def load_net(attack_model, filename, path):
if(attack_model == "CNN"):
from deeprobust.image.netmodels.CNN import Net
model = Net()
if(attack_model == "ResNet18"):
import deeprobust.image.netmodels.resnet as Net
model = Net.ResNet18()
model.load_state_dict(torch.load(path + filename))
model.eval()
return model
parser.add_argument(
"--destination",
default='./trained_models/',
help="choose destination to load the pretrained models.")
parser.add_argument("--filename", default="MNIST_CNN_epoch_20.pt")
return parser.parse_args()
args = parameter_parser() # read argument and creat an argparse object
model = Net()
model.load_state_dict(torch.load(args.destination + args.filename))
model.eval()
print("Finish loading network.")
xx = datasets.MNIST('./', download=False).data[999:1000].to('cuda')
xx = xx.unsqueeze_(1).float() / 255
#print(xx.size())
## Set Target
yy = datasets.MNIST('./', download=False).targets[999:1000].to('cuda')
"""
Generate adversarial examples
"""
F1 = FGSM(model, device="cuda") ### or cuda
AdvExArray = F1.generate(xx, yy, **attack_params['FGSM_MNIST'])
import numpy as np
import torch
import torch.nn as nn
from torchvision import datasets, models, transforms
from deeprobust.image.attack.Nattack import NATTACK
from deeprobust.image.netmodels.CNN import Net
#initialize model
model = Net()
model.load_state_dict(
torch.load("defense_model/mnist_pgdtraining_0.3.pt",
map_location=torch.device('cuda')))
model.eval()
print("----------model_parameters-----------")
for names, parameters in model.named_parameters():
print(names, ',', parameters.type())
print("-------------------------------------")
data_loader = torch.utils.data.DataLoader(datasets.MNIST(
'deeprobust/image/data',
train=True,
download=True,
transform=transforms.Compose([transforms.ToTensor()])),
batch_size=1,
shuffle=True)
attack = NATTACK(model)
attack.generate(dataloader=data_loader, classnum=10)
from deeprobust.image.attack.cw import CarliniWagner
from deeprobust.image.netmodels.CNN import Net
from deeprobust.image.config import attack_params
# print log
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
logger.info("Start test cw attack")
# load model
model = Net()
model.load_state_dict(
torch.load("./trained_models/MNIST_CNN_epoch_20.pt",
map_location=torch.device('cuda')))
model.eval()
xx = datasets.MNIST('deeprobust/image/data', download=False).data[1234]
xx = xx.unsqueeze_(0).float() / 255
xx = xx.unsqueeze_(0).float().to('cuda')
## Set Targetå
yy = datasets.MNIST('deeprobust/image/data', download=False).targets[1234]
yy = yy.float()
attack = CarliniWagner(model, device='cuda')
AdvExArray = attack.generate(xx,
yy,
target_label=1,
parser.add_argument("--download destination",
default = '~/lyx/projects/models/download',
help = "choose destination to load the pretrained models.")
parser.add_argument("--file name",
default = "MNIST_CNN")
return parser.parse_args()
args = parameter_parser() # read argument and creat an argparse object
model = Net()
print("Download network from Google Drive.")
model.load_state_dict(torch.load(destination + filename))
model.eval()
print("Finish loading network.")
xx = datasets.MNIST('deeprobust/image/data', download = False).data[999:1000].to('cuda')
xx = xx.unsqueeze_(1).float()/255
print(xx.size())
## Set Targetå
yy = datasets.MNIST('deeprobust/image/data', download = False).targets[999:1000].to('cuda')
F1 = FGM(model, device = "cuda") ### or cuda
AdvExArray = F1.generate(xx, yy, **attack_params['FGSM_MNIST'])
predict0 = model(xx)
from deeprobust.image.attack.cw import CarliniWagner
from deeprobust.image.netmodels.CNN import Net
from deeprobust.image.config import attack_params
# print log
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
logger.info("Start test cw attack")
# load model
model = Net()
model.load_state_dict(
torch.load("deeprobust/image/save_models/MNIST_CNN_epoch_20.pt",
map_location=torch.device('cuda')))
model.eval()
xx = datasets.MNIST('deeprobust/image/data', download=False).data[1234]
xx = xx.unsqueeze_(0).float() / 255
xx = xx.unsqueeze_(0).float().to('cuda')
## Set Targetå
yy = datasets.MNIST('deeprobust/image/data', download=False).targets[1234]
yy = yy.float()
attack = CarliniWagner(model, device='cuda')
AdvExArray = attack.generate(xx,
yy,
target=1,
import numpy as np
import torch
import torch.nn as nn
from torchvision import datasets, models, transforms
from deeprobust.image.attack.Nattack import NATTACK
from deeprobust.image.netmodels.CNN import Net
#initialize model
model = Net()
model.load_state_dict(
torch.load("trained_models/mnist_fgsmtraining_0.2.pt",
map_location=torch.device('cuda')))
model.eval()
print("----------model_parameters-----------")
for names, parameters in model.named_parameters():
print(names, ',', parameters.type())
print("-------------------------------------")
data_loader = torch.utils.data.DataLoader(datasets.MNIST(
'deeprobust/image/data',
train=True,
download=True,
transform=transforms.Compose([transforms.ToTensor()])),
batch_size=1,
shuffle=True)
attack = NATTACK(model)
attack.generate(dataloader=data_loader, classnum=10)
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F #233
import torch.optim as optim
from torchvision import datasets, models, transforms
from PIL import Image
from lbfgs import LBFGS
from deeprobust.image.netmodels.CNN import Net
from deeprobust.image.config import attack_params
#load model
model = Net()
model.load_state_dict(
torch.load(
"/home/bizon/Desktop/liyaxin/deeprobust_trained_model/MNIST_CNN_epoch_20.pt",
map_location=torch.device('cpu')))
model.eval()
import ipdb
ipdb.set_trace()
xx = datasets.MNIST('deeprobust/image/data', download=True).data[8888]
xx = xx.unsqueeze_(0).float() / 255
xx = xx.unsqueeze_(0).float()
## Set Targetå
yy = datasets.MNIST('deeprobust/image/data', download=False).targets[8888]
yy = yy.float()
predict0 = model(xx)