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| # Pytorch Multi-Scale Structural Similarity Index (SSIM) | |
| # This code is written by jorge-pessoa (https://github.com/jorge-pessoa/pytorch-msssim) | |
| # MIT licence | |
| import math | |
| from math import exp | |
| import torch | |
| import torch.nn.functional as F | |
| from torch.autograd import Variable | |
| # +++++++++++++++++++++++++++++++++++++ | |
| # SSIM | |
| # ------------------------------------- | |
| 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, size_average=True, full=False): | |
| padd = 0 | |
| mu1 = F.conv2d(img1, window, padding=padd, groups=channel) | |
| mu2 = F.conv2d(img2, window, padding=padd, groups=channel) | |
| mu1_sq = mu1.pow(2) | |
| mu2_sq = mu2.pow(2) | |
| mu1_mu2 = mu1 * mu2 | |
| sigma1_sq = F.conv2d(img1 * img1, window, padding=padd, groups=channel) - mu1_sq | |
| sigma2_sq = F.conv2d(img2 * img2, window, padding=padd, groups=channel) - mu2_sq | |
| sigma12 = F.conv2d(img1 * img2, window, padding=padd, 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)) | |
| v1 = 2.0 * sigma12 + C2 | |
| v2 = sigma1_sq + sigma2_sq + C2 | |
| cs = torch.mean(v1 / v2) | |
| if size_average: | |
| ret = ssim_map.mean() | |
| else: | |
| ret = ssim_map.mean(1).mean(1).mean(1) | |
| if full: | |
| return ret, cs | |
| return ret | |
| 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): | |
| (_, 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, self.size_average) | |
| def ssim(img1, img2, window_size=11, size_average=True, full=False): | |
| (_, channel, height, width) = img1.size() | |
| real_size = min(window_size, height, width) | |
| window = create_window(real_size, channel) | |
| if img1.is_cuda: | |
| window = window.cuda(img1.get_device()) | |
| window = window.type_as(img1) | |
| return _ssim(img1, img2, window, real_size, channel, size_average, full=full) | |
| def msssim(img1, img2, window_size=11, size_average=True): | |
| # TODO: fix NAN results | |
| if img1.size() != img2.size(): | |
| raise RuntimeError('Input images must have the same shape (%s vs. %s).' % | |
| (img1.size(), img2.size())) | |
| if len(img1.size()) != 4: | |
| raise RuntimeError('Input images must have four dimensions, not %d' % | |
| len(img1.size())) | |
| weights = torch.tensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333], dtype=img1.dtype) | |
| if img1.is_cuda: | |
| weights = weights.cuda(img1.get_device()) | |
| levels = weights.size()[0] | |
| mssim = [] | |
| mcs = [] | |
| for _ in range(levels): | |
| sim, cs = ssim(img1, img2, window_size=window_size, size_average=size_average, full=True) | |
| mssim.append(sim) | |
| mcs.append(cs) | |
| img1 = F.avg_pool2d(img1, (2, 2)) | |
| img2 = F.avg_pool2d(img2, (2, 2)) | |
| mssim = torch.stack(mssim) | |
| mcs = torch.stack(mcs) | |
| return (torch.prod(mcs[0:levels - 1] ** weights[0:levels - 1]) * | |
| (mssim[levels - 1] ** weights[levels - 1])) | |
| class MSSSIM(torch.nn.Module): | |
| def __init__(self, window_size=11, size_average=True, channel=3): | |
| super(MSSSIM, self).__init__() | |
| self.window_size = window_size | |
| self.size_average = size_average | |
| self.channel = channel | |
| def forward(self, img1, img2): | |
| # TODO: store window between calls if possible | |
| return msssim(img1, img2, window_size=self.window_size, size_average=self.size_average) | |
| def calc_psnr(sr, hr, scale=0, benchmark=False): | |
| # adapt from EDSR: https://github.com/thstkdgus35/EDSR-PyTorch | |
| diff = (sr - hr).data | |
| if benchmark: | |
| shave = scale | |
| if diff.size(1) > 1: | |
| convert = diff.new(1, 3, 1, 1) | |
| convert[0, 0, 0, 0] = 65.738 | |
| convert[0, 1, 0, 0] = 129.057 | |
| convert[0, 2, 0, 0] = 25.064 | |
| diff.mul_(convert).div_(256) | |
| diff = diff.sum(dim=1, keepdim=True) | |
| else: | |
| shave = scale + 6 | |
| valid = diff[:, :, shave:-shave, shave:-shave] | |
| mse = valid.pow(2).mean() | |
| return -10 * math.log10(mse) | |
| # +++++++++++++++++++++++++++++++++++++ | |
| # PSNR | |
| # ------------------------------------- | |
| from torch import nn | |
| def psnr(predict, target): | |
| with torch.no_grad(): | |
| criteria = nn.MSELoss() | |
| mse = criteria(predict, target) | |
| return -10 * torch.log10(mse) | |