text: # Copyright 2020 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 pytest
  import numpy as np
  
  import mindspore
  import mindspore.nn as nn
  import mindspore.context as context
  
  from mindspore import Tensor
  from mindspore.ops.composite import GradOperation
  
  @pytest.mark.level0
  @pytest.mark.platform_x86_gpu_training
  @pytest.mark.env_onecard
  def test_mirror_pad():
      context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
  
      test1_arr_in = [[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]]
      test_1_paddings = ((0, 0), (0, 0), (1, 1), (2, 2))
      test1_arr_exp = [[[[6, 5, 4, 5, 6, 5, 4], [3, 2, 1, 2, 3, 2, 1], [6, 5, 4, 5, 6, 5, 4],
                         [9, 8, 7, 8, 9, 8, 7], [6, 5, 4, 5, 6, 5, 4]]]]
  
      test2_arr_in = [[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]]
      test_2_paddings = ((0, 0), (0, 0), (1, 1), (2, 2))
      test2_arr_exp = [[[[2, 1, 1, 2, 3, 3, 2], [2, 1, 1, 2, 3, 3, 2], [5, 4, 4, 5, 6, 6, 5],
                         [8, 7, 7, 8, 9, 9, 8], [8, 7, 7, 8, 9, 9, 8]]]]
  
      reflectOp = nn.Pad(mode='REFLECT', paddings=test_1_paddings)
      symmOp = nn.Pad(mode='SYMMETRIC', paddings=test_2_paddings)
  
      x_test_1 = Tensor(np.array(test1_arr_in), dtype=mindspore.float32)
      x_test_2 = Tensor(np.array(test2_arr_in), dtype=mindspore.float32)
  
      y_test_1 = reflectOp(x_test_1).asnumpy()
      y_test_2 = symmOp(x_test_2).asnumpy()
  
      print(np.array(test1_arr_in))
      print(y_test_1)
  
      np.testing.assert_equal(np.array(test1_arr_exp), y_test_1)
      np.testing.assert_equal(np.array(test2_arr_exp), y_test_2)
  
  
  class Grad(nn.Cell):
      def __init__(self, network):
          super(Grad, self).__init__()
          self.grad = GradOperation(get_all=True, sens_param=True)
          self.network = network
      def construct(self, input_, output_grad):
          return self.grad(self.network)(input_, output_grad)
  
  class Net(nn.Cell):
      def __init__(self):
          super(Net, self).__init__()
          self.pad = nn.Pad(mode="REFLECT", paddings=((0, 0), (0, 0), (1, 0), (0, 2)))
      def construct(self, x):
          return self.pad(x)
  
  
  @pytest.mark.level0
  @pytest.mark.platform_x86_gpu_training
  @pytest.mark.env_onecard
  def test_mirror_pad_backprop():
      context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
      test_arr_in = [[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]] # size -> 3*3
      test_arr_in = Tensor(test_arr_in, dtype=mindspore.float32)
      dy = (np.ones((1, 1, 4, 5)) * 0.1).astype(np.float32)
      expected_dx = np.array([[[[0.2, 0.2, 0.1],
                                [0.4, 0.4, 0.2],
                                [0.2, 0.2, 0.1]]]])
      net = Grad(Net())
      dx = net(test_arr_in, Tensor(dy))
      dx = dx[0].asnumpy()
      np.testing.assert_array_almost_equal(dx, expected_dx)
  