提交获取caption和删除多余文本和测试模型的脚本

clean_text.py

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+import pickle
+
+def clean_text(text):
+    # 将文本反转
+    reversed_text = text[::-1]
+    # 查找第一个句号的位置
+    dot_pos = reversed_text.find('.')
+    
+    if dot_pos == -1:  # 如果没有找到句号
+        return text
+        
+    # 删除句号之前的所有文本，然后再次反转
+    cleaned_text = reversed_text[dot_pos:][::-1]
+    return cleaned_text.strip()
+
+# 加载原始数据
+print("正在加载数据...")
+with open('batch_1.pkl', 'rb') as f:
+    data = pickle.load(f)
+
+# 处理文本
+print("正在处理文本...")
+cleaned_data = []
+for item in data:
+    cleaned_item = item.copy()  # 复制原始数据项
+    cleaned_item['response'] = clean_text(item['response'])
+    cleaned_data.append(cleaned_item)
+
+# 保存处理后的数据
+print("正在保存清理后的数据...")
+with open('batch_1_cleaned.pkl', 'wb') as f:
+    pickle.dump(cleaned_data, f)
+
+# 打印示例
+print("\n处理示例:")
+for i in range(min(3, len(data))):
+    print(f"\n原始文本 {i+1}:")
+    print(data[i]['response'])
+    print(f"\n处理后文本 {i+1}:")
+    print(cleaned_data[i]['response'])
+
+print(f"\n总数据量: {len(data)}")
+print("数据已保存到 batch_1_cleaned.pkl") 

test2.py

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+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '3'
+
+import numpy as np
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from torchvision.transforms.functional import InterpolationMode
+from transformers import AutoModel, AutoTokenizer
+import requests
+from io import BytesIO
+from urllib.parse import urlparse
+from torch.profiler import profile, record_function, ProfilerActivity
+import time
+import pickle
+import random
+from datetime import datetime
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+def load_image(image_file, input_size=448, max_num=12):
+    # 如果已经是 PIL Image 对象
+    if isinstance(image_file, Image.Image):
+        image = image_file.convert('RGB')
+    # 如果是 URL
+    elif isinstance(image_file, str) and bool(urlparse(image_file).netloc):
+        try:
+            response = requests.get(image_file, timeout=10)
+            response.raise_for_status()  # 检查请求是否成功
+            image = Image.open(BytesIO(response.content)).convert('RGB')
+        except Exception as e:
+            raise ValueError(f"无法从URL加载图片: {str(e)}")
+    # 如果是本地文件路径
+    elif isinstance(image_file, str):
+        image = Image.open(image_file).convert('RGB')
+    # 如果是字节数据
+    elif isinstance(image_file, bytes):
+        image = Image.open(BytesIO(image_file)).convert('RGB')
+    else:
+        raise ValueError(f"不支持的图片格式: {type(image_file)}")
+
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values
+
+# If you want to load a model using multiple GPUs, please refer to the `Multiple GPUs` section.
+path = 'Internvl2_5'
+model = AutoModel.from_pretrained(
+    path,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    use_flash_attn=True,
+    trust_remote_code=True,
+    vision_model = None,
+    language_model = None).eval().cuda()
+
+# 加载要测试的权重
+state_dict = torch.load("mlp_epoch_5.pth")  # 加载训练好的权重
+model.load_state_dict(state_dict)
+generation_config = dict(max_new_tokens=1024, do_sample=True)
+tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
+
+print(model)
+
+# 测试
+# 加载数据
+with open('batch_1.pkl', 'rb') as f:
+    data = pickle.load(f)
+
+# 随机选择100条数据
+test_samples = random.sample(data, 100)
+
+# 创建结果文件
+timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+results_file = f'test_results_{timestamp}.txt'
+
+with open(results_file, 'w', encoding='utf-8') as f:
+    for i, test_item in enumerate(test_samples, 1):
+        question = "Identify the brand of the product in the picture, and write a caption including brand information in 200 words."
+        expected_answer = test_item['response']
+        
+        try:
+            pixel_values = load_image(test_item['image'], max_num=12).to(torch.bfloat16).cuda()
+            response, _ = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
+            
+            # 写入结果
+            f.write(f"Test case {i}/100:\n")
+            f.write(f"Question: {question}\n")
+            f.write(f"Expected: {expected_answer}\n")
+            f.write(f"Response: {response}\n")
+            f.write("-" * 50 + "\n")
+            
+            # 打印进度
+            print(f"Processed {i}/100 samples")
+            
+        except Exception as e:
+            f.write(f"Test case {i}/100 - ERROR:\n")
+            f.write(f"Error message: {str(e)}\n")
+            f.write("-" * 50 + "\n")
+            print(f"Error processing sample {i}: {str(e)}")
+
+print(f"测试完成！结果已保存到文件：{results_file}")

webdata.py

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+from openai import OpenAI
+import base64
+from datasets import load_dataset
+import requests
+from PIL import Image
+from io import BytesIO
+import pickle
+import logging
+from datetime import datetime
+import concurrent.futures
+import threading
+from tqdm import tqdm
+
+ds = load_dataset("DBQ/My.Theresa.Product.prices.France")
+# 打印数据集信息
+print(f"数据集加载完成，共包含 {len(ds)} 条数据")
+
+# 初始化客户端
+client = OpenAI(
+    api_key='sk-xqbujijjqqmlmlvkhvxeogqjtzslnhdtqxqgiyuhwpoqcjvf',
+    base_url='https://api.siliconflow.cn/v1'  # 通义千问API的基础URL
+)
+
+# 添加线程锁用于安全打印和保存
+print_lock = threading.Lock()
+results_lock = threading.Lock()
+
+def encode_image_to_base64(image_path):
+    """将本地图片转换为base64编码"""
+    try:
+        with open(image_path, "rb") as image_file:
+            return base64.b64encode(image_file.read()).decode('utf-8')
+    except Exception as e:
+        return None
+
+# 使用ChatGPT模型
+def chat_with_gpt(prompt, image_inputs=None):
+    try:
+        messages = []
+        content = [{"type": "text", "text": prompt}]
+        
+        if image_inputs:
+            # 确保image_inputs是列表
+            if not isinstance(image_inputs, list):
+                image_inputs = [image_inputs]
+                
+            # 处理每张图片
+            for image_input in image_inputs:
+                if image_input.startswith(('http://', 'https://')):
+                    image_data = {
+                        "type": "image_url",
+                        "image_url": {"url": image_input}
+                    }
+                else:
+                    # 处理本地图片
+                    base64_image = encode_image_to_base64(image_input)
+                    if base64_image:
+                        image_data = {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:image/jpeg;base64,{base64_image}"
+                            }
+                        }
+                    else:
+                        raise Exception("无法读取本地图片")
+                content.append(image_data)
+                
+        messages.append({
+            "role": "user",
+            "content": content
+        })
+            
+        response = client.chat.completions.create(
+            model="Qwen/QVQ-72B-Preview",
+            messages=messages,
+            temperature=0.1,  # 降低温度使输出更加确定性
+            top_p=0.2,        # 降低采样范围，使输出更加保守
+            max_tokens=200,   # 控制回答长度
+            presence_penalty=0.0,  # 不鼓励模型谈论新主题
+            frequency_penalty=0.0,  # 不惩罚频繁词汇
+            stream=False
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"发生错误：{str(e)}"
+
+# 设置日志配置
+def setup_logging():
+    # 创建日志文件名（包含时间戳）
+    log_filename = f'process_log_{datetime.now().strftime("%Y%m%d_%H%M%S")}.txt'
+    
+    # 配置日志
+    logging.basicConfig(
+        level=logging.INFO,
+        format='%(asctime)s - %(levelname)s - %(message)s',
+        handlers=[
+            logging.FileHandler(log_filename, encoding='utf-8'),
+            logging.StreamHandler()  # 同时输出到控制台
+        ]
+    )
+    return log_filename
+
+def process_single_item(item):
+    """处理单个数据项的函数"""
+    try:
+        image_url = item['imageurl']
+        response = requests.get(image_url)
+        img = Image.open(BytesIO(response.content))
+        
+        prompt = f"The brand of the product in the picture is {item['brand']}, write a caption including brand information."
+        gpt_response = chat_with_gpt(prompt, image_url)
+        
+        return {
+            'brand': item['brand'],
+            'image': img,
+            'response': gpt_response
+        }
+    except Exception as e:
+        with print_lock:
+            logging.error(f"处理数据时出错: {str(e)}")
+        return None
+
+def process_dataset(ds, batch_size=20000, max_workers=10):
+    results = []
+    valid_count = 0
+    total_items = len(ds['train'])
+    
+    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+        # 创建进度条
+        with tqdm(total=total_items, desc="处理数据") as pbar:
+            # 提交所有任务
+            future_to_item = {executor.submit(process_single_item, item): i 
+                            for i, item in enumerate(ds['train'])}
+            
+            # 处理完成的任务
+            for future in concurrent.futures.as_completed(future_to_item):
+                result = future.result()
+                if result:
+                    with results_lock:
+                        results.append(result)
+                        valid_count += 1
+                        
+                        # 达到batch_size时保存
+                        if len(results) >= batch_size:
+                            save_results(results, valid_count // batch_size)
+                            results = []
+                
+                pbar.update(1)
+    
+    # 保存剩余结果
+    if results:
+        save_results(results, (valid_count // batch_size) + 1)
+    
+    logging.info(f"处理完成，共处理 {valid_count} 条有效数据")
+
+def save_results(results, batch_num):
+    # 保存为pickle文件
+    with open(f'batch_{batch_num}.pkl', 'wb') as f:
+        pickle.dump(results, f)
+
+# 运行处理
+if __name__ == "__main__":
+    # 设置日志
+    log_file = setup_logging()
+    logging.info("开始处理数据集")
+    logging.info(f"数据集加载完成，共包含 {len(ds)} 条数据")
+    
+    process_dataset(ds)