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get ssim residual map #27

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5d8f102
return ssim_map without mean reduction. i.e for ssim residual map
Dec 18, 2019
9998795
reduction parameter added
Dec 18, 2019
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4 changes: 2 additions & 2 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -51,11 +51,11 @@ img1 = Variable( img1, requires_grad=False)
img2 = Variable( img2, requires_grad = True)


# Functional: pytorch_ssim.ssim(img1, img2, window_size = 11, size_average = True)
# Functional: pytorch_ssim.ssim(img1, img2, window_size = 11, size_average = True,reduction='mean')
ssim_value = pytorch_ssim.ssim(img1, img2).data[0]
print("Initial ssim:", ssim_value)

# Module: pytorch_ssim.SSIM(window_size = 11, size_average = True)
# Module: pytorch_ssim.SSIM(window_size = 11, size_average = True,reduction='mean')
ssim_loss = pytorch_ssim.SSIM()

optimizer = optim.Adam([img2], lr=0.01)
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27 changes: 15 additions & 12 deletions pytorch_ssim/__init__.py
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Expand Up @@ -14,7 +14,7 @@ def create_window(window_size, channel):
window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
return window

def _ssim(img1, img2, window, window_size, channel, size_average = True):
def _ssim(img1, img2, window, window_size, channel, size_average = True, reduction = 'mean'):
mu1 = F.conv2d(img1, window, padding = window_size//2, groups = channel)
mu2 = F.conv2d(img2, window, padding = window_size//2, groups = channel)

Expand All @@ -30,19 +30,22 @@ def _ssim(img1, img2, window, window_size, channel, size_average = True):
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()
else:
return ssim_map.mean(1).mean(1).mean(1)

if reduction == 'mean':
if size_average:
return ssim_map.mean()
else:
return ssim_map.mean(1).mean(1).mean(1)
elif reduction == 'none':
return ssim_map
# reduction can be 'mean' or 'none'
class SSIM(torch.nn.Module):
def __init__(self, window_size = 11, size_average = True):
def __init__(self, window_size = 11, size_average = True, reduction= 'mean'):
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.reduction = reduction

def forward(self, img1, img2):
(_, channel, _, _) = img1.size()
Expand All @@ -60,14 +63,14 @@ def forward(self, img1, img2):
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):
return _ssim(img1, img2, window, self.window_size, channel, self.size_average , self.reduction)
# reduction can be 'mean' or 'none'
def ssim(img1, img2, window_size = 11, size_average = True, reduction='mean'):
(_, 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, size_average)
return _ssim(img1, img2, window, window_size, channel, size_average, reduction)