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)