|
|
from autoattack import AutoAttack |
|
|
import numpy as np |
|
|
from .base import Attack,LabelMixin |
|
|
import torch.nn as nn |
|
|
from utils.distributed import DistributedMetric |
|
|
from tqdm import tqdm |
|
|
from torchpack import distributed as dist |
|
|
from utils import accuracy |
|
|
from typing import Dict |
|
|
from .utils import ctx_noparamgrad_and_eval |
|
|
class Squre_Attack(Attack, LabelMixin): |
|
|
|
|
|
def __init__( |
|
|
self, predict, loss_fn=None, eps=0.3, nb_iter=40, eps_iter=0.01, rand_init=True, clip_min=0., clip_max=1., |
|
|
ord=np.inf, targeted=False, rand_init_type='uniform'): |
|
|
super(Squre_Attack, self).__init__(predict, loss_fn, clip_min, clip_max) |
|
|
self.eps = eps |
|
|
self.nb_iter = nb_iter |
|
|
self.eps_iter = eps_iter |
|
|
self.rand_init = rand_init |
|
|
self.rand_init_type = rand_init_type |
|
|
self.ord = ord |
|
|
self.targeted = targeted |
|
|
if self.loss_fn is None: |
|
|
self.loss_fn = nn.CrossEntropyLoss(reduction="sum") |
|
|
self.adversary = AutoAttack(predict, norm='Linf', eps=self.eps, version='standard') |
|
|
def perturb(self, x, y=None): |
|
|
self.adversary.attacks_to_run=['square'] |
|
|
adversarial_examples = self.adversary.run_standard_evaluation(x, y, bs=100) |
|
|
return adversarial_examples,adversarial_examples |
|
|
def eval_squred(self,data_loader_dict: Dict)-> Dict: |
|
|
|
|
|
test_criterion = nn.CrossEntropyLoss().cuda() |
|
|
val_loss = DistributedMetric() |
|
|
val_top1 = DistributedMetric() |
|
|
val_top5 = DistributedMetric() |
|
|
val_advloss = DistributedMetric() |
|
|
val_advtop1 = DistributedMetric() |
|
|
val_advtop5 = DistributedMetric() |
|
|
self.predict.eval() |
|
|
with tqdm( |
|
|
total=len(data_loader_dict["val"]), |
|
|
desc="Eval", |
|
|
disable=not dist.is_master(), |
|
|
) as t: |
|
|
for images, labels in data_loader_dict["val"]: |
|
|
images, labels = images.cuda(), labels.cuda() |
|
|
|
|
|
output = self.predict(images) |
|
|
loss = test_criterion(output, labels) |
|
|
val_loss.update(loss, images.shape[0]) |
|
|
acc1, acc5 = accuracy(output, labels, topk=(1, 5)) |
|
|
val_top5.update(acc5[0], images.shape[0]) |
|
|
val_top1.update(acc1[0], images.shape[0]) |
|
|
with ctx_noparamgrad_and_eval(self.predict): |
|
|
images_adv,_ = self.perturb(images, labels) |
|
|
output_adv = self.predict(images_adv) |
|
|
loss_adv = test_criterion(output_adv,labels) |
|
|
val_advloss.update(loss_adv, images.shape[0]) |
|
|
acc1_adv, acc5_adv = accuracy(output_adv, labels, topk=(1, 5)) |
|
|
val_advtop1.update(acc1_adv[0], images.shape[0]) |
|
|
val_advtop5.update(acc5_adv[0], images.shape[0]) |
|
|
t.set_postfix( |
|
|
{ |
|
|
"loss": val_loss.avg.item(), |
|
|
"top1": val_top1.avg.item(), |
|
|
"top5": val_top5.avg.item(), |
|
|
"adv_loss": val_advloss.avg.item(), |
|
|
"adv_top1": val_advtop1.avg.item(), |
|
|
"adv_top5": val_advtop5.avg.item(), |
|
|
"#samples": val_top1.count.item(), |
|
|
"batch_size": images.shape[0], |
|
|
"img_size": images.shape[2], |
|
|
} |
|
|
) |
|
|
t.update() |
|
|
|
|
|
val_results = { |
|
|
"val_top1": val_top1.avg.item(), |
|
|
"val_top5": val_top5.avg.item(), |
|
|
"val_loss": val_loss.avg.item(), |
|
|
"val_advtop1": val_advtop1.avg.item(), |
|
|
"val_advtop5": val_advtop5.avg.item(), |
|
|
"val_advloss": val_advloss.avg.item(), |
|
|
} |
|
|
return val_results |
