diff --git a/mindarmour/adv_robustness/attacks/deep_fool.py b/mindarmour/adv_robustness/attacks/deep_fool.py index 67eaba5..db6a63e 100644 --- a/mindarmour/adv_robustness/attacks/deep_fool.py +++ b/mindarmour/adv_robustness/attacks/deep_fool.py @@ -105,9 +105,9 @@ class DeepFool(Attack): max_iters (int): Max iterations, which should be greater than zero. Default: 50. overshoot (float): Overshoot parameter. Default: 0.02. - norm_level (int): Order of the vector norm. Possible values: np.inf + norm_level (Union[int, str]): Order of the vector norm. Possible values: np.inf or 2. Default: 2. - bounds (tuple): Upper and lower bounds of data range. In form of (clip_min, + bounds (Union[tuple, list]): Upper and lower bounds of data range. In form of (clip_min, clip_max). Default: None. sparse (bool): If True, input labels are sparse-coded. If False, input labels are onehot-coded. Default: True. diff --git a/mindarmour/utils/_check_param.py b/mindarmour/utils/_check_param.py index 9c1318c..a72ca61 100644 --- a/mindarmour/utils/_check_param.py +++ b/mindarmour/utils/_check_param.py @@ -149,13 +149,11 @@ def check_numpy_param(arg_name, arg_value): ValueError: If value type is not in (list, tuple, numpy.ndarray). """ _ = _check_array_not_empty(arg_name, arg_value) - if isinstance(arg_value, (list, tuple)): - arg_value = np.asarray(arg_value) - elif isinstance(arg_value, np.ndarray): + if isinstance(arg_value, np.ndarray): arg_value = np.copy(arg_value) else: - msg = 'type of {} must be in (list, tuple, numpy.ndarray)'.format( - arg_name) + msg = 'type of {} must be numpy.ndarray, but got {}'.format( + arg_name, type(arg_value)) LOGGER.error(TAG, msg) raise TypeError(msg) return arg_value @@ -220,6 +218,8 @@ def check_norm_level(norm_level): """ check norm_level of regularization. """ + if not isinstance(norm_level, (int, str)): + msg = 'Type of norm_level must be in [int, str], but got {}'.format(type(norm_level)) accept_norm = [1, 2, '1', '2', 'l1', 'l2', 'inf', 'linf', np.inf] if norm_level not in accept_norm: msg = 'norm_level must be in {}, but got {}'.format(accept_norm, diff --git a/tests/ut/python/adv_robustness/attacks/black/test_nes.py b/tests/ut/python/adv_robustness/attacks/black/test_nes.py index 47a2b67..93c4905 100644 --- a/tests/ut/python/adv_robustness/attacks/black/test_nes.py +++ b/tests/ut/python/adv_robustness/attacks/black/test_nes.py @@ -147,7 +147,7 @@ def nes_mnist_attack(scene, top_k): target_class) nes_instance.set_target_images(target_image) - tag, adv, queries = nes_instance.generate(initial_img, target_class) + tag, adv, queries = nes_instance.generate(np.array(initial_img), np.array(target_class)) if tag[0]: success += 1 queries_num += queries[0] diff --git a/tests/ut/python/adv_robustness/attacks/test_batch_generate_attack.py b/tests/ut/python/adv_robustness/attacks/test_batch_generate_attack.py index f2c92e9..dcb4fca 100644 --- a/tests/ut/python/adv_robustness/attacks/test_batch_generate_attack.py +++ b/tests/ut/python/adv_robustness/attacks/test_batch_generate_attack.py @@ -17,9 +17,10 @@ Batch-generate-attack test. import numpy as np import pytest +import mindspore.context as context import mindspore.ops.operations as P +from mindspore.ops.composite import GradOperation from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits -import mindspore.context as context from mindarmour.adv_robustness.attacks import FastGradientMethod @@ -54,6 +55,60 @@ class Net(Cell): return out +class Net2(Cell): + """ + Construct the network of target model. A network with multiple input data. + + Examples: + >>> net = Net2() + """ + + def __init__(self): + super(Net2, self).__init__() + self._softmax = P.Softmax() + + def construct(self, inputs1, inputs2): + out1 = self._softmax(inputs1) + out2 = self._softmax(inputs2) + return out1 + out2, out1 - out2 + + +class LossNet(Cell): + """ + Loss function for test. + """ + def construct(self, loss1, loss2, labels1, labels2): + return loss1 + loss2 - labels1 - labels2 + + +class WithLossCell(Cell): + """Wrap the network with loss function""" + def __init__(self, backbone, loss_fn): + super(WithLossCell, self).__init__(auto_prefix=False) + self._backbone = backbone + self._loss_fn = loss_fn + + def construct(self, inputs1, inputs2, labels1, labels2): + out = self._backbone(inputs1, inputs2) + return self._loss_fn(*out, labels1, labels2) + + +class GradWrapWithLoss(Cell): + """ + Construct a network to compute the gradient of loss function in \ + input space and weighted by 'weight'. + """ + + def __init__(self, network): + super(GradWrapWithLoss, self).__init__() + self._grad_all = GradOperation(get_all=True, sens_param=False) + self._network = network + + def construct(self, *inputs): + gout = self._grad_all(self._network)(*inputs) + return gout[0] + + @pytest.mark.level0 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @@ -71,4 +126,30 @@ def test_batch_generate_attack(): ms_adv_x = attack.batch_generate(input_np, label, batch_size=32) assert np.any(ms_adv_x != input_np), 'Fast gradient method: generate value' \ + ' must not be equal to original value.' + + +@pytest.mark.level0 +@pytest.mark.platform_arm_ascend_training +@pytest.mark.platform_x86_ascend_training +@pytest.mark.env_card +@pytest.mark.component_mindarmour +def test_batch_generate_attack_multi_inputs(): + """ + Attack with batch-generate by multi-inputs. + """ + context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") + inputs1 = np.random.random((128, 10)).astype(np.float32) + inputs2 = np.random.random((128, 10)).astype(np.float32) + labels1 = np.random.randint(0, 10, 128).astype(np.int32) + labels2 = np.random.randint(0, 10, 128).astype(np.int32) + labels1 = np.eye(10)[labels1].astype(np.float32) + labels2 = np.eye(10)[labels2].astype(np.float32) + + with_loss_cell = WithLossCell(Net2(), LossNet()) + grad_with_loss_net = GradWrapWithLoss(with_loss_cell) + attack = FastGradientMethod(grad_with_loss_net) + ms_adv_x = attack.batch_generate((inputs1, inputs2), (labels1, labels2), batch_size=32) + + assert np.any(ms_adv_x != inputs1), 'Fast gradient method: generate value' \ ' must not be equal to original value.' diff --git a/tests/ut/python/adv_robustness/attacks/test_gradient_method.py b/tests/ut/python/adv_robustness/attacks/test_gradient_method.py index 8c6ec25..96f527d 100644 --- a/tests/ut/python/adv_robustness/attacks/test_gradient_method.py +++ b/tests/ut/python/adv_robustness/attacks/test_gradient_method.py @@ -312,52 +312,6 @@ def test_fast_gradient_method_multi_inputs(): @pytest.mark.platform_x86_ascend_training @pytest.mark.env_card @pytest.mark.component_mindarmour -def test_batch_generate(): - """ - Fast gradient method unit test. - """ - context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") - input_np = np.random.random([10, 3]).astype(np.float32) - label = np.random.randint(0, 3, [10]) - label = np.eye(3)[label].astype(np.float32) - - loss_fn = SoftmaxCrossEntropyWithLogits(sparse=False) - attack = FastGradientMethod(Net(), loss_fn=loss_fn) - ms_adv_x = attack.batch_generate(input_np, label, 4) - - assert np.any(ms_adv_x != input_np), 'Fast gradient method: generate value' \ - ' must not be equal to original value.' - - -@pytest.mark.level0 -@pytest.mark.platform_arm_ascend_training -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_card -@pytest.mark.component_mindarmour -def test_batch_generate_multi_inputs(): - """ - Fast gradient method unit test. - """ - context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") - inputs1 = np.asarray([[0.1, 0.2, 0.7]]).astype(np.float32) - inputs2 = np.asarray([[0.4, 0.8, 0.5]]).astype(np.float32) - labels1 = np.expand_dims(np.eye(3)[1].astype(np.float32), axis=0) - labels2 = np.expand_dims(np.eye(3)[2].astype(np.float32), axis=0) - - with_loss_cell = WithLossCell(Net2(), LossNet()) - grad_with_loss_net = GradWrapWithLoss(with_loss_cell) - attack = FastGradientMethod(grad_with_loss_net) - ms_adv_x = attack.generate((inputs1, inputs2), (labels1, labels2)) - - assert np.any(ms_adv_x != inputs1), 'Fast gradient method: generate value' \ - ' must not be equal to original value.' - - -@pytest.mark.level0 -@pytest.mark.platform_arm_ascend_training -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_card -@pytest.mark.component_mindarmour def test_assert_error(): """ Random least likely class method unit test. diff --git a/tests/ut/python/adv_robustness/evaluations/test_radar_metric.py b/tests/ut/python/adv_robustness/evaluations/test_radar_metric.py index bdcb120..aeb5029 100644 --- a/tests/ut/python/adv_robustness/evaluations/test_radar_metric.py +++ b/tests/ut/python/adv_robustness/evaluations/test_radar_metric.py @@ -14,6 +14,7 @@ """ Radar map test. """ +import numpy as np import pytest from mindarmour.adv_robustness.evaluations import RadarMetric @@ -28,7 +29,7 @@ def test_radar_metric(): metrics_name = ['MR', 'ACAC', 'ASS', 'NTE', 'RGB'] def_metrics = [0.9, 0.85, 0.6, 0.7, 0.8] raw_metrics = [0.5, 0.3, 0.55, 0.65, 0.7] - metrics_data = [def_metrics, raw_metrics] + metrics_data = np.array([def_metrics, raw_metrics]) metrics_labels = ['before', 'after'] # create obj @@ -46,7 +47,7 @@ def test_value_error(): metrics_name = ['MR', 'ACAC', 'ASS', 'NTE', 'RGB'] def_metrics = [0.9, 0.85, 0.6, 0.7, 0.8] raw_metrics = [0.5, 0.3, 0.55, 0.65, 0.7] - metrics_data = [def_metrics, raw_metrics] + metrics_data = np.array([def_metrics, raw_metrics]) metrics_labels = ['before', 'after'] with pytest.raises(ValueError):