text: # encoding: utf-8
  """
  @author:  l1aoxingyu
  @contact: sherlockliao01@gmail.com
  """
  
  import torch
  import torch.nn.functional as F
  from torch import nn
  
  
  class Flatten(nn.Module):
      def forward(self, input):
          return input.view(input.size(0), -1)
  
  
  class GeneralizedMeanPooling(nn.Module):
      r"""Applies a 2D power-average adaptive pooling over an input signal composed of several input planes.
      The function computed is: :math:`f(X) = pow(sum(pow(X, p)), 1/p)`
          - At p = infinity, one gets Max Pooling
          - At p = 1, one gets Average Pooling
      The output is of size H x W, for any input size.
      The number of output features is equal to the number of input planes.
      Args:
          output_size: the target output size of the image of the form H x W.
                       Can be a tuple (H, W) or a single H for a square image H x H
                       H and W can be either a ``int``, or ``None`` which means the size will
                       be the same as that of the input.
      """
  
      def __init__(self, norm, output_size=1, eps=1e-6):
          super(GeneralizedMeanPooling, self).__init__()
          assert norm > 0
          self.p = float(norm)
          self.output_size = output_size
          self.eps = eps
  
      def forward(self, x):
          x = x.clamp(min=self.eps).pow(self.p)
          return torch.nn.functional.adaptive_avg_pool2d(x, self.output_size).pow(1. / self.p)
  
      def __repr__(self):
          return self.__class__.__name__ + '(' \
                 + str(self.p) + ', ' \
                 + 'output_size=' + str(self.output_size) + ')'
  
  
  class GeneralizedMeanPoolingP(GeneralizedMeanPooling):
      """ Same, but norm is trainable
      """
  
      def __init__(self, norm=3, output_size=1, eps=1e-6):
          super(GeneralizedMeanPoolingP, self).__init__(norm, output_size, eps)
          self.p = nn.Parameter(torch.ones(1) * norm)
  
  
  class AdaptiveAvgMaxPool2d(nn.Module):
      def __init__(self, output_size=1):
          super(AdaptiveAvgMaxPool2d, self).__init__()
          self.output_size = output_size
          self.avgpool = FastGlobalAvgPool2d()
  
      def forward(self, x):
          x_avg = self.avgpool(x)
          x_max = F.adaptive_max_pool2d(x, 1)
          x = x_max + x_avg
          return x
  
  
  class FastGlobalAvgPool2d(nn.Module):
      def __init__(self, flatten=False):
          super(FastGlobalAvgPool2d, self).__init__()
          self.flatten = flatten
  
      def forward(self, x):
          if self.flatten:
              in_size = x.size()
              return x.view((in_size[0], in_size[1], -1)).mean(dim=2)
          else:
              return x.view(x.size(0), x.size(1), -1).mean(-1).view(x.size(0), x.size(1), 1, 1)
  