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Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation (ECCV 2022)

This repository is for ProCA introduced in the following paper

Zhengkai Jiang, Yuxi Li, Ceyuan Yang, Peng Gao, Yabiao Wang, Ying Tai, Chengjie Wang, "Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation", ECCV 2022 [arxiv].

Prerequisites

• Python 3.6
• Pytorch 1.7.1
• torchvision from master
• yacs
• matplotlib
• GCC >= 4.9
• OpenCV
• CUDA >= 10.1

Step-by-step installation

conda create --name ProCA -y python=3.6
conda activate ProCA

# this installs the right pip and dependencies for the fresh python
conda install -y ipython pip

pip install ninja yacs cython matplotlib tqdm opencv-python imageio mmcv tqdm torchvision==0.8.2 torch==1.7.1

Data Preparation

• Download The Cityscapes Dataset
• Download The SYNTHIA-RAND-CITYSCAPES Dataset
• Download The GTAV Dataset

The data folder should be structured as follows:

├── datasets/
│   ├── cityscapes/     
|   |   ├── gtFine/
|   |   ├── leftImg8bit/
│   ├── synthia/
|   |   ├── RAND_CITYSCAPES/
|   |   ├── synthia_label_info.p
│   ├── gtav/
|   |   ├── images/
|   |   ├── labels/
|   |   ├── gtav_label_info.p

Symlink the required dataset

ln -s /path_to_cityscapes_dataset datasets/cityscapes
ln -s /path_to_synthia_dataset datasets/synthia
ln -s /path_to_gtav_dataset datasets/gtav

Generate the label statics file for SYNTHIA and GTAV Datasets by running

python3 datasets/generate_synthia_label_info.py -d datasets/synthia -o datasets/synthia/
python3 datasets/generate_gtav_label_info.py -d datasets/gtav -o datasets/gtav/

Inference Using Pretrained Model

Download the pretrained model (ResNet-101) (52.0 mIoU of single scale, extraction code:3e9h) or Google Drive and save it in results/. Then run the command

python test.py -cfg configs/deeplabv2_r101_ssl_synthia.yaml resume results/model_proca_ssl.pth

multi-scale testing results should preduce result

Download the pretrained model (ResNet-101) (55.1 mIoU of sinle scale, extraction code: f2ve) or Google Drive and save it in results/. Then run the command

python test.py -cfg configs/deeplabv2_r101_ssl.yaml resume results/model_proca_ssl.pth

multi-scale testing results should preduce result

Train

We provide the training script using 4 Tesla V100 GPUs.

bash run_proca_resnet101_gta5.sh

We also provide Memory-Bank implementation which can be seen in train_memory_bank.

Acknowledgements

This code is partly based on the open-source implementations from FADA and SDCA.

Citation

If you find this code or idea useful, please cite our work:

@inproceedings{jiang2022prototypical,
  title={Prototypical Contrast Adaptation for Domain Adaptive Segmentation},
  author={Jiang, Zhengkai and Li, Yuxi and Yang, Ceyuan and Gao, Peng and Wang, Yabiao and Tai, Ying and Wang, Chengjie},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2022}
}