Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation"

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Deep LearningDSP
Overview

DSP

Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation". Accepted by ACM Multimedia 2021.

Authors: Li Gao, Jing Zhang, Lefei Zhang, Dacheng Tao.

Prerequisite

  • CUDA/CUDNN
  • Python3
  • PyTorch==1.7
  • Packages found in requirements.txt
  1. Creat a new conda environment
conda create -n dsp_env python=3.7
conda activate dsp_env
conda install pytorch=1.7 torchvision torchaudio cudatoolkit -c pytorch
pip install -r requirements.txt
  1. Download the code from github and change the directory
git clone https://github.com/GaoLii/DSP/
cd DSP
  1. Prepare dataset

Download Cityscapes, GTA5 and SYNTHIA dataset, then organize the folder as follows:

├── ../../dataset/
│   ├── Cityscapes/     
|   |   ├── gtFine/
|   |   ├── leftImg8bit/
│   ├── GTA5/
|   |   ├── images/
|   |   ├── labels/
│   ├── RAND_CITYSCAPES/ 
|   |   ├── GT/
|   |   ├── RGB/
...

Training and Evaluation example

Training and evaluation are on a single Tesla V100 GPU with 16G memory.

Train with unsupervised domain adaptation

GTA5->CityScapes

python train_DSP.py --config ./configs/configUDA_gta.json --name UDA_gta

SYNTHIA->CityScapes

python train_DSP.py --config ./configs/configUDA_syn.json --name UDA_syn

Evaluation

python evaluateUDA.py --model-path checkpoint.pth

Pretrained models

This model should be unzipped in the '../saved' folder.

License

The code is heavily borrowed from DACS.

If you use this code in your research please consider citing

@article{Gao_2021,
   title={DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation},
   url={https://arxiv.org/abs/2107.09600},
   DOI={10.1145/3474085.3475186},
   journal={Proceedings of the 29th ACM International Conference on Multimedia},
   publisher={ACM},
   author={Gao, Li and Zhang, Jing and Zhang, Lefei and Tao, Dacheng},
   year={2021},
   month={Oct}
}
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