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--- |
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license: apache-2.0 |
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datasets: |
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- ai4colonoscopy/ColonINST-v1 |
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language: |
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- en |
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metrics: |
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- accuracy |
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base_model: |
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- microsoft/phi-1_5 |
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library_name: adapter-transformers |
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tags: |
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- medical |
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- colonoscopy |
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- polyp |
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--- |
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# ColonGPT (A colonoscopy-specific multimodal Language Model) |
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<p align="center"> |
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<img src="./assert/ColonGPT.gif" width="666px"/> <br /> |
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<em>Details of our multimodal language model, ColonGPT.</em> |
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</p> |
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📖 [Paper](https://arxiv.org) | 🏠 [Home](https://github.com/ai4colonoscopy/IntelliScope) |
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> This is the merged weights of [ColonGPT-v1-phi1.5-siglip-lora](https://drive.google.com/drive/folders/1Emi7o7DpN0zlCPIYqsCfNMr9LTPt3SCT?usp=sharing), including vision encoder (siglip) + language model (phi-1.5), and other fine-tuned weights on our ColonINST. |
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Our ColonGPT is a standard multimodal language model, which contains four basic components: a language tokenizer, an visual encoder (🤗 [SigLIP-SO](https://huggingface.co/google/siglip-so400m-patch14-384)), a multimodal connector, and a language model (🤗 [Phi1.5](https://huggingface.co/microsoft/phi-1_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](https://github.com/BAAI-DCAI/Bunny). There, you'll find comprehensive usage instructions for our model and the latest advancements in intelligent colonoscopy technology. |
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# Quick start |
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Here is a code snippet to show you how to quickly try-on our ColonGPT model with transformers. 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](https://github.com/ai4colonoscopy/IntelliScope/blob/main/docs/guideline-for-ColonGPT.md) to explore more. |
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- Before running the snippet, you only need to install the following minimium dependencies. |
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```shell |
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conda create -n quickstart python=3.10 |
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conda activate quickstart |
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pip install torch transformers accelerate pillow |
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``` |
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- Then you can use `python script/quick_start/quickstart.py` to start. |
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```python |
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import torch |
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import transformers |
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from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria |
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from PIL import Image |
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import warnings |
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transformers.logging.set_verbosity_error() |
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transformers.logging.disable_progress_bar() |
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warnings.filterwarnings('ignore') |
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device = 'cuda' # or cpu |
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torch.set_default_device(device) |
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model_name = "ai4colonoscopy/ColonGPT-v1" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype=torch.float16, # or float32 for cpu |
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device_map='auto', |
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trust_remote_code=True |
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) |
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tokenizer = AutoTokenizer.from_pretrained( |
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model_name, |
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trust_remote_code=True |
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) |
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class KeywordsStoppingCriteria(StoppingCriteria): |
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def __init__(self, keyword, tokenizer, input_ids): |
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self.keyword_id = tokenizer(keyword).input_ids |
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self.tokenizer = tokenizer |
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self.start_len = input_ids.shape[1] |
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: |
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for keyword_id in self.keyword_id: |
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if keyword_id in input_ids[0, -len(self.keyword_id):]: |
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return True |
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return False |
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prompt = "Describe what you see in the image." |
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text = f"USER: <image>\n{prompt} ASSISTANT:" |
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text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')] |
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input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0).to(device) |
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image = Image.open('cache/examples/example2.png') |
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image_tensor = model.process_images([image], model.config).to(dtype=model.dtype, device=device) |
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stop_str = "<|endoftext|>" |
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stopping_criteria = KeywordsStoppingCriteria(stop_str, tokenizer, input_ids) |
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output_ids = model.generate( |
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input_ids, |
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images=image_tensor, |
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do_sample=False, |
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temperature=0, |
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max_new_tokens=512, |
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use_cache=True, |
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stopping_criteria=[stopping_criteria] |
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) |
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outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).replace("<|endoftext|>", "").strip() |
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print(outputs) |
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``` |
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# License |
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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. |
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The content of this project itself is licensed under the Apache license 2.0. |
