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import numpy as np |
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import torch |
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import torch.nn.functional as F |
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class SSIM(torch.nn.Module): |
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"""SSIM. Modified from: |
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https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py |
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""" |
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def __init__(self, window_size=11, size_average=True): |
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super().__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.register_buffer('window', self._create_window(window_size, self.channel)) |
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def forward(self, img1, img2): |
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assert len(img1.shape) == 4 |
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channel = img1.size()[1] |
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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 = self._create_window(self.window_size, channel) |
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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 self._ssim(img1, img2, window, self.window_size, channel, self.size_average) |
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def _gaussian(self, window_size, sigma): |
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gauss = torch.Tensor([ |
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np.exp(-(x - (window_size // 2)) ** 2 / float(2 * sigma ** 2)) for x in range(window_size) |
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]) |
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return gauss / gauss.sum() |
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def _create_window(self, window_size, channel): |
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_1D_window = self._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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return _2D_window.expand(channel, 1, window_size, window_size).contiguous() |
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def _ssim(self, 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( |
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img1 * img1, window, padding=(window_size // 2), groups=channel) - mu1_sq |
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sigma2_sq = F.conv2d( |
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img2 * img2, window, padding=(window_size // 2), groups=channel) - mu2_sq |
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sigma12 = F.conv2d( |
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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)) / \ |
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((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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return ssim_map.mean(1).mean(1).mean(1) |
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def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): |
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return |
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