This repo is based on the pycls project. If you already have pycls installed you will need to create a new python environment to install this codebase. Most original pycls functionality should remain intact however, this has not been thoroughly tested.
Clone the repository:
git clone https://github.com/wagner-group/robust-self-attention
Install PyTorch from pytorch.org.
Install additional dependencies:
pip install -r requirements.txt
Set up modules:
python setup.py develop --user
Please see DATA.md for setting up the ImageNet dataset.
The ImageNet-100 subset is specified by setting the MODEL.NUM_CLASSES config option to 100.
python tools/test_net.py --cfg configs/patchadv/eval_resnet.yaml ADV.VAL_PATCH_SIZE 10
Download ImageNet-100 weights from here and adversarially finetune model with:
python tools/train_net.py --cfg configs/patchadv/train_resnet_adv.yaml
Additional evaluation and training configs used in the paper are available in the configs folder.
This repo includes several changes to the original pycls functionality:
- support for adversarial training and evaluation
- WandB logging
- load RGB images by default instead of BGR
- more flexible checkpoint loading
- slurm submission scripts using submitit
as well as many new options accessible through the config system- see diff history for config.py for full list of new options.