''' git clone https://modelscope.cn/models/LLM-Research/Meta-Llama-3.1-8B python run_caption_ds.py "svjack/Genshin-Impact-Couple-with-Tags-IID-Gender-Only-Two" --caption_column="joy-caption" --output_path="gen_couple_cap_dir" ''' import argparse from pathlib import Path import torch from torch import nn from transformers import AutoModel, AutoProcessor, AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast, AutoModelForCausalLM from datasets import load_dataset # 引入 Hugging Face Dataset from tqdm import tqdm # 引入 tqdm 用于显示进度条 # Constants CLIP_PATH = "google/siglip-so400m-patch14-384" VLM_PROMPT = "A descriptive caption for this image:\n" #MODEL_PATH = "meta-llama/Meta-Llama-3.1-8B" MODEL_PATH = "Meta-Llama-3.1-8B" CHECKPOINT_PATH = Path("wpkklhc6") # Image Adapter class ImageAdapter(nn.Module): def __init__(self, input_features: int, output_features: int): super().__init__() self.linear1 = nn.Linear(input_features, output_features) self.activation = nn.GELU() self.linear2 = nn.Linear(output_features, output_features) def forward(self, vision_outputs: torch.Tensor): x = self.linear1(vision_outputs) x = self.activation(x) x = self.linear2(x) return x # Load models def load_models(): print("Loading CLIP") clip_processor = AutoProcessor.from_pretrained(CLIP_PATH) clip_model = AutoModel.from_pretrained(CLIP_PATH) clip_model = clip_model.vision_model clip_model.eval() clip_model.requires_grad_(False) clip_model.to("cuda") print("Loading tokenizer") tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, use_fast=False) assert isinstance(tokenizer, PreTrainedTokenizer) or isinstance(tokenizer, PreTrainedTokenizerFast), f"Tokenizer is of type {type(tokenizer)}" print("Loading LLM") text_model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto", torch_dtype=torch.bfloat16) text_model.eval() print("Loading image adapter") image_adapter = ImageAdapter(clip_model.config.hidden_size, text_model.config.hidden_size) image_adapter.load_state_dict(torch.load(CHECKPOINT_PATH / "image_adapter.pt", map_location="cpu")) image_adapter.eval() image_adapter.to("cuda") return clip_processor, clip_model, tokenizer, text_model, image_adapter # Generate caption @torch.no_grad() def generate_caption(input_image, clip_processor, clip_model, tokenizer, text_model, image_adapter): torch.cuda.empty_cache() # Preprocess image image = clip_processor(images=input_image, return_tensors='pt').pixel_values image = image.to('cuda') # Tokenize the prompt prompt = tokenizer.encode(VLM_PROMPT, return_tensors='pt', padding=False, truncation=False, add_special_tokens=False) # Embed image with torch.amp.autocast_mode.autocast('cuda', enabled=True): vision_outputs = clip_model(pixel_values=image, output_hidden_states=True) image_features = vision_outputs.hidden_states[-2] embedded_images = image_adapter(image_features) embedded_images = embedded_images.to('cuda') # Embed prompt prompt_embeds = text_model.model.embed_tokens(prompt.to('cuda')) assert prompt_embeds.shape == (1, prompt.shape[1], text_model.config.hidden_size), f"Prompt shape is {prompt_embeds.shape}, expected {(1, prompt.shape[1], text_model.config.hidden_size)}" embedded_bos = text_model.model.embed_tokens(torch.tensor([[tokenizer.bos_token_id]], device=text_model.device, dtype=torch.int64)) # Construct prompts inputs_embeds = torch.cat([ embedded_bos.expand(embedded_images.shape[0], -1, -1), embedded_images.to(dtype=embedded_bos.dtype), prompt_embeds.expand(embedded_images.shape[0], -1, -1), ], dim=1) input_ids = torch.cat([ torch.tensor([[tokenizer.bos_token_id]], dtype=torch.long), torch.zeros((1, embedded_images.shape[1]), dtype=torch.long), prompt, ], dim=1).to('cuda') attention_mask = torch.ones_like(input_ids) generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=300, do_sample=True, top_k=10, temperature=0.5, suppress_tokens=None) # Trim off the prompt generate_ids = generate_ids[:, input_ids.shape[1]:] if generate_ids[0][-1] == tokenizer.eos_token_id: generate_ids = generate_ids[:, :-1] caption = tokenizer.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0] return caption.strip() # Main function def main(): parser = argparse.ArgumentParser(description="Generate captions for images in a Hugging Face Dataset.") parser.add_argument("dataset_name", type=str, help="Name of the Hugging Face Dataset") parser.add_argument("--image_column", type=str, default="image", help="Name of the column containing images (default: 'image')") parser.add_argument("--caption_column", type=str, default="caption", help="Name of the column to save captions (default: 'caption')") parser.add_argument("--output_path", type=str, required=True, help="Path to save the dataset with captions") args = parser.parse_args() # Load models clip_processor, clip_model, tokenizer, text_model, image_adapter = load_models() # Load dataset print(f"Loading dataset: {args.dataset_name}") dataset = load_dataset(args.dataset_name) len_ = len(dataset["train"]) #len_ = 10 # Initialize a list to store captions captions = [] # Generate captions for each image in the dataset print("Generating captions...") for idx, example in enumerate(tqdm(dataset["train"].select(range(len_)), desc="Processing images")): # 假设数据集是 "train" 拆分 try: # Generate caption caption = generate_caption(example[args.image_column], clip_processor, clip_model, tokenizer, text_model, image_adapter) captions.append(caption) # Print the generated caption print(f"Caption for image {idx + 1}: {caption}") except Exception as e: print(f"Error processing image {idx + 1}: {e}") captions.append("") # 如果出错，保存空字符串 print(f"Caption for image {idx + 1}: [Error]") # Add captions to the dataset print("Adding captions to the dataset...") dataset = dataset["train"].select(range(len_)).add_column(args.caption_column, captions) # 将 captions 添加到数据集 # Save the dataset with captions print(f"Saving dataset to {args.output_path}") dataset.save_to_disk(args.output_path) print("Done!") if __name__ == "__main__": main()