text: # Copyright 2019 Huawei Technologies Co., Ltd
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  # http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """
  Gradient-Attack test.
  """
  import numpy as np
  import pytest
  
  import mindspore.nn as nn
  from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits
  import mindspore.context as context
  from mindspore.ops.composite import GradOperation
  
  from mindarmour.adv_robustness.attacks import FastGradientMethod
  from mindarmour.adv_robustness.attacks import FastGradientSignMethod
  from mindarmour.adv_robustness.attacks import LeastLikelyClassMethod
  from mindarmour.adv_robustness.attacks import RandomFastGradientMethod
  from mindarmour.adv_robustness.attacks import RandomFastGradientSignMethod
  from mindarmour.adv_robustness.attacks import RandomLeastLikelyClassMethod
  
  
  # for user
  class Net(Cell):
      """
      Construct the network of target model.
  
      Examples:
          >>> net = Net()
      """
  
      def __init__(self):
          """
          Introduce the layers used for network construction.
          """
          super(Net, self).__init__()
          self._relu = nn.ReLU()
  
      def construct(self, inputs):
          """
          Construct network.
  
          Args:
              inputs (Tensor): Input data.
          """
          out = self._relu(inputs)
          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._relu = nn.ReLU()
  
      def construct(self, inputs1, inputs2):
          out1 = self._relu(inputs1)
          out2 = self._relu(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
  @pytest.mark.env_card
  @pytest.mark.component_mindarmour
  def test_fast_gradient_method():
      """
      Fast gradient method unit test.
      """
      context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
      input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32)
      label = np.asarray([2], np.int32)
      label = np.eye(3)[label].astype(np.float32)
  
      attack = FastGradientMethod(Net(), loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False))
      ms_adv_x = attack.generate(input_np, label)
  
      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_x86_gpu_training
  @pytest.mark.env_card
  @pytest.mark.component_mindarmour
  def test_fast_gradient_method_gpu():
      """
      Fast gradient method unit test.
      """
      context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
      input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32)
      label = np.asarray([2], np.int32)
      label = np.eye(3)[label].astype(np.float32)
  
      attack = FastGradientMethod(Net(), loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False))
      ms_adv_x = attack.generate(input_np, label)
  
      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_x86_cpu
  @pytest.mark.env_card
  @pytest.mark.component_mindarmour
  def test_fast_gradient_method_cpu():
      """
      Fast gradient method unit test.
      """
      context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
      input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32)
      label = np.asarray([2], np.int32)
  
      loss = SoftmaxCrossEntropyWithLogits(sparse=True)
      attack = FastGradientMethod(Net(), loss_fn=loss)
      ms_adv_x = attack.generate(input_np, label)
  
      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_random_fast_gradient_method():
      """
      Random fast gradient method unit test.
      """
      context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
      input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32)
      label = np.asarray([2], np.int32)
      label = np.eye(3)[label].astype(np.float32)
  
      attack = RandomFastGradientMethod(Net(), loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False))
      ms_adv_x = attack.generate(input_np, label)
  
      assert np.any(ms_adv_x != input_np), 'Random 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_fast_gradient_sign_method():
      """
      Fast gradient sign method unit test.
      """
      context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
      input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32)
      label = np.asarray([2], np.int32)
      label = np.eye(3)[label].astype(np.float32)
  
      attack = FastGradientSignMethod(Net(), loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False))
      ms_adv_x = attack.generate(input_np, label)
  
      assert np.any(ms_adv_x != input_np), 'Fast gradient sign 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_random_fast_gradient_sign_method():
      """
      Random fast gradient sign method unit test.
      """
      context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
      input_np = np.random.random((1, 28)).astype(np.float32)
      label = np.asarray([2], np.int32)
      label = np.eye(28)[label].astype(np.float32)
  
      attack = RandomFastGradientSignMethod(Net(), loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False))
      ms_adv_x = attack.generate(input_np, label)
  
      assert np.any(ms_adv_x != input_np), 'Random fast gradient sign 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_least_likely_class_method():
      """
      Least likely class method unit test.
      """
      context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
      input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32)
      label = np.asarray([2], np.int32)
      label = np.eye(3)[label].astype(np.float32)
  
      attack = LeastLikelyClassMethod(Net(), loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False))
      ms_adv_x = attack.generate(input_np, label)
  
      assert np.any(ms_adv_x != input_np), 'Least likely class 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_random_least_likely_class_method():
      """
      Random least likely class method unit test.
      """
      context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
      input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32)
      label = np.asarray([2], np.int32)
      label = np.eye(3)[label].astype(np.float32)
  
      attack = RandomLeastLikelyClassMethod(Net(), eps=0.1, alpha=0.01, \
          loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False))
      ms_adv_x = attack.generate(input_np, label)
  
      assert np.any(ms_adv_x != input_np), 'Random least likely class 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_fast_gradient_method_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.
      """
      context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
      with pytest.raises(ValueError) as e:
          assert RandomLeastLikelyClassMethod(Net(), eps=0.05, alpha=0.21, \
              loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False))
      assert str(e.value) == 'eps must be larger than alpha!'
  