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 pytest
  from mindspore import Tensor
  from mindspore.ops import operations as P
  import mindspore.nn as nn
  import numpy as np
  import mindspore.context as context
  from mindspore.common.initializer import initializer
  from mindspore.common.parameter import Parameter
  
  context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
  
  class Net5(nn.Cell):
      def __init__(self):
          super(Net5, self).__init__()
          out_channel = 4
          kernel_size = 1
          self.conv_input = P.Conv2DBackpropInput(out_channel,
                                                    kernel_size,
                                                    pad_mode="valid",
                                                    pad=0,
                                                    mode=1,
                                                    stride=1,
                                                    dilation=1,
                                                    group=1)
          self.w = Parameter(initializer(Tensor(np.array([[[[1, 0, -1], [1, 0, -1], [1, 0, -1]]]]).astype(np.float32)), [1, 1, 3, 3]), name='w')
          self.x = Parameter(initializer(Tensor(np.array([[[
              [3, 0, 1, 2, 7, 4],
              [1, 5, 8, 9, 3, 1],
              [2, 7, 2, 5, 1, 3],
              [0, 1, 3, 1, 7, 8],
              [4, 2, 1, 6, 2, 8],
              [2, 4, 5, 2, 3, 9]]]]).astype(np.float32)), [1,1,6,6]), name='x')
          self.out = Parameter(initializer(Tensor(np.array([[[
              [ -5, -4,  0,  8],
              [-10, -2,  2,  3],
              [  0, -2, -4, -7],
              [ -3, -2, -3, -16]]]]).astype(np.float32)),[1,1,4,4]), name='y')
          self.get_shape = P.Shape()
  
      def construct(self):
          return self.conv_input(self.out, self.w, self.get_shape(self.x))
  
  
  @pytest.mark.level0
  @pytest.mark.platform_x86_cpu
  @pytest.mark.env_onecard
  def test_conv2d_backprop_input():
      conv2d_input = Net5()
      output = conv2d_input()
      print("================================")
      """
      expect output:
      [[[[ -5,  -4,   5,  12,   0,  -8]
         [-15,  -6,  17,  17,  -2, -11]
         [-15,  -8,  13,  12,   2,  -4]
         [-13,  -6,   8, -14,   5,  20]
         [ -3,  -4,  -4, -19,   7,  23]
         [ -3,  -2,   0, -14,   3,  16]]]]
      """
      expect = np.array([[[[ -5,  -4,   5,  12,   0,  -8],
         [-15,  -6,  17,  17,  -2, -11],
         [-15,  -8,  13,  12,   2,  -4],
         [-13,  -6,   8, -14,   5,  20],
         [ -3,  -4,  -4, -19,   7,  23],
         [ -3,  -2,   0, -14,   3,  16]]]]).astype(np.float32)
      print(output)
      assert (output.asnumpy() == expect).all()
  