Category-anchor Guided Unsupervised Domain Adaptation for Semantic Segmentation

Qiming Zhang*, Jing Zhang*, Wei Liu, Dacheng Tao

paper

Table of Contents

• Introduction
• Requirements
• Usage
• License
• Notes

Introduction

This respository contains the CAG-UDA method as described in the NeurIPS 2019 paper "Category-anchor Guided Unsupervised Domain Adaptation for Semantic Segmentation".

Requirements

The code is implemented based on Pytorch 0.4.1 with CUDA 9.0, Python 3.6.7. The code is trained using a NVIDIA Tesla V100 with 16 GB memory. Please see the 'requirements.txt' file for other requirements.

Usage

Assuming you are in the CAG-UDA master folder.

1. Preparation:

• Download the GTA5 dataset as the source domain, and the Cityscapes dataset as the target domain.
• Then put them into a folder (dataset/GTA5 for example). Please carefully check the directory of the folder whether containing invalid characters.
• Please notice that images in GTA5 have slightly different resolutions, which has been resolved in our code.
• Download pretrained models here and put them in the 'pretrained/' folder. There are four models for warmup, stage 1, stage 2, and stage 3 respectively.

2. Setup the config file with directory 'config/adaptation_from_city_to_gta.yml'.

• Set the dataset path in the config file (data:source:rootpath and data:target:rootpath).
• Set the pretrained model path to 'training:resume' and 'training:Pred_resume' in the config file. 'Pred_resume' model is used to assign pseudo-labels..
• To better understand the meaning of each parameter in the config file, please see 'config/readme'.

3. Training

• To run the code:

python train.py

• During the training, the generated files (log file) will be written in the folder 'runs/..'.

4. Evaluation

• Set the config file for test (configs/test_from_city_to_gta.yml): (1). Set the dataset path as illustrated before. (2). Set the model path in 'test:path:'.
• Run:

python test.py

to see the results.

4. Constructing anchors

• Setting the config file 'configs/CAC_from_gta_to_city.yml' as illustrated before.
• Run:

python cac.py

• The anchor file would be in 'run/cac_from_gta_to_city/..'

License

MIT

The code is heavily borrowed from the repository (https://github.com/meetshah1995/pytorch-semseg).

If you use this code and find it usefule, please cite:

@inproceedings{zhang2019category,
  title={Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation},
  author={Zhang, Qiming and Zhang, Jing and Liu, Wei and Tao, Dacheng},
  booktitle={Advances in Neural Information Processing Systems},
  pages={433--443},
  year={2019}
}

Notes

The category anchors are stored in the file 'category_anchors'. It is calculated as the mean value of features with respect to each category from the source domain.

Contact: qzha2506@uni.sydney.edu.au / qmzhangzz@gmail.com