def test_adversarial(model, criterion, image, label):
# model = bn_model
# criterion = bn_criterion
# image = bn_image
# label = bn_label
adversarial = Adversarial(model, criterion, image, label, verbose=False)
assert not adversarial.predictions(image)[1]
assert adversarial.image is None
assert adversarial.distance == MSE(value=np.inf)
assert adversarial.original_image is image
assert adversarial.original_class == label
assert adversarial.target_class() is None
assert adversarial.normalized_distance(image) == MSE(value=0)
assert adversarial.normalized_distance(image).value == 0
np.random.seed(22)
perturbation = np.random.uniform(-1, 1, size=image.shape)
perturbed = np.clip(image + perturbation, 0, 255).astype(np.float32)
d1 = adversarial.normalized_distance(perturbed).value
assert d1 != 0
assert adversarial.original_image.dtype == np.float32
adversarial.set_distance_dtype(np.float32)
assert adversarial.normalized_distance(perturbed).value == d1
adversarial.set_distance_dtype(np.float64)
assert adversarial.normalized_distance(perturbed).value != d1
adversarial.reset_distance_dtype()
assert adversarial.normalized_distance(perturbed).value == d1
label = 22 # wrong label
adversarial = Adversarial(model, criterion, image, label, verbose=True)
assert adversarial.image is not None
assert adversarial.distance == MSE(value=0)
assert adversarial.original_image is image
assert adversarial.original_class == label
assert adversarial.target_class() is None
assert adversarial.normalized_distance(image) == MSE(value=0)
assert adversarial.normalized_distance(image).value == 0
predictions, is_adversarial = adversarial.predictions(image)
first_predictions = predictions
assert is_adversarial
predictions, is_adversarial, _, _ = adversarial.predictions(
image, return_details=True) # noqa: E501
first_predictions = predictions
assert is_adversarial
predictions, is_adversarial = adversarial.batch_predictions(
image[np.newaxis]) # noqa: E501
assert (predictions == first_predictions[np.newaxis]).all()
assert np.all(is_adversarial == np.array([True]))
predictions, is_adversarial, index = adversarial.batch_predictions(
image[np.newaxis], greedy=True) # noqa: E501
assert (predictions == first_predictions[np.newaxis]).all()
assert is_adversarial
assert index == 0
predictions, is_adversarial, index, _, _ = adversarial.batch_predictions(
image[np.newaxis], greedy=True, return_details=True) # noqa: E501
assert (predictions == first_predictions[np.newaxis]).all()
assert is_adversarial
assert index == 0
predictions, gradient, is_adversarial = adversarial.predictions_and_gradient(
image, label) # noqa: E501
assert (predictions == first_predictions).all()
assert gradient.shape == image.shape
assert is_adversarial
predictions, gradient, is_adversarial, _, _ = adversarial.predictions_and_gradient(
image, label, return_details=True) # noqa: E501
assert (predictions == first_predictions).all()
assert gradient.shape == image.shape
assert is_adversarial
predictions, gradient, is_adversarial = adversarial.predictions_and_gradient(
) # noqa: E501
assert (predictions == first_predictions).all()
assert gradient.shape == image.shape
assert is_adversarial
gradient_pre = np.ones_like(predictions) * 0.3
gradient = adversarial.backward(gradient_pre, image)
gradient2 = adversarial.backward(gradient_pre)
assert gradient.shape == image.shape
assert (gradient == gradient2).all()
gradient = adversarial.gradient()
assert gradient.shape == image.shape
assert is_adversarial
assert adversarial.num_classes() == 1000
assert adversarial.has_gradient()
assert adversarial.channel_axis(batch=True) == 3
assert adversarial.channel_axis(batch=False) == 2
# without adversarials
criterion.is_adversarial = Mock(return_value=False)
adversarial = Adversarial(model, criterion, image, label)
predictions, is_adversarial, index = adversarial.batch_predictions(
image[np.newaxis], greedy=True) # noqa: E501
assert (predictions == first_predictions[np.newaxis]).all()
assert not is_adversarial
assert index is None
# without gradient
del model.predictions_and_gradient
assert not adversarial.has_gradient()
def test_adversarial(model, criterion, image, label):
# model = bn_model
# criterion = bn_criterion
# image = bn_image
# label = bn_label
adversarial = Adversarial(model, criterion, image, label)
assert not adversarial.predictions(image)[1]
assert adversarial.image is None
assert adversarial.distance == MSE(value=np.inf)
assert adversarial.original_image is image
assert adversarial.original_class == label
assert adversarial.target_class() is None
assert adversarial.normalized_distance(image) == MSE(value=0)
assert adversarial.normalized_distance(image).value == 0
label = 22 # wrong label
adversarial = Adversarial(model, criterion, image, label)
assert adversarial.image is not None
assert adversarial.distance == MSE(value=0)
assert adversarial.original_image is image
assert adversarial.original_class == label
assert adversarial.target_class() is None
assert adversarial.normalized_distance(image) == MSE(value=0)
assert adversarial.normalized_distance(image).value == 0
predictions, is_adversarial = adversarial.predictions(image)
first_predictions = predictions
assert is_adversarial
predictions, is_adversarial = adversarial.batch_predictions(
image[np.newaxis]) # noqa: E501
assert (predictions == first_predictions[np.newaxis]).all()
assert np.all(is_adversarial == np.array([True]))
predictions, is_adversarial, index = adversarial.batch_predictions(
image[np.newaxis], increasing=True) # noqa: E501
assert (predictions == first_predictions[np.newaxis]).all()
assert is_adversarial
assert index == 0
predictions, gradient, is_adversarial = adversarial.predictions_and_gradient(
image, label) # noqa: E501
assert (predictions == first_predictions).all()
assert gradient.shape == image.shape
assert is_adversarial
predictions, gradient, is_adversarial = adversarial.predictions_and_gradient(
) # noqa: E501
assert (predictions == first_predictions).all()
assert gradient.shape == image.shape
assert is_adversarial
gradient = adversarial.gradient()
assert gradient.shape == image.shape
assert is_adversarial
assert adversarial.num_classes() == 1000
assert adversarial.has_gradient()
assert adversarial.channel_axis(batch=True) == 3
assert adversarial.channel_axis(batch=False) == 2
# without adversarials
criterion.is_adversarial = Mock(return_value=False)
adversarial = Adversarial(model, criterion, image, label)
predictions, is_adversarial, index = adversarial.batch_predictions(
image[np.newaxis], increasing=True) # noqa: E501
assert (predictions == first_predictions[np.newaxis]).all()
assert not is_adversarial
assert index is None
# without gradient
del model.predictions_and_gradient
assert not adversarial.has_gradient()
def test_adversarial(model, criterion, image, label):
# model = bn_model
# criterion = bn_criterion
# image = bn_image
# label = bn_label
adversarial = Adversarial(model, criterion, image, label, verbose=False)
assert not adversarial.predictions(image)[1]
assert adversarial.image is None
assert adversarial.distance == MSE(value=np.inf)
assert adversarial.original_image is image
assert adversarial.original_class == label
assert adversarial.target_class() is None
assert adversarial.normalized_distance(image) == MSE(value=0)
assert adversarial.normalized_distance(image).value == 0
np.random.seed(22)
perturbation = np.random.uniform(-1, 1, size=image.shape)
perturbed = np.clip(image + perturbation, 0, 255).astype(np.float32)
d1 = adversarial.normalized_distance(perturbed).value
assert d1 != 0
assert adversarial.original_image.dtype == np.float32
adversarial.set_distance_dtype(np.float32)
assert adversarial.normalized_distance(perturbed).value == d1
adversarial.set_distance_dtype(np.float64)
assert adversarial.normalized_distance(perturbed).value != d1
adversarial.reset_distance_dtype()
assert adversarial.normalized_distance(perturbed).value == d1
label = 22 # wrong label
adversarial = Adversarial(model, criterion, image, label, verbose=True)
assert adversarial.image is not None
assert adversarial.distance == MSE(value=0)
assert adversarial.original_image is image
assert adversarial.original_class == label
assert adversarial.target_class() is None
assert adversarial.normalized_distance(image) == MSE(value=0)
assert adversarial.normalized_distance(image).value == 0
predictions, is_adversarial = adversarial.predictions(image)
first_predictions = predictions
assert is_adversarial
predictions, is_adversarial, _, _ = adversarial.predictions(image, return_details=True) # noqa: E501
first_predictions = predictions
assert is_adversarial
predictions, is_adversarial = adversarial.batch_predictions(image[np.newaxis]) # noqa: E501
assert (predictions == first_predictions[np.newaxis]).all()
assert np.all(is_adversarial == np.array([True]))
predictions, is_adversarial, index = adversarial.batch_predictions(image[np.newaxis], greedy=True) # noqa: E501
assert (predictions == first_predictions[np.newaxis]).all()
assert is_adversarial
assert index == 0
predictions, is_adversarial, index, _, _ = adversarial.batch_predictions(image[np.newaxis], greedy=True, return_details=True) # noqa: E501
assert (predictions == first_predictions[np.newaxis]).all()
assert is_adversarial
assert index == 0
predictions, gradient, is_adversarial = adversarial.predictions_and_gradient(image, label) # noqa: E501
assert (predictions == first_predictions).all()
assert gradient.shape == image.shape
assert is_adversarial
predictions, gradient, is_adversarial, _, _ = adversarial.predictions_and_gradient(image, label, return_details=True) # noqa: E501
assert (predictions == first_predictions).all()
assert gradient.shape == image.shape
assert is_adversarial
predictions, gradient, is_adversarial = adversarial.predictions_and_gradient() # noqa: E501
assert (predictions == first_predictions).all()
assert gradient.shape == image.shape
assert is_adversarial
gradient_pre = np.ones_like(predictions) * 0.3
gradient = adversarial.backward(gradient_pre, image)
gradient2 = adversarial.backward(gradient_pre)
assert gradient.shape == image.shape
assert (gradient == gradient2).all()
gradient = adversarial.gradient()
assert gradient.shape == image.shape
assert is_adversarial
assert adversarial.num_classes() == 1000
assert adversarial.has_gradient()
assert adversarial.channel_axis(batch=True) == 3
assert adversarial.channel_axis(batch=False) == 2
# without adversarials
criterion.is_adversarial = Mock(return_value=False)
adversarial = Adversarial(model, criterion, image, label)
predictions, is_adversarial, index = adversarial.batch_predictions(image[np.newaxis], greedy=True) # noqa: E501
assert (predictions == first_predictions[np.newaxis]).all()
assert not is_adversarial
assert index is None
# without gradient
del model.predictions_and_gradient
assert not adversarial.has_gradient()
