提交获取caption和删除多余文本和测试模型的脚本
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clean_text.py
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clean_text.py
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import pickle
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def clean_text(text):
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# 将文本反转
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reversed_text = text[::-1]
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# 查找第一个句号的位置
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dot_pos = reversed_text.find('.')
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if dot_pos == -1: # 如果没有找到句号
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return text
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# 删除句号之前的所有文本，然后再次反转
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cleaned_text = reversed_text[dot_pos:][::-1]
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return cleaned_text.strip()
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# 加载原始数据
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print("正在加载数据...")
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with open('batch_1.pkl', 'rb') as f:
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data = pickle.load(f)
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# 处理文本
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print("正在处理文本...")
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cleaned_data = []
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for item in data:
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cleaned_item = item.copy() # 复制原始数据项
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cleaned_item['response'] = clean_text(item['response'])
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cleaned_data.append(cleaned_item)
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# 保存处理后的数据
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print("正在保存清理后的数据...")
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with open('batch_1_cleaned.pkl', 'wb') as f:
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pickle.dump(cleaned_data, f)
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# 打印示例
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print("\n处理示例:")
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for i in range(min(3, len(data))):
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print(f"\n原始文本 {i+1}:")
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print(data[i]['response'])
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print(f"\n处理后文本 {i+1}:")
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print(cleaned_data[i]['response'])
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print(f"\n总数据量: {len(data)}")
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print("数据已保存到 batch_1_cleaned.pkl")
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168
test2.py
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test2.py
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import os
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os.environ['CUDA_VISIBLE_DEVICES'] = '3'
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import numpy as np
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import torch
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import torchvision.transforms as T
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from PIL import Image
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from torchvision.transforms.functional import InterpolationMode
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from transformers import AutoModel, AutoTokenizer
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import requests
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from io import BytesIO
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from urllib.parse import urlparse
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from torch.profiler import profile, record_function, ProfilerActivity
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import time
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import pickle
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import random
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from datetime import datetime
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IMAGENET_MEAN = (0.485, 0.456, 0.406)
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IMAGENET_STD = (0.229, 0.224, 0.225)
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def build_transform(input_size):
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MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
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transform = T.Compose([
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T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
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T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
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T.ToTensor(),
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T.Normalize(mean=MEAN, std=STD)
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])
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return transform
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def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
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best_ratio_diff = float('inf')
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best_ratio = (1, 1)
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area = width * height
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for ratio in target_ratios:
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target_aspect_ratio = ratio[0] / ratio[1]
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ratio_diff = abs(aspect_ratio - target_aspect_ratio)
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if ratio_diff < best_ratio_diff:
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best_ratio_diff = ratio_diff
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best_ratio = ratio
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elif ratio_diff == best_ratio_diff:
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if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
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best_ratio = ratio
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return best_ratio
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def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
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orig_width, orig_height = image.size
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aspect_ratio = orig_width / orig_height
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# calculate the existing image aspect ratio
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target_ratios = set(
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(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
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i * j <= max_num and i * j >= min_num)
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target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
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# find the closest aspect ratio to the target
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target_aspect_ratio = find_closest_aspect_ratio(
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aspect_ratio, target_ratios, orig_width, orig_height, image_size)
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# calculate the target width and height
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target_width = image_size * target_aspect_ratio[0]
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target_height = image_size * target_aspect_ratio[1]
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blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
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# resize the image
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resized_img = image.resize((target_width, target_height))
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processed_images = []
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for i in range(blocks):
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box = (
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(i % (target_width // image_size)) * image_size,
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(i // (target_width // image_size)) * image_size,
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((i % (target_width // image_size)) + 1) * image_size,
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((i // (target_width // image_size)) + 1) * image_size
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)
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# split the image
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split_img = resized_img.crop(box)
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processed_images.append(split_img)
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assert len(processed_images) == blocks
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if use_thumbnail and len(processed_images) != 1:
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thumbnail_img = image.resize((image_size, image_size))
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processed_images.append(thumbnail_img)
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return processed_images
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def load_image(image_file, input_size=448, max_num=12):
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# 如果已经是 PIL Image 对象
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if isinstance(image_file, Image.Image):
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image = image_file.convert('RGB')
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# 如果是 URL
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elif isinstance(image_file, str) and bool(urlparse(image_file).netloc):
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try:
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response = requests.get(image_file, timeout=10)
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response.raise_for_status() # 检查请求是否成功
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image = Image.open(BytesIO(response.content)).convert('RGB')
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except Exception as e:
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raise ValueError(f"无法从URL加载图片: {str(e)}")
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# 如果是本地文件路径
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elif isinstance(image_file, str):
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image = Image.open(image_file).convert('RGB')
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# 如果是字节数据
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elif isinstance(image_file, bytes):
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image = Image.open(BytesIO(image_file)).convert('RGB')
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else:
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raise ValueError(f"不支持的图片格式: {type(image_file)}")
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transform = build_transform(input_size=input_size)
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images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
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pixel_values = [transform(image) for image in images]
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pixel_values = torch.stack(pixel_values)
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return pixel_values
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# If you want to load a model using multiple GPUs, please refer to the `Multiple GPUs` section.
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path = 'Internvl2_5'
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model = AutoModel.from_pretrained(
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path,
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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use_flash_attn=True,
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trust_remote_code=True,
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vision_model = None,
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language_model = None).eval().cuda()
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# 加载要测试的权重
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state_dict = torch.load("mlp_epoch_5.pth") # 加载训练好的权重
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model.load_state_dict(state_dict)
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generation_config = dict(max_new_tokens=1024, do_sample=True)
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tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
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print(model)
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# 测试
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# 加载数据
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with open('batch_1.pkl', 'rb') as f:
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data = pickle.load(f)
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# 随机选择100条数据
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test_samples = random.sample(data, 100)
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# 创建结果文件
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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results_file = f'test_results_{timestamp}.txt'
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with open(results_file, 'w', encoding='utf-8') as f:
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for i, test_item in enumerate(test_samples, 1):
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question = "Identify the brand of the product in the picture, and write a caption including brand information in 200 words."
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expected_answer = test_item['response']
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try:
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pixel_values = load_image(test_item['image'], max_num=12).to(torch.bfloat16).cuda()
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response, _ = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
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# 写入结果
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f.write(f"Test case {i}/100:\n")
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f.write(f"Question: {question}\n")
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f.write(f"Expected: {expected_answer}\n")
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f.write(f"Response: {response}\n")
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f.write("-" * 50 + "\n")
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# 打印进度
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print(f"Processed {i}/100 samples")
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except Exception as e:
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f.write(f"Test case {i}/100 - ERROR:\n")
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f.write(f"Error message: {str(e)}\n")
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f.write("-" * 50 + "\n")
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print(f"Error processing sample {i}: {str(e)}")
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print(f"测试完成！结果已保存到文件：{results_file}")
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168
webdata.py
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webdata.py
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from openai import OpenAI
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import base64
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from datasets import load_dataset
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import requests
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from PIL import Image
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from io import BytesIO
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import pickle
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import logging
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from datetime import datetime
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import concurrent.futures
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import threading
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from tqdm import tqdm
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ds = load_dataset("DBQ/My.Theresa.Product.prices.France")
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# 打印数据集信息
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print(f"数据集加载完成，共包含 {len(ds)} 条数据")
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# 初始化客户端
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client = OpenAI(
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api_key='sk-xqbujijjqqmlmlvkhvxeogqjtzslnhdtqxqgiyuhwpoqcjvf',
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base_url='https://api.siliconflow.cn/v1' # 通义千问API的基础URL
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)
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# 添加线程锁用于安全打印和保存
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print_lock = threading.Lock()
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results_lock = threading.Lock()
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def encode_image_to_base64(image_path):
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"""将本地图片转换为base64编码"""
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try:
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with open(image_path, "rb") as image_file:
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return base64.b64encode(image_file.read()).decode('utf-8')
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except Exception as e:
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return None
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# 使用ChatGPT模型
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def chat_with_gpt(prompt, image_inputs=None):
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try:
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messages = []
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content = [{"type": "text", "text": prompt}]
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if image_inputs:
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# 确保image_inputs是列表
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if not isinstance(image_inputs, list):
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image_inputs = [image_inputs]
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# 处理每张图片
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for image_input in image_inputs:
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if image_input.startswith(('http://', 'https://')):
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image_data = {
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"type": "image_url",
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"image_url": {"url": image_input}
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}
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else:
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# 处理本地图片
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base64_image = encode_image_to_base64(image_input)
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if base64_image:
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image_data = {
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}"
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}
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}
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else:
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raise Exception("无法读取本地图片")
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content.append(image_data)
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messages.append({
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"role": "user",
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"content": content
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})
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response = client.chat.completions.create(
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model="Qwen/QVQ-72B-Preview",
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messages=messages,
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temperature=0.1, # 降低温度使输出更加确定性
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top_p=0.2, # 降低采样范围，使输出更加保守
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max_tokens=200, # 控制回答长度
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presence_penalty=0.0, # 不鼓励模型谈论新主题
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frequency_penalty=0.0, # 不惩罚频繁词汇
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stream=False
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)
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return response.choices[0].message.content
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except Exception as e:
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return f"发生错误：{str(e)}"
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# 设置日志配置
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def setup_logging():
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# 创建日志文件名（包含时间戳）
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log_filename = f'process_log_{datetime.now().strftime("%Y%m%d_%H%M%S")}.txt'
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler(log_filename, encoding='utf-8'),
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logging.StreamHandler() # 同时输出到控制台
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]
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)
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return log_filename
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def process_single_item(item):
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"""处理单个数据项的函数"""
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try:
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image_url = item['imageurl']
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response = requests.get(image_url)
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img = Image.open(BytesIO(response.content))
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prompt = f"The brand of the product in the picture is {item['brand']}, write a caption including brand information."
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gpt_response = chat_with_gpt(prompt, image_url)
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return {
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'brand': item['brand'],
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'image': img,
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'response': gpt_response
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}
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except Exception as e:
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with print_lock:
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logging.error(f"处理数据时出错: {str(e)}")
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return None
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def process_dataset(ds, batch_size=20000, max_workers=10):
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results = []
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valid_count = 0
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total_items = len(ds['train'])
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with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
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# 创建进度条
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with tqdm(total=total_items, desc="处理数据") as pbar:
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# 提交所有任务
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future_to_item = {executor.submit(process_single_item, item): i
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for i, item in enumerate(ds['train'])}
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# 处理完成的任务
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for future in concurrent.futures.as_completed(future_to_item):
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result = future.result()
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if result:
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with results_lock:
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results.append(result)
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valid_count += 1
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# 达到batch_size时保存
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if len(results) >= batch_size:
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save_results(results, valid_count // batch_size)
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results = []
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pbar.update(1)
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# 保存剩余结果
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if results:
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save_results(results, (valid_count // batch_size) + 1)
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logging.info(f"处理完成，共处理 {valid_count} 条有效数据")
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def save_results(results, batch_num):
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# 保存为pickle文件
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with open(f'batch_{batch_num}.pkl', 'wb') as f:
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pickle.dump(results, f)
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# 运行处理
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if __name__ == "__main__":
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# 设置日志
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log_file = setup_logging()
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logging.info("开始处理数据集")
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logging.info(f"数据集加载完成，共包含 {len(ds)} 条数据")
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process_dataset(ds)
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