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| import torch | |
| import torch.nn.functional as F | |
| from torch.autograd import Variable | |
| import numpy as np | |
| from math import exp | |
| """ | |
| # ============================================ | |
| # SSIM loss | |
| # https://github.com/Po-Hsun-Su/pytorch-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): | |
| 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 size_average: | |
| return ssim_map.mean() | |
| else: | |
| return ssim_map.mean(1).mean(1).mean(1) | |
| class SSIMLoss(torch.nn.Module): | |
| def __init__(self, window_size=11, size_average=True): | |
| super(SSIMLoss, 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): | |
| (_, 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, size_average) | |
| if __name__ == '__main__': | |
| import cv2 | |
| from torch import optim | |
| from skimage import io | |
| npImg1 = cv2.imread("einstein.png") | |
| img1 = torch.from_numpy(np.rollaxis(npImg1, 2)).float().unsqueeze(0)/255.0 | |
| img2 = torch.rand(img1.size()) | |
| if torch.cuda.is_available(): | |
| img1 = img1.cuda() | |
| img2 = img2.cuda() | |
| img1 = Variable(img1, requires_grad=False) | |
| img2 = Variable(img2, requires_grad=True) | |
| ssim_value = ssim(img1, img2).item() | |
| print("Initial ssim:", ssim_value) | |
| ssim_loss = SSIMLoss() | |
| optimizer = optim.Adam([img2], lr=0.01) | |
| while ssim_value < 0.99: | |
| optimizer.zero_grad() | |
| ssim_out = -ssim_loss(img1, img2) | |
| ssim_value = -ssim_out.item() | |
| print('{:<4.4f}'.format(ssim_value)) | |
| ssim_out.backward() | |
| optimizer.step() | |
| img = np.transpose(img2.detach().cpu().squeeze().float().numpy(), (1,2,0)) | |
| io.imshow(np.uint8(np.clip(img*255, 0, 255))) | |