forked from mrq/DL-Art-School
80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
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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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import numpy as np
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from math import exp
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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, window_size, channel, size_average=True, raw=False):
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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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elif raw:
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return ssim_map
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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, raw=False):
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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.raw = raw
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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):
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(_, channel, _, _) = img1.size()
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if channel == self.channel and self.window.data.type() == img1.data.type():
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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(img1, img2, window, self.window_size, channel, self.size_average, self.raw)
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def ssim(img1, img2, window_size=11, 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, size_average)
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