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| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| class SSIM(torch.nn.Module): | |
| """SSIM. Modified from: | |
| https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py | |
| """ | |
| def __init__(self, window_size=11, size_average=True): | |
| super().__init__() | |
| self.window_size = window_size | |
| self.size_average = size_average | |
| self.channel = 1 | |
| self.register_buffer('window', self._create_window(window_size, self.channel)) | |
| def forward(self, img1, img2): | |
| assert len(img1.shape) == 4 | |
| channel = img1.size()[1] | |
| if channel == self.channel and self.window.data.type() == img1.data.type(): | |
| window = self.window | |
| else: | |
| window = self._create_window(self.window_size, channel) | |
| # window = window.to(img1.get_device()) | |
| window = window.type_as(img1) | |
| self.window = window | |
| self.channel = channel | |
| return self._ssim(img1, img2, window, self.window_size, channel, self.size_average) | |
| def _gaussian(self, window_size, sigma): | |
| gauss = torch.Tensor([ | |
| np.exp(-(x - (window_size // 2)) ** 2 / float(2 * sigma ** 2)) for x in range(window_size) | |
| ]) | |
| return gauss / gauss.sum() | |
| def _create_window(self, window_size, channel): | |
| _1D_window = self._gaussian(window_size, 1.5).unsqueeze(1) | |
| _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) | |
| return _2D_window.expand(channel, 1, window_size, window_size).contiguous() | |
| def _ssim(self, 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() | |
| return ssim_map.mean(1).mean(1).mean(1) | |
| def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): | |
| return | |