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test_CIFAR10.py
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test_CIFAR10.py
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import argparse
import copy
import logging
import os
import time
import gc
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
from torch.utils.data import DataLoader, Subset
from models import *
from utils import *
from main import *
from cure import *
from preactresnet import *
# from resnet import *
import warnings
warnings.filterwarnings('ignore')
def attack_pgd_Alireza(model, X, y, epsilon, alpha, attack_iters, restarts, norm, early_stop=False, fgsm_init=None):
max_loss = torch.zeros(y.shape[0]).cuda()
max_delta = torch.zeros_like(X).cuda()
for _ in range(restarts):
delta = torch.zeros_like(X).cuda()
if attack_iters>1 or fgsm_init=='random':
if norm == "l_inf":
delta.uniform_(-epsilon, epsilon)
elif norm == "l_2":
delta.normal_()
d_flat = delta.view(delta.size(0),-1)
n = d_flat.norm(p=2,dim=1).view(delta.size(0),1,1,1)
r = torch.zeros_like(n).uniform_(0, 1)
delta *= r/n*epsilon
else:
raise ValueError
delta = clamp(delta, lower_limit-X, upper_limit-X)
delta.requires_grad = True
for _ in range(attack_iters):
output = model(normalize(X + delta))
if early_stop:
index = torch.where(output.max(1)[1] == y)[0]
else:
index = slice(None,None,None)
if not isinstance(index, slice) and len(index) == 0:
break
loss = F.cross_entropy(output, y)
loss.backward()
grad = delta.grad.detach()
d = delta[index, :, :, :]
g = grad[index, :, :, :]
x = X[index, :, :, :]
if norm == "l_inf":
d = torch.clamp(d + alpha * torch.sign(g), min=-epsilon, max=epsilon)
elif norm == "l_2":
g_norm = torch.norm(g.view(g.shape[0],-1),dim=1).view(-1,1,1,1)
scaled_g = g/(g_norm + 1e-10)
d = (d + scaled_g*alpha).view(d.size(0),-1).renorm(p=2,dim=0,maxnorm=epsilon).view_as(d)
d = clamp(d, lower_limit - x, upper_limit - x)
delta.data[index, :, :, :] = d
delta.grad.zero_()
with torch.no_grad():
all_loss = F.cross_entropy(model(normalize(X+delta)), y, reduction='none')
max_delta[all_loss >= max_loss] = torch.clone(delta.detach()[all_loss >= max_loss])
max_loss = torch.max(max_loss, all_loss)
return max_delta
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--batch-size', default=256, type=int)
parser.add_argument('--data-dir', default='cifar-data', type=str)
parser.add_argument('--epsilon', default=8, type=int)
parser.add_argument('--alpha', default=2, type=float, help='Step size')
parser.add_argument('--out-dir', default='train_fgsm_output', type=str, help='Output directory')
parser.add_argument('--seed', default=0, type=int, help='Random seed')
parser.add_argument('--eval', action='store_true')
return parser.parse_args()
def main():
args = get_args()
gc.collect()
state = {k: v for k, v in args._get_kwargs()}
print(state)
results_csv = 'AutoAttack_PGD_20_full_epsilon_RN18.csv'
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
train_loader, test_loader, train_dataset, test_dataset = get_loaders(args.data_dir, args.batch_size)
# epsilon = (args.epsilon / 255.) / std
# alpha = (args.alpha / 255.) / std
# pgd_alpha = (2 / 255.) / std
# Evaluation
model_test = PreActResNet18()
model_test.load_state_dict(torch.load('train_fgsm_output/model_best_PRN18_lamda_900_epsilon_16_g3_direction_1_10_alpha_4.pth'))
# model_test = WideResNet().cuda()
# model_test = resnet(name='resnet18', num_classes=10).cuda()
# model_test.float()
# model_test.eval()
model_test.cuda()
model_test.eval()
metrics = pd.DataFrame(columns=['epsilon','ACC_PGD','ACC_AA'])
# Select 1024 random indices from the test set
indices = torch.randperm(len(test_dataset))[:9000]
# Create the subset
test_subset = Subset(test_dataset, indices)
testloader = DataLoader(test_subset, batch_size=args.batch_size, shuffle=False)
# test_loss, test_acc = evaluate_standard(testloader, model_test)
# print(f'test_loss is :{test_loss} and clean_acc is:{test_acc*100}')
# Epsilons = range(2,20,2)
Epsilons = [16.0]
# pgd_alpha = (10 / 255.)
criterion = nn.CrossEntropyLoss()
for epsilon in Epsilons:
epsilon = (epsilon/255.)
pgd_alpha = (4./255.)
print(f'pgd_alpha is :{pgd_alpha}')
test_loss = 0
test_acc = 0
test_robust_loss = 0
test_robust_acc = 0
test_n = 0
for i, (X, y) in tqdm(enumerate(testloader)):
X = X.cuda()
y = y.cuda()
# delta = attack_pgd_Alireza(model_test, X, y, epsilon, pgd_alpha, 20, 1, 'l_inf', early_stop=False, fgsm_init=None)
# delta = delta.detach()
# robust_output = model_test(normalize(torch.clamp(X + delta[:X.size(0)], min=lower_limit, max=upper_limit)))
# robust_loss = criterion(robust_output, y)
# clean_output = model_test(normalize(X))
# clean_loss = criterion(clean_output, y)
# test_robust_loss += robust_loss.item() * y.size(0)
# test_robust_acc += (robust_output.max(1)[1] == y).sum().item()
# test_loss += clean_loss.item() * y.size(0)
# test_acc += (clean_output.max(1)[1] == y).sum().item()
# test_n += y.size(0)
# print("="*80)
# print(f'Test Accuracy on PGD samples for epsilon:{epsilon} is :{(test_robust_acc/test_n)*100}%')
# print("="*80)
# print(f'Test Accuracy on clean samples is :{(test_acc/test_n)*100}%')
# print("="*80)
# print('='*50)
# print(f'pgd_loss for epsilon={epsilon} is :{pgd_loss} and pgd_acc(robust acc) for epsilon={epsilon} is :{pgd_acc*100}')
print('='*50)
ACC_AA = evaluate_robust_accuracy_AA_Complete(model_test, testloader, 'cuda', epsilon=epsilon)
print(f'Robust accuray for AA at epsilon={epsilon} is :{ACC_AA}')
print('='*50)
# metrics = metrics._append({'epsilon': epsilon, 'ACC_PGD': pgd_acc,'ACC_AA':ACC_AA}, ignore_index=True)
# metrics.to_csv(results_csv, index=False)
if __name__ == "__main__":
main()