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