!19 Remove redundant code of files under mindarmour/example/mnist_demo/

Merge pull request !19 from jxlang910/master

example/mnist_demo/lenet5_mnist_coverage.py

@@ -33,7 +33,6 @@ LOGGER.set_level('INFO')
 
 
 def test_lenet_mnist_coverage():
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     # upload trained network
     ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
     net = LeNet5()
@@ -85,4 +84,6 @@ def test_lenet_mnist_coverage():
 
 
 if __name__ == '__main__':
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     test_lenet_mnist_coverage()

example/mnist_demo/lenet5_mnist_fuzzing.py

@@ -32,7 +32,6 @@ LOGGER.set_level('INFO')
 
 
 def test_lenet_mnist_fuzzing():
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     # upload trained network
     ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
     net = LeNet5()
@@ -87,4 +86,6 @@ def test_lenet_mnist_fuzzing():
 
 
 if __name__ == '__main__':
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     test_lenet_mnist_fuzzing()

example/mnist_demo/mnist_attack_cw.py

@@ -15,7 +15,6 @@ import sys
 import time
 
 import numpy as np
-import pytest
 from mindspore import Model
 from mindspore import Tensor
 from mindspore import context
@@ -36,89 +35,10 @@ LOGGER.set_level('INFO')
 TAG = 'CW_Test'
 
 
-@pytest.mark.level1
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_card
-@pytest.mark.component_mindarmour
 def test_carlini_wagner_attack():
     """
     CW-Attack test
     """
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    # upload trained network
-    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
-    net = LeNet5()
-    load_dict = load_checkpoint(ckpt_name)
-    load_param_into_net(net, load_dict)
-
-    # get test data
-    data_list = "./MNIST_unzip/test"
-    batch_size = 32
-    ds = generate_mnist_dataset(data_list, batch_size=batch_size)
-
-    # prediction accuracy before attack
-    model = Model(net)
-    batch_num = 3  # the number of batches of attacking samples
-    test_images = []
-    test_labels = []
-    predict_labels = []
-    i = 0
-    for data in ds.create_tuple_iterator():
-        i += 1
-        images = data[0].astype(np.float32)
-        labels = data[1]
-        test_images.append(images)
-        test_labels.append(labels)
-        pred_labels = np.argmax(model.predict(Tensor(images)).asnumpy(),
-                                axis=1)
-        predict_labels.append(pred_labels)
-        if i >= batch_num:
-            break
-    predict_labels = np.concatenate(predict_labels)
-    true_labels = np.concatenate(test_labels)
-    accuracy = np.mean(np.equal(predict_labels, true_labels))
-    LOGGER.info(TAG, "prediction accuracy before attacking is : %s", accuracy)
-
-    # attacking
-    num_classes = 10
-    attack = CarliniWagnerL2Attack(net, num_classes, targeted=False)
-    start_time = time.clock()
-    adv_data = attack.batch_generate(np.concatenate(test_images),
-                                     np.concatenate(test_labels), batch_size=32)
-    stop_time = time.clock()
-    pred_logits_adv = model.predict(Tensor(adv_data)).asnumpy()
-    # rescale predict confidences into (0, 1).
-    pred_logits_adv = softmax(pred_logits_adv, axis=1)
-    pred_labels_adv = np.argmax(pred_logits_adv, axis=1)
-    accuracy_adv = np.mean(np.equal(pred_labels_adv, true_labels))
-    LOGGER.info(TAG, "prediction accuracy after attacking is : %s",
-                accuracy_adv)
-    test_labels = np.eye(10)[np.concatenate(test_labels)]
-    attack_evaluate = AttackEvaluate(np.concatenate(test_images).transpose(0, 2, 3, 1),
-                                     test_labels, adv_data.transpose(0, 2, 3, 1),
-                                     pred_logits_adv)
-    LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
-                attack_evaluate.mis_classification_rate())
-    LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',
-                attack_evaluate.avg_conf_adv_class())
-    LOGGER.info(TAG, 'The average confidence of true class is : %s',
-                attack_evaluate.avg_conf_true_class())
-    LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_lp_distance())
-    LOGGER.info(TAG, 'The average structural similarity between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_ssim())
-    LOGGER.info(TAG, 'The average costing time is %s',
-                (stop_time - start_time)/(batch_num*batch_size))
-
-
-def test_carlini_wagner_attack_cpu():
-    """
-    CW-Attack test for CPU device.
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     # upload trained network
     ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
     net = LeNet5()
@@ -188,4 +108,6 @@ def test_carlini_wagner_attack_cpu():
 
 
 if __name__ == '__main__':
-    test_carlini_wagner_attack_cpu()
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    test_carlini_wagner_attack()

example/mnist_demo/mnist_attack_deepfool.py

@@ -15,7 +15,6 @@ import sys
 import time
 
 import numpy as np
-import pytest
 from mindspore import Model
 from mindspore import Tensor
 from mindspore import context
@@ -36,90 +35,10 @@ LOGGER.set_level('INFO')
 TAG = 'DeepFool_Test'
 
 
-@pytest.mark.level1
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_card
-@pytest.mark.component_mindarmour
 def test_deepfool_attack():
     """
     DeepFool-Attack test
     """
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    # upload trained network
-    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
-    net = LeNet5()
-    load_dict = load_checkpoint(ckpt_name)
-    load_param_into_net(net, load_dict)
-
-    # get test data
-    data_list = "./MNIST_unzip/test"
-    batch_size = 32
-    ds = generate_mnist_dataset(data_list, batch_size=batch_size)
-
-    # prediction accuracy before attack
-    model = Model(net)
-    batch_num = 3  # the number of batches of attacking samples
-    test_images = []
-    test_labels = []
-    predict_labels = []
-    i = 0
-    for data in ds.create_tuple_iterator():
-        i += 1
-        images = data[0].astype(np.float32)
-        labels = data[1]
-        test_images.append(images)
-        test_labels.append(labels)
-        pred_labels = np.argmax(model.predict(Tensor(images)).asnumpy(),
-                                axis=1)
-        predict_labels.append(pred_labels)
-        if i >= batch_num:
-            break
-    predict_labels = np.concatenate(predict_labels)
-    true_labels = np.concatenate(test_labels)
-    accuracy = np.mean(np.equal(predict_labels, true_labels))
-    LOGGER.info(TAG, "prediction accuracy before attacking is : %s", accuracy)
-
-    # attacking
-    classes = 10
-    attack = DeepFool(net, classes, norm_level=2,
-                      bounds=(0.0, 1.0))
-    start_time = time.clock()
-    adv_data = attack.batch_generate(np.concatenate(test_images),
-                                     np.concatenate(test_labels), batch_size=32)
-    stop_time = time.clock()
-    pred_logits_adv = model.predict(Tensor(adv_data)).asnumpy()
-    # rescale predict confidences into (0, 1).
-    pred_logits_adv = softmax(pred_logits_adv, axis=1)
-    pred_labels_adv = np.argmax(pred_logits_adv, axis=1)
-    accuracy_adv = np.mean(np.equal(pred_labels_adv, true_labels))
-    LOGGER.info(TAG, "prediction accuracy after attacking is : %s",
-                accuracy_adv)
-    test_labels = np.eye(10)[np.concatenate(test_labels)]
-    attack_evaluate = AttackEvaluate(np.concatenate(test_images).transpose(0, 2, 3, 1),
-                                     test_labels, adv_data.transpose(0, 2, 3, 1),
-                                     pred_logits_adv)
-    LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
-                attack_evaluate.mis_classification_rate())
-    LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',
-                attack_evaluate.avg_conf_adv_class())
-    LOGGER.info(TAG, 'The average confidence of true class is : %s',
-                attack_evaluate.avg_conf_true_class())
-    LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_lp_distance())
-    LOGGER.info(TAG, 'The average structural similarity between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_ssim())
-    LOGGER.info(TAG, 'The average costing time is %s',
-                (stop_time - start_time)/(batch_num*batch_size))
-
-
-def test_deepfool_attack_cpu():
-    """
-    DeepFool-Attack test for CPU device.
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     # upload trained network
     ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
     net = LeNet5()
@@ -190,4 +109,6 @@ def test_deepfool_attack_cpu():
 
 
 if __name__ == '__main__':
-    test_deepfool_attack_cpu()
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    test_deepfool_attack()

example/mnist_demo/mnist_attack_fgsm.py

@@ -15,7 +15,6 @@ import sys
 import time
 
 import numpy as np
-import pytest
 from mindspore import Model
 from mindspore import Tensor
 from mindspore import context
@@ -37,88 +36,10 @@ LOGGER.set_level('INFO')
 TAG = 'FGSM_Test'
 
 
-@pytest.mark.level1
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_card
-@pytest.mark.component_mindarmour
 def test_fast_gradient_sign_method():
-    """
-    FGSM-Attack test
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    # upload trained network
-    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
-    net = LeNet5()
-    load_dict = load_checkpoint(ckpt_name)
-    load_param_into_net(net, load_dict)
-
-    # get test data
-    data_list = "./MNIST_unzip/test"
-    batch_size = 32
-    ds = generate_mnist_dataset(data_list, batch_size, sparse=False)
-
-    # prediction accuracy before attack
-    model = Model(net)
-    batch_num = 3  # the number of batches of attacking samples
-    test_images = []
-    test_labels = []
-    predict_labels = []
-    i = 0
-    for data in ds.create_tuple_iterator():
-        i += 1
-        images = data[0].astype(np.float32)
-        labels = data[1]
-        test_images.append(images)
-        test_labels.append(labels)
-        pred_labels = np.argmax(model.predict(Tensor(images)).asnumpy(),
-                                axis=1)
-        predict_labels.append(pred_labels)
-        if i >= batch_num:
-            break
-    predict_labels = np.concatenate(predict_labels)
-    true_labels = np.argmax(np.concatenate(test_labels), axis=1)
-    accuracy = np.mean(np.equal(predict_labels, true_labels))
-    LOGGER.info(TAG, "prediction accuracy before attacking is : %s", accuracy)
-
-    # attacking
-    attack = FastGradientSignMethod(net, eps=0.3)
-    start_time = time.clock()
-    adv_data = attack.batch_generate(np.concatenate(test_images),
-                                     np.concatenate(test_labels), batch_size=32)
-    stop_time = time.clock()
-    np.save('./adv_data', adv_data)
-    pred_logits_adv = model.predict(Tensor(adv_data)).asnumpy()
-    # rescale predict confidences into (0, 1).
-    pred_logits_adv = softmax(pred_logits_adv, axis=1)
-    pred_labels_adv = np.argmax(pred_logits_adv, axis=1)
-    accuracy_adv = np.mean(np.equal(pred_labels_adv, true_labels))
-    LOGGER.info(TAG, "prediction accuracy after attacking is : %s", accuracy_adv)
-    attack_evaluate = AttackEvaluate(np.concatenate(test_images).transpose(0, 2, 3, 1),
-                                     np.concatenate(test_labels),
-                                     adv_data.transpose(0, 2, 3, 1),
-                                     pred_logits_adv)
-    LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
-                attack_evaluate.mis_classification_rate())
-    LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',
-                attack_evaluate.avg_conf_adv_class())
-    LOGGER.info(TAG, 'The average confidence of true class is : %s',
-                attack_evaluate.avg_conf_true_class())
-    LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_lp_distance())
-    LOGGER.info(TAG, 'The average structural similarity between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_ssim())
-    LOGGER.info(TAG, 'The average costing time is %s',
-                (stop_time - start_time)/(batch_num*batch_size))
-
-
-def test_fast_gradient_sign_method_cpu():
     """
     FGSM-Attack test for CPU device.
     """
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     # upload trained network
     ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
     net = LeNet5()
@@ -188,4 +109,6 @@ def test_fast_gradient_sign_method_cpu():
 
 
 if __name__ == '__main__':
-    test_fast_gradient_sign_method_cpu()
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    test_fast_gradient_sign_method()

example/mnist_demo/mnist_attack_genetic.py

@@ -15,7 +15,6 @@ import sys
 import time
 
 import numpy as np
-import pytest
 from mindspore import Tensor
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
@@ -49,96 +48,10 @@ class ModelToBeAttacked(BlackModel):
         return result.asnumpy()
 
 
-@pytest.mark.level1
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_card
-@pytest.mark.component_mindarmour
 def test_genetic_attack_on_mnist():
     """
     Genetic-Attack test
     """
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    # upload trained network
-    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
-    net = LeNet5()
-    load_dict = load_checkpoint(ckpt_name)
-    load_param_into_net(net, load_dict)
-
-    # get test data
-    data_list = "./MNIST_unzip/test"
-    batch_size = 32
-    ds = generate_mnist_dataset(data_list, batch_size=batch_size)
-
-    # prediction accuracy before attack
-    model = ModelToBeAttacked(net)
-    batch_num = 3  # the number of batches of attacking samples
-    test_images = []
-    test_labels = []
-    predict_labels = []
-    i = 0
-    for data in ds.create_tuple_iterator():
-        i += 1
-        images = data[0].astype(np.float32)
-        labels = data[1]
-        test_images.append(images)
-        test_labels.append(labels)
-        pred_labels = np.argmax(model.predict(images), axis=1)
-        predict_labels.append(pred_labels)
-        if i >= batch_num:
-            break
-    predict_labels = np.concatenate(predict_labels)
-    true_labels = np.concatenate(test_labels)
-    accuracy = np.mean(np.equal(predict_labels, true_labels))
-    LOGGER.info(TAG, "prediction accuracy before attacking is : %g", accuracy)
-
-    # attacking
-    attack = GeneticAttack(model=model, pop_size=6, mutation_rate=0.05,
-                           per_bounds=0.1, step_size=0.25, temp=0.1,
-                           sparse=True)
-    targeted_labels = np.random.randint(0, 10, size=len(true_labels))
-    for i, true_l in enumerate(true_labels):
-        if targeted_labels[i] == true_l:
-            targeted_labels[i] = (targeted_labels[i] + 1) % 10
-    start_time = time.clock()
-    success_list, adv_data, query_list = attack.generate(
-        np.concatenate(test_images), targeted_labels)
-    stop_time = time.clock()
-    LOGGER.info(TAG, 'success_list: %s', success_list)
-    LOGGER.info(TAG, 'average of query times is : %s', np.mean(query_list))
-    pred_logits_adv = model.predict(adv_data)
-    # rescale predict confidences into (0, 1).
-    pred_logits_adv = softmax(pred_logits_adv, axis=1)
-    pred_lables_adv = np.argmax(pred_logits_adv, axis=1)
-    accuracy_adv = np.mean(np.equal(pred_lables_adv, true_labels))
-    LOGGER.info(TAG, "prediction accuracy after attacking is : %g",
-                accuracy_adv)
-    test_labels_onehot = np.eye(10)[true_labels]
-    attack_evaluate = AttackEvaluate(np.concatenate(test_images),
-                                     test_labels_onehot, adv_data,
-                                     pred_logits_adv, targeted=True,
-                                     target_label=targeted_labels)
-    LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
-                attack_evaluate.mis_classification_rate())
-    LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',
-                attack_evaluate.avg_conf_adv_class())
-    LOGGER.info(TAG, 'The average confidence of true class is : %s',
-                attack_evaluate.avg_conf_true_class())
-    LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_lp_distance())
-    LOGGER.info(TAG, 'The average structural similarity between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_ssim())
-    LOGGER.info(TAG, 'The average costing time is %s',
-                (stop_time - start_time)/(batch_num*batch_size))
-
-
-def test_genetic_attack_on_mnist_cpu():
-    """
-    Genetic-Attack test for CPU device.
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     # upload trained network
     ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
     net = LeNet5()
@@ -215,4 +128,6 @@ def test_genetic_attack_on_mnist_cpu():
 
 
 if __name__ == '__main__':
-    test_genetic_attack_on_mnist_cpu()
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    test_genetic_attack_on_mnist()

example/mnist_demo/mnist_attack_hsja.py

@@ -14,7 +14,6 @@
 import sys
 
 import numpy as np
-import pytest
 from mindspore import Tensor
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
@@ -68,90 +67,10 @@ def create_target_images(dataset, data_labels, target_labels):
     return np.array(res)
 
 
-@pytest.mark.level1
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_card
-@pytest.mark.component_mindarmour
 def test_hsja_mnist_attack():
     """
     hsja-Attack test
     """
-    context.set_context(mode=context.GRAPH_MODE)
-    context.set_context(device_target="Ascend")
-    # upload trained network
-    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
-    net = LeNet5()
-    load_dict = load_checkpoint(ckpt_name)
-    load_param_into_net(net, load_dict)
-    net.set_train(False)
-
-    # get test data
-    data_list = "./MNIST_unzip/test"
-    batch_size = 32
-    ds = generate_mnist_dataset(data_list, batch_size=batch_size)
-
-    # prediction accuracy before attack
-    model = ModelToBeAttacked(net)
-    batch_num = 5  # the number of batches of attacking samples
-    test_images = []
-    test_labels = []
-    predict_labels = []
-    i = 0
-    for data in ds.create_tuple_iterator():
-        i += 1
-        images = data[0].astype(np.float32)
-        labels = data[1]
-        test_images.append(images)
-        test_labels.append(labels)
-        pred_labels = np.argmax(model.predict(images), axis=1)
-        predict_labels.append(pred_labels)
-        if i >= batch_num:
-            break
-    predict_labels = np.concatenate(predict_labels)
-    true_labels = np.concatenate(test_labels)
-    accuracy = np.mean(np.equal(predict_labels, true_labels))
-    LOGGER.info(TAG, "prediction accuracy before attacking is : %s",
-                accuracy)
-    test_images = np.concatenate(test_images)
-
-    # attacking
-    norm = 'l2'
-    search = 'grid_search'
-    target = False
-    attack = HopSkipJumpAttack(model, constraint=norm, stepsize_search=search)
-    if target:
-        target_labels = random_target_labels(true_labels)
-        target_images = create_target_images(test_images, predict_labels,
-                                             target_labels)
-        attack.set_target_images(target_images)
-        success_list, adv_data, _ = attack.generate(test_images, target_labels)
-    else:
-        success_list, adv_data, _ = attack.generate(test_images, None)
-
-    adv_datas = []
-    gts = []
-    for success, adv, gt in zip(success_list, adv_data, true_labels):
-        if success:
-            adv_datas.append(adv)
-            gts.append(gt)
-    if gts:
-        adv_datas = np.concatenate(np.asarray(adv_datas), axis=0)
-        gts = np.asarray(gts)
-        pred_logits_adv = model.predict(adv_datas)
-        pred_lables_adv = np.argmax(pred_logits_adv, axis=1)
-        accuracy_adv = np.mean(np.equal(pred_lables_adv, gts))
-        mis_rate = (1 - accuracy_adv)*(len(adv_datas) / len(success_list))
-        LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
-                    mis_rate)
-
-
-def test_hsja_mnist_attack_cpu():
-    """
-    hsja-Attack test
-    """
-    context.set_context(mode=context.GRAPH_MODE)
-    context.set_context(device_target="CPU")
     # upload trained network
     ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
     net = LeNet5()
@@ -220,4 +139,6 @@ def test_hsja_mnist_attack_cpu():
 
 
 if __name__ == '__main__':
-    test_hsja_mnist_attack_cpu()
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    test_hsja_mnist_attack()

example/mnist_demo/mnist_attack_jsma.py

@@ -15,7 +15,6 @@ import sys
 import time
 
 import numpy as np
-import pytest
 from mindspore import Model
 from mindspore import Tensor
 from mindspore import context
@@ -38,94 +37,10 @@ LOGGER.set_level('INFO')
 TAG = 'JSMA_Test'
 
 
-@pytest.mark.level1
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_card
-@pytest.mark.component_mindarmour
 def test_jsma_attack():
     """
     JSMA-Attack test
     """
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    # upload trained network
-    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
-    net = LeNet5()
-    load_dict = load_checkpoint(ckpt_name)
-    load_param_into_net(net, load_dict)
-
-    # get test data
-    data_list = "./MNIST_unzip/test"
-    batch_size = 32
-    ds = generate_mnist_dataset(data_list, batch_size=batch_size)
-
-    # prediction accuracy before attack
-    model = Model(net)
-    batch_num = 3  # the number of batches of attacking samples
-    test_images = []
-    test_labels = []
-    predict_labels = []
-    i = 0
-    for data in ds.create_tuple_iterator():
-        i += 1
-        images = data[0].astype(np.float32)
-        labels = data[1]
-        test_images.append(images)
-        test_labels.append(labels)
-        pred_labels = np.argmax(model.predict(Tensor(images)).asnumpy(),
-                                axis=1)
-        predict_labels.append(pred_labels)
-        if i >= batch_num:
-            break
-    predict_labels = np.concatenate(predict_labels)
-    true_labels = np.concatenate(test_labels)
-    targeted_labels = np.random.randint(0, 10, size=len(true_labels))
-    for i, true_l in enumerate(true_labels):
-        if targeted_labels[i] == true_l:
-            targeted_labels[i] = (targeted_labels[i] + 1) % 10
-    accuracy = np.mean(np.equal(predict_labels, true_labels))
-    LOGGER.info(TAG, "prediction accuracy before attacking is : %g", accuracy)
-
-    # attacking
-    classes = 10
-    attack = JSMAAttack(net, classes)
-    start_time = time.clock()
-    adv_data = attack.batch_generate(np.concatenate(test_images),
-                                     targeted_labels, batch_size=32)
-    stop_time = time.clock()
-    pred_logits_adv = model.predict(Tensor(adv_data)).asnumpy()
-    # rescale predict confidences into (0, 1).
-    pred_logits_adv = softmax(pred_logits_adv, axis=1)
-    pred_lables_adv = np.argmax(pred_logits_adv, axis=1)
-    accuracy_adv = np.mean(np.equal(pred_lables_adv, true_labels))
-    LOGGER.info(TAG, "prediction accuracy after attacking is : %g",
-                accuracy_adv)
-    test_labels = np.eye(10)[np.concatenate(test_labels)]
-    attack_evaluate = AttackEvaluate(
-        np.concatenate(test_images).transpose(0, 2, 3, 1),
-        test_labels, adv_data.transpose(0, 2, 3, 1),
-        pred_logits_adv, targeted=True, target_label=targeted_labels)
-    LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
-                attack_evaluate.mis_classification_rate())
-    LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',
-                attack_evaluate.avg_conf_adv_class())
-    LOGGER.info(TAG, 'The average confidence of true class is : %s',
-                attack_evaluate.avg_conf_true_class())
-    LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_lp_distance())
-    LOGGER.info(TAG, 'The average structural similarity between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_ssim())
-    LOGGER.info(TAG, 'The average costing time is %s',
-                (stop_time - start_time) / (batch_num*batch_size))
-
-
-def test_jsma_attack_cpu():
-    """
-    JSMA-Attack test for CPU device.
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     # upload trained network
     ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
     net = LeNet5()
@@ -200,4 +115,6 @@ def test_jsma_attack_cpu():
 
 
 if __name__ == '__main__':
-    test_jsma_attack_cpu()
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    test_jsma_attack()

example/mnist_demo/mnist_attack_lbfgs.py

@@ -15,7 +15,6 @@ import sys
 import time
 
 import numpy as np
-import pytest
 from mindspore import Model
 from mindspore import Tensor
 from mindspore import context
@@ -37,102 +36,10 @@ LOGGER.set_level('INFO')
 TAG = 'LBFGS_Test'
 
 
-@pytest.mark.level1
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_card
-@pytest.mark.component_mindarmour
 def test_lbfgs_attack():
-    """
-    LBFGS-Attack test
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    # upload trained network
-    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
-    net = LeNet5()
-    load_dict = load_checkpoint(ckpt_name)
-    load_param_into_net(net, load_dict)
-
-    # get test data
-    data_list = "./MNIST_unzip/test"
-    batch_size = 32
-    ds = generate_mnist_dataset(data_list, batch_size=batch_size, sparse=False)
-
-    # prediction accuracy before attack
-    model = Model(net)
-    batch_num = 3  # the number of batches of attacking samples
-    test_images = []
-    test_labels = []
-    predict_labels = []
-    i = 0
-    for data in ds.create_tuple_iterator():
-        i += 1
-        images = data[0].astype(np.float32)
-        labels = data[1]
-        test_images.append(images)
-        test_labels.append(labels)
-        pred_labels = np.argmax(model.predict(Tensor(images)).asnumpy(),
-                                axis=1)
-        predict_labels.append(pred_labels)
-        if i >= batch_num:
-            break
-    predict_labels = np.concatenate(predict_labels)
-    true_labels = np.argmax(np.concatenate(test_labels), axis=1)
-    accuracy = np.mean(np.equal(predict_labels, true_labels))
-    LOGGER.info(TAG, "prediction accuracy before attacking is : %s", accuracy)
-
-    # attacking
-    is_targeted = True
-    if is_targeted:
-        targeted_labels = np.random.randint(0, 10, size=len(true_labels)).astype(np.int32)
-        for i, true_l in enumerate(true_labels):
-            if targeted_labels[i] == true_l:
-                targeted_labels[i] = (targeted_labels[i] + 1) % 10
-    else:
-        targeted_labels = true_labels.astype(np.int32)
-    targeted_labels = np.eye(10)[targeted_labels].astype(np.float32)
-    attack = LBFGS(net, is_targeted=is_targeted)
-    start_time = time.clock()
-    adv_data = attack.batch_generate(np.concatenate(test_images),
-                                     targeted_labels,
-                                     batch_size=batch_size)
-    stop_time = time.clock()
-    pred_logits_adv = model.predict(Tensor(adv_data)).asnumpy()
-    # rescale predict confidences into (0, 1).
-    pred_logits_adv = softmax(pred_logits_adv, axis=1)
-    pred_labels_adv = np.argmax(pred_logits_adv, axis=1)
-
-    accuracy_adv = np.mean(np.equal(pred_labels_adv, true_labels))
-    LOGGER.info(TAG, "prediction accuracy after attacking is : %s",
-                accuracy_adv)
-    attack_evaluate = AttackEvaluate(np.concatenate(test_images).transpose(0, 2, 3, 1),
-                                     np.concatenate(test_labels),
-                                     adv_data.transpose(0, 2, 3, 1),
-                                     pred_logits_adv,
-                                     targeted=is_targeted,
-                                     target_label=np.argmax(targeted_labels,
-                                                            axis=1))
-    LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
-                attack_evaluate.mis_classification_rate())
-    LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',
-                attack_evaluate.avg_conf_adv_class())
-    LOGGER.info(TAG, 'The average confidence of true class is : %s',
-                attack_evaluate.avg_conf_true_class())
-    LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_lp_distance())
-    LOGGER.info(TAG, 'The average structural similarity between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_ssim())
-    LOGGER.info(TAG, 'The average costing time is %s',
-                (stop_time - start_time)/(batch_num*batch_size))
-
-
-def test_lbfgs_attack_cpu():
     """
     LBFGS-Attack test for CPU device.
     """
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     # upload trained network
     ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
     net = LeNet5()
@@ -214,4 +121,6 @@ def test_lbfgs_attack_cpu():
 
 
 if __name__ == '__main__':
-    test_lbfgs_attack_cpu()
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    test_lbfgs_attack()

example/mnist_demo/mnist_attack_mdi2fgsm.py

@@ -15,7 +15,6 @@ import sys
 import time
 
 import numpy as np
-import pytest
 from mindspore import Model
 from mindspore import Tensor
 from mindspore import context
@@ -37,83 +36,8 @@ LOGGER = LogUtil.get_instance()
 TAG = 'M_DI2_FGSM_Test'
 LOGGER.set_level('INFO')
 
-@pytest.mark.level1
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_card
-@pytest.mark.component_mindarmour
-def test_momentum_diverse_input_iterative_method():
-    """
-    M-DI2-FGSM Attack Test
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    # upload trained network
-    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
-    net = LeNet5()
-    load_dict = load_checkpoint(ckpt_name)
-    load_param_into_net(net, load_dict)
 
-    # get test data
-    data_list = "./MNIST_unzip/test"
-    batch_size = 32
-    ds = generate_mnist_dataset(data_list, batch_size, sparse=False)
-
-    # prediction accuracy before attack
-    model = Model(net)
-    batch_num = 32  # the number of batches of attacking samples
-    test_images = []
-    test_labels = []
-    predict_labels = []
-    i = 0
-    for data in ds.create_tuple_iterator():
-        i += 1
-        images = data[0].astype(np.float32)
-        labels = data[1]
-        test_images.append(images)
-        test_labels.append(labels)
-        pred_labels = np.argmax(model.predict(Tensor(images)).asnumpy(),
-                                axis=1)
-        predict_labels.append(pred_labels)
-        if i >= batch_num:
-            break
-    predict_labels = np.concatenate(predict_labels)
-    true_labels = np.argmax(np.concatenate(test_labels), axis=1)
-    accuracy = np.mean(np.equal(predict_labels, true_labels))
-    LOGGER.info(TAG, "prediction accuracy before attacking is : %s", accuracy)
-
-    # attacking
-    attack = MomentumDiverseInputIterativeMethod(net)
-    start_time = time.clock()
-    adv_data = attack.batch_generate(np.concatenate(test_images),
-                                     np.concatenate(test_labels), batch_size=32)
-    stop_time = time.clock()
-    pred_logits_adv = model.predict(Tensor(adv_data)).asnumpy()
-    # rescale predict confidences into (0, 1).
-    pred_logits_adv = softmax(pred_logits_adv, axis=1)
-    pred_labels_adv = np.argmax(pred_logits_adv, axis=1)
-    accuracy_adv = np.mean(np.equal(pred_labels_adv, true_labels))
-    LOGGER.info(TAG, "prediction accuracy after attacking is : %s", accuracy_adv)
-    attack_evaluate = AttackEvaluate(np.concatenate(test_images).transpose(0, 2, 3, 1),
-                                     np.concatenate(test_labels),
-                                     adv_data.transpose(0, 2, 3, 1),
-                                     pred_logits_adv)
-    LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
-                attack_evaluate.mis_classification_rate())
-    LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',
-                attack_evaluate.avg_conf_adv_class())
-    LOGGER.info(TAG, 'The average confidence of true class is : %s',
-                attack_evaluate.avg_conf_true_class())
-    LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_lp_distance())
-    LOGGER.info(TAG, 'The average structural similarity between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_ssim())
-    LOGGER.info(TAG, 'The average costing time is %s',
-                (stop_time - start_time)/(batch_num*batch_size))
-
-
-def test_momentum_diverse_input_iterative_method_cpu():
+def test_momentum_diverse_input_iterative_method():
     """
     M-DI2-FGSM Attack Test for CPU device.
     """
@@ -186,4 +110,6 @@ def test_momentum_diverse_input_iterative_method_cpu():
 
 
 if __name__ == '__main__':
-    test_momentum_diverse_input_iterative_method_cpu()
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    test_momentum_diverse_input_iterative_method()

example/mnist_demo/mnist_attack_nes.py

@@ -14,7 +14,6 @@
 import sys
 
 import numpy as np
-import pytest
 from mindspore import Tensor
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
@@ -78,98 +77,10 @@ def create_target_images(dataset, data_labels, target_labels):
     return np.array(res)
 
 
-@pytest.mark.level1
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_card
-@pytest.mark.component_mindarmour
 def test_nes_mnist_attack():
     """
     hsja-Attack test
     """
-    context.set_context(mode=context.GRAPH_MODE)
-    context.set_context(device_target="Ascend")
-    # upload trained network
-    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
-    net = LeNet5()
-    load_dict = load_checkpoint(ckpt_name)
-    load_param_into_net(net, load_dict)
-    net.set_train(False)
-
-    # get test data
-    data_list = "./MNIST_unzip/test"
-    batch_size = 32
-    ds = generate_mnist_dataset(data_list, batch_size=batch_size)
-
-    # prediction accuracy before attack
-    model = ModelToBeAttacked(net)
-    # the number of batches of attacking samples
-    batch_num = 5
-    test_images = []
-    test_labels = []
-    predict_labels = []
-    i = 0
-    for data in ds.create_tuple_iterator():
-        i += 1
-        images = data[0].astype(np.float32)
-        labels = data[1]
-        test_images.append(images)
-        test_labels.append(labels)
-        pred_labels = np.argmax(model.predict(images), axis=1)
-        predict_labels.append(pred_labels)
-        if i >= batch_num:
-            break
-    predict_labels = np.concatenate(predict_labels)
-    true_labels = np.concatenate(test_labels)
-
-    accuracy = np.mean(np.equal(predict_labels, true_labels))
-    LOGGER.info(TAG, "prediction accuracy before attacking is : %s",
-                accuracy)
-    test_images = np.concatenate(test_images)
-
-    # attacking
-    scene = 'Query_Limit'
-    if scene == 'Query_Limit':
-        top_k = -1
-    elif scene == 'Partial_Info':
-        top_k = 5
-    elif scene == 'Label_Only':
-        top_k = 5
-
-    success = 0
-    queries_num = 0
-
-    nes_instance = NES(model, scene, top_k=top_k)
-    test_length = 32
-    advs = []
-    for img_index in range(test_length):
-        # Initial image and class selection
-        initial_img = test_images[img_index]
-        orig_class = true_labels[img_index]
-        initial_img = [initial_img]
-        target_class = random_target_labels([orig_class], true_labels)
-        target_image = create_target_images(test_images, true_labels,
-                                            target_class)
-        nes_instance.set_target_images(target_image)
-        tag, adv, queries = nes_instance.generate(initial_img, target_class)
-        if tag[0]:
-            success += 1
-        queries_num += queries[0]
-        advs.append(adv)
-
-    advs = np.reshape(advs, (len(advs), 1, 32, 32))
-    adv_pred = np.argmax(model.predict(advs), axis=1)
-    adv_accuracy = np.mean(np.equal(adv_pred, true_labels[:test_length]))
-    LOGGER.info(TAG, "prediction accuracy after attacking is : %s",
-                adv_accuracy)
-
-
-def test_nes_mnist_attack_cpu():
-    """
-    hsja-Attack test for CPU device.
-    """
-    context.set_context(mode=context.GRAPH_MODE)
-    context.set_context(device_target="CPU")
     # upload trained network
     ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
     net = LeNet5()
@@ -246,4 +157,6 @@ def test_nes_mnist_attack_cpu():
 
 
 if __name__ == '__main__':
-    test_nes_mnist_attack_cpu()
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    test_nes_mnist_attack()

example/mnist_demo/mnist_attack_pgd.py

@@ -15,7 +15,6 @@ import sys
 import time
 
 import numpy as np
-import pytest
 from mindspore import Model
 from mindspore import Tensor
 from mindspore import context
@@ -37,88 +36,10 @@ LOGGER.set_level('INFO')
 TAG = 'PGD_Test'
 
 
-@pytest.mark.level1
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_card
-@pytest.mark.component_mindarmour
 def test_projected_gradient_descent_method():
-    """
-    PGD-Attack test
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    # upload trained network
-    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
-    net = LeNet5()
-    load_dict = load_checkpoint(ckpt_name)
-    load_param_into_net(net, load_dict)
-
-    # get test data
-    data_list = "./MNIST_unzip/test"
-    batch_size = 32
-    ds = generate_mnist_dataset(data_list, batch_size, sparse=False)
-
-    # prediction accuracy before attack
-    model = Model(net)
-    batch_num = 32  # the number of batches of attacking samples
-    test_images = []
-    test_labels = []
-    predict_labels = []
-    i = 0
-    for data in ds.create_tuple_iterator():
-        i += 1
-        images = data[0].astype(np.float32)
-        labels = data[1]
-        test_images.append(images)
-        test_labels.append(labels)
-        pred_labels = np.argmax(model.predict(Tensor(images)).asnumpy(),
-                                axis=1)
-        predict_labels.append(pred_labels)
-        if i >= batch_num:
-            break
-    predict_labels = np.concatenate(predict_labels)
-    true_labels = np.argmax(np.concatenate(test_labels), axis=1)
-    accuracy = np.mean(np.equal(predict_labels, true_labels))
-    LOGGER.info(TAG, "prediction accuracy before attacking is : %s", accuracy)
-
-    # attacking
-    attack = ProjectedGradientDescent(net, eps=0.3)
-    start_time = time.clock()
-    adv_data = attack.batch_generate(np.concatenate(test_images),
-                                     np.concatenate(test_labels), batch_size=32)
-    stop_time = time.clock()
-    np.save('./adv_data', adv_data)
-    pred_logits_adv = model.predict(Tensor(adv_data)).asnumpy()
-    # rescale predict confidences into (0, 1).
-    pred_logits_adv = softmax(pred_logits_adv, axis=1)
-    pred_labels_adv = np.argmax(pred_logits_adv, axis=1)
-    accuracy_adv = np.mean(np.equal(pred_labels_adv, true_labels))
-    LOGGER.info(TAG, "prediction accuracy after attacking is : %s", accuracy_adv)
-    attack_evaluate = AttackEvaluate(np.concatenate(test_images).transpose(0, 2, 3, 1),
-                                     np.concatenate(test_labels),
-                                     adv_data.transpose(0, 2, 3, 1),
-                                     pred_logits_adv)
-    LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
-                attack_evaluate.mis_classification_rate())
-    LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',
-                attack_evaluate.avg_conf_adv_class())
-    LOGGER.info(TAG, 'The average confidence of true class is : %s',
-                attack_evaluate.avg_conf_true_class())
-    LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_lp_distance())
-    LOGGER.info(TAG, 'The average structural similarity between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_ssim())
-    LOGGER.info(TAG, 'The average costing time is %s',
-                (stop_time - start_time)/(batch_num*batch_size))
-
-
-def test_projected_gradient_descent_method_cpu():
     """
     PGD-Attack test for CPU device.
     """
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     # upload trained network
     ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
     net = LeNet5()
@@ -188,4 +109,6 @@ def test_projected_gradient_descent_method_cpu():
 
 
 if __name__ == '__main__':
-    test_projected_gradient_descent_method_cpu()
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    test_projected_gradient_descent_method()

example/mnist_demo/mnist_attack_pointwise.py

@@ -14,7 +14,6 @@
 import sys
 
 import numpy as np
-import pytest
 from mindspore import Tensor
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
@@ -49,94 +48,10 @@ class ModelToBeAttacked(BlackModel):
         return result.asnumpy()
 
 
-@pytest.mark.level1
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_card
-@pytest.mark.component_mindarmour
 def test_pointwise_attack_on_mnist():
     """
     Salt-and-Pepper-Attack test
     """
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    # upload trained network
-    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
-    net = LeNet5()
-    load_dict = load_checkpoint(ckpt_name)
-    load_param_into_net(net, load_dict)
-
-    # get test data
-    data_list = "./MNIST_unzip/test"
-    batch_size = 32
-    ds = generate_mnist_dataset(data_list, batch_size=batch_size)
-
-    # prediction accuracy before attack
-    model = ModelToBeAttacked(net)
-    batch_num = 3  # the number of batches of attacking samples
-    test_images = []
-    test_labels = []
-    predict_labels = []
-    i = 0
-    for data in ds.create_tuple_iterator():
-        i += 1
-        images = data[0].astype(np.float32)
-        labels = data[1]
-        test_images.append(images)
-        test_labels.append(labels)
-        pred_labels = np.argmax(model.predict(images), axis=1)
-        predict_labels.append(pred_labels)
-        if i >= batch_num:
-            break
-    predict_labels = np.concatenate(predict_labels)
-    true_labels = np.concatenate(test_labels)
-    accuracy = np.mean(np.equal(predict_labels, true_labels))
-    LOGGER.info(TAG, "prediction accuracy before attacking is : %g", accuracy)
-
-    # attacking
-    is_target = False
-    attack = PointWiseAttack(model=model, is_targeted=is_target)
-    if is_target:
-        targeted_labels = np.random.randint(0, 10, size=len(true_labels))
-        for i, true_l in enumerate(true_labels):
-            if targeted_labels[i] == true_l:
-                targeted_labels[i] = (targeted_labels[i] + 1) % 10
-    else:
-        targeted_labels = true_labels
-    success_list, adv_data, query_list = attack.generate(
-        np.concatenate(test_images), targeted_labels)
-    success_list = np.arange(success_list.shape[0])[success_list]
-    LOGGER.info(TAG, 'success_list: %s', success_list)
-    LOGGER.info(TAG, 'average of query times is : %s', np.mean(query_list))
-    adv_preds = []
-    for ite_data in adv_data:
-        pred_logits_adv = model.predict(ite_data)
-        # rescale predict confidences into (0, 1).
-        pred_logits_adv = softmax(pred_logits_adv, axis=1)
-        adv_preds.extend(pred_logits_adv)
-    accuracy_adv = np.mean(np.equal(np.max(adv_preds, axis=1), true_labels))
-    LOGGER.info(TAG, "prediction accuracy after attacking is : %g",
-                accuracy_adv)
-    test_labels_onehot = np.eye(10)[true_labels]
-    attack_evaluate = AttackEvaluate(np.concatenate(test_images),
-                                     test_labels_onehot, adv_data,
-                                     adv_preds, targeted=is_target,
-                                     target_label=targeted_labels)
-    LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
-                attack_evaluate.mis_classification_rate())
-    LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',
-                attack_evaluate.avg_conf_adv_class())
-    LOGGER.info(TAG, 'The average confidence of true class is : %s',
-                attack_evaluate.avg_conf_true_class())
-    LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_lp_distance())
-
-
-def test_pointwise_attack_on_mnist_cpu():
-    """
-    Salt-and-Pepper-Attack test for CPU device.
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     # upload trained network
     ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
     net = LeNet5()
@@ -211,4 +126,6 @@ def test_pointwise_attack_on_mnist_cpu():
 
 
 if __name__ == '__main__':
-    test_pointwise_attack_on_mnist_cpu()
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    test_pointwise_attack_on_mnist()

example/mnist_demo/mnist_attack_pso.py

@@ -15,7 +15,6 @@ import sys
 import time
 
 import numpy as np
-import pytest
 from mindspore import Tensor
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
@@ -49,89 +48,10 @@ class ModelToBeAttacked(BlackModel):
         return result.asnumpy()
 
 
-@pytest.mark.level1
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_card
-@pytest.mark.component_mindarmour
 def test_pso_attack_on_mnist():
     """
     PSO-Attack test
     """
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    # upload trained network
-    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
-    net = LeNet5()
-    load_dict = load_checkpoint(ckpt_name)
-    load_param_into_net(net, load_dict)
-
-    # get test data
-    data_list = "./MNIST_unzip/test"
-    batch_size = 32
-    ds = generate_mnist_dataset(data_list, batch_size=batch_size)
-
-    # prediction accuracy before attack
-    model = ModelToBeAttacked(net)
-    batch_num = 3  # the number of batches of attacking samples
-    test_images = []
-    test_labels = []
-    predict_labels = []
-    i = 0
-    for data in ds.create_tuple_iterator():
-        i += 1
-        images = data[0].astype(np.float32)
-        labels = data[1]
-        test_images.append(images)
-        test_labels.append(labels)
-        pred_labels = np.argmax(model.predict(images), axis=1)
-        predict_labels.append(pred_labels)
-        if i >= batch_num:
-            break
-    predict_labels = np.concatenate(predict_labels)
-    true_labels = np.concatenate(test_labels)
-    accuracy = np.mean(np.equal(predict_labels, true_labels))
-    LOGGER.info(TAG, "prediction accuracy before attacking is : %s", accuracy)
-
-    # attacking
-    attack = PSOAttack(model, bounds=(0.0, 1.0), pm=0.5, sparse=True)
-    start_time = time.clock()
-    success_list, adv_data, query_list = attack.generate(
-        np.concatenate(test_images), np.concatenate(test_labels))
-    stop_time = time.clock()
-    LOGGER.info(TAG, 'success_list: %s', success_list)
-    LOGGER.info(TAG, 'average of query times is : %s', np.mean(query_list))
-    pred_logits_adv = model.predict(adv_data)
-    # rescale predict confidences into (0, 1).
-    pred_logits_adv = softmax(pred_logits_adv, axis=1)
-    pred_labels_adv = np.argmax(pred_logits_adv, axis=1)
-    accuracy_adv = np.mean(np.equal(pred_labels_adv, true_labels))
-    LOGGER.info(TAG, "prediction accuracy after attacking is : %s",
-                accuracy_adv)
-    test_labels_onehot = np.eye(10)[np.concatenate(test_labels)]
-    attack_evaluate = AttackEvaluate(np.concatenate(test_images),
-                                     test_labels_onehot, adv_data,
-                                     pred_logits_adv)
-    LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
-                attack_evaluate.mis_classification_rate())
-    LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',
-                attack_evaluate.avg_conf_adv_class())
-    LOGGER.info(TAG, 'The average confidence of true class is : %s',
-                attack_evaluate.avg_conf_true_class())
-    LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_lp_distance())
-    LOGGER.info(TAG, 'The average structural similarity between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_ssim())
-    LOGGER.info(TAG, 'The average costing time is %s',
-                (stop_time - start_time)/(batch_num*batch_size))
-
-
-def test_pso_attack_on_mnist_cpu():
-    """
-    PSO-Attack test for CPU device.
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     # upload trained network
     ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
     net = LeNet5()
@@ -201,4 +121,6 @@ def test_pso_attack_on_mnist_cpu():
 
 
 if __name__ == '__main__':
-    test_pso_attack_on_mnist_cpu()
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    test_pso_attack_on_mnist()

example/mnist_demo/mnist_attack_salt_and_pepper.py

@@ -14,7 +14,6 @@
 import sys
 
 import numpy as np
-import pytest
 from mindspore import Tensor
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
@@ -49,98 +48,10 @@ class ModelToBeAttacked(BlackModel):
         return result.asnumpy()
 
 
-@pytest.mark.level1
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_card
-@pytest.mark.component_mindarmour
 def test_salt_and_pepper_attack_on_mnist():
     """
     Salt-and-Pepper-Attack test
     """
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    # upload trained network
-    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
-    net = LeNet5()
-    load_dict = load_checkpoint(ckpt_name)
-    load_param_into_net(net, load_dict)
-
-    # get test data
-    data_list = "./MNIST_unzip/test"
-    batch_size = 32
-    ds = generate_mnist_dataset(data_list, batch_size=batch_size)
-
-    # prediction accuracy before attack
-    model = ModelToBeAttacked(net)
-    batch_num = 3  # the number of batches of attacking samples
-    test_images = []
-    test_labels = []
-    predict_labels = []
-    i = 0
-    for data in ds.create_tuple_iterator():
-        i += 1
-        images = data[0].astype(np.float32)
-        labels = data[1]
-        test_images.append(images)
-        test_labels.append(labels)
-        pred_labels = np.argmax(model.predict(images), axis=1)
-        predict_labels.append(pred_labels)
-        if i >= batch_num:
-            break
-    LOGGER.debug(TAG, 'model input image shape is: {}'.format(np.array(test_images).shape))
-    predict_labels = np.concatenate(predict_labels)
-    true_labels = np.concatenate(test_labels)
-    accuracy = np.mean(np.equal(predict_labels, true_labels))
-    LOGGER.info(TAG, "prediction accuracy before attacking is : %g", accuracy)
-
-    # attacking
-    is_target = False
-    attack = SaltAndPepperNoiseAttack(model=model,
-                                      is_targeted=is_target,
-                                      sparse=True)
-    if is_target:
-        targeted_labels = np.random.randint(0, 10, size=len(true_labels))
-        for i, true_l in enumerate(true_labels):
-            if targeted_labels[i] == true_l:
-                targeted_labels[i] = (targeted_labels[i] + 1) % 10
-    else:
-        targeted_labels = true_labels
-    LOGGER.debug(TAG, 'input shape is: {}'.format(np.concatenate(test_images).shape))
-    success_list, adv_data, query_list = attack.generate(
-        np.concatenate(test_images), targeted_labels)
-    success_list = np.arange(success_list.shape[0])[success_list]
-    LOGGER.info(TAG, 'success_list: %s', success_list)
-    LOGGER.info(TAG, 'average of query times is : %s', np.mean(query_list))
-    adv_preds = []
-    for ite_data in adv_data:
-        pred_logits_adv = model.predict(ite_data)
-        # rescale predict confidences into (0, 1).
-        pred_logits_adv = softmax(pred_logits_adv, axis=1)
-        adv_preds.extend(pred_logits_adv)
-    accuracy_adv = np.mean(np.equal(np.max(adv_preds, axis=1), true_labels))
-    LOGGER.info(TAG, "prediction accuracy after attacking is : %g",
-                accuracy_adv)
-    test_labels_onehot = np.eye(10)[true_labels]
-    attack_evaluate = AttackEvaluate(np.concatenate(test_images),
-                                     test_labels_onehot, adv_data,
-                                     adv_preds, targeted=is_target,
-                                     target_label=targeted_labels)
-    LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
-                attack_evaluate.mis_classification_rate())
-    LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',
-                attack_evaluate.avg_conf_adv_class())
-    LOGGER.info(TAG, 'The average confidence of true class is : %s',
-                attack_evaluate.avg_conf_true_class())
-    LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '
-                     'samples and adversarial samples are: %s',
-                attack_evaluate.avg_lp_distance())
-
-
-def test_salt_and_pepper_attack_on_mnist_cpu():
-    """
-    Salt-and-Pepper-Attack test for CPU device.
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     # upload trained network
     ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
     net = LeNet5()
@@ -219,4 +130,6 @@ def test_salt_and_pepper_attack_on_mnist_cpu():
 
 
 if __name__ == '__main__':
-    test_salt_and_pepper_attack_on_mnist_cpu()
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    test_salt_and_pepper_attack_on_mnist()

example/mnist_demo/mnist_defense_nad.py

@@ -12,11 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """defense example using nad"""
-import logging
 import sys
 
 import numpy as np
-import pytest
 from mindspore import Tensor
 from mindspore import context
 from mindspore import nn
@@ -36,111 +34,10 @@ LOGGER = LogUtil.get_instance()
 TAG = 'Nad_Example'
 
 
-@pytest.mark.level1
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_card
-@pytest.mark.component_mindarmour
 def test_nad_method():
-    """
-    NAD-Defense test.
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    # 1. load trained network
-    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
-    net = LeNet5()
-    load_dict = load_checkpoint(ckpt_name)
-    load_param_into_net(net, load_dict)
-
-    loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=False)
-    opt = nn.Momentum(net.trainable_params(), 0.01, 0.09)
-
-    nad = NaturalAdversarialDefense(net, loss_fn=loss, optimizer=opt,
-                                    bounds=(0.0, 1.0), eps=0.3)
-
-    # 2. get test data
-    data_list = "./MNIST_unzip/test"
-    batch_size = 32
-    ds_test = generate_mnist_dataset(data_list, batch_size=batch_size,
-                                     sparse=False)
-    inputs = []
-    labels = []
-    for data in ds_test.create_tuple_iterator():
-        inputs.append(data[0].astype(np.float32))
-        labels.append(data[1])
-    inputs = np.concatenate(inputs)
-    labels = np.concatenate(labels)
-
-    # 3. get accuracy of test data on original model
-    net.set_train(False)
-    acc_list = []
-    batchs = inputs.shape[0] // batch_size
-    for i in range(batchs):
-        batch_inputs = inputs[i*batch_size : (i + 1)*batch_size]
-        batch_labels = np.argmax(labels[i*batch_size : (i + 1)*batch_size], axis=1)
-        logits = net(Tensor(batch_inputs)).asnumpy()
-        label_pred = np.argmax(logits, axis=1)
-        acc_list.append(np.mean(batch_labels == label_pred))
-
-    LOGGER.debug(TAG, 'accuracy of TEST data on original model is : %s',
-                 np.mean(acc_list))
-
-    # 4. get adv of test data
-    attack = FastGradientSignMethod(net, eps=0.3)
-    adv_data = attack.batch_generate(inputs, labels)
-    LOGGER.debug(TAG, 'adv_data.shape is : %s', adv_data.shape)
-
-    # 5. get accuracy of adv data on original model
-    net.set_train(False)
-    acc_list = []
-    batchs = adv_data.shape[0] // batch_size
-    for i in range(batchs):
-        batch_inputs = adv_data[i*batch_size : (i + 1)*batch_size]
-        batch_labels = np.argmax(labels[i*batch_size : (i + 1)*batch_size], axis=1)
-        logits = net(Tensor(batch_inputs)).asnumpy()
-        label_pred = np.argmax(logits, axis=1)
-        acc_list.append(np.mean(batch_labels == label_pred))
-
-    LOGGER.debug(TAG, 'accuracy of adv data on original model is : %s',
-                 np.mean(acc_list))
-
-    # 6. defense
-    net.set_train()
-    nad.batch_defense(inputs, labels, batch_size=32, epochs=10)
-
-    # 7. get accuracy of test data on defensed model
-    net.set_train(False)
-    acc_list = []
-    batchs = inputs.shape[0] // batch_size
-    for i in range(batchs):
-        batch_inputs = inputs[i*batch_size : (i + 1)*batch_size]
-        batch_labels = np.argmax(labels[i*batch_size : (i + 1)*batch_size], axis=1)
-        logits = net(Tensor(batch_inputs)).asnumpy()
-        label_pred = np.argmax(logits, axis=1)
-        acc_list.append(np.mean(batch_labels == label_pred))
-
-    LOGGER.debug(TAG, 'accuracy of TEST data on defensed model is : %s',
-                 np.mean(acc_list))
-
-    # 8. get accuracy of adv data on defensed model
-    acc_list = []
-    batchs = adv_data.shape[0] // batch_size
-    for i in range(batchs):
-        batch_inputs = adv_data[i*batch_size : (i + 1)*batch_size]
-        batch_labels = np.argmax(labels[i*batch_size : (i + 1)*batch_size], axis=1)
-        logits = net(Tensor(batch_inputs)).asnumpy()
-        label_pred = np.argmax(logits, axis=1)
-        acc_list.append(np.mean(batch_labels == label_pred))
-
-    LOGGER.debug(TAG, 'accuracy of adv data on defensed model is : %s',
-                 np.mean(acc_list))
-
-
-def test_nad_method_cpu():
     """
     NAD-Defense test for CPU device.
     """
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     # 1. load trained network
     ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
     net = LeNet5()
@@ -231,5 +128,6 @@ def test_nad_method_cpu():
 
 
 if __name__ == '__main__':
-    LOGGER.set_level(logging.DEBUG)
-    test_nad_method_cpu()
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    test_nad_method()

example/mnist_demo/mnist_evaluation.py

@@ -40,7 +40,6 @@ from mindarmour.utils.logger import LogUtil
 sys.path.append("..")
 from data_processing import generate_mnist_dataset
 
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 LOGGER = LogUtil.get_instance()
 TAG = 'Defense_Evaluate_Example'
@@ -140,20 +139,18 @@ def test_black_defense():
     # get test data
     data_list = "./MNIST_unzip/test"
     batch_size = 32
-    ds_test = generate_mnist_dataset(data_list, batch_size=batch_size,
-                                     sparse=False)
+    ds_test = generate_mnist_dataset(data_list, batch_size=batch_size)
     inputs = []
     labels = []
     for data in ds_test.create_tuple_iterator():
         inputs.append(data[0].astype(np.float32))
         labels.append(data[1])
     inputs = np.concatenate(inputs).astype(np.float32)
-    labels = np.concatenate(labels).astype(np.float32)
-    labels_sparse = np.argmax(labels, axis=1)
+    labels = np.concatenate(labels).astype(np.int32)
 
-    target_label = np.random.randint(0, 10, size=labels_sparse.shape[0])
-    for idx in range(labels_sparse.shape[0]):
-        while target_label[idx] == labels_sparse[idx]:
+    target_label = np.random.randint(0, 10, size=labels.shape[0])
+    for idx in range(labels.shape[0]):
+        while target_label[idx] == labels[idx]:
             target_label[idx] = np.random.randint(0, 10)
     target_label = np.eye(10)[target_label].astype(np.float32)
 
@@ -167,23 +164,23 @@ def test_black_defense():
     wb_model = ModelToBeAttacked(wb_net)
 
     # gen white-box adversarial examples of test data
-    wb_attack = FastGradientSignMethod(wb_net, eps=0.3)
+    loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
+    wb_attack = FastGradientSignMethod(wb_net, eps=0.3, loss_fn=loss)
     wb_adv_sample = wb_attack.generate(attacked_sample,
                                        attacked_true_label)
 
     wb_raw_preds = softmax(wb_model.predict(wb_adv_sample), axis=1)
     accuracy_test = np.mean(
         np.equal(np.argmax(wb_model.predict(attacked_sample), axis=1),
-                 np.argmax(attacked_true_label, axis=1)))
+                 attacked_true_label))
     LOGGER.info(TAG, "prediction accuracy before white-box attack is : %s",
                 accuracy_test)
     accuracy_adv = np.mean(np.equal(np.argmax(wb_raw_preds, axis=1),
-                                    np.argmax(attacked_true_label, axis=1)))
+                                    attacked_true_label))
     LOGGER.info(TAG, "prediction accuracy after white-box attack is : %s",
                 accuracy_adv)
 
     # improve the robustness of model with white-box adversarial examples
-    loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=False)
     opt = nn.Momentum(wb_net.trainable_params(), 0.01, 0.09)
 
     nad = NaturalAdversarialDefense(wb_net, loss_fn=loss, optimizer=opt,
@@ -194,12 +191,12 @@ def test_black_defense():
     wb_def_preds = wb_net(Tensor(wb_adv_sample)).asnumpy()
     wb_def_preds = softmax(wb_def_preds, axis=1)
     accuracy_def = np.mean(np.equal(np.argmax(wb_def_preds, axis=1),
-                                    np.argmax(attacked_true_label, axis=1)))
+                                    attacked_true_label))
     LOGGER.info(TAG, "prediction accuracy after defense is : %s", accuracy_def)
 
     # calculate defense evaluation metrics for defense against white-box attack
     wb_def_evaluate = DefenseEvaluate(wb_raw_preds, wb_def_preds,
-                                      np.argmax(attacked_true_label, axis=1))
+                                      attacked_true_label)
     LOGGER.info(TAG, 'defense evaluation for white-box adversarial attack')
     LOGGER.info(TAG,
                 'classification accuracy variance (CAV) is : {:.2f}'.format(
@@ -232,7 +229,7 @@ def test_black_defense():
                               per_bounds=0.1, step_size=0.25, temp=0.1,
                               sparse=False)
     attack_target_label = target_label[:attacked_size]
-    true_label = labels_sparse[:attacked_size + benign_size]
+    true_label = labels[:attacked_size + benign_size]
     # evaluate robustness of original model
     # gen black-box adversarial examples of test data
     for idx in range(attacked_size):
@@ -323,4 +320,8 @@ def test_black_defense():
 
 
 if __name__ == '__main__':
-    test_black_defense()
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    DEVICE = context.get_context("device_target")
+    if DEVICE in ("Ascend", "GPU"):
+        test_black_defense()

example/mnist_demo/mnist_similarity_detector.py

@@ -14,7 +14,6 @@
 import sys
 
 import numpy as np
-import pytest
 from mindspore import Model
 from mindspore import Tensor
 from mindspore import context
@@ -29,7 +28,6 @@ from mindarmour.attacks.black.pso_attack import PSOAttack
 from mindarmour.detectors.black.similarity_detector import SimilarityDetector
 from mindarmour.utils.logger import LogUtil
 
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 sys.path.append("..")
 from data_processing import generate_mnist_dataset
@@ -92,11 +90,6 @@ class EncoderNet(Cell):
         return self._encode_dim
 
 
-@pytest.mark.level1
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_card
-@pytest.mark.component_mindarmour
 def test_similarity_detector():
     """
     Similarity Detector test.
@@ -178,4 +171,8 @@ def test_similarity_detector():
 
 
 if __name__ == '__main__':
-    test_similarity_detector()
+    # device_target can be "CPU", "GPU" or "Ascend"
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    DEVICE = context.get_context("device_target")
+    if DEVICE in ("Ascend", "GPU"):
+        test_similarity_detector()

example/mnist_demo/mnist_train.py

@@ -31,12 +31,6 @@ TAG = "Lenet5_train"
 
 
 def mnist_train(epoch_size, batch_size, lr, momentum):
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
-                        enable_mem_reuse=False)
-
-    lr = lr
-    momentum = momentum
-    epoch_size = epoch_size
     mnist_path = "./MNIST_unzip/"
     ds = generate_mnist_dataset(os.path.join(mnist_path, "train"),
                                 batch_size=batch_size, repeat_size=1)
@@ -67,4 +61,6 @@ def mnist_train(epoch_size, batch_size, lr, momentum):
 
 
 if __name__ == '__main__':
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU",
+                        enable_mem_reuse=False)
     mnist_train(10, 32, 0.01, 0.9)