MeLo: Low-rank Adaptation is Better than Finetuning for Medical Image
Useful links
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, 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, 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: 147456this 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)
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: 6.459Save and load LoRA:
lora_model.save_lora_parameters('mytask.lora.safetensors') # save
lora_model.load_lora_parameters('mytask.lora.safetensors') # loadPerformance
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