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| # MIT Licence | |
| # Methods to predict the SSIM, taken from | |
| # https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py | |
| from math import exp | |
| import torch | |
| import torch.nn.functional as F | |
| from torch.autograd import Variable | |
| 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): | |
| _1D_window = gaussian(window_size, 1.5).unsqueeze(1) | |
| _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) | |
| window = Variable( | |
| _2D_window.expand(channel, 1, window_size, window_size).contiguous() | |
| ) | |
| return window | |
| def _ssim( | |
| img1, img2, window, window_size, channel, mask=None, size_average=True | |
| ): | |
| mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) | |
| mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) | |
| mu1_sq = mu1.pow(2) | |
| mu2_sq = mu2.pow(2) | |
| mu1_mu2 = mu1 * mu2 | |
| sigma1_sq = ( | |
| F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) | |
| - mu1_sq | |
| ) | |
| sigma2_sq = ( | |
| F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) | |
| - mu2_sq | |
| ) | |
| sigma12 = ( | |
| F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) | |
| - mu1_mu2 | |
| ) | |
| C1 = (0.01) ** 2 | |
| C2 = (0.03) ** 2 | |
| ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ( | |
| (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2) | |
| ) | |
| if not (mask is None): | |
| b = mask.size(0) | |
| ssim_map = ssim_map.mean(dim=1, keepdim=True) * mask | |
| ssim_map = ssim_map.view(b, -1).sum(dim=1) / mask.view(b, -1).sum( | |
| dim=1 | |
| ).clamp(min=1) | |
| return ssim_map | |
| import pdb | |
| pdb.set_trace | |
| if size_average: | |
| return ssim_map.mean() | |
| else: | |
| return ssim_map.mean(1).mean(1).mean(1) | |
| class SSIM(torch.nn.Module): | |
| def __init__(self, window_size=11, size_average=True): | |
| super(SSIM, self).__init__() | |
| self.window_size = window_size | |
| self.size_average = size_average | |
| self.channel = 1 | |
| self.window = create_window(window_size, self.channel) | |
| def forward(self, img1, img2, mask=None): | |
| (_, channel, _, _) = img1.size() | |
| if ( | |
| channel == self.channel | |
| and self.window.data.type() == img1.data.type() | |
| ): | |
| window = self.window | |
| else: | |
| window = create_window(self.window_size, channel) | |
| if img1.is_cuda: | |
| window = window.cuda(img1.get_device()) | |
| window = window.type_as(img1) | |
| self.window = window | |
| self.channel = channel | |
| return _ssim( | |
| img1, | |
| img2, | |
| window, | |
| self.window_size, | |
| channel, | |
| mask, | |
| self.size_average, | |
| ) | |
| def ssim(img1, img2, window_size=11, mask=None, size_average=True): | |
| (_, channel, _, _) = img1.size() | |
| window = create_window(window_size, channel) | |
| if img1.is_cuda: | |
| window = window.cuda(img1.get_device()) | |
| window = window.type_as(img1) | |
| return _ssim(img1, img2, window, window_size, channel, mask, size_average) | |