File size: 4,026 Bytes
7771996 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from autoattack import AutoAttack
import numpy as np
import logging
from .base import Attack,LabelMixin
import torch
import torch.nn as nn
from typing import Dict
from .utils import ctx_noparamgrad_and_eval
from utils.distributed import DistributedMetric
from tqdm import tqdm
from torchpack import distributed as dist
from utils import accuracy
class AutoAttacks(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(AutoAttacks, 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):
adversarial_examples = self.adversary.run_standard_evaluation(x, y, bs=100)
return adversarial_examples,adversarial_examples
def eval_AutoAttack(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()
# compute output
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
|