def test_load(self):
def compare_state_dicts(d1, d2):
cond = (set(d1.keys()) == set(d2.keys()))
for i in d1.keys():
cond = cond and bool(torch.all(d1[i] == d2[i]).item())
return cond
model = Model()
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)
classifier = PyTorchClassifier((0, 1), model, loss_fn, optimizer,
(1, 28, 28), 10)
import tempfile
import os
import copy
orig_params = copy.deepcopy(classifier._model.state_dict())
t_file = tempfile.NamedTemporaryFile()
full_path = t_file.name
t_file.close()
base_name = os.path.basename(full_path)
dir_name = os.path.dirname(full_path)
classifier.save(base_name, path=dir_name)
model = Model() # Reinitialize the weights
classifier = PyTorchClassifier((0, 1), model, loss_fn, optimizer,
(1, 28, 28), 10)
new_params = copy.deepcopy(classifier._model.state_dict())
assert (not compare_state_dicts(new_params, orig_params))
classifier.load_model_weights(base_name, path=dir_name)
new_params = copy.deepcopy(classifier._model.state_dict())
assert (compare_state_dicts(new_params, orig_params))
os.remove(full_path + '.model')
# Test the classifier
predictions = cifar_classifier.predict(x_test)
accuracy = np.sum(
np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
print('Accuracy before attack: {}%'.format(accuracy * 100))
# Craft the adversarial examples
epsilon = 0.2 # Maximum perturbation
adv_crafter = FastGradientMethod(cifar_classifier, eps=epsilon)
x_test_adv = adv_crafter.generate(x=x_test)
# Test the classifier on adversarial exmaples
predictions = cifar_classifier.predict(x_test_adv)
accuracy = np.sum(
np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
print('Accuracy after attack: {}%'.format(accuracy * 100))
cifar_classifier.save('cifar_fgsm_state_dict', 'models')
preprocess = Autoencoder()
preprocess.load_state_dict(torch.load('models/conv_autoencoder.pth'))
x_test_adv = torch.from_numpy(x_test_adv)
x_test_denoised = preprocess(x_test_adv)
x_test_denoised = x_test_denoised.detach().numpy()
predictions = cifar_classifier.predict(x_test_denoised)
accuracy = np.sum(
np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
print('Accuracy after defense: {}%'.format(accuracy * 100))
# Test the classifier
predictions = mnist_classifier.predict(x_test)
accuracy = np.sum(
np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
print('Accuracy before attack: {}%'.format(accuracy * 100))
# Craft the adversarial examples
epsilon = 0.2 # Maximum perturbation
adv_crafter = FastGradientMethod(mnist_classifier, eps=epsilon)
x_test_adv = adv_crafter.generate(x=x_test)
# Test the classifier on adversarial exmaples
predictions = mnist_classifier.predict(x_test_adv)
accuracy = np.sum(
np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
print('Accuracy after attack: {}%'.format(accuracy * 100))
mnist_classifier.save('mnist_fgsm_state_dict', 'models')
preprocess = Autoencoder()
preprocess.load_state_dict(torch.load('models/conv_autoencoder.pth'))
x_test_adv = torch.from_numpy(x_test_adv)
x_test_denoised = preprocess(x_test_adv)
x_test_denoised = x_test_denoised.detach().numpy()
predictions = mnist_classifier.predict(x_test_denoised)
accuracy = np.sum(
np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
print('Accuracy after defense: {}%'.format(accuracy * 100))
