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MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning

Introduction

This is the official implementation of the paper: MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning developed at Brown University SCALE lab.

This repository provides a Python-based implementation of MTLoRA including MTLoRALinear (the main module) and MTL architectures.

The repository is built on top of Swin-Transformer and uses some modules from Multi-Task-Learning-PyTorch.

How to Run

Running MTLoRA code, is very simmilar to Swin's codebase:

  1. Clone the repository

    git clone https://github.com/scale-lab/MTLoRA.git
cd MTLoRA

  2. Install the prerequisites

    • Install PyTorch>=1.12.0 and torchvision>=0.13.0 with CUDA>=11.6
    • Install dependencies: pip install -r requirements.txt

  3. Run the code

    python -m torch.distributed.launch --nproc_per_node 1 --master_port 12345 main.py --cfg configs/mtlora/tiny_448/<config>.yaml --pascal <path to pascal database> --tasks semseg,normals,sal,human_parts --batch-size 32 --ckpt-freq=20 --epoch=300 --resume-backbone <path to the weights of the chosen Swin variant>

    Swin variants and their weights can be found at the official Swin Transformer repository.

    The outputs will be saved in output/ folder unless overridden by the argument --output.

  4. Using the pre-trained model

    You can download the model weights from the following link.

    To run and evaluate the pre-trained model (assuming the model weight file is at ./mtlora.pth), use --eval and --resume <checkpoint> as follows:

    python -m torch.distributed.launch --nproc_per_node 1 --master_port 12345 main.py --cfg configs/mtlora/tiny_448/mtlora_tiny_448_r64_scale4_pertask.yaml --pascal <path to pascal database> --tasks semseg,normals,sal,human_parts --batch-size 32 --resume ./mtlora.pth --eval

Authorship

Since the release commit is squashed, the GitHub contributors tab doesn't reflect the authors' contributions. The following authors contributed equally to this codebase:

Citation

If you find MTLoRA helpful in your research, please cite our paper:

@inproceedings{agiza2024mtlora,
  title={MTLoRA: Low-Rank Adaptation Approach for Efficient Multi-Task Learning},
  author={Agiza, Ahmed and Neseem, Marina and Reda, Sherief},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={16196--16205},
  year={2024}
}

License

MIT License. See LICENSE file