| 51 | return _ssim(img1, img2, window, window_size, channel, size_average) |
| 52 | |
| 53 | def _ssim(img1, img2, window, window_size, channel, size_average=True): |
| 54 | mu1 = func.conv2d(img1, window, padding=window_size // 2, groups=channel) |
| 55 | mu2 = func.conv2d(img2, window, padding=window_size // 2, groups=channel) |
| 56 | |
| 57 | mu1_sq = mu1.pow(2) |
| 58 | mu2_sq = mu2.pow(2) |
| 59 | mu1_mu2 = mu1 * mu2 |
| 60 | |
| 61 | sigma1_sq = func.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq |
| 62 | sigma2_sq = func.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq |
| 63 | sigma12 = func.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2 |
| 64 | |
| 65 | c1 = 0.01 ** 2 |
| 66 | c2 = 0.03 ** 2 |
| 67 | |
| 68 | ssim_map = ((2 * mu1_mu2 + c1) * (2 * sigma12 + c2)) / ((mu1_sq + mu2_sq + c1) * (sigma1_sq + sigma2_sq + c2)) |
| 69 | |
| 70 | if size_average: |
| 71 | return ssim_map.mean() |
| 72 | else: |
| 73 | return ssim_map.mean(1).mean(1).mean(1) |
| 74 | |
| 75 | def update_params_and_optimizer(new_params, params, optimizer): |
| 76 | for k, v in new_params.items(): |