Code for Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach.
The code is developed under the following configuration.
4-8 GPUs(With at least 11G GPU memories), which is set for the correspoinding batch size.
Python(3.6) and Pytorch(0.4.1) is necessary before running the scripts. To install the required pythonn packages(expect Pytorch), run
pip install -r requirements.txt
To monitor the convergence of the network, we split 500 images out of Cityscapes training dataset as our validation set and test on Cityscapes valdiation set.
You can check it in the
To train on your own enviorment, please download the dataset and modify the dataset path in the corresponding cfgs docunment. Downloaded pretrained model
sh run.sh train_source_only.py cfgs/source_only_exp001.yaml
sh run.sh train_adabn.py cfgs/adabn_exp001.yaml
sh run.sh train_pycda_local.py cfgs/pycda_local_exp001.yaml
Convert batchnorm statistics
sh run.sh test_adabn.py $your_script
This project is licensed under the MIT License - see the LICENSE.md file for details