Consistency Regularization for Domain Adaptation

[Arxiv]

Official GitHub repository for "Consistency Regularization for Domain Adaptation" accepted to OOD-CV 2022 Workshop.

Repository heavily based on DAFormer's GitHub repository. Please refer to their repository for a more detailed Readme.

We would like to thanks DAFormer for their open source project.

Setup Environment

We follow DAFormer's environment setup:

python -m venv ~/venv/daformer
source ~/venv/daformer/bin/activate

In that environment, the requirements can be installed with:

pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html
pip install mmcv-full==1.3.7  # requires the other packages to be installed first

All experiments were executed on a NVIDIA GeForce RTX 3090.

Setup Datasets

Cityscapes: Download leftImg8bit_trainvaltest.zip and gt_trainvaltest.zip from here and extract them to ${CITYSCAPES_PATH}.

GTA: Download all image and label packages from here and extract them to ${GTA_PATH}.

Synthia: Download SYNTHIA-RAND-CITYSCAPES from here and extract it to ${SYNTHIA_PATH}.

Then update data_root fields in uda_gta_to_cityscapes_512x512.py and uda_synthia_to_cityscapes_512x512.py with your ${CITYSCAPES_PATH}, ${GTA_PATH} and ${SYNTHIA_PATH}.

Data Preprocessing: Finally, please run the following scripts to convert the label IDs to the train IDs and to generate the class index for RCS:

python tools/convert_datasets/gta.py ${GTA_PATH} --nproc 8
python tools/convert_datasets/cityscapes.py ${CITYSCAPES_PATH} --nproc 8
python tools/convert_datasets/synthia.py ${SYNTHIA_PATH} --nproc 8

Training

For the experiments in our paper, we use DAFormer's pre-defined configs:

python run_experiments.py --exp 7

More information about DAFormer's available experiments and their assigned IDs, can be found in experiments.py. The generated configs will be stored in configs/generated/.

Consistency Regularization for Domain Adaptation

Code implementation for consistency regularization can be found in mmseg\models\uda\dacs.py, mostly under DACS.forward_train.

Unfortunately, hyperparameters introduced for consistency regularization have been hard coded. To change them, update n_pair and lambda_sc in DACS.forward_train.

Testing & Predictions

Trained models can be tested after the training has finished using the following shell command:

sh test.sh path/to/checkpoint_directory