wehub-resource-sync
152 lines
7.1 KiB
Python
152 lines
7.1 KiB
Python
import os
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import argparse
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import torch
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from transformers import MllamaForConditionalGeneration, MllamaProcessor
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from tqdm.auto import tqdm
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import csv
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from PIL import Image
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import torch.multiprocessing as mp
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from concurrent.futures import ProcessPoolExecutor
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import shutil
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import time
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USER_TEXT = """
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You are an expert fashion captioner, we are writing descriptions of clothes, look at the image closely and write a caption for it.
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Write the following Title, Size, Category, Gender, Type, Description in JSON FORMAT, PLEASE DO NOT FORGET JSON,
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ALSO START WITH THE JSON AND NOT ANY THING ELSE, FIRST CHAR IN YOUR RESPONSE IS ITS OPENING BRACE
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FOLLOW THESE STEPS CLOSELY WHEN WRITING THE CAPTION:
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1. Only start your response with a dictionary like the example below, nothing else, I NEED TO PARSE IT LATER, SO DONT ADD ANYTHING ELSE-IT WILL BREAK MY CODE
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Remember-DO NOT SAY ANYTHING ELSE ABOUT WHAT IS GOING ON, just the opening brace is the first thing in your response nothing else ok?
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2. REMEMBER TO CLOSE THE DICTIONARY WITH '}'BRACE, IT GOES AFTER THE END OF DESCRIPTION-YOU ALWAYS FORGET IT, THIS WILL CAUSE A LOT OF ISSUES
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3. If you cant tell the size from image, guess it! its okay but dont literally write that you guessed it
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4. Do not make the caption very literal, all of these are product photos, DO NOT CAPTION HOW OR WHERE THEY ARE PLACED, FOCUS ON WRITING ABOUT THE PIECE OF CLOTHING
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5. BE CREATIVE WITH THE DESCRIPTION BUT FOLLOW EVERYTHING CLOSELY FOR STRUCTURE
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6. Return your answer in dictionary format, see the example below
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{"Title": "Title of item of clothing", "Size": {'S', 'M', 'L', 'XL'}, #select one randomly if you cant tell from the image. DO NOT TELL ME YOU ESTIMATE OR GUESSED IT ONLY THE LETTER IS ENOUGH", Category": {T-Shirt, Shoes, Tops, Pants, Jeans, Shorts, Skirts, Shoes, Footwear}, "Gender": {M, F, U}, "Type": {Casual, Formal, Work Casual, Lounge}, "Description": "Write it here"}
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Example: ALWAYS RETURN ANSWERS IN THE DICTIONARY FORMAT BELOW OK?
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{"Title": "Casual White pant with logo on it", "size": "L", "Category": "Jeans", "Gender": "U", "Type": "Work Casual", "Description": "Write it here, this is where your stuff goes"}
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"""
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def is_image_corrupt(image_path):
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try:
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with Image.open(image_path) as img:
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img.verify()
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return False
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except (IOError, SyntaxError, Image.UnidentifiedImageError):
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return True
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def find_and_move_corrupt_images(folder_path, corrupt_folder):
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image_files = [os.path.join(folder_path, f) for f in os.listdir(folder_path)
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if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
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num_cores = mp.cpu_count()
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with tqdm(total=len(image_files), desc="Checking for corrupt images", unit="file",
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bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]") as pbar:
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with ProcessPoolExecutor(max_workers=num_cores) as executor:
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results = list(executor.map(is_image_corrupt, image_files))
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pbar.update(len(image_files))
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corrupt_images = [img for img, is_corrupt in zip(image_files, results) if is_corrupt]
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os.makedirs(corrupt_folder, exist_ok=True)
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for img in tqdm(corrupt_images, desc="Moving corrupt images", unit="file",
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bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]"):
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shutil.move(img, os.path.join(corrupt_folder, os.path.basename(img)))
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print(f"Moved {len(corrupt_images)} corrupt images to {corrupt_folder}")
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def get_image(image_path):
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return Image.open(image_path).convert('RGB')
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def llama_progress_bar(total, desc, position=0):
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"""Custom progress bar with llama emojis."""
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bar_format = "{desc}: |{bar}| {percentage:3.0f}% [{n_fmt}/{total_fmt}, {rate_fmt}{postfix}]"
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return tqdm(total=total, desc=desc, position=position, bar_format=bar_format, ascii="🦙·")
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def process_images(rank, world_size, args, model_name, input_files, output_csv):
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model = MllamaForConditionalGeneration.from_pretrained(model_name, device_map=f"cuda:{rank}", torch_dtype=torch.bfloat16, token=args.hf_token)
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processor = MllamaProcessor.from_pretrained(model_name, token=args.hf_token)
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chunk_size = len(input_files) // world_size
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start_idx = rank * chunk_size
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end_idx = start_idx + chunk_size if rank < world_size - 1 else len(input_files)
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results = []
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pbar = llama_progress_bar(total=end_idx - start_idx, desc=f"GPU {rank}", position=rank)
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for filename in input_files[start_idx:end_idx]:
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image_path = os.path.join(args.input_path, filename)
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image = get_image(image_path)
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conversation = [
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{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": USER_TEXT}]}
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]
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prompt = processor.apply_chat_template(conversation, add_special_tokens=False, add_generation_prompt=True, tokenize=False)
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inputs = processor(image, prompt, return_tensors="pt").to(model.device)
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output = model.generate(**inputs, temperature=1, top_p=0.9, max_new_tokens=512)
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decoded_output = processor.decode(output[0])[len(prompt):]
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results.append((filename, decoded_output))
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pbar.update(1)
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pbar.set_postfix({"Last File": filename})
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pbar.close()
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with open(output_csv, 'w', newline='', encoding='utf-8') as f:
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writer = csv.writer(f)
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writer.writerow(['Filename', 'Caption'])
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writer.writerows(results)
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def main():
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parser = argparse.ArgumentParser(description="Multi-GPU Image Captioning")
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parser.add_argument("--hf_token", required=True, help="Hugging Face API token")
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parser.add_argument("--input_path", required=True, help="Path to input image folder")
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parser.add_argument("--output_path", required=True, help="Path to output CSV folder")
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parser.add_argument("--num_gpus", type=int, required=True, help="Number of GPUs to use")
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parser.add_argument("--corrupt_folder", default="corrupt_images", help="Folder to move corrupt images")
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args = parser.parse_args()
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model_name = "meta-llama/Llama-3.2-11b-Vision-Instruct"
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print("🦙 Starting image processing pipeline...")
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start_time = time.time()
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# Find and move corrupt images
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corrupt_folder = os.path.join(args.input_path, args.corrupt_folder)
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find_and_move_corrupt_images(args.input_path, corrupt_folder)
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# Get list of remaining (non-corrupt) image files
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input_files = [f for f in os.listdir(args.input_path) if f.lower().endswith(('.jpg', '.jpeg', '.png'))]
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print(f"\n🦙 Processing {len(input_files)} images using {args.num_gpus} GPUs...")
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mp.set_start_method('spawn', force=True)
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processes = []
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for rank in range(args.num_gpus):
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output_csv = os.path.join(args.output_path, f"captions_gpu_{rank}.csv")
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p = mp.Process(target=process_images, args=(rank, args.num_gpus, args, model_name, input_files, output_csv))
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p.start()
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processes.append(p)
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for p in processes:
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p.join()
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end_time = time.time()
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total_time = end_time - start_time
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print(f"\n🦙 Total processing time: {total_time:.2f} seconds")
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print("🦙 Image captioning completed successfully!")
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if __name__ == "__main__":
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main()
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