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from __future__ import annotations
import argparse
import json
from pathlib import Path
import re
from collections import Counter
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from .common import (
dtype_for_device,
read_json,
retrieval_metrics,
write_json,
)
from .io import load_feature_pair, select_rows
from .models import load_bridge, load_prefix
TOKEN_RE = re.compile(r"[a-z0-9]+")
def parse_args() -> argparse.Namespace:
p = argparse.ArgumentParser()
p.add_argument("--manifest", default="artifacts/manifest.json")
p.add_argument("--vision", default="artifacts/vision.pt")
p.add_argument("--text", default="artifacts/text.pt")
p.add_argument("--bridge", required=True)
p.add_argument("--prefix")
p.add_argument("--split", choices=["val", "test"], default="test")
p.add_argument("--device", default="cuda:1")
p.add_argument("--generation-samples", type=int, default=100)
p.add_argument("--max-new-tokens", type=int, default=32)
p.add_argument(
"--shuffle-mapped",
action="store_true",
help="Permute image-conditioned latents before retrieval/generation as a null control.",
)
p.add_argument("--seed", type=int, default=20260728)
p.add_argument("--output", default="artifacts/evaluation.json")
return p.parse_args()
def unigram_f1(candidate: str, references: list[str]) -> float:
candidate_tokens = TOKEN_RE.findall(candidate.lower())
if not candidate_tokens:
return 0.0
candidate_count = Counter(candidate_tokens)
best = 0.0
for reference in references:
reference_count = Counter(TOKEN_RE.findall(reference.lower()))
overlap = sum((candidate_count & reference_count).values())
precision = overlap / max(sum(candidate_count.values()), 1)
recall = overlap / max(sum(reference_count.values()), 1)
f1 = 2 * precision * recall / max(precision + recall, 1e-12)
best = max(best, f1)
return best
def main() -> None:
args = parse_args()
manifest = read_json(args.manifest)
vision, text, vlookup, tlookup = load_feature_pair(args.vision, args.text)
rows = manifest[args.split]
x = select_rows(vision["features"], vlookup, rows)
y = select_rows(text["features"], tlookup, rows)
bridge, bridge_state = load_bridge(args.bridge, args.device)
mapped = []
with torch.inference_mode():
for chunk in x.split(512):
mapped.append(bridge(chunk.to(args.device)).cpu())
mapped = torch.cat(mapped)
if args.shuffle_mapped:
generator = torch.Generator().manual_seed(args.seed)
mapped = mapped[torch.randperm(len(mapped), generator=generator)]
result: dict = {
"split": args.split,
"samples": len(rows),
"bridge_mode": bridge_state["mode"],
"shuffle_mapped": args.shuffle_mapped,
"retrieval": retrieval_metrics(mapped, y),
}
if args.prefix:
prefix, prefix_state = load_prefix(args.prefix, args.device)
if prefix_state["text_model"] != text["model"]:
raise ValueError("Prefix adapter and text feature model differ")
tokenizer = AutoTokenizer.from_pretrained(text["model"])
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
dtype = dtype_for_device(args.device)
lm = AutoModelForCausalLM.from_pretrained(
text["model"], torch_dtype=dtype
).to(args.device)
lm.eval()
generated: list[str] = []
n = min(args.generation_samples, len(rows))
with torch.inference_mode():
for chunk in mapped[:n].split(16):
prefix_embeds = prefix(chunk.to(args.device)).to(dtype)
attention = torch.ones(
prefix_embeds.shape[:2],
dtype=torch.long,
device=args.device,
)
output = lm.generate(
inputs_embeds=prefix_embeds,
attention_mask=attention,
max_new_tokens=args.max_new_tokens,
do_sample=False,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
)
generated.extend(tokenizer.batch_decode(output, skip_special_tokens=True))
caption_lookup = {
int(row): caps
for row, caps in zip(text["rows"], text["all_captions"])
}
records = []
scores = []
for row, caption in zip(rows[:n], generated):
refs = caption_lookup[int(row)]
score = unigram_f1(caption, refs)
scores.append(score)
records.append(
{
"row": int(row),
"generated": caption,
"references": refs,
"unigram_f1": score,
}
)
result["generation"] = {
"samples": n,
"mean_best_reference_unigram_f1": sum(scores) / max(len(scores), 1),
"examples": records[:25],
}
Path(args.output).parent.mkdir(parents=True, exist_ok=True)
write_json(args.output, result)
print(json.dumps(result, indent=2, ensure_ascii=False))
if __name__ == "__main__":
main()
|
