"model_dir": str(d),
"metrics": metrics,
}
)
no_h = metrics["t5_local_rewrite_no_heur"]
with_h = metrics["t5_local_rewrite_with_heur"]
print(
f"{d.name}: "
f"no_heur R={no_h['set_recall']:.4f} F1={no_h['set_f1']:.4f} "
f"| with_heur R={with_h['set_recall']:.4f} F1={with_h['set_f1']:.4f}"
)
out_base = args.out_json if args.out_json.is_absolute() else (REPO_ROOT / args.out_json).resolve()
if out_base.suffix.lower() == ".json":
out_path = out_base
out_path.parent.mkdir(parents=True, exist_ok=True)
else:
out_base.mkdir(parents=True, exist_ok=True)
ts = datetime.now().strftime("%Y%m%d_%H%M%S")
out_path = out_base / f"rewrite_only_compare_n30_t5_sweep_{ts}.json"
payload = {
"meta": {
"timestamp": datetime.now().isoformat(),
"samples_path": str(samples_path),
"caption_field": args.caption_field,
"n_samples": len(rows),
"num_beams": args.num_beams,
"max_new_tokens": args.max_new_tokens,
"max_source_length": args.max_source_length,
"cuda_visible_devices": os.environ.get("CUDA_VISIBLE_DEVICES"),
},
"rows": result_rows,
}
out_path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
print(f"Saved: {out_path}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
|