This is a pytorch implementation of Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation.
- Python 3.6
- Pytorch 1.2.0
- torchvision from master
- yacs
- matplotlib
- GCC >= 4.9
- OpenCV
- CUDA >= 9.0
conda create --name undoing -y python=3.6
conda activate undoing
# 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-
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
│ └──
...
-
For original ASPP, please download the pretrained models of FADA for Synthia -> CityScapes task(s2c) and GTA5 -> CityScapes task(g2c).
-
For modified ASPP, please reproduce FADA by changing the classifer. And we also provide the pretrainded models here.
python test_trainlabelfre.py -cfg configs/deeplabv2_r101_adv.yaml resume g2c_adv.pth
- Improve the adversial result of FADA by IA
python test_IA.py -cfg configs/deeplabv2_r101_tgt_self_distill.yaml resume g2c_adv.pth
- Improve the pseudo label by IA
python test_IA.py -cfg configs/deeplabv2_r101_adv.yaml --saveres resume g2c_adv.pth OUTPUT_DIR datasets/cityscapes/soft_labels DATASETS.TEST cityscapes_train
python -m torch.distributed.launch --nproc_per_node=4 train_self_distill.py -cfg configs/deeplabv2_r101_tgt_self_distill_2.yaml OUTPUT_DIR results/sd_test
- Improve the adversial result of FADA by CR
python -m torch.distributed.launch --nproc_per_node=4 train_CR.py -cfg configs/deeplabv2_r101_adv.yaml OUTPUT_DIR results/adv_test_CR resume g2c_adv.pth
python test.py -cfg configs/deeplabv2_r101_tgt_self_distill.yaml resume results/adv_test_CR
- Improve the pseudo label by CR
python -m torch.distributed.launch --nproc_per_node=4 train_CR.py -cfg configs/deeplabv2_r101_adv.yaml OUTPUT_DIR results/adv_test_CR resume g2c_adv.pth
python test.py -cfg configs/deeplabv2_r101_adv.yaml --saveres resume results/adv_test_CR/xxx.pth OUTPUT_DIR datasets/cityscapes/soft_labels DATASETS.TEST cityscapes_train
python -m torch.distributed.launch --nproc_per_node=4 train_self_distill.py -cfg configs/deeplabv2_r101_tgt_self_distill_2.yaml OUTPUT_DIR results/sd_test
- Please refer to the code of IAST and ProDA.
- PS: for ProDA, you should change the model structure and the data preprocessing as FADA.
- Please contact [email protected] for more details.
python test.py -cfg configs/deeplabv2_r101_tgt_self_distill.yaml resume g2c_adv.pth
python test.py -cfg configs/deeplabv2_r101_tgt_self_distill_2.yaml resume g2c_sd.pth
Codes are adapted from FADA, IAST and ProDA. We thank them for their excellent projects.
If you find this code useful please consider citing
@InProceedings{Liu_2022_CVPR
author = {Liu, Yahao and Deng, Jinhong and Tao, Jiale and Chu, Tong and Duan, Lixin and Li, Wen},
title = {Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation },
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition(CVPR)},
year = {2022}
}
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