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--- |
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license: apache-2.0 |
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--- |
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# RP3D-DiagModel |
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## About Checkpoint |
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The detailed parameter we use for training is in the following: |
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``` |
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start_class: 0 |
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end_clas: 5569 |
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backbone: 'resnet' |
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level: 'articles' # represents the disorder level |
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depth: 32 |
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ltype: 'MultiLabel' # represents the Binary Cross Entropy Loss |
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augment: True # represents the medical data augmentation |
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split: 'late' # represents the late fusion strategy |
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``` |
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### Load Model |
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``` |
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# Load backnone |
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model = RadNet(num_cls=num_classes, backbone=backbone, depth=depth, ltype=ltype, augment=augment, fuse=fuse, ke=ke, encoded=encoded, adapter=adapter) |
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pretrained_weights = torch.load("path/to/pytorch_model_32_late.bin") |
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missing, unexpect = model.load_state_dict(pretrained_weights,strict=False) |
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print("missing_cpt:", missing) |
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print("unexpect_cpt:", unexpect) |
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# If KE is set True, load text encoder |
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medcpt = MedCPT_clinical(bert_model_name = 'ncbi/MedCPT-Query-Encoder') |
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checkpoint = torch.load('path/to/epoch_state.pt',map_location='cpu')['state_dict'] |
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load_checkpoint = {key.replace('module.', ''): value for key, value in checkpoint.items()} |
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missing, unexpect = medcpt.load_state_dict(load_checkpoint, strict=False) |
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print("missing_cpt:", missing) |
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print("unexpect_cpt:", unexpect) |
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``` |
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## Why we provide this checkpoint? |
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All the early fusion checkpoint can be further finetuned from this checkpoint. If you need other checkpoints using different parameter settings, there are two possible ways: |
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### Finetune from this checkpoint |
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''' |
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checkpoint: "None" |
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safetensor: path to this checkpoint(pytorch_model.bin) |
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''' |
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### Contact Us |
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Email the author: [email protected] |
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## About Dataset |
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Please refer to [RP3D-DiagDS](https://huggingface.co/datasets/QiaoyuZheng/RP3D-DiagDS) |
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For more information, please refer to our instructions on [github](https://github.com/qiaoyu-zheng/RP3D-Diag) to download and use. |
