Create label_script.py

end-to-end-use-cases/Multi-Modal-RAG/scripts/label_script.py

@@ -0,0 +1,151 @@
+import os
+import argparse
+import torch
+from transformers import MllamaForConditionalGeneration, MllamaProcessor
+from tqdm.auto import tqdm
+import csv
+from PIL import Image
+import torch.multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+import shutil
+import time
+
+USER_TEXT = """
+You are an expert fashion captioner, we are writing descriptions of clothes, look at the image closely and write a caption for it.
+
+Write the following Title, Size, Category, Gender, Type, Description in JSON FORMAT, PLEASE DO NOT FORGET JSON, 
+
+ALSO START WITH THE JSON AND NOT ANY THING ELSE, FIRST CHAR IN YOUR RESPONSE IS ITS OPENING BRACE
+
+FOLLOW THESE STEPS CLOSELY WHEN WRITING THE CAPTION: 
+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
+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?
+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
+3. If you cant tell the size from image, guess it! its okay but dont literally write that you guessed it
+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
+5. BE CREATIVE WITH THE DESCRIPTION BUT FOLLOW EVERYTHING CLOSELY FOR STRUCTURE
+6. Return your answer in dictionary format, see the example below
+
+{"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"}
+
+Example: ALWAYS RETURN ANSWERS IN THE DICTIONARY FORMAT BELOW OK?
+
+{"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"} 
+"""
+
+def is_image_corrupt(image_path):
+    try:
+        with Image.open(image_path) as img:
+            img.verify()
+        return False
+    except (IOError, SyntaxError, Image.UnidentifiedImageError):
+        return True
+
+def find_and_move_corrupt_images(folder_path, corrupt_folder):
+    image_files = [os.path.join(folder_path, f) for f in os.listdir(folder_path) 
+                   if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
+    
+    num_cores = mp.cpu_count()
+    with tqdm(total=len(image_files), desc="Checking for corrupt images", unit="file", 
+              bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]") as pbar:
+        with ProcessPoolExecutor(max_workers=num_cores) as executor:
+            results = list(executor.map(is_image_corrupt, image_files))
+            pbar.update(len(image_files))
+    
+    corrupt_images = [img for img, is_corrupt in zip(image_files, results) if is_corrupt]
+    
+    os.makedirs(corrupt_folder, exist_ok=True)
+    for img in tqdm(corrupt_images, desc="Moving corrupt images", unit="file",
+                    bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]"):
+        shutil.move(img, os.path.join(corrupt_folder, os.path.basename(img)))
+    
+    print(f"Moved {len(corrupt_images)} corrupt images to {corrupt_folder}")
+
+def get_image(image_path):
+    return Image.open(image_path).convert('RGB')
+
+def llama_progress_bar(total, desc, position=0):
+    """Custom progress bar with llama emojis."""
+    bar_format = "{desc}: |{bar}| {percentage:3.0f}% [{n_fmt}/{total_fmt}, {rate_fmt}{postfix}]"
+    return tqdm(total=total, desc=desc, position=position, bar_format=bar_format, ascii="🦙·")
+
+def process_images(rank, world_size, args, model_name, input_files, output_csv):
+    model = MllamaForConditionalGeneration.from_pretrained(model_name, device_map=f"cuda:{rank}", torch_dtype=torch.bfloat16, token=args.hf_token)
+    processor = MllamaProcessor.from_pretrained(model_name, token=args.hf_token)
+
+    chunk_size = len(input_files) // world_size
+    start_idx = rank * chunk_size
+    end_idx = start_idx + chunk_size if rank < world_size - 1 else len(input_files)
+    
+    results = []
+    
+    pbar = llama_progress_bar(total=end_idx - start_idx, desc=f"GPU {rank}", position=rank)
+    
+    for filename in input_files[start_idx:end_idx]:
+        image_path = os.path.join(args.input_path, filename)
+        image = get_image(image_path)
+
+        conversation = [
+            {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": USER_TEXT}]}
+        ]
+
+        prompt = processor.apply_chat_template(conversation, add_special_tokens=False, add_generation_prompt=True, tokenize=False)
+        inputs = processor(image, prompt, return_tensors="pt").to(model.device)
+
+        output = model.generate(**inputs, temperature=1, top_p=0.9, max_new_tokens=512)
+        decoded_output = processor.decode(output[0])[len(prompt):]
+
+        results.append((filename, decoded_output))
+        
+        pbar.update(1)
+        pbar.set_postfix({"Last File": filename})
+
+    pbar.close()
+
+    with open(output_csv, 'w', newline='', encoding='utf-8') as f:
+        writer = csv.writer(f)
+        writer.writerow(['Filename', 'Caption'])
+        writer.writerows(results)
+
+def main():
+    parser = argparse.ArgumentParser(description="Multi-GPU Image Captioning")
+    parser.add_argument("--hf_token", required=True, help="Hugging Face API token")
+    parser.add_argument("--input_path", required=True, help="Path to input image folder")
+    parser.add_argument("--output_path", required=True, help="Path to output CSV folder")
+    parser.add_argument("--num_gpus", type=int, required=True, help="Number of GPUs to use")
+    parser.add_argument("--corrupt_folder", default="corrupt_images", help="Folder to move corrupt images")
+    args = parser.parse_args()
+
+    model_name = "meta-llama/Llama-3.2-11b-Vision-Instruct"
+
+    print("🦙 Starting image processing pipeline...")
+    start_time = time.time()
+
+    # Find and move corrupt images
+    corrupt_folder = os.path.join(args.input_path, args.corrupt_folder)
+    find_and_move_corrupt_images(args.input_path, corrupt_folder)
+
+    # Get list of remaining (non-corrupt) image files
+    input_files = [f for f in os.listdir(args.input_path) if f.lower().endswith(('.jpg', '.jpeg', '.png'))]
+
+    print(f"\n🦙 Processing {len(input_files)} images using {args.num_gpus} GPUs...")
+
+    mp.set_start_method('spawn', force=True)
+    processes = []
+
+    for rank in range(args.num_gpus):
+        output_csv = os.path.join(args.output_path, f"captions_gpu_{rank}.csv")
+        p = mp.Process(target=process_images, args=(rank, args.num_gpus, args, model_name, input_files, output_csv))
+        p.start()
+        processes.append(p)
+
+    for p in processes:
+        p.join()
+
+    end_time = time.time()
+    total_time = end_time - start_time
+    print(f"\n🦙 Total processing time: {total_time:.2f} seconds")
+    print("🦙 Image captioning completed successfully!")
+
+if __name__ == "__main__":
+    main()