README.md

MeLo: Low-rank Adaptation is Better than Finetuning for Medical Image

Useful links

[Homepage]      [arXiv]     

Feature

• Supported DeepLab segmentation for lukemelas/PyTorch-Pretrained-ViT. 2023-03-15
• Supported timm. 2023-03-16
• Supported multi-lora. 2023-11-15
• Repo clean up.

Installation

Gii clone. My torch.__version__==1.13.0, other version newer than torch.__version__==1.10.0 should also work, I guess. You may also need a safetensors from huggingface to load and save weight.

Examples

You may find examples in examples.ipynb

Usage

You may use Vision Transformer from timm:

import timm
import torch
from lora import LoRA_ViT_timm
img = torch.randn(2, 3, 224, 224)
model = timm.create_model('vit_base_patch16_224', pretrained=True)
lora_vit = LoRA_ViT_timm(vit_model=model, r=4, alpha=4, num_classes=10)
pred = lora_vit(img)
print(pred.shape)

If timm is too complicated, you can use a simpler implementation of ViT from lukemelas/PyTorch-Pretrained-ViT. Wrap you ViT using LoRA-ViT, this a simple example of classifer

from base_vit import ViT
import torch
from lora import LoRA_ViT

model = ViT('B_16_imagenet1k')
model.load_state_dict(torch.load('B_16_imagenet1k.pth'))
preds = model(img) # preds.shape = torch.Size([1, 1000])

num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"trainable parameters: {num_params}") #trainable parameters: 86859496


lora_model = LoRA_ViT(model, r=4, alpha=4, num_classes=10)
num_params = sum(p.numel() for p in lora_model.parameters() if p.requires_grad)
print(f"trainable parameters: {num_params}") # trainable parameters: 147456

this an example for segmentation tasks, using deeplabv3

model = ViT('B_16_imagenet1k')
model.load_state_dict(torch.load('B_16_imagenet1k.pth'))
lora_model = LoRA_ViT(model, r=4, alpha=4)
seg_lora_model = SegWrapForViT(vit_model=lora_model, image_size=384,
                            patches=16, dim=768, n_classes=10)

num_params = sum(p.numel() for p in seg_lora_model.parameters() if p.requires_grad)
print(f"Number of trainable parameters: {num_params/2**20:.3f}") # trainable parameters: 6.459

Save and load LoRA:

lora_model.save_lora_parameters('mytask.lora.safetensors') # save
lora_model.load_lora_parameters('mytask.lora.safetensors') # load

Performance

In M1 Pro, LoRA is about 1.8x~1.9x faster. python performance_profile.py should do the time profiler now. More test will come soon.

Citation

Use this bibtex to cite this repository:

@misc{zhu2023melo,
      title={MeLo: Low-rank Adaptation is Better than Fine-tuning for Medical Image Diagnosis}, 
      author={Yitao Zhu and Zhenrong Shen and Zihao Zhao and Sheng Wang and Xin Wang and Xiangyu Zhao and Dinggang Shen and Qian Wang},
      year={2023},
      eprint={2311.08236},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Credit

ViT code and imagenet pretrained weight come from lukemelas/PyTorch-Pretrained-ViT