def test_pad():
"""UT for projected adversarial defense."""
num_classes = 10
batch_size = 32
sparse = False
context.set_context(mode=context.GRAPH_MODE)
context.set_context(device_target='Ascend')
# create test data
inputs = np.random.rand(batch_size, 1, 32, 32).astype(np.float32)
labels = np.random.randint(num_classes, size=batch_size).astype(np.int32)
if not sparse:
labels = np.eye(num_classes)[labels].astype(np.float32)
# construct network
net = Net()
loss_fn = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=sparse)
optimizer = Momentum(net.trainable_params(), 0.001, 0.9)
# defense
pad = ProjectedAdversarialDefense(net, loss_fn=loss_fn, optimizer=optimizer)
LOGGER.set_level(logging.DEBUG)
LOGGER.debug(TAG, '---start projected adversarial defense--')
loss = pad.defense(inputs, labels)
LOGGER.debug(TAG, '---end projected adversarial defense--')
assert np.any(loss >= 0.0)
def test_ad():
"""UT for adversarial defense."""
num_classes = 10
batch_size = 32
sparse = False
context.set_context(mode=context.GRAPH_MODE)
context.set_context(device_target='Ascend')
# create test data
inputs = np.random.rand(batch_size, 1, 32, 32).astype(np.float32)
labels = np.random.randint(num_classes, size=batch_size).astype(np.int32)
if not sparse:
labels = np.eye(num_classes)[labels].astype(np.float32)
net = Net()
loss_fn = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=sparse)
optimizer = Momentum(learning_rate=Tensor(np.array([0.001], np.float32)),
momentum=0.9,
params=net.trainable_params())
ad_defense = AdversarialDefense(net, loss_fn=loss_fn, optimizer=optimizer)
LOGGER.set_level(logging.DEBUG)
LOGGER.debug(TAG, '--start adversarial defense--')
loss = ad_defense.defense(inputs, labels)
LOGGER.debug(TAG, '--end adversarial defense--')
assert np.any(loss >= 0.0)
def test_ead():
"""UT for ensemble adversarial defense."""
num_classes = 10
batch_size = 64
sparse = False
context.set_context(mode=context.GRAPH_MODE)
context.set_context(device_target='Ascend')
# create test data
inputs = np.random.rand(batch_size, 1, 32, 32).astype(np.float32)
labels = np.random.randint(num_classes, size=batch_size).astype(np.int32)
if not sparse:
labels = np.eye(num_classes)[labels].astype(np.float32)
net = Net()
loss_fn = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=sparse)
optimizer = Momentum(net.trainable_params(), 0.001, 0.9)
net = Net()
fgsm = FastGradientSignMethod(net)
pgd = ProjectedGradientDescent(net)
ead = EnsembleAdversarialDefense(net, [fgsm, pgd],
loss_fn=loss_fn,
optimizer=optimizer)
LOGGER.set_level(logging.DEBUG)
LOGGER.debug(TAG, '---start ensemble adversarial defense--')
loss = ead.defense(inputs, labels)
LOGGER.debug(TAG, '---end ensemble adversarial defense--')
assert np.any(loss >= 0.0)