code: import torch
  import torch.nn.functional as F
  from torch.autograd import Variable
  import numpy as np
  from math import exp
  
  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, 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 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):
          (_, 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, self.size_average)
  
  def ssim(img1, img2, window_size = 11, 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, size_average)
apis: torch.nn.functional.conv2d