import torch import torchvision.transforms as T from PIL import Image from torchvision.transforms.functional import InterpolationMode import requests from io import BytesIO from urllib.parse import urlparse IMAGENET_MEAN = (0.485, 0.456, 0.406) IMAGENET_STD = (0.229, 0.224, 0.225) def build_transform(input_size): 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=IMAGENET_MEAN, std=IMAGENET_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 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]) target_aspect_ratio = find_closest_aspect_ratio( aspect_ratio, target_ratios, orig_width, orig_height, image_size) 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] 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_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): # 处理不同类型的输入 if isinstance(image_file, Image.Image): image = image_file.convert('RGB') elif isinstance(image_file, str) and bool(urlparse(image_file).netloc): try: response = requests.get(image_file, timeout=10) response.raise_for_status() 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)}") elif isinstance(image_file, str): image = Image.open(image_file).convert('RGB') 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 def prepare_training_data(question: str, answer: str, tokenizer, image_path=None, input_size=448, max_num=12, num_image_token: int = 256): """ 准备完整的训练数据（包括输入和标签） Args: question (str): 用户输入的问题 answer (str): 助手的回答文本 tokenizer: 分词器 image_path: 图片路径、URL或PIL Image对象 input_size (int): 图片输入尺寸 max_num (int): 最大图片块数 num_image_token (int): 每张图片的token数量 Returns: dict: 包含模型训练所需的所有输入 """ # 1. 处理图像输入 pixel_values = None if image_path is not None: pixel_values = load_image(image_path, input_size=input_size, max_num=max_num)[-1:] ### 只取最后一张图片 if torch.cuda.is_available(): pixel_values = pixel_values.to(torch.bfloat16) # 2. 确保问题包含图片标记 if pixel_values is not None and '' not in question: question = '\n' + question # 3. 根据pixel_values确定num_patches num_patches = pixel_values.shape[0] if pixel_values is not None else 0 # 4. 构造完整的对话内容 system_msg = "你是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。" # 5. 替换图片标记 if num_patches > 0: image_tokens = "" + "" * (num_image_token * num_patches) + "" question = question.replace('', image_tokens, 1) # 6. 构造完整的query（包含答案） full_prompt = ( f"<|im_start|>system\n{system_msg}<|im_end|>\n" f"<|im_start|>user\n{question}<|im_end|>\n" f"<|im_start|>assistant\n{answer}<|im_end|>\n" ) # 7. 转换为模型输入格式 model_inputs = tokenizer( full_prompt, return_tensors="pt", add_special_tokens=False ) # 8. 构造labels input_ids = model_inputs["input_ids"] labels = input_ids.clone() assistant_start_token = "<|im_start|>assistant\n" assistant_token_ids = tokenizer(assistant_start_token, add_special_tokens=False)["input_ids"] assistant_start_pos = None for i in range(len(input_ids[0]) - len(assistant_token_ids)): if input_ids[0][i:i+len(assistant_token_ids)].tolist() == assistant_token_ids: assistant_start_pos = i break if assistant_start_pos is not None: labels[0, :assistant_start_pos] = -100 return { "input_ids": input_ids, "attention_mask": model_inputs["attention_mask"], "labels": labels, "pixel_values": pixel_values } # 使用示例： """ import torch from transformers import AutoTokenizer # 初始化tokenizer tokenizer = AutoTokenizer.from_pretrained("path_to_tokenizer") # 准备示例数据 question = "请描述这张图片中的内容" answer = "这是一张美丽的风景照，画面中有青山绿水。" image_path = "./examples/image1.jpg" # 或URL，或PIL Image对象 # 准备训练数据 training_data = prepare_training_data( question=question, answer=answer, tokenizer=tokenizer, image_path=image_path, input_size=448, max_num=12 ) # training_data 包含: # { # "input_ids": tensor([[...]]), # 完整对话的token ids # "attention_mask": tensor([[...]]), # 注意力掩码 # "labels": tensor([[...]]), # 带有-100标记的标签 # "pixel_values": tensor([[...]]) # 图像数据 # } """