ColonGPT

ColonGPT (A colonoscopy-specific multimodal Language Model)

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This is the merged weights of ColonGPT-v1-phi1.5-siglip-lora-stg2, including vision encoder (siglip) + language model (phi-1.5), and other fine-tuned weights on our ColonINST.

Our ColonGPT is a standard multimodal language model, which contains four basic components: a language tokenizer, an visual encoder (🤗 SigLIP-SO), a multimodal connector, and a language model (🤗 Phi1.5). In this huggingface page, we provide a quick start for convenient of new users. For further details about ColonGPT, we highly recommend visiting our homepage. There, you'll find comprehensive usage instructions for our model and the latest advancements in intelligent colonoscopy technology.

Quick start

Here is a code snippet to show you how to quickly try-on our ColonGPT model with transformers. The model focuses on three downstream tasks: image classification (CLS), referring expression generation (REG), and referring expression comprehension (REC). If you need a caption generator, please refer to ColonGPT-V1-stg1. For convenience, we manually combined some configuration and code files and merged the weights. Please note that this is a quick code, we recommend you installing ColonGPT's source code to explore more.

• Before running the snippet, you only need to install the following minimium dependencies.

  conda create -n quickstart python=3.10
  conda activate quickstart
  pip install torch transformers accelerate pillow
  

• Then you can use python script/quick_start/quickstart.py to start.

  import torch
  import transformers
  from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria
  from PIL import Image
  import warnings
  
  transformers.logging.set_verbosity_error()
  transformers.logging.disable_progress_bar()
  warnings.filterwarnings('ignore')
  
  device = 'cuda'  # or cpu
  torch.set_default_device(device)
  
  model_name = "ai4colonoscopy/ColonGPT-v1"
  
  model = AutoModelForCausalLM.from_pretrained(
      model_name,
      torch_dtype=torch.float16,  # or float32 for cpu
      device_map='auto',
      trust_remote_code=True
  )
  
  tokenizer = AutoTokenizer.from_pretrained(
      model_name,
      trust_remote_code=True
  )
  
  class KeywordsStoppingCriteria(StoppingCriteria):
      def __init__(self, keyword, tokenizer, input_ids):
          self.keyword_id = tokenizer(keyword).input_ids
          self.tokenizer = tokenizer
          self.start_len = input_ids.shape[1]
  
      def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
          for keyword_id in self.keyword_id:
              if keyword_id in input_ids[0, -len(self.keyword_id):]:
                  return True
          return False
  
  prompt = "Categorize the object."
  text = f"USER: <image>\n{prompt} ASSISTANT:"
  text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
  input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0).to(device)
  
  image = Image.open('/home/projects/u7248002/Project/ColonGPT-tmp/cache/examples/example2.png')
  image_tensor = model.process_images([image], model.config).to(dtype=model.dtype, device=device)
  
  stop_str = "<|endoftext|>"
  stopping_criteria = KeywordsStoppingCriteria(stop_str, tokenizer, input_ids)
  
  output_ids = model.generate(
      input_ids,
      images=image_tensor,
      do_sample=False,
      temperature=0,
      max_new_tokens=512,
      use_cache=True,
      stopping_criteria=[stopping_criteria]
  )
  
  outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).replace("<|endoftext|>", "").strip()
  print(outputs)
  

License

This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses. The content of this project itself is licensed under the Apache license 2.0.