A New Framework for Domain Adaptive Semantic Segmentation via Semantic Prototype-based Contrastive Learning by Binhui Xie, Mingjia Li, Shuang Li.
2021/11/25: arXiv version of SPCL is available.
2022/06/24: Code is released.
If you find it useful for your research, please cite
@article{xie2021spcl,
title={SPCL: A New Framework for Domain Adaptive Semantic Segmentation via Semantic Prototype-based Contrastive},
author={Binhui Xie, Mingjia Li, Shuang Li},
journal={arXiv preprint arXiv:2111.12358},
year={2021}
}
- Python 3.6
- torch 1.7.1
- torchvision 0.8.2
- yacs
- matplotlib
- GCC >= 4.9
- OpenCV
- CUDA >= 9.1
conda create --name spcl -y python=3.6
conda activate spcl
# this installs the right pip and dependencies for the fresh python
conda install -y ipython pip
pip install torch==1.7.1 torchvision==0.8.2 ninja yacs cython matplotlib tqdm opencv-python imageio mmcv-
Download The GTA5 Dataset
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Download The SYNTHIA Dataset
-
Download The Synscapes Dataset
-
Download The Cityscapes Dataset
-
Symlink the required dataset
ln -s /path_to_gta5_dataset datasets/gta5
ln -s /path_to_synthia_dataset datasets/synthia
ln -s /path_to_synscapes_dataset datasets/synscapes
ln -s /path_to_cityscapes_dataset datasets/cityscapes- Generate the label statics file for GTA5 and SYNTHIA Datasets by running
python datasets/generate_gta5_label_info.py -d datasets/gta5 -o datasets/gta5/
python datasets/generate_synthia_label_info.py -d datasets/synthia -o datasets/synthia/The data folder should be structured as follows:
├── datasets/
│ ├── cityscapes/
| | ├── gtFine/
| | ├── leftImg8bit/
│ ├── gta5/
| | ├── images/
| | ├── labels/
| | ├── gtav_label_info.p
│ ├── synthia/
| | ├── RAND_CITYSCAPES/
| | ├── synthia_label_info.p
│ ├── synscapes/
| | ├── img/rgb-2k
| | ├── img/class
│ └──
...
We provide the training script using 4 Tesla V100 GPUs.
bash train_with_ssl.shTip: For those who are interested in how performance change during the process of adversarial training, test.py also accepts directory as the input and the results will be stored in a csv file.
python test.py -cfg configs/deeplabv2_r101_tgt_ssl.yaml resume results/r101_g2c_ours_ssl/ OUTPUT_DIR results/r101_g2c_ours_ssl/ SOLVER.BATCH_SIZE 8This project is based on the following open-source projects: FADA and SDCA. We thank authors for making the source code publically available.
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