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Official PyTorch Implementation Code for Developing Super Fast Adversarial Training with Distributed Data Parallel, Channel Last Memory Format, Mixed Precision Training + Adversarial Attack, Faster Adversarial Training, and Fast Forward Computer Vision (FFCV).
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This is an Official PyTorch Implementation code for developing super fast adversarial training. This code is combined with below state-of-the-art technologies for accelerating adversarial attacks and defenses with Deep Neural Networks on Volta GPU architecture.
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@software{SuperFastAT_ByungKwanLee_2022,
author = {Byung-Kwan Lee},
title = {Super Fast Adversarial Training with Distributed Data Parallel and Mixed Precision},
howpublished = {\url{https://github.com/ByungKwanLee/Super-Fast-Adversarial-Training}},
year = {2022}
}
This library is developed based on the well-known package of torchattacks [link] due to its simple scalability.
Current Available Attacks Below
conda create -y -n ffcv python=3.8 cupy pkg-config compilers libjpeg-turbo opencv pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.1 numba -c pytorch -c conda-forge
conda activate ffcv
conda install cudnn -c conda-forge
pip install ffcv torchattacks==3.1.0
pip install -r requirements.txt
fast_dataset_converter.py to generate dataset with .betson extension, instead of using original dataset [FFCV].# Future import build
from __future__ import print_function
# Import built-in module
import os
import argparse
# fetch args
parser = argparse.ArgumentParser()
# parameter
parser.add_argument('--dataset', default='imagenet', type=str)
parser.add_argument('--gpu', default='0', type=str)
args = parser.parse_args()
# GPU configurations
os.environ["CUDA_VISIBLE_DEVICES"]=args.gpu
# init fast dataloader
from utils.fast_data_utils import save_data_for_beton
save_data_for_beton(dataset=args.dataset)
fast_pretrain_standard.py(Standard Training) or fast_pretrain_adv.py (Adversarial Training)# model parameter
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', default='imagenet', type=str)
parser.add_argument('--network', default='resnet', type=str)
parser.add_argument('--depth', default=50, type=int)
parser.add_argument('--gpu', default='0,1,2,3,4', type=str)
# learning parameter
parser.add_argument('--learning_rate', default=0.1, type=float)
parser.add_argument('--weight_decay', default=0.0002, type=float)
parser.add_argument('--batch_size', default=512, type=float)
parser.add_argument('--test_batch_size', default=128, type=float)
parser.add_argument('--epoch', default=100, type=int)
or
# model parameter
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', default='imagenet', type=str)
parser.add_argument('--network', default='resnet', type=str)
parser.add_argument('--depth', default=18, type=int)
parser.add_argument('--gpu', default='0,1,2,3,4', type=str)
# learning parameter
parser.add_argument('--learning_rate', default=0.1, type=float)
parser.add_argument('--weight_decay', default=0.0002, type=float)
parser.add_argument('--batch_size', default=1024, type=float)
parser.add_argument('--test_batch_size', default=512, type=float)
parser.add_argument('--epoch', default=60, type=int)
# attack parameter
parser.add_argument('--attack', default='pgd', type=str)
parser.add_argument('--eps', default=0.03, type=float)
parser.add_argument('--steps', default=10, type=int)
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