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August 15, 2024 13:06
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joy-caption-pre-alpha
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| #import spaces | |
| #import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| from torch import nn | |
| from transformers import AutoModel, AutoProcessor, AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast, AutoModelForCausalLM | |
| from pathlib import Path | |
| import torch | |
| import torch.amp.autocast_mode | |
| from PIL import Image | |
| import os, time | |
| CLIP_PATH = "google/siglip-so400m-patch14-384" | |
| VLM_PROMPT = "A short caption for this image:\n" | |
| MODEL_PATH = "NousResearch/Meta-Llama-3.1-8B"#"mlabonne/Daredevil-8B"#"meta-llama/Meta-Llama-3.1-8B" | |
| CHECKPOINT_PATH = Path("wpkklhc6") | |
| TITLE = "<h1><center>JoyCaption Pre-Alpha (2024-07-30a)</center></h1>" | |
| HF_TOKEN = os.environ.get("HF_TOKEN", None) | |
| MAX_NEW_TOKENS = 200 | |
| IMAGE_FOLDER = "/workspace/1" | |
| 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 CLIP | |
| 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") | |
| # Tokenizer | |
| 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)}" | |
| # LLM | |
| print("Loading LLM") | |
| text_model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto", torch_dtype=torch.bfloat16) | |
| text_model.eval() | |
| # Image Adapter | |
| 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") | |
| torch.cuda.empty_cache() | |
| # Tokenize the prompt | |
| prompt = tokenizer.encode(VLM_PROMPT, return_tensors='pt', padding=False, truncation=False, add_special_tokens=False) | |
| # 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)) | |
| def caption_get(image_path): | |
| input_image = Image.open(image_path) | |
| # Preprocess image | |
| image = clip_processor(images=input_image, return_tensors='pt').pixel_values | |
| image = image.to('cuda') | |
| # 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') | |
| # 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=MAX_NEW_TOKENS, 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() | |
| # Чтение всех изображений из папки | |
| image_folder = Path(IMAGE_FOLDER).resolve() | |
| jpeg_images = image_folder.glob('*.jpeg') | |
| jpg_images = image_folder.glob('*.jpg') | |
| png_images = image_folder.glob('*.png') | |
| all_images = list(jpeg_images) + list(png_images) + list(jpg_images) | |
| # Фильтрация списка all_images, чтобы оставить только файлы, начинающиеся с | |
| #all_images = [img for img in all_images if img.name.startswith('nus')] | |
| all_images = sorted(all_images) | |
| #all_images = all_images[:10] | |
| num_images = len(all_images) | |
| start_time = time.time() | |
| images_processed = 0 | |
| for image_path in all_images: # Для файлов .jpg, измените расширение при необходимости | |
| images_processed+=1 | |
| caption = caption_get(image_path) | |
| text_filename = str(image_path.with_suffix('.txt')) | |
| with open(text_filename, 'w' if Path(text_filename).exists() else 'w') as file_text: | |
| file_text.write(f"{caption}") | |
| if images_processed % 1 ==0: | |
| elapsed_time = time.time() - start_time | |
| estimated_total_time = (elapsed_time / images_processed) * num_images | |
| remaining_time = estimated_total_time - elapsed_time | |
| print(f"File: {image_path}, Caption: {caption}\n") | |
| print(f"Processed {images_processed}/{num_images} files, approximate remaining time: {time.strftime('%H:%M:%S', time.gmtime(remaining_time))}") |
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