add garbage collection for attack module

tests/ut/python/adv_robustness/attacks/black/test_genetic_attack.py

@@ -14,8 +14,10 @@
 """
 Genetic-Attack test.
 """
+import gc
 import numpy as np
 import pytest
+
 import mindspore.ops.operations as M
 from mindspore import Tensor
 from mindspore import context
@@ -112,6 +114,8 @@ def test_genetic_attack():
                            sparse=False)
     _, adv_data, _ = attack.generate(inputs, labels)
     assert np.any(inputs != adv_data)
+    del inputs, labels, adv_data
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -136,7 +140,8 @@ def test_supplement():
                            sparse=False)
     # raise error
     _, _, _ = attack.generate(inputs, labels)
-
+    del inputs, labels
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -163,7 +168,8 @@ def test_value_error():
     # raise error
     with pytest.raises(ValueError):
         assert attack.generate(inputs, labels)
-
+    del inputs, labels
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -189,3 +195,5 @@ def test_genetic_attack_detection_cpu():
         _, adv_img, _ = attack.generate(img_data, (pre_gt_boxes, pre_gt_labels))
         adv_imgs.append(adv_img)
     assert np.any(inputs != np.array(adv_imgs))
+    del inputs, adv_imgs
+    gc.collect()

tests/ut/python/adv_robustness/attacks/black/test_hsja.py

@@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
-
+import gc
 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
@@ -153,7 +154,8 @@ def test_hsja_mnist_attack_ascend():
         accuracy_adv = np.mean(np.equal(pred_lables_adv, gts))
         LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
                     accuracy_adv)
-
+    del pred_labels, adv_data, predict_labels, true_labels
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -226,6 +228,8 @@ def test_hsja_mnist_attack_cpu():
         accuracy_adv = np.mean(np.equal(pred_lables_adv, gts))
         LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
                     accuracy_adv)
+    del pred_labels, adv_data, predict_labels, true_labels
+    gc.collect()
 
 
 @pytest.mark.level0

tests/ut/python/adv_robustness/attacks/black/test_pointwise_attack.py

@@ -15,6 +15,7 @@
 PointWise Attack test
 """
 import os
+import gc
 
 import numpy as np
 import pytest
@@ -86,6 +87,8 @@ def test_pointwise_attack_method_ascend():
     assert np.any(adv_data[is_adv][0] != input_np[is_adv][0]), 'Pointwise attack method: ' \
                                              'generate value must not be equal' \
                                              ' to original value.'
+    del input_np, labels, adv_data
+    gc.collect()
 
 
 @pytest.mark.level0
@@ -126,3 +129,5 @@ def test_pointwise_attack_method_cpu():
     assert np.any(adv_data[is_adv][0] != input_np[is_adv][0]), 'Pointwise attack method: ' \
                                              'generate value must not be equal' \
                                              ' to original value.'
+    del input_np, labels, adv_data
+    gc.collect()

tests/ut/python/adv_robustness/attacks/black/test_pso_attack.py

@@ -14,6 +14,7 @@
 """
 PSO-Attack test.
 """
+import gc
 import numpy as np
 import pytest
 
@@ -115,7 +116,8 @@ def test_pso_attack():
     attack = PSOAttack(model, bounds=(0.0, 1.0), pm=0.5, sparse=False)
     _, adv_data, _ = attack.generate(inputs, labels)
     assert np.any(inputs != adv_data)
-
+    del inputs, labels, adv_data
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -141,7 +143,8 @@ def test_pso_attack_targeted():
                        sparse=False)
     _, adv_data, _ = attack.generate(inputs, labels)
     assert np.any(inputs != adv_data)
-
+    del inputs, labels, adv_data
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
@@ -165,7 +168,8 @@ def test_pso_attack_gpu():
     attack = PSOAttack(model, bounds=(0.0, 1.0), pm=0.5, sparse=False)
     _, adv_data, _ = attack.generate(inputs, labels)
     assert np.any(inputs != adv_data)
-
+    del inputs, labels, adv_data
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -189,7 +193,8 @@ def test_pso_attack_cpu():
     attack = PSOAttack(model, bounds=(0.0, 1.0), pm=0.5, sparse=False)
     _, adv_data, _ = attack.generate(inputs, labels)
     assert np.any(inputs != adv_data)
-
+    del inputs, labels, adv_data
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu

tests/ut/python/adv_robustness/attacks/black/test_salt_and_pepper_attack.py

@@ -15,6 +15,7 @@
 SaltAndPepper Attack Test
 """
 import os
+import gc
 import numpy as np
 import pytest
 
@@ -73,6 +74,9 @@ def test_salt_and_pepper_attack_method_ascend():
     _, adv_data, _ = attack.generate(inputs, labels)
     assert np.any(adv_data[0] != inputs[0]), 'Salt and pepper attack method:  generate value must not be equal' \
                                              ' to original value.'
+    del inputs, labels, adv_data
+    gc.collect()
+
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -104,3 +108,5 @@ def test_salt_and_pepper_attack_method_cpu():
     _, adv_data, _ = attack.generate(inputs, labels)
     assert np.any(adv_data[0] != inputs[0]), 'Salt and pepper attack method:  generate value must not be equal' \
                                              ' to original value.'
+    del inputs, labels, adv_data
+    gc.collect()

tests/ut/python/adv_robustness/attacks/test_batch_generate_attack.py

@@ -14,6 +14,7 @@
 """
 Batch-generate-attack test.
 """
+import gc
 import numpy as np
 import pytest
 
@@ -129,6 +130,9 @@ def test_batch_generate_attack_ascend():
 
     assert np.any(ms_adv_x != input_np), 'Fast gradient method: generate value' \
                                             ' must not be equal to original value.'
+    del input_np, label, ms_adv_x
+    gc.collect()
+
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -151,7 +155,8 @@ def test_batch_generate_attack_cpu():
 
     assert np.any(ms_adv_x != input_np), 'Fast gradient method: generate value' \
                                             ' must not be equal to original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 
 @pytest.mark.level0
@@ -181,7 +186,8 @@ def test_batch_generate_attack_multi_inputs_ascend():
 
     assert np.any(ms_adv_x != inputs1), 'Fast gradient method: generate value' \
                                          ' must not be equal to original value.'
-
+    del inputs1, inputs2, labels1, labels2, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -209,3 +215,5 @@ def test_batch_generate_attack_multi_inputs_cpu():
 
     assert np.any(ms_adv_x != inputs1), 'Fast gradient method: generate value' \
                                          ' must not be equal to original value.'
+    del inputs1, inputs2, labels1, labels2, ms_adv_x
+    gc.collect()

tests/ut/python/adv_robustness/attacks/test_cw.py

@@ -14,6 +14,7 @@
 """
 CW-Attack test.
 """
+import gc
 import numpy as np
 import pytest
 
@@ -72,7 +73,8 @@ def test_cw_attack_ascend():
     attack = CarliniWagnerL2Attack(net, num_classes, targeted=False)
     adv_data = attack.generate(input_np, label_np)
     assert np.any(input_np != adv_data)
-
+    del input_np, label_np, adv_data
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -93,7 +95,8 @@ def test_cw_attack_cpu():
     attack = CarliniWagnerL2Attack(net, num_classes, targeted=False)
     adv_data = attack.generate(input_np, label_np)
     assert np.any(input_np != adv_data)
-
+    del input_np, label_np, adv_data
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -115,7 +118,8 @@ def test_cw_attack_targeted_ascend():
     attack = CarliniWagnerL2Attack(net, num_classes, targeted=True)
     adv_data = attack.generate(input_np, target_np)
     assert np.any(input_np != adv_data)
-
+    del input_np, target_np, adv_data
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -136,3 +140,5 @@ def test_cw_attack_targeted_cpu():
     attack = CarliniWagnerL2Attack(net, num_classes, targeted=True)
     adv_data = attack.generate(input_np, target_np)
     assert np.any(input_np != adv_data)
+    del input_np, target_np, adv_data
+    gc.collect()

tests/ut/python/adv_robustness/attacks/test_deep_fool.py

@@ -14,6 +14,7 @@
 """
 DeepFool-Attack test.
 """
+import gc
 import numpy as np
 import pytest
 
@@ -97,6 +98,8 @@ def test_deepfool_attack_ascend():
     assert np.allclose(adv_data, expect_value), 'mindspore deepfool_method' \
         ' implementation error, ms_adv_x != expect_value'
 
+    del input_np, true_labels, adv_data, expect_value
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -124,7 +127,8 @@ def test_deepfool_attack_cpu():
                                 0.40406296]])
     assert np.allclose(adv_data, expect_value), 'mindspore deepfool_method' \
         ' implementation error, ms_adv_x != expect_value'
-
+    del input_np, true_labels, adv_data, expect_value
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -151,7 +155,8 @@ def test_deepfool_attack_detection_ascend():
                       bounds=(0.0, 1.0))
     adv_data = attack.generate((inputs1_np, inputs2_np), (gt_boxes, gt_labels))
     assert np.any(adv_data != inputs1_np)
-
+    del inputs1_np, inputs2_np, gt_labels, adv_data
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -177,7 +182,8 @@ def test_deepfool_attack_detection_cpu():
                       bounds=(0.0, 1.0))
     adv_data = attack.generate((inputs1_np, inputs2_np), (gt_boxes, gt_labels))
     assert np.any(adv_data != inputs1_np)
-
+    del inputs1_np, inputs2_np, gt_labels, adv_data
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -202,7 +208,8 @@ def test_deepfool_attack_inf_ascend():
                       bounds=(0.0, 1.0))
     adv_data = attack.generate(input_np, true_labels)
     assert np.any(input_np != adv_data)
-
+    del input_np, true_labels, adv_data
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -226,7 +233,8 @@ def test_deepfool_attack_inf_cpu():
                       bounds=(0.0, 1.0))
     adv_data = attack.generate(input_np, true_labels)
     assert np.any(input_np != adv_data)
-
+    del input_np, true_labels, adv_data
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -251,7 +259,8 @@ def test_value_error_ascend():
         attack = DeepFool(net, classes, max_iters=10, norm_level=1,
                           bounds=(0.0, 1.0))
         assert attack.generate(input_np, true_labels)
-
+    del input_np, true_labels
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -275,3 +284,5 @@ def test_value_error_cpu():
         attack = DeepFool(net, classes, max_iters=10, norm_level=1,
                           bounds=(0.0, 1.0))
         assert attack.generate(input_np, true_labels)
+    del input_np, true_labels
+    gc.collect()

tests/ut/python/adv_robustness/attacks/test_gradient_method.py

@@ -14,6 +14,7 @@
 """
 Gradient-Attack test.
 """
+import gc
 import numpy as np
 import pytest
 
@@ -130,6 +131,8 @@ def test_fast_gradient_method():
 
     assert np.any(ms_adv_x != input_np), 'Fast gradient method: generate value' \
                                          ' must not be equal to original value.'
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 
 @pytest.mark.level0
@@ -150,7 +153,8 @@ def test_fast_gradient_method_gpu():
 
     assert np.any(ms_adv_x != input_np), 'Fast gradient method: generate value' \
                                          ' must not be equal to original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -170,7 +174,8 @@ def test_fast_gradient_method_cpu():
 
     assert np.any(ms_adv_x != input_np), 'Fast gradient method: generate value' \
                                          ' must not be equal to original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -192,7 +197,8 @@ def test_random_fast_gradient_method():
     assert np.any(ms_adv_x != input_np), 'Random fast gradient method: ' \
                                          'generate value must not be equal to' \
                                          ' original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -213,8 +219,9 @@ def test_fast_gradient_sign_method():
 
     assert np.any(ms_adv_x != input_np), 'Fast gradient sign method: generate' \
                                          ' value must not be equal to' \
-                                         ' original value.'
-
+                                        ' original value.'
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -236,7 +243,8 @@ def test_random_fast_gradient_sign_method():
     assert np.any(ms_adv_x != input_np), 'Random fast gradient sign method: ' \
                                          'generate value must not be equal to' \
                                          ' original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -258,7 +266,8 @@ def test_least_likely_class_method():
     assert np.any(ms_adv_x != input_np), 'Least likely class method: generate' \
                                          ' value must not be equal to' \
                                          ' original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -281,7 +290,8 @@ def test_random_least_likely_class_method():
     assert np.any(ms_adv_x != input_np), 'Random least likely class method: ' \
                                          'generate value must not be equal to' \
                                          ' original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -305,7 +315,8 @@ def test_fast_gradient_method_multi_inputs():
 
     assert np.any(ms_adv_x != inputs1), 'Fast gradient method: generate value' \
                                          ' must not be equal to original value.'
-
+    del inputs1, inputs2, labels1, labels2, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training

tests/ut/python/adv_robustness/attacks/test_iterative_gradient_method.py

@@ -14,6 +14,7 @@
 """
 Iterative-gradient Attack test.
 """
+import gc
 import numpy as np
 import pytest
 
@@ -79,7 +80,8 @@ def test_basic_iterative_method_ascend():
         assert np.any(
             ms_adv_x != input_np), 'Basic iterative method: generate value' \
                                    ' must not be equal to original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -104,7 +106,8 @@ def test_basic_iterative_method_cpu():
         assert np.any(
             ms_adv_x != input_np), 'Basic iterative method: generate value' \
                                    ' must not be equal to original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -129,7 +132,8 @@ def test_momentum_iterative_method_ascend():
         assert np.any(ms_adv_x != input_np), 'Momentum iterative method: generate' \
                                              ' value must not be equal to' \
                                              ' original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -153,7 +157,8 @@ def test_momentum_iterative_method_cpu():
         assert np.any(ms_adv_x != input_np), 'Momentum iterative method: generate' \
                                              ' value must not be equal to' \
                                              ' original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -180,7 +185,8 @@ def test_projected_gradient_descent_method_ascend():
             ms_adv_x != input_np), 'Projected gradient descent method: ' \
                                    'generate value must not be equal to' \
                                    ' original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -206,7 +212,8 @@ def test_projected_gradient_descent_method_cpu():
             ms_adv_x != input_np), 'Projected gradient descent method: ' \
                                    'generate value must not be equal to' \
                                    ' original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -230,7 +237,8 @@ def test_diverse_input_iterative_method_ascend():
     assert np.any(ms_adv_x != input_np), 'Diverse input iterative method: generate' \
                                              ' value must not be equal to' \
                                              ' original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -253,7 +261,8 @@ def test_diverse_input_iterative_method_cpu():
     assert np.any(ms_adv_x != input_np), 'Diverse input iterative method: generate' \
                                              ' value must not be equal to' \
                                              ' original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -277,7 +286,8 @@ def test_momentum_diverse_input_iterative_method_ascend():
     assert np.any(ms_adv_x != input_np), 'Momentum diverse input iterative method: ' \
                                              'generate value must not be equal to' \
                                              ' original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -301,7 +311,8 @@ def test_momentum_diverse_input_iterative_method_cpu():
     assert np.any(ms_adv_x != input_np), 'Momentum diverse input iterative method: ' \
                                              'generate value must not be equal to' \
                                              ' original value.'
-
+    del input_np, label, ms_adv_x
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -321,7 +332,8 @@ def test_error_ascend():
         label = np.asarray([2], np.int32)
         label = np.eye(3)[label].astype(np.float32)
         assert attack.generate(input_np, label)
-
+        del input_np, label
+        gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -340,3 +352,5 @@ def test_error_cpu():
         label = np.asarray([2], np.int32)
         label = np.eye(3)[label].astype(np.float32)
         assert attack.generate(input_np, label)
+        del input_np, label
+        gc.collect()

tests/ut/python/adv_robustness/attacks/test_jsma.py

@@ -14,6 +14,7 @@
 """
 JSMA-Attack test.
 """
+import gc
 import numpy as np
 import pytest
 
@@ -77,7 +78,8 @@ def test_jsma_attack():
     attack = JSMAAttack(net, classes, max_iteration=5)
     adv_data = attack.generate(input_np, label_np)
     assert np.any(input_np != adv_data)
-
+    del input_np, label_np, adv_data
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -105,6 +107,8 @@ def test_jsma_attack_2():
     attack = JSMAAttack(net, classes, max_iteration=5, increase=False)
     adv_data = attack.generate(input_np, label_np)
     assert np.any(input_np != adv_data)
+    del input_np, label_np, adv_data
+    gc.collect()
 
 
 @pytest.mark.level0
@@ -132,6 +136,8 @@ def test_jsma_attack_gpu():
     attack = JSMAAttack(net, classes, max_iteration=5)
     adv_data = attack.generate(input_np, label_np)
     assert np.any(input_np != adv_data)
+    del input_np, label_np, adv_data
+    gc.collect()
 
 
 @pytest.mark.level0
@@ -159,3 +165,5 @@ def test_jsma_attack_cpu():
     attack = JSMAAttack(net, classes, max_iteration=5)
     adv_data = attack.generate(input_np, label_np)
     assert np.any(input_np != adv_data)
+    del input_np, label_np, adv_data
+    gc.collect()

tests/ut/python/adv_robustness/attacks/test_lbfgs.py

@@ -15,9 +15,10 @@
 LBFGS-Attack test.
 """
 import os
-
+import gc
 import numpy as np
 import pytest
+
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 
@@ -71,6 +72,8 @@ def test_lbfgs_attack_ascend():
     LOGGER.debug(TAG, 'target_np is :{}'.format(target_np[0]))
     _ = attack.generate(input_np, target_np)
 
+    del input_np, label_np, target_np
+    gc.collect()
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
@@ -107,3 +110,5 @@ def test_lbfgs_attack_cpu():
     attack = LBFGS(net, is_targeted=True)
     LOGGER.debug(TAG, 'target_np is :{}'.format(target_np[0]))
     _ = attack.generate(input_np, target_np)
+    del input_np, label_np, target_np
+    gc.collect()