text: import math
  import torch
  import torch.nn.functional as F
  
  def psnr(gt, img):
      """
      calculate psnr between two images
      :param gt: groundtruth image
      :param img: inference image
      :return: psnr
      """
      mse = torch.mean( (gt - img) ** 2 )
      if mse < 1.0e-10:
          return 100
      PIXEL_MAX = 1.0
      return 20 * torch.log10(PIXEL_MAX / torch.sqrt(mse))
  
  
  def gaussian(window_size, sigma):
      gauss = torch.Tensor([math.exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
      return gauss / gauss.sum()
  
  
  # create gaussian kernel by multiply two gaussian distribution
  # extend to 3 channel
  def create_window(window_size, channel=1):
      _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
      _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
      window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()
      return window
  
  
  def ssim(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None):
      # Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh).
      if val_range is None:
          if torch.max(img1) > 128:
              max_val = 255
          else:
              max_val = 1
  
          if torch.min(img1) < -0.5:
              min_val = -1
          else:
              min_val = 0
          L = max_val - min_val
      else:
          L = val_range
  
      padd = 0
      (_, channel, height, width) = img1.size()
      if window is None:
          real_size = min(window_size, height, width)
          window = create_window(real_size, channel=channel).to(img1.device)
  
      mu1 = F.conv2d(img1, window, padding=padd, groups=channel)
      mu2 = F.conv2d(img2, window, padding=padd, groups=channel)
  
      mu1_sq = mu1.pow(2)
      mu2_sq = mu2.pow(2)
      mu1_mu2 = mu1 * mu2
  
      sigma1_sq = F.conv2d(img1 * img1, window, padding=padd, groups=channel) - mu1_sq
      sigma2_sq = F.conv2d(img2 * img2, window, padding=padd, groups=channel) - mu2_sq
      sigma12 = F.conv2d(img1 * img2, window, padding=padd, groups=channel) - mu1_mu2
  
      C1 = (0.01 * L) ** 2
      C2 = (0.03 * L) ** 2
  
      v1 = 2.0 * sigma12 + C2
      v2 = sigma1_sq + sigma2_sq + C2
      cs = torch.mean(v1 / v2)  # contrast sensitivity
  
      ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2)
  
      if size_average:
          ret = ssim_map.mean()
      else:
          ret = ssim_map.mean(1).mean(1).mean(1)
  
      if full:
          return ret, cs
      return ret
  
  if __name__ == '__main__':
      device = 'cuda'
      img = torch.ones([5, 3, 64, 64]).to(device)*0.5
      img_ = torch.ones([5, 3, 64, 64]).to(device)*0.5 +torch.randn([5, 3, 64, 64]).to(device)*0.005
      PSNR = psnr(img,img_)
      SSIM = ssim(img, img_)
      print("finished")
  