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Category-anchor Guided Unsupervised Domain Adaptation for Semantic Segmentation

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

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.

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.

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'.

Training

To run the code:

python train.py

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

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.

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.