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.
  # ============================================================================
  
  import numpy as np
  import pytest
  
  import mindspore.context as context
  import mindspore.nn as nn
  from mindspore import Tensor
  from mindspore.common.initializer import initializer
  from mindspore.common.parameter import Parameter
  from mindspore.ops import operations as P
  from mindspore.ops.operations import _grad_ops as G
  
  context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
  
  
  class NetReluGrad(nn.Cell):
      def __init__(self):
          super(NetReluGrad, self).__init__()
          self.rekuGrad = G.ReluGrad()
          self.x = Parameter(initializer(Tensor(np.array([[[[-1, 1, 1],
                                                            [1, -1, 1],
                                                            [1, 1, -1]]]]).astype(np.float32)), [1, 1, 3, 3]), name='x')
          self.dy = Parameter(initializer(Tensor(np.array([[[[1, 0, 1],
                                                             [0, 1, 0],
                                                             [1, 1, 1]]]]).astype(np.float32)), [1, 1, 3, 3]), name='dy')
  
      def construct(self):
          return self.rekuGrad(self.dy, self.x)
  
  
  @pytest.mark.level0
  @pytest.mark.platform_x86_cpu
  @pytest.mark.env_onecard
  def test_relu_grad():
      relu_grad = NetReluGrad()
      output = relu_grad()
      expect = np.array([[[[0, 0, 1, ], [0, 0, 0, ], [1, 1, 0.]]]]).astype(np.float32)
      error = np.ones(shape=[3, 3]) * 1.0e-6
      diff = output.asnumpy() - expect
      assert np.all(diff < error)
  