File size: 3,897 Bytes
4afe981 | 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 | #!/home/devcontainers/tinyllm/.venv/bin/python
"""DuoVLM-40M 命令行：给一张图 + 一个问题 → 回答。
python ask_cli.py --image photo.jpg --question "How many people are in the image?"
python ask_cli.py --image photo.jpg # 不给问题 = 描述这张图
python ask_cli.py --image photo.jpg --blind # 并排跑一遍"不看图"对照
python ask_cli.py --dir ./photos --question "What is happening?"
python ask_cli.py --text "The history of the city begins in" --continue-n 60
提示：40M 小模型擅长物体/场景识别、计数、是-否、简短描述；不擅长精细颜色、
读文字(OCR)、长句推理；长输出必须带 no_repeat_ngram=3（默认已开）。
"""
import argparse
import json
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent))
from duovlm_infer import DESC_Q, DuoVLMInfer # noqa: E402
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--image", action="append", default=[])
ap.add_argument("--dir", default="")
ap.add_argument("--question", default="")
ap.add_argument("--questions", default="", help="同一张图连问多个，用 | 分隔")
ap.add_argument("--text", default="", help="纯文本续写种子（不用图）")
ap.add_argument("--continue-n", type=int, default=40)
ap.add_argument("--blind", action="store_true")
ap.add_argument("--no-image", action="store_true")
ap.add_argument("--max-new", type=int, default=12)
ap.add_argument("--ngram", type=int, default=3)
ap.add_argument("--rep-penalty", type=float, default=1.0)
ap.add_argument("--temperature", type=float, default=0.0)
ap.add_argument("--top-k", type=int, default=0)
ap.add_argument("--no-loop-break", action="store_true")
ap.add_argument("--weights", default="")
ap.add_argument("--json", action="store_true", help="输出 JSON（给脚本/服务用）")
a = ap.parse_args()
imgs = list(a.image)
if a.dir:
imgs += [str(p) for p in sorted(Path(a.dir).glob("*.jpg"))[:20]]
if not imgs and not a.text and not a.no_image:
print("需要 --image / --dir（或 --text / --no-image 走无图模式）", file=sys.stderr)
raise SystemExit(2)
qs = [x.strip() for x in a.questions.split("|") if x.strip()] or [a.question]
kw = dict(ngram=a.ngram, rep_penalty=a.rep_penalty, temperature=a.temperature,
top_k=a.top_k, loop_break=not a.no_loop_break)
vlm = DuoVLMInfer(weights=a.weights or None, verbose=not a.json)
if not a.json:
print(vlm.param_report())
print("-" * 88, flush=True)
rows = []
if a.text:
for seed in [x for x in a.text.split("|") if x]:
r = vlm.continue_text(seed, max_new=a.continue_n, **kw)
r["seed"] = seed
rows.append(r)
if not a.json:
print(f"种子: {seed}\n续写: {r['answer']}\n ({r['ms']}ms)\n", flush=True)
else:
for p in imgs or [None]:
for i, q in enumerate(qs):
r = vlm.ask(p, q, no_image=a.no_image, max_new=a.max_new, **kw)
r.update({"image": p, "question": q or DESC_Q})
if a.blind and p:
b = vlm.ask(p, q, blind=True, max_new=a.max_new, **kw)
r["blind_answer"] = b["answer"]
rows.append(r)
if not a.json:
tag = "" if i else Path(p).name if p else "(无图)"
print(f"{tag:30s} Q: {r['question']}")
print(f"{'':30s} [{r['source']}] {r['ms']}ms 答: {r['answer']}")
if "blind_answer" in r:
print(f"{'':30s} 不看图: {r['blind_answer']}")
if a.json:
print(json.dumps(rows, ensure_ascii=False, indent=1))
if __name__ == "__main__":
main()
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