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frank_wolfe.py
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frank_wolfe.py
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import torch
import torch.nn as nn
from data import str2dataset
from model import str2model
from utils import *
from wasserstein_attack import WassersteinAttack
from lmo import entr_support_func
from projection import dual_capacity_constrained_projection
class FrankWolfe(WassersteinAttack):
def __init__(self,
predict, loss_fn,
eps, kernel_size,
nb_iter, entrp_gamma, dual_max_iter, grad_tol, int_tol,
device="cuda",
postprocess=False,
verbose=True,
):
super().__init__(predict=predict, loss_fn=loss_fn,
eps=eps, kernel_size=kernel_size,
device=device,
postprocess=postprocess,
verbose=verbose,
)
self.nb_iter = nb_iter
self.entrp_gamma = entrp_gamma
self.dual_max_iter = dual_max_iter
self.grad_tol = grad_tol
self.int_tol = int_tol
self.inf = 1000000
def perturb(self, X, y):
batch_size, c, h, w = X.size()
self.initialize_cost(X, inf=self.inf)
pi = self.initialize_coupling(X).clone().detach().requires_grad_(True)
normalization = X.sum(dim=(1, 2, 3), keepdim=True)
for t in range(self.nb_iter):
adv_example = self.coupling2adversarial(pi, X)
scores = self.predict(adv_example.clamp(min=self.clip_min, max=self.clip_max))
loss = self.loss_fn(scores, y)
loss.backward()
with torch.no_grad():
self.lst_loss.append(loss.item())
self.lst_acc.append((scores.max(dim=1)[1] == y).sum().item())
"""Add a small constant to enhance numerical stability"""
pi.grad /= (tensor_norm(pi.grad, p='inf').view(batch_size, 1, 1, 1) + 1e-35)
assert (pi.grad == pi.grad).all() and (pi.grad != float('inf')).all() and (pi.grad != float('-inf')).all()
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
optimal_pi, num_iter = entr_support_func(pi.grad,
X,
cost=self.cost,
inf=self.inf,
eps=self.eps * normalization.squeeze(),
gamma=self.entrp_gamma,
dual_max_iter=self.dual_max_iter,
grad_tol=self.grad_tol,
int_tol=self.int_tol)
end.record()
torch.cuda.synchronize()
self.run_time += start.elapsed_time(end)
self.num_iter += num_iter
self.func_calls += 1
if self.verbose and (t + 1) % 10 == 0:
print("num of iters : {:4d}, ".format(t + 1),
"loss : {:12.6f}, ".format(loss.item()),
"acc : {:5.2f}%, ".format((scores.max(dim=1)[1] == y).sum().item() / batch_size * 100),
"dual iter : {:2d}, ".format(num_iter),
"per iter time : {:7.3f}ms".format(start.elapsed_time(end) / num_iter))
step = 2. / (t + 2)
pi += step * (optimal_pi - pi)
pi.grad.zero_()
self.check_nonnegativity(pi / normalization, tol=1e-6, verbose=False)
self.check_marginal_constraint(pi / normalization, X / normalization, tol=1e-5, verbose=False)
self.check_transport_cost(pi / normalization, tol=1e-3, verbose=False)
with torch.no_grad():
adv_example = self.coupling2adversarial(pi, X)
check_hypercube(adv_example, verbose=self.verbose)
self.check_nonnegativity(pi / normalization, tol=1e-4, verbose=self.verbose)
self.check_marginal_constraint(pi / normalization, X / normalization, tol=1e-4, verbose=self.verbose)
self.check_transport_cost(pi / normalization, tol=self.eps * 1e-3, verbose=self.verbose)
if self.postprocess is True:
if self.verbose:
print("==========> post-processing projection........")
pi = dual_capacity_constrained_projection(pi,
X,
self.cost,
eps=self.eps * normalization.squeeze(),
transpose_idx=self.forward_idx,
detranspose_idx=self.backward_idx,
coupling2adversarial=self.coupling2adversarial)
adv_example = self.coupling2adversarial(pi, X)
check_hypercube(adv_example, tol=self.eps * 1e-1, verbose=self.verbose)
self.check_nonnegativity(pi / normalization, tol=1e-6, verbose=self.verbose)
self.check_marginal_constraint(pi / normalization, X / normalization, tol=1e-6, verbose=self.verbose)
self.check_transport_cost(pi / normalization, tol=self.eps * 1e-3, verbose=self.verbose)
"""Do not clip the adversarial examples to preserve pixel mass"""
return adv_example
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='MNIST')
parser.add_argument('--checkpoint', type=str, default='mnist_vanilla')
parser.add_argument('--batch_size', type=int, default=20)
parser.add_argument('--num_batch', type=int_or_none, default=5)
parser.add_argument('--eps', type=float, default=0.5, help='the perturbation size')
parser.add_argument('--kernel_size', type=int_or_none, default=5)
parser.add_argument('--nb_iter', type=int, default=20)
parser.add_argument('--entrp_gamma', type=float, default=1e-6)
parser.add_argument('--dual_max_iter', type=int, default=15)
parser.add_argument('--grad_tol', type=float, default=1e-5)
parser.add_argument('--int_tol', type=float, default=1e-5)
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--postprocess', type=str2bool, default=False)
parser.add_argument('--save_img_loc', type=str_or_none, default=None)
parser.add_argument('--save_info_loc', type=str_or_none, default=None)
args = parser.parse_args()
print(args)
device = "cuda"
set_seed(args.seed)
testset, normalize, unnormalize = str2dataset(args.dataset)
testloader = torch.utils.data.DataLoader(testset, batch_size=args.batch_size, shuffle=False, num_workers=0)
net = str2model(args.checkpoint, dataset=args.dataset, pretrained=True).eval().to(device)
for param in net.parameters():
param.requires_grad = False
frank_wolfe = FrankWolfe(predict=lambda x: net(normalize(x)),
loss_fn=nn.CrossEntropyLoss(reduction="sum"),
eps=args.eps,
kernel_size=args.kernel_size,
nb_iter=args.nb_iter,
entrp_gamma=args.entrp_gamma,
dual_max_iter=args.dual_max_iter,
grad_tol=args.grad_tol,
int_tol=args.int_tol,
device=device,
postprocess=args.postprocess,
verbose=True)
acc = test(lambda x: net(normalize(x)),
testloader,
device=device,
attacker=frank_wolfe,
num_batch=args.num_batch,
save_img_loc=args.save_img_loc)
frank_wolfe.print_info(acc)
if args.save_info_loc is not None:
frank_wolfe.save_info(acc, args.save_info_loc)