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ldm/modules/evaluate/ssim.py

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+# MIT Licence
+
+# Methods to predict the SSIM, taken from
+# https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py
+
+from math import exp
+
+import torch
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+def gaussian(window_size, sigma):
+    gauss = torch.Tensor(
+        [
+            exp(-((x - window_size // 2) ** 2) / float(2 * sigma ** 2))
+            for x in range(window_size)
+        ]
+    )
+    return gauss / gauss.sum()
+
+
+def create_window(window_size, channel):
+    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
+    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
+    window = Variable(
+        _2D_window.expand(channel, 1, window_size, window_size).contiguous()
+    )
+    return window
+
+
+def _ssim(
+    img1, img2, window, window_size, channel, mask=None, 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 not (mask is None):
+        b = mask.size(0)
+        ssim_map = ssim_map.mean(dim=1, keepdim=True) * mask
+        ssim_map = ssim_map.view(b, -1).sum(dim=1) / mask.view(b, -1).sum(
+            dim=1
+        ).clamp(min=1)
+        return ssim_map
+
+    import pdb
+
+    pdb.set_trace
+
+    if size_average:
+        return ssim_map.mean()
+    else:
+        return ssim_map.mean(1).mean(1).mean(1)
+
+
+class SSIM(torch.nn.Module):
+    def __init__(self, window_size=11, size_average=True):
+        super(SSIM, self).__init__()
+        self.window_size = window_size
+        self.size_average = size_average
+        self.channel = 1
+        self.window = create_window(window_size, self.channel)
+
+    def forward(self, img1, img2, mask=None):
+        (_, channel, _, _) = img1.size()
+
+        if (
+            channel == self.channel
+            and self.window.data.type() == img1.data.type()
+        ):
+            window = self.window
+        else:
+            window = create_window(self.window_size, channel)
+
+            if img1.is_cuda:
+                window = window.cuda(img1.get_device())
+            window = window.type_as(img1)
+
+            self.window = window
+            self.channel = channel
+
+        return _ssim(
+            img1,
+            img2,
+            window,
+            self.window_size,
+            channel,
+            mask,
+            self.size_average,
+        )
+
+
+def ssim(img1, img2, window_size=11, mask=None, size_average=True):
+    (_, channel, _, _) = img1.size()
+    window = create_window(window_size, channel)
+
+    if img1.is_cuda:
+        window = window.cuda(img1.get_device())
+    window = window.type_as(img1)
+
+    return _ssim(img1, img2, window, window_size, channel, mask, size_average)