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| import torch | |
| import torch.nn.functional as F | |
| from math import exp | |
| import numpy as np | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| 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=1): | |
| _1D_window = gaussian(window_size, 1.5).unsqueeze(1) | |
| _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0).to(device) | |
| window = _2D_window.expand(channel, 1, window_size, window_size).contiguous() | |
| return window | |
| def create_window_3d(window_size, channel=1): | |
| _1D_window = gaussian(window_size, 1.5).unsqueeze(1) | |
| _2D_window = _1D_window.mm(_1D_window.t()) | |
| _3D_window = _2D_window.unsqueeze(2) @ (_1D_window.t()) | |
| window = _3D_window.expand(1, channel, window_size, window_size, window_size).contiguous().to(device) | |
| return window | |
| def ssim(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None): | |
| # Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh). | |
| if val_range is None: | |
| if torch.max(img1) > 128: | |
| max_val = 255 | |
| else: | |
| max_val = 1 | |
| if torch.min(img1) < -0.5: | |
| min_val = -1 | |
| else: | |
| min_val = 0 | |
| L = max_val - min_val | |
| else: | |
| L = val_range | |
| padd = 0 | |
| (_, channel, height, width) = img1.size() | |
| if window is None: | |
| real_size = min(window_size, height, width) | |
| window = create_window(real_size, channel=channel).to(img1.device) | |
| # mu1 = F.conv2d(img1, window, padding=padd, groups=channel) | |
| # mu2 = F.conv2d(img2, window, padding=padd, groups=channel) | |
| mu1 = F.conv2d(F.pad(img1, (5, 5, 5, 5), mode="replicate"), window, padding=padd, groups=channel) | |
| mu2 = F.conv2d(F.pad(img2, (5, 5, 5, 5), mode="replicate"), window, padding=padd, groups=channel) | |
| mu1_sq = mu1.pow(2) | |
| mu2_sq = mu2.pow(2) | |
| mu1_mu2 = mu1 * mu2 | |
| sigma1_sq = F.conv2d(F.pad(img1 * img1, (5, 5, 5, 5), "replicate"), window, padding=padd, groups=channel) - mu1_sq | |
| sigma2_sq = F.conv2d(F.pad(img2 * img2, (5, 5, 5, 5), "replicate"), window, padding=padd, groups=channel) - mu2_sq | |
| sigma12 = F.conv2d(F.pad(img1 * img2, (5, 5, 5, 5), "replicate"), window, padding=padd, groups=channel) - mu1_mu2 | |
| C1 = (0.01 * L) ** 2 | |
| C2 = (0.03 * L) ** 2 | |
| v1 = 2.0 * sigma12 + C2 | |
| v2 = sigma1_sq + sigma2_sq + C2 | |
| cs = torch.mean(v1 / v2) # contrast sensitivity | |
| ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * 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 | |
| def ssim_matlab(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None): | |
| # Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh). | |
| if val_range is None: | |
| if torch.max(img1) > 128: | |
| max_val = 255 | |
| else: | |
| max_val = 1 | |
| if torch.min(img1) < -0.5: | |
| min_val = -1 | |
| else: | |
| min_val = 0 | |
| L = max_val - min_val | |
| else: | |
| L = val_range | |
| padd = 0 | |
| (_, _, height, width) = img1.size() | |
| if window is None: | |
| real_size = min(window_size, height, width) | |
| window = create_window_3d(real_size, channel=1).to(img1.device, dtype=img1.dtype) | |
| # Channel is set to 1 since we consider color images as volumetric images | |
| img1 = img1.unsqueeze(1) | |
| img2 = img2.unsqueeze(1) | |
| mu1 = F.conv3d(F.pad(img1, (5, 5, 5, 5, 5, 5), mode="replicate"), window, padding=padd, groups=1) | |
| mu2 = F.conv3d(F.pad(img2, (5, 5, 5, 5, 5, 5), mode="replicate"), window, padding=padd, groups=1) | |
| mu1_sq = mu1.pow(2) | |
| mu2_sq = mu2.pow(2) | |
| mu1_mu2 = mu1 * mu2 | |
| sigma1_sq = F.conv3d(F.pad(img1 * img1, (5, 5, 5, 5, 5, 5), "replicate"), window, padding=padd, groups=1) - mu1_sq | |
| sigma2_sq = F.conv3d(F.pad(img2 * img2, (5, 5, 5, 5, 5, 5), "replicate"), window, padding=padd, groups=1) - mu2_sq | |
| sigma12 = F.conv3d(F.pad(img1 * img2, (5, 5, 5, 5, 5, 5), "replicate"), window, padding=padd, groups=1) - mu1_mu2 | |
| C1 = (0.01 * L) ** 2 | |
| C2 = (0.03 * L) ** 2 | |
| v1 = 2.0 * sigma12 + C2 | |
| v2 = sigma1_sq + sigma2_sq + C2 | |
| cs = torch.mean(v1 / v2) # contrast sensitivity | |
| ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * 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 | |
| def msssim(img1, img2, window_size=11, size_average=True, val_range=None, normalize=False): | |
| device = img1.device | |
| weights = torch.FloatTensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333]).to(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, val_range=val_range) | |
| 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) | |
| # Normalize (to avoid NaNs during training unstable models, not compliant with original definition) | |
| if normalize: | |
| mssim = (mssim + 1) / 2 | |
| mcs = (mcs + 1) / 2 | |
| pow1 = mcs**weights | |
| pow2 = mssim**weights | |
| # From Matlab implementation https://ece.uwaterloo.ca/~z70wang/research/iwssim/ | |
| output = torch.prod(pow1[:-1] * pow2[-1]) | |
| return output | |
| # Classes to re-use window | |
| class SSIM(torch.nn.Module): | |
| def __init__(self, window_size=11, size_average=True, val_range=None): | |
| super(SSIM, self).__init__() | |
| self.window_size = window_size | |
| self.size_average = size_average | |
| self.val_range = val_range | |
| # Assume 3 channel for SSIM | |
| self.channel = 3 | |
| self.window = create_window(window_size, channel=self.channel) | |
| def forward(self, img1, img2): | |
| (_, channel, _, _) = img1.size() | |
| if channel == self.channel and self.window.dtype == img1.dtype: | |
| window = self.window | |
| else: | |
| window = create_window(self.window_size, channel).to(img1.device).type(img1.dtype) | |
| self.window = window | |
| self.channel = channel | |
| _ssim = ssim(img1, img2, window=window, window_size=self.window_size, size_average=self.size_average) | |
| dssim = (1 - _ssim) / 2 | |
| return dssim | |
| 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): | |
| return msssim(img1, img2, window_size=self.window_size, size_average=self.size_average) | |