| 47 | return _ssim(img1, img2, window, window_size, channel, size_average) |
| 48 | |
| 49 | def _ssim(img1, img2, window, window_size, channel, size_average=True): |
| 50 | mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) |
| 51 | mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) |
| 52 | |
| 53 | mu1_sq = mu1.pow(2) |
| 54 | mu2_sq = mu2.pow(2) |
| 55 | mu1_mu2 = mu1 * mu2 |
| 56 | |
| 57 | sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq |
| 58 | sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq |
| 59 | sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2 |
| 60 | |
| 61 | C1 = 0.01 ** 2 |
| 62 | C2 = 0.03 ** 2 |
| 63 | |
| 64 | ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)) |
| 65 | |
| 66 | if size_average: |
| 67 | return ssim_map.mean() |
| 68 | else: |
| 69 | return ssim_map.mean(1).mean(1).mean(1) |
| 70 | |
| 71 | |
| 72 | loss_fn_vgg = None |