This is a pytorch implementation of ASH.
- Python 3.6
- GPU Memory >= 16G
- Pytorch 1.6.0
-
Download The GTA5 Dataset
-
Download The SYNTHIA Dataset
-
Download The Cityscapes Dataset
The data folder is structured as follows:
├── data/
│ ├── Cityscapes/
| | ├── gtFine/
| | ├── leftImg8bit/
│ ├── GTA5/
| | ├── images/
| | ├── labels/
│ ├── SYNTHIA/
| | ├── RAND_CITYSCAPES/
│ └──
└── model/
│ ├── DeepLab_resnet_pretrained.pth
...
CUDA_VISIBLE_DEVICES=0 python train.py --snapshot-dir ./snapshots/GTA2Cityscapes
This code is heavily borrowed from the baseline CLAN https://github.com/RoyalVane/CLAN ) and pytorch_adain (https://github.com/naoto0804/pytorch-AdaIN)
GTA trained model https://drive.google.com/file/d/1eVnDMC3ytyl5Wx8H5VPKoTNq1UGXmS56/view?usp=sharing
SYN trained model https://drive.google.com/file/d/1XTmJhXCGeD2KrK7M7Xklq0Gayvd0n86e/view?usp=sharing
If you use this code in your research please consider citing
@inproceedings{ASH,
author = {Gabriel Tjio and
Ping Liu and
Joey Tianyi Zhou and
Rick Siow Mong Goh},
title = {Adversarial Semantic Hallucination for Domain Generalized Semantic
Segmentation},
booktitle = {IEEE Winter Conf. on Applications of Computer Vision},
year = {2022}
}