Awesome
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_sd.pth
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.
Pretrained weights
Our pretrained models for Synthia -> CityScapes task(s2c) and GTA5 -> CityScapes task(g2c) are available via Google Drive.
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}
}