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attack_mnist_baseline.py
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attack_mnist_baseline.py
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'''Attack a MNIST model with a Wasserstein adversary.'''
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
from models import get_model
from utils import progress_bar
from pgd import attack
parser = argparse.ArgumentParser(description='Attack a MNIST model with a Wasserstein adversary.')
parser.add_argument('--model', default='lenet')
parser.add_argument('--checkpoint', required=True)
# Directories
parser.add_argument('--outdir', default='epsilons/', help='output dir')
parser.add_argument('--datadir', default='data/', help='output dir')
# Threat model
parser.add_argument('--binarize', action='store_true')
parser.add_argument('--norm', default='grad')
parser.add_argument('--ball', default='wasserstein')
parser.add_argument('--p', default=1, type=float, help='p-wasserstein distance')
parser.add_argument('--alpha', default=0.06, type=float, help='PGD step size')
# Sinkhorn projection
parser.add_argument('--reg', default=1000, type=float, help='entropy regularization')
parser.add_argument('--kernel', default=5, type=int, help='width of the local transport plan')
# Attack schedule
parser.add_argument('--init-epsilon', default=0.01, type=float, help='initial epsilon')
parser.add_argument('--epsilon-iters', default=1, type=int, help='freq to ramp up epsilon')
parser.add_argument('--epsilon-factor', default=.01, type=float, help='factor to ramp up epsilon')
parser.add_argument('--maxiters', default=200, type=int, help='PGD num of steps')
# MISC
parser.add_argument('--override', action='store_true')
parser.add_argument('--preset', default='new_clamping')
parser.add_argument('--unconstrained', action='store_true')
parser.add_argument('--no-clamping', action='store_true')
args = parser.parse_args()
if not args.override:
if args.norm == 'linfinity':
args.init_epsilon = 0.1
args.alpha = 0.1
elif args.norm == 'grad':
args.alpha = 0.06
elif args.norm == 'enhanced_linfinity':
args.alpha = 0.04
if 'binarize' in args.checkpoint:
args.binarize = True
if args.preset == 'new_clamping':
args.unconstrained = False
args.no_clamping = False
elif args.preset == 'old_clamping':
args.unconstrained = True
args.no_clamping = False
elif args.preset == 'old_linf':
args.unconstrained = True
args.no_clamping = True
else:
assert False, f'Unknown preset: {args.preset}'
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Data
print('==> Preparing data..')
transform_train = transforms.Compose([
transforms.ToTensor(),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
])
trainset = torchvision.datasets.MNIST(root=args.datadir, train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2, pin_memory=True)
testset = torchvision.datasets.MNIST(root=args.datadir, train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=2000, shuffle=False, num_workers=4, pin_memory=True)
# Model
print('==> Building model..')
net = get_model('MNIST', args.model)
net = net.to(device)
regularization = args.reg
print('==> regularization set to {}'.format(regularization))
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
save_name = os.path.join(args.outdir, '{}_reg_{}_p_{}_alpha_{}_norm_{}_ball_{}_{}-form_{}.txt'.format(
args.checkpoint.split('/')[-1], regularization, args.p,
args.alpha, args.norm, args.ball,
'old' if args.unconstrained else 'new',
'linf-renorming' if args.no_clamping else 'clamping'))
print('==> loading model {}'.format(args.checkpoint))
print('==> saving epsilon to {}'.format(save_name))
d = torch.load(args.checkpoint)
if 'state_dict' in d:
net.load_state_dict(d['state_dict'][0])
if device == 'cuda':
net = torch.nn.DataParallel(net)
cudnn.benchmark = True
elif 'robust' in args.checkpoint:
net.load_state_dict(d)
if device == 'cuda':
net = torch.nn.DataParallel(net)
cudnn.benchmark = True
else:
if device == 'cuda':
net = torch.nn.DataParallel(net)
cudnn.benchmark = True
net.load_state_dict(d['net'])
criterion = nn.CrossEntropyLoss()
def test():
net.eval()
test_loss = 0
correct = 0
total = 0
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
if args.binarize:
inputs = (inputs >= 0.5).float()
outputs = net(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (test_loss/(batch_idx+1), 100.*correct/total, correct, total))
# Save checkpoint.
acc = 100.*correct/total
def test_attack():
net.eval()
test_loss = 0
correct = 0
total = 0
all_epsilons = []
succeed_epsilons = []
L1_delta = []
W_delta = []
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
if args.binarize:
inputs = (inputs >= 0.5).float()
inputs_pgd, _, epsilons = attack(torch.clamp(inputs,min=0), targets, net,
regularization=regularization,
p=args.p,
alpha=args.alpha,
norm=args.norm,
ball=args.ball,
epsilon_iters=args.epsilon_iters,
epsilon_factor=args.epsilon_factor,
epsilon=args.init_epsilon,
maxiters=args.maxiters,
kernel_size=args.kernel,
use_tqdm=True,
clamping=not args.no_clamping,
constrained_sinkhorn=not args.unconstrained)
outputs_pgd = net(inputs_pgd)
loss = criterion(outputs_pgd, targets)
test_loss += loss.item()
_, predicted = outputs_pgd.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
epsilons[predicted == targets] = float('inf')
which_correct = epsilons == float('inf')
succeed_epsilons.append(epsilons[~which_correct])
all_epsilons.append(epsilons)
progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d) | Avg epsilon: %.3f'
% (test_loss/(batch_idx+1), 100.*correct/total, correct, total, torch.cat(succeed_epsilons).float().mean().item()))
acc = 100.*correct/total
all_epsilons = torch.cat(all_epsilons)
with open(save_name, 'w') as f:
f.write('index\tradius\n')
for i in range(len(all_epsilons)):
f.write(f'{i+1}\t{all_epsilons[i].item()}\n')
test()
test_attack()