torch::Tensor img1 = gt.permute({2, 0, 1}).index({None, "..."});

torch::Tensor img2 = rendered.permute({2, 0, 1}).index({None, "..."});

if (img1.device() != window.device()){

window = window.to(img1.device());

}

torch::Tensor mu1 = torch::nn::functional::conv2d(img1, window, torch::nn::functional::Conv2dFuncOptions().padding(windowSize / 2).groups(channel));

torch::Tensor mu2 = torch::nn::functional::conv2d(img2, window, torch::nn::functional::Conv2dFuncOptions().padding(windowSize / 2).groups(channel));

torch::Tensor mu1Sq = mu1.pow(2);

torch::Tensor mu2Sq = mu2.pow(2);

torch::Tensor mu1mu2 = mu1 * mu2;

torch::Tensor sigma1Sq = torch::nn::functional::conv2d(img1 * img1, window, torch::nn::functional::Conv2dFuncOptions().padding(windowSize / 2).groups(channel)) - mu1Sq;

torch::Tensor sigma2Sq = torch::nn::functional::conv2d(img2 * img2, window, torch::nn::functional::Conv2dFuncOptions().padding(windowSize / 2).groups(channel)) - mu2Sq;

torch::Tensor sigma12 = torch::nn::functional::conv2d(img1 * img2, window, torch::nn::functional::Conv2dFuncOptions().padding(windowSize / 2).groups(channel)) - mu1mu2;

const float C1 = 0.01 * 0.01;

const float C2 = 0.03 * 0.03;

torch::Tensor ssimMap = ((2.0f * mu1mu2 + C1) * (2.0f * sigma12 + C2)) / ((mu1Sq + mu2Sq + C1) * (sigma1Sq + sigma2Sq + C2));

return ssimMap.mean();

torch::Tensor _1DWindow = gaussian(1.5f).unsqueeze(1);

torch::Tensor _2DWindow = _1DWindow.mm(_1DWindow.t()).unsqueeze(0).unsqueeze(0);

return _2DWindow.expand({channel, 1, windowSize, windowSize}).contiguous();

torch::Tensor gauss = torch::zeros(windowSize);

for (int i = 0; i < windowSize; i++) {

gauss[i] = std::exp(-(std::pow(std::floor(static_cast<float>(i - windowSize) / 2.0f), 2.0f)) / (2.0f * sigma * sigma));

}

return gauss / gauss.sum();

}