def forward(self, input, target, max_val=None):
max_val = self.max_val if max_val is None else max_val
input = input.unsqueeze(1)
print (input.max())
target = target.unsqueeze(1)
return 1- ssim_loss(input, target, max_val=max_val, filter_size=self.filter_size, k1=self.k1, k2=self.k2,
sigma=self.sigma, reduction=self.reduction)
def forward(self, input, target, max_val=None):
max_val = self.max_val if max_val is None else max_val
return 1 - ssim_loss(input,
target,
max_val=max_val,
filter_size=self.filter_size,
k1=self.k1,
k2=self.k2,
sigma=self.sigma,
reduction=self.reduction)
def _get_slice_metrics(img_recons, img_targets):
img_recons = img_recons.detach() # Just in case.
img_targets = img_targets.detach()
max_range = img_targets.max() - img_targets.min()
slice_ssim = ssim_loss(img_recons, img_targets, max_val=max_range)
slice_psnr = psnr(img_recons, img_targets, data_range=max_range)
slice_nmse = nmse(img_recons, img_targets)
return {'slice_ssim': slice_ssim, 'slice_nmse': slice_nmse, 'slice_psnr': slice_psnr}
def forward(self, input, target, max_val=None):
max_val = self.max_val if max_val is None else max_val
cssim = 1 - ssim_loss(input,
target,
max_val=max_val,
filter_size=self.filter_size,
k1=self.k1,
k2=self.k2,
sigma=self.sigma,
reduction=self.reduction)
l1_loss = F.l1_loss(input, target, reduction=self.reduction)
return cssim + self.l1_weight * l1_loss