content: import torch
  import torch.nn.functional as F
  import numpy as np
  from math import exp, sqrt
  
  
  class NCC(torch.nn.Module):
      def __init__(self, h_patch_size, mode='rgb'):
          super(NCC, self).__init__()
          self.window_size = 2 * h_patch_size + 1
          self.mode = mode  # 'rgb' or 'gray'
          self.channel = 3
          self.register_buffer("window", create_window(self.window_size, self.channel))
  
      def forward(self, img_pred, img_gt):
          """
          :param img_pred: [Npx, nviews, npatch, c]
          :param img_gt: [Npx, npatch, c]
          :return:
          """
          ntotpx, nviews, npatch, channels = img_pred.shape
  
          patch_size = int(sqrt(npatch))
          patch_img_pred = img_pred.reshape(ntotpx, nviews, patch_size, patch_size, channels).permute(0, 1, 4, 2,
                                                                                                      3).contiguous()
          patch_img_gt = img_gt.reshape(ntotpx, patch_size, patch_size, channels).permute(0, 3, 1, 2)
  
          return _ncc(patch_img_pred, patch_img_gt, self.window, self.channel)
  
  
  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, std=1.5):
      _1D_window = gaussian(window_size, std).unsqueeze(1)
      _2D_window = _1D_window.mm(_1D_window.t()).unsqueeze(0).unsqueeze(0)
      window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()
      return window
  
  
  def _ncc(pred, gt, window, channel):
      ntotpx, nviews, nc, h, w = pred.shape
      flat_pred = pred.view(-1, nc, h, w)
      mu1 = F.conv2d(flat_pred, window, padding=0, groups=channel).view(ntotpx, nviews, nc)
      mu2 = F.conv2d(gt, window, padding=0, groups=channel).view(ntotpx, nc)
  
      mu1_sq = mu1.pow(2)
      mu2_sq = mu2.pow(2).unsqueeze(1)  # (ntotpx, 1, nc)
  
      sigma1_sq = F.conv2d(flat_pred * flat_pred, window, padding=0, groups=channel).view(ntotpx, nviews, nc) - mu1_sq
      sigma2_sq = F.conv2d(gt * gt, window, padding=0, groups=channel).view(ntotpx, 1, 3) - mu2_sq
  
      sigma1 = torch.sqrt(sigma1_sq + 1e-4)
      sigma2 = torch.sqrt(sigma2_sq + 1e-4)
  
      pred_norm = (pred - mu1[:, :, :, None, None]) / (sigma1[:, :, :, None, None] + 1e-8)  # [ntotpx, nviews, nc, h, w]
      gt_norm = (gt[:, None, :, :, :] - mu2[:, None, :, None, None]) / (
              sigma2[:, :, :, None, None] + 1e-8)  # ntotpx, nc, h, w
  
      ncc = F.conv2d((pred_norm * gt_norm).view(-1, nc, h, w), window, padding=0, groups=channel).view(
          ntotpx, nviews, nc)
  
      return torch.mean(ncc, dim=2)
  