content: # MIT Licence
  
  # Methods to predict the SSIM, taken from
  # https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py
  
  from math import exp
  
  import torch
  import torch.nn.functional as F
  from torch.autograd import Variable
  
  def gaussian(window_size, sigma):
      gauss = torch.Tensor(
          [
              exp(-((x - window_size // 2) ** 2) / float(2 * sigma ** 2))
              for x in range(window_size)
          ]
      )
      return gauss / gauss.sum()
  
  
  def create_window(window_size, channel):
      _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
      _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
      window = Variable(
          _2D_window.expand(channel, 1, window_size, window_size).contiguous()
      )
      return window
  
  
  def _ssim(
      img1, img2, window, window_size, channel, mask=None, size_average=True
  ):
      mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
      mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
  
      mu1_sq = mu1.pow(2)
      mu2_sq = mu2.pow(2)
      mu1_mu2 = mu1 * mu2
  
      sigma1_sq = (
          F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel)
          - mu1_sq
      )
      sigma2_sq = (
          F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel)
          - mu2_sq
      )
      sigma12 = (
          F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel)
          - mu1_mu2
      )
  
      C1 = (0.01) ** 2
      C2 = (0.03) ** 2
  
      ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / (
          (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)
      )
  
      if not (mask is None):
          b = mask.size(0)
          ssim_map = ssim_map.mean(dim=1, keepdim=True) * mask
          ssim_map = ssim_map.view(b, -1).sum(dim=1) / mask.view(b, -1).sum(
              dim=1
          ).clamp(min=1)
          return ssim_map
  
      import pdb
  
      pdb.set_trace
  
      if size_average:
          return ssim_map.mean()
      else:
          return ssim_map.mean(1).mean(1).mean(1)
  
  
  class SSIM(torch.nn.Module):
      def __init__(self, window_size=11, size_average=True):
          super(SSIM, self).__init__()
          self.window_size = window_size
          self.size_average = size_average
          self.channel = 1
          self.window = create_window(window_size, self.channel)
  
      def forward(self, img1, img2, mask=None):
          (_, channel, _, _) = img1.size()
  
          if (
              channel == self.channel
              and self.window.data.type() == img1.data.type()
          ):
              window = self.window
          else:
              window = create_window(self.window_size, channel)
  
              if img1.is_cuda:
                  window = window.cuda(img1.get_device())
              window = window.type_as(img1)
  
              self.window = window
              self.channel = channel
  
          return _ssim(
              img1,
              img2,
              window,
              self.window_size,
              channel,
              mask,
              self.size_average,
          )
  
  
  def ssim(img1, img2, window_size=11, mask=None, size_average=True):
      (_, channel, _, _) = img1.size()
      window = create_window(window_size, channel)
  
      if img1.is_cuda:
          window = window.cuda(img1.get_device())
      window = window.type_as(img1)
  
      return _ssim(img1, img2, window, window_size, channel, mask, size_average)
  