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import torch |
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import torch.nn.functional as F |
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from torch.autograd import Variable |
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from math import exp |
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def l1_loss(network_output, gt): |
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return torch.abs((network_output - gt)).mean() |
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def l2_loss(network_output, gt): |
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return ((network_output - gt) ** 2).mean() |
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def gaussian(window_size, sigma): |
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gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)]) |
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return gauss / gauss.sum() |
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def create_window(window_size, channel): |
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_1D_window = gaussian(window_size, 1.5).unsqueeze(1) |
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_2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) |
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window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous()) |
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return window |
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def ssim(img1, img2, window_size=11, size_average=True): |
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channel = img1.size(-3) |
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window = create_window(window_size, channel) |
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if img1.is_cuda: |
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window = window.cuda(img1.get_device()) |
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window = window.type_as(img1) |
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return _ssim(img1, img2, window, window_size, channel, size_average) |
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def _ssim(img1, img2, window, window_size, channel, size_average=True): |
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mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) |
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mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) |
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mu1_sq = mu1.pow(2) |
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mu2_sq = mu2.pow(2) |
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mu1_mu2 = mu1 * mu2 |
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sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq |
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sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq |
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sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2 |
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C1 = 0.01 ** 2 |
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C2 = 0.03 ** 2 |
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ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)) |
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if size_average: |
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return ssim_map.mean() |
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else: |
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return ssim_map.mean(1).mean(1).mean(1) |
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