e5b0e4f15d23be74d3bd34beb8e4a4b17634575b,tests/attacks/evasion/test_frame_saliency.py,,test_iterative_saliency_refresh,#Any#Any#,89

Before Change

@pytest.mark.skipMlFramework("pytorch")
def test_iterative_saliency_refresh(fix_get_mnist_subset, image_dl_estimator_for_attack):
    classifier_list = image_dl_estimator_for_attack(FastGradientMethod)
    // TODO this if statement must be removed once we have a classifier for both image and tabular data
    if classifier_list is None:
        logging.warning("Couldn"t perform  this test because no classifier is defined")
        return

    expected_values_axis_1 = {"nb_perturbed_frames": ExpectedValue(np.asarray([5, 1, 3, 10, 8, 1, 3, 8, 4, 7, 7]), 2)}

    expected_values_axis_2 = {"nb_perturbed_frames": ExpectedValue(np.asarray([11, 1, 2, 6, 14, 2, 2, 13, 4, 8, 4]), 2)}

    for classifier in classifier_list:
        attacker = FastGradientMethod(classifier, eps=0.3, batch_size=128)
        attack = FrameSaliencyAttack(classifier, attacker, "iterative_saliency_refresh")
        backend_check_adverse_frames(attack, fix_get_mnist_subset, expected_values_axis_1)

        // test with non-default frame index:
        attack = FrameSaliencyAttack(classifier, attacker, "iterative_saliency", frame_index=2)
        backend_check_adverse_frames(attack, fix_get_mnist_subset, expected_values_axis_2)


def test_classifier_type_check_fail():
    backend_test_classifier_type_check_fail(FastGradientMethod, [LossGradientsMixin, BaseEstimator])

After Change

@pytest.mark.skipMlFramework("pytorch")
@pytest.mark.framework_agnostic
def test_iterative_saliency_refresh(fix_get_mnist_subset, image_dl_estimator_for_attack):
    try:
        classifier = image_dl_estimator_for_attack(FastGradientMethod)

        expected_values_axis_1 = {"nb_perturbed_frames": ExpectedValue(np.asarray([5, 1, 3, 10, 8, 1, 3, 8, 4, 7, 7]), 2)}

        expected_values_axis_2 = {"nb_perturbed_frames": ExpectedValue(np.asarray([11, 1, 2, 6, 14, 2, 2, 13, 4, 8, 4]), 2)}

        attacker = FastGradientMethod(classifier, eps=0.3, batch_size=128)
        attack = FrameSaliencyAttack(classifier, attacker, "iterative_saliency_refresh")
        backend_check_adverse_frames(attack, fix_get_mnist_subset, expected_values_axis_1)

        // test with non-default frame index:
        attack = FrameSaliencyAttack(classifier, attacker, "iterative_saliency", frame_index=2)
        backend_check_adverse_frames(attack, fix_get_mnist_subset, expected_values_axis_2)
    except ARTTestException as e:
        add_warning(e)


@pytest.mark.framework_agnostic
def test_classifier_type_check_fail():
    try:
        backend_test_classifier_type_check_fail(FastGradientMethod, [LossGradientsMixin, BaseEstimator])

Instances