evaluation/qwen2.5_vl.py

import os
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from utils.utils import COUNTING_BASE_PROMPT, RELATION_BASE_PROMPT, eval_loop, parse_model_answer, eval_pipeline
from utils.vision_process import process_vision_info

current_dir = os.path.dirname(os.path.abspath(__file__))
parent_dir = os.path.split(current_dir)[0]



def process(model, item, task_type="counting", **kwargs):
    processor = kwargs.get('processor')
    question = item['question']
    image_path = os.path.join(parent_dir, item['image_path'])
    if task_type == "counting":
        formatted_question = COUNTING_BASE_PROMPT + question
    else:  # relation task
        formatted_question = RELATION_BASE_PROMPT + question
        
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": image_path,
                },
                {"type": "text", "text": formatted_question},
            ],
        }
    ]

    text = processor.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    image_inputs, video_inputs = process_vision_info(messages)
    inputs = processor(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
    )
    inputs = inputs.to(model.device)

    generated_ids = model.generate(**inputs, max_new_tokens=1000)
    generated_ids_trimmed = [
        out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]
    output_text = processor.batch_decode(
        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
    )
    model_answer = output_text[0]
    parsed_answer = parse_model_answer(model_answer, task_type)
    result = {'model_answer': model_answer, 'parsed_answer': parsed_answer}
    return result


def main():
    model_name_or_path = 'Qwen/Qwen2.5-VL-3B-Instruct'
    model_name = model_name_or_path.split('/')[-1]
    cache_dir = './cache/'

    model = Qwen2_5_VLForConditionalGeneration.from_pretrained(model_name_or_path, torch_dtype="auto", device_map="auto", cache_dir=cache_dir)
    processor = AutoProcessor.from_pretrained(model_name_or_path)

    params = {
        'model': model,
        'processor': processor,
        'process_fn': process,
    }
    eval_pipeline(model_name, current_dir, params)

if __name__ == "__main__":
    main()