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--- |
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license: cc-by-2.0 |
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--- |
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## Usage of PathGen-CLIP |
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``` |
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pip install open_clip_torch |
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``` |
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```python |
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import torch |
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from PIL import Image |
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import open_clip |
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model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-16', pretrained='path/pathgen-clip.pt') |
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model.eval() # model in train mode by default, impacts some models with BatchNorm or stochastic depth active |
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tokenizer = open_clip.get_tokenizer('ViT-B-32') |
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image = preprocess(Image.open("example.png")).unsqueeze(0) |
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text = tokenizer(["An H&E image of tumor patch", "An H&E image of normal patch"]) |
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with torch.no_grad(), torch.cuda.amp.autocast(): |
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image_features = model.encode_image(image) |
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text_features = model.encode_text(text) |
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image_features /= image_features.norm(dim=-1, keepdim=True) |
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text_features /= text_features.norm(dim=-1, keepdim=True) |
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text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1) |
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print("Label probs:", text_probs) |
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``` |
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## Cite |
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``` |
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@misc{sun2024pathgen16m16millionpathology, |
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title={PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration}, |
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author={Yuxuan Sun and Yunlong Zhang and Yixuan Si and Chenglu Zhu and Zhongyi Shui and Kai Zhang and Jingxiong Li and Xingheng Lyu and Tao Lin and Lin Yang}, |
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year={2024}, |
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eprint={2407.00203}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2407.00203}, |
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} |
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``` |
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