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 numpy as np
  import pytest
  
  import mindspore.context as context
  import mindspore.nn as nn
  from mindspore import Tensor
  from mindspore.common.api import ms_function
  from mindspore.ops.operations import _quant_ops as Q
  
  context.set_context(device_target='GPU')
  
  
  class Net(nn.Cell):
      def __init__(self):
          super(Net, self).__init__()
          self.op_w = Q.CorrectionMulGrad()
  
      @ms_function
      def construct(self, dy, x, batch_std, running_std):
          dx, d_batch_std = self.op_w(dy, x, batch_std, running_std)
          return dx, d_batch_std
  
  
  @pytest.mark.level0
  @pytest.mark.platform_x86_gpu_training
  @pytest.mark.env_onecard
  def test_correction_mul_grad():
      net = Net()
      co, ci, h, w = 64, 1, 32, 32
      dout = np.random.uniform(-0.1, 0.1, size=[co, ci, h, w]).astype('float32')
      x = np.random.uniform(1, 1, size=[co, ci, h, w]).astype('float32')
      batch_std = np.random.uniform(1, 10, size=[co]).astype('float32')
      running_std = np.random.uniform(1, 10, size=[co]).astype('float32')
      output = net(Tensor(dout), Tensor(x), Tensor(batch_std), Tensor(running_std))
      expect = [0, 0]
      expect[0] = (dout * np.reshape(batch_std / running_std, (co, 1, 1, 1)))
      expect[1] = (np.sum(dout * x, (1, 2, 3)) / running_std)
      for i, _ in enumerate(output):
          assert np.allclose(output[i].asnumpy(), expect[i], rtol=1.e-5, atol=1.e-5)
  