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  # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
  
  # 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 paddle
  
  # set device
  paddle.set_device('gpu' if paddle.is_compiled_with_cuda() else 'cpu')
  
  
  def miniBatchStdDev(x, subGroupSize=4):
      r"""
      Add a minibatch standard deviation channel to the current layer.
      In other words:
          1) Compute the standard deviation of the feature map over the minibatch
          2) Get the mean, over all pixels and all channels of thsi ValueError
          3) expand the layer and cocatenate it with the input
  
      Args:
  
          - x (tensor): previous layer
          - subGroupSize (int): size of the mini-batches on which the standard deviation
          should be computed
      """
      size = x.shape
      subGroupSize = min(size[0], subGroupSize)
      if size[0] % subGroupSize != 0:
          subGroupSize = size[0]
      G = int(size[0] / subGroupSize)
      if subGroupSize > 1:
          y = x.reshape((-1, subGroupSize, size[1], size[2], size[3]))
          y = paddle.var(y, 1)
          y = paddle.sqrt(y + 1e-8)
          y = y.reshape((G, -1))
          y = paddle.mean(y, 1).reshape((G, 1))
          y = y.expand((G, size[2]*size[3])).reshape((G, 1, 1, size[2], size[3]))
          y = y.expand((G, subGroupSize, -1, -1, -1))
          y = y.reshape((-1, 1, size[2], size[3]))
      else:
          y = paddle.zeros((x.shape[0], 1, x.shape[2], x.shape[3]))
  
      return paddle.concat([x, y], axis=1)
  