def test_untargeted_resnet18(image, label=None):
import torch
import torchvision.models as models
from perceptron.models.classification import PyTorchModel
mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
resnet18 = models.resnet18(pretrained=True).eval()
if torch.cuda.is_available():
resnet18 = resnet18.cuda()
model = PyTorchModel(
resnet18, bounds=(0, 1), num_classes=1000, preprocessing=(mean, std))
print(np.argmax(model.predictions(image)))
attack = Attack(model, criterion=Misclassification())
adversarial = attack(image, label, unpack=True)
def test_untargeted_resnet50(image, label=None):
import torch
import torchvision.models as models
from perceptron.models.classification import PyTorchModel
mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
model_pyt = models.resnet50(pretrained=True).eval()
if torch.cuda.is_available():
model_pyt = model_pyt.cuda()
model = PyTorchModel(model_pyt,
bounds=(0, 1),
num_classes=1000,
preprocessing=(mean, std))
print(np.argmax(model.predictions(image)))
attack = Attack(model, criterion=Misclassification())
adversarial_obj = attack(image, label, unpack=False, epsilons=10000)
distance = adversarial_obj.distance
adversarial = adversarial_obj.image
return distance, adversarial