scripts/query_image_vqa.py

#!/usr/bin/env python3
from __future__ import annotations

import argparse
from pathlib import Path

import torch
from PIL import Image
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration


def main() -> None:
    parser = argparse.ArgumentParser(description="Run a local Qwen2.5-VL/Jarvis VLP world-knowledge checkpoint on one image.")
    parser.add_argument("--model", default="/data/zianguan/shared_vlp_world_knowledge_clean_best_20260520/best_vqa_world_knowledge_ckpt")
    parser.add_argument("--image", required=True)
    parser.add_argument("--prompt", required=True)
    parser.add_argument("--max-new-tokens", type=int, default=128)
    args = parser.parse_args()

    image = Image.open(Path(args.image)).convert("RGB")
    processor = AutoProcessor.from_pretrained(args.model, trust_remote_code=True)
    model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
        args.model,
        torch_dtype=torch.bfloat16,
        device_map="auto",
        trust_remote_code=True,
    )

    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": image},
                {"type": "text", "text": args.prompt},
            ],
        }
    ]
    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

    with torch.inference_mode():
        output_ids = model.generate(**inputs, max_new_tokens=args.max_new_tokens)
    generated = output_ids[:, inputs.input_ids.shape[1] :]
    print(processor.batch_decode(generated, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0])


if __name__ == "__main__":
    main()