MTFormer

This is a pytorch project for the paper MTFormer: Multi-Task Learning via Transformer and Cross-Task Reasoning by Xiaogang Xu, Hengshuang Zhao, Vibhav Vineet, Ser-Nam Lim, Antonio Torralba presented at ECCV2022.

Introduction

In this paper, we propose a novel transformer-based architecture named MTFormer for MTL. In the framework, multiple tasks share the same transformer encoder and transformer decoder, and lightweight branches are introduced to harvest task-specific outputs, which increases the MTL performance and reduces the time-space complexity. Furthermore, information from different task domains can benefit each other, and we conduct cross-task reasoning. We propose a cross-task attention mechanism for further boosting the MTL results. The cross-task attention mechanism brings little parameters and computations while introducing extra performance improvements. Besides, we design a self-supervised cross-task contrastive learning algorithm for further boosting the MTL performance. Extensive experiments are conducted on two multi-task learning datasets, on which MTFormer achieves state-of-the-art results with limited network parameters and computations.

paper link

Dataset

We utilize the datasets processed by https://github.com/SimonVandenhende/Multi-Task-Learning-PyTorch, and you can download the datasets of NYUD-v2 and PASCAL following the corresponding instructions.

Project Setup

First install Python 3. We advise you to install Python 3 and PyTorch with Anaconda:

conda create --name py36 python=3.6
source activate py36

Clone the repo and install the complementary requirements:

cd $HOME
git clone --recursive git@github.com:xiaogang00/MTFormer.git
cd MTFormer
pip install -r requirements.txt

This code is validated on RTX2080 with CUDA10.2.

Please modify the corresponding path in "utils/mypath.py" (db_root and seism_root), "utils/common_config.py" (pretrain_path), and "configs/env.yml" (root_dir)

Usage

Train

Train the MTFormer on NYUD-v2:

python main_CL_nyud.py --config_env configs/env.yml --config_exp configs/nyud/multi_task_MTFormer.yml

Train the MTFormer on PASCAL:

python main_CL_pascal.py --config_env configs/env.yml --config_exp configs/pascal/multi_task_MTFormer.yml

Test

During the training, the evaluation will be conducted (if eval_final_10_epochs_only: True in the corresponding config, the evaluation will be made during the last 10 epochs; otherwise, the evaluation will be conducted in after every epoch)

Pre-trained Models

We provide the pre-trained models for NYUD-v2 and PASCAL. Please download and put them under the dictionary of 'output/NYUD/swim_transformer2' and 'output/PASCALContext/swim_transformer2'

Quantitative Results

For each dictionary, load 'checkpoint.pth.tar' and the best results can be viewed by

import torch
result=torch.load('checkpoint.pth.tar')
print(result['best_result'])

The best model's weights are stored in 'best_model.pth.tar'. 'log_file.txt' is the training log file which record the training loss and evaluation results.

Qualitative Results

You can run the code which can automatically compute the visual results using the trained models (stored in 'results' of each dictionary)

python main_CL_nyud.py --config_env configs/env.yml --config_exp configs/nyud/multi_task_MTFormer.yml

python main_CL_pascal.py --config_env configs/env.yml --config_exp configs/pascal/multi_task_MTFormer.yml

Citation Information

If you find the project useful, please cite:

@inproceedings{xu2022mtformer,
  title={MTFormer: Multi-Task Learning via Transformer and Cross-Task Reasoning},
  author={Xiaogang Xu, Hengshuang Zhao, Vibhav Vineet, Ser-Nam Lim, and Antonio Torralba},
  booktitle={ECCV},
  year={2022}
}

Acknowledgments

This source code is inspired by MTI-Net, Swin-Transformer.

Contributions

If you have any questions/comments/bug reports, feel free to e-mail the author Xiaogang Xu (xiaogangxu00@gmail.com).

xiaogang00 / MTFormer

readme