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Updated to work with current pytorch #45

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Updated to work with current pytorch #45

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c83f7bf
Updated to work with current pytorch
adishourya Dec 3, 2023
8c0e144
fixed missing imports
adishourya Feb 22, 2024
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47 changes: 19 additions & 28 deletions pytorch_ssim/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,69 +5,60 @@
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)