124 lines
3.3 KiB
Python
124 lines
3.3 KiB
Python
# MIT Licence
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# Methods to predict the SSIM, taken from
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# https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py
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from math import exp
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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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def gaussian(window_size, sigma):
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gauss = torch.Tensor(
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[
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exp(-((x - window_size // 2) ** 2) / float(2 * sigma ** 2))
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for x in range(window_size)
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]
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)
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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(
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_2D_window.expand(channel, 1, window_size, window_size).contiguous()
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)
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return window
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def _ssim(
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img1, img2, window, window_size, channel, mask=None, size_average=True
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):
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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 = (
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F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel)
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- mu1_sq
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)
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sigma2_sq = (
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F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel)
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- mu2_sq
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)
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sigma12 = (
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F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel)
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- mu1_mu2
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)
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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)) / (
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(mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)
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)
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if not (mask is None):
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b = mask.size(0)
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ssim_map = ssim_map.mean(dim=1, keepdim=True) * mask
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ssim_map = ssim_map.view(b, -1).sum(dim=1) / mask.view(b, -1).sum(
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dim=1
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).clamp(min=1)
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return ssim_map
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import pdb
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pdb.set_trace
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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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class SSIM(torch.nn.Module):
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def __init__(self, window_size=11, size_average=True):
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super(SSIM, self).__init__()
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self.window_size = window_size
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self.size_average = size_average
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self.channel = 1
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self.window = create_window(window_size, self.channel)
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def forward(self, img1, img2, mask=None):
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(_, channel, _, _) = img1.size()
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if (
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channel == self.channel
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and self.window.data.type() == img1.data.type()
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):
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window = self.window
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else:
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window = create_window(self.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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self.window = window
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self.channel = channel
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return _ssim(
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img1,
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img2,
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window,
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self.window_size,
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channel,
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mask,
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self.size_average,
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)
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def ssim(img1, img2, window_size=11, mask=None, size_average=True):
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(_, channel, _, _) = img1.size()
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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, mask, size_average)
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