xrosliang / fada

(ECCV 2020) Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation

Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation (ECCV 2020)

This is a pytorch implementation of FADA.

Prerequisites

• Python 3.6
• Pytorch 1.2.0
• torchvision from master
• yacs
• matplotlib
• GCC >= 4.9
• OpenCV
• CUDA >= 9.0

Step-by-step installation

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

# 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

# follow PyTorch installation in https://pytorch.org/get-started/locally/
# we give the instructions for CUDA 9.2
conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=9.2 -c pytorch

Getting started

• Download The GTA5 Dataset

• Download The SYNTHIA 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_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
│   └── 			
...

Train

We provide the training script using 4 Tesla P40 GPUs. Note that when generating pseudo labels for self distillation, the link to the pseudo label directory should be updated here.

bash train_with_sd.sh

Evaluate

python test.py -cfg configs/deeplabv2_r101_tgt_self_distill.yaml resume g2c_tgt_self_distill_r101_model_iter018000.pth

Our pretrained model is available via polybox.

Tip: 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.

Visualization results

Acknowledge

Some codes are adapted from maskrcnn-benchmark and semseg. We thank them for their excellent projects.

Citation

If you find this code useful please consider citing

@InProceedings{Haoran_2020_ECCV,
  author = {Wang, Haoran and Shen, Tong and Zhang, Wei and Duan, Lingyu and Mei, Tao},
  title = {Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation},
  booktitle = {The European Conference on Computer Vision (ECCV)},
  month = {August},
  year = {2020}
}