Awesome

SDCA

Pytorch Implementation for Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation.

Update on 2021/05/12: ArXiv Version of SDCA is available at this https URL.

Introduction

In this paper, we present a new semantic distribution-aware contrastive adaptation algorithm, dubbed as SDCA, that enables pixel-wise representation alignment across domains under the guidance of the semantic distributions. To be precise, we first design a novel contrastive loss at pixel level by considering the correspondences between the semantic distributions and pixel-wise representations from both domains. Essentially, clusters of pixel representations from the same category are obliged to cluster together and those from different categories are obliged to spread out, boosting segmentation capability of the model. Next, an upper bound on this formulation is derived by implicitly involving the simultaneous learning of an infinite number of (dis)similar pixel pairs, making it highly efficient.

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 sdca -y python=3.6
conda activate sdca

# 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
• Download The Cross-city 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
│   ├── crosscity/	
|   |   ├── Rio/
|   |   ├── Rome/
|   |   ├── Taipei/
|   |   ├── Tokyo/	
│   └──	

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
ln -s /path_to_NTHUDatasets datasets/crosscity

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

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

Inference Using Pretrained Model

Download the pretrained model (VGG-16) (43.5 mIoU / 50.3 mIoU*) and save it in results/. Then run the command

python test.py -cfg configs/deeplabv2_vgg16_ssl_synthia.yaml resume results/vgg_s2c_best.pth

Download the pretrained model (ResNet-101) (50.2 mIoU / 56.8 mIoU*) and save it in results/. Then run the command

python test.py -cfg configs/deeplabv2_r101_ssl_synthia.yaml resume results/r101_s2c_best.pth

Download the pretrained model (VGG-16) (47.3 mIoU) and save it in results/. Then run the command

python test.py -cfg configs/deeplabv2_vgg16_ssl.yaml resume results/vgg_g2c_best.pth

Download the pretrained model (ResNet-101) (52.9 mIoU) and save it in results/. Then run the command

python test.py -cfg configs/deeplabv2_r101_ssl.yaml resume results/r101_g2c_best.pth

Train

We provide the training script using 4 Tesla V100 GPUs.

bash train_with_ssl.sh

Evaluate

python test.py -cfg configs/deeplabv2_r101_ssl.yaml resume results/r101_g2c_ours_ssl

Acknowledgments

This code is heavily borrowed from FADA.

Citation

If you find it useful to your research, please give credit to the following paper

@article{Li_2021_SDCA,
  title={Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation},
  author={Shuang Li, Binhui Xie, Bin Zang, Chi Harold Liu, Xinjing Cheng, Ruigang Yang, and Guoren Wang},
  journal={arXiv preprint arXiv:2105.05013},
  year={2021}
}