Updated to work with current pytorch

pytorch_ssim/__init__.py

@@ -1,73 +1,59 @@
-import torch
-import torch.nn.functional as F
-from torch.autograd import Variable
-import numpy as np
-from math import exp
 
 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()
+    gauss = torch.exp(-(torch.arange(window_size) - window_size // 2)**2 / (2.0 * sigma**2))
+    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())
+    window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()
     return window
 
-def _ssim(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)
+def _ssim(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
+    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
+    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
+    C1 = (0.01 * 255)**2
+    C2 = (0.03 * 255)**2
 
-    ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))
+    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()
     else:
         return ssim_map.mean(1).mean(1).mean(1)
 
 class SSIM(torch.nn.Module):
-    def __init__(self, window_size = 11, size_average = True):
+    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)
+        self.register_buffer('window', create_window(window_size, self.channel))
 
     def forward(self, img1, img2):
         (_, channel, _, _) = img1.size()
 
-        if channel == self.channel and self.window.data.type() == img1.data.type():
+        if channel == self.channel and self.window.type() == img1.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)
-
+            window = window.to(img1.device).type_as(img1)
             self.window = window
             self.channel = channel
 
-
         return _ssim(img1, img2, window, self.window_size, channel, self.size_average)
 
-def ssim(img1, img2, window_size = 11, size_average = True):
+def ssim(img1, img2, window_size=11, 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)
-
+    window = window.to(img1.device).type_as(img1)
     return _ssim(img1, img2, window, window_size, channel, size_average)