Delete simple_inference.py

simple_inference.py

@@ -1,238 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import argparse
-import json
-import os
-import torch
-import base64
-from io import BytesIO
-from PIL import Image
-
-# 条件导入，根据选择的推理引擎
-try:
-    from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
-    transformers_available = True
-except ImportError:
-    transformers_available = False
-    print("警告: transformers相关库未安装，无法使用transformers引擎")
-
-try:
-    from vllm import LLM, SamplingParams
-    vllm_available = True
-except ImportError:
-    vllm_available = False
-    print("警告: vllm相关库未安装，无法使用vllm引擎")
-
-# 合并 qwen_vl_utils 的代码
-def process_vision_info(messages):
-    """处理多模态消息中的图像和视频信息
-
-    Args:
-        messages: 包含图像或视频的消息列表
-
-    Returns:
-        images_data: 处理后的图像数据
-        videos_data: 处理后的视频数据
-    """
-    images_list, videos_list = [], []
-    for message in messages:
-        content = message.get("content", None)
-        if isinstance(content, str):
-            # 纯文本消息，不处理
-            continue
-        elif isinstance(content, list):
-            # 混合消息，可能包含图像或视频
-            for item in content:
-                if not isinstance(item, dict):
-                    continue
-                
-                # 处理图像
-                if item.get("type") == "image" and "image" in item:
-                    image = item["image"]
-                    if isinstance(image, str):
-                        # 图像URL或路径，尝试加载
-                        try:
-                            image = Image.open(image)
-                        except Exception as e:
-                            print(f"图像加载失败: {e}")
-                            continue
-                    
-                    # 转换PIL图像为base64编码
-                    if isinstance(image, Image.Image):
-                        buffered = BytesIO()
-                        image.save(buffered, format="PNG")
-                        image_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-                        images_list.append(image_str)
-                
-                # 处理视频（如有需要）
-                elif item.get("type") == "video" and "video" in item:
-                    # 暂不支持视频
-                    pass
-                    
-    return images_list or None, videos_list or None
-
-def predict_location(
-    image_path, 
-    model_name="Qwen/Qwen2.5-VL-7B-Instruct", 
-    inference_engine="transformers"
-):
-    """
-    对单个图片进行位置识别预测
-    
-    参数:
-        image_path: 图片文件路径
-        model_name: 模型名称或路径
-        inference_engine: 推理引擎，"vllm" 或 "transformers"
-        
-    返回:
-        预测结果文本
-    """
-    # 检查图片是否存在
-    if not os.path.exists(image_path):
-        return f"错误: 图片文件不存在: {image_path}"
-    
-    # 加载图片
-    try:
-        image = Image.open(image_path)
-        print(f"成功加载图片: {image_path}")
-    except Exception as e:
-        return f"错误: 无法加载图片: {str(e)}"
-    
-    # 加载处理器
-    print(f"加载处理器: {model_name}")
-    processor = AutoProcessor.from_pretrained(model_name, padding_side='left')
-    
-    # 构建提示消息 - 简化版本，没有SFT和COT
-    question_text = "In which country and within which first-level administrative region of that country was this picture taken?Please answer in the format of <answer>$country,administrative_area_level_1$</answer>?"
-    system_message = "You are a helpful assistant good at solving problems with step-by-step reasoning. You should first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags."
-    
-    # 构建简化后的提示消息
-    prompt_messages = [
-        {
-            "role": "system",
-            "content": [
-                {"type": "text", "text": system_message}
-            ]
-        },
-        {
-            "role": "user",
-            "content": [
-                {"type": "image", "image": image},
-                {"type": "text", "text": question_text}
-            ]
-        }
-    ]
-    
-    # 根据选定的引擎进行推理
-    if inference_engine == "vllm":
-        if not vllm_available:
-            return "错误: vLLM库不可用，请安装vllm或选择transformers引擎"
-        
-        # 使用vLLM进行推理
-        print(f"使用vLLM加载模型: {model_name}")
-        llm = LLM(
-            model=model_name,
-            limit_mm_per_prompt={"image": 10, "video": 10},
-            dtype="auto",
-            gpu_memory_utilization=0.95,
-        )
-        
-        # 设置采样参数
-        sampling_params = SamplingParams(
-            temperature=0.7,
-            top_p=0.8,
-            repetition_penalty=1.05,
-            max_tokens=2048,
-            stop_token_ids=[],
-        )
-        
-        # 处理消息为vLLM格式
-        prompt = processor.apply_chat_template(
-            prompt_messages,
-            tokenize=False,
-            add_generation_prompt=True,
-        )
-        
-        # 处理图像数据
-        image_inputs, video_inputs = process_vision_info(prompt_messages)
-        
-        mm_data = {}
-        if image_inputs is not None:
-            mm_data["image"] = image_inputs
-        
-        # 构建vLLM输入
-        llm_input = {
-            "prompt": prompt,
-            "multi_modal_data": mm_data,
-        }
-        
-        # 生成回答
-        outputs = llm.generate([llm_input], sampling_params=sampling_params)
-        response = outputs[0].outputs[0].text
-        
-    else:  # transformers
-        if not transformers_available:
-            return "错误: Transformers相关库不可用，请安装必要的包"
-        
-        # 使用transformers加载模型
-        print(f"使用transformers加载模型: {model_name}")
-        model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-            model_name, torch_dtype="auto", device_map="auto"
-        )
-        
-        # 准备输入
-        text = processor.apply_chat_template(
-            prompt_messages, tokenize=False, add_generation_prompt=True
-        )
-        
-        # 处理输入
-        inputs = processor(
-            text=text,
-            images=prompt_messages[1]['content'][0]['image'],
-            return_tensors="pt",
-        )
-        
-        inputs = inputs.to(model.device)
-        
-        # 生成回答
-        with torch.no_grad():
-            generated_ids = model.generate(**inputs, max_new_tokens=2048)
-        
-        # 处理输出
-        generated_ids_trimmed = generated_ids[0][len(inputs['input_ids'][0]):]
-        response = processor.decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
-        
-        # 清理GPU缓存
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-    
-    print("\n=== 推理结果 ===")
-    print(response)
-    print("=================\n")
-    
-    return response
-
-# if __name__ == "__main__":
-#     # 命令行参数设置
-#     parser = argparse.ArgumentParser(description='对单个图片进行位置识别预测')
-#     parser.add_argument('--image_path', type=str, required=True,
-#                         help='图片文件路径')
-#     parser.add_argument('--model_name', type=str, default="Qwen/Qwen2.5-VL-7B-Instruct",
-#                         help='模型名称或路径')
-#     parser.add_argument('--inference_engine', type=str, default="transformers", choices=["vllm", "transformers"],
-#                         help='推理引擎: vllm 或 transformers')
-    
-#     args = parser.parse_args()
-    
-#     # 单个图片推理
-#     result = predict_location(
-#         image_path=args.image_path,
-#         model_name=args.model_name,
-#         inference_engine=args.inference_engine
-#     )
-    
-#     print(f"最终预测结果: {result}")
-
-# 使用示例:
-# python simple_inference.py --image_path /data/phd/tiankaibin/dataset/data/streetview_images_first_tier_cities/testaccio_rome_italy_h45_r100_20250317_183133.jpg --model_name TheEighthDay/SeekWorld_RL_PLUS --inference_engine vllm