def forward(
self,
input: torch.Tensor,
target: torch.Tensor,
) -> torch.Tensor:
r"""Defines the computation performed at every call.
"""
_assert_type(
[input, target],
device=self.kernel.device,
dim_range=(4, 4),
n_channels=3,
value_range=(0., self.value_range),
)
# Downsample
_, _, h, w = input.size()
M = round(min(h, w) / 256)
if M > 1:
input = F.avg_pool2d(input, kernel_size=M, ceil_mode=True)
target = F.avg_pool2d(target, kernel_size=M, ceil_mode=True)
# RGB to LHM
input = self.convert(input)
target = self.convert(target)
# MDSI
l = mdsi(input, target, kernel=self.kernel, **self.kwargs)
return _reduce(l, self.reduction)
def forward(
self,
input: torch.Tensor,
target: torch.Tensor,
) -> torch.Tensor:
r"""Defines the computation performed at every call.
"""
_assert_type(
[input, target],
device=self.kernel.device,
dim_range=(4, 4),
n_channels=self.kernel.size(0),
value_range=(0., self.value_range),
)
l = ms_ssim(
input,
target,
kernel=self.kernel,
weights=self.weights,
**self.kwargs,
)
return _reduce(l, self.reduction)
def forward(
self,
input: torch.Tensor,
target: torch.Tensor,
) -> torch.Tensor:
r"""Defines the computation performed at every call.
"""
_assert_type(
[input, target],
device=self.shift.device,
dim_range=(4, 4),
n_channels=3,
value_range=(0., 1.) if self.scaling else (0., -1.),
)
# ImageNet scaling
if self.scaling:
input = (input - self.shift) / self.scale
target = (target - self.shift) / self.scale
# LPIPS
residuals = []
for lin, fx, fy in zip(self.lins, self.net(input), self.net(target)):
fx = fx / torch.linalg.norm(fx, dim=1, keepdim=True)
fy = fy / torch.linalg.norm(fy, dim=1, keepdim=True)
mse = ((fx - fy)**2).mean(dim=(-1, -2), keepdim=True)
residuals.append(lin(mse).flatten())
l = torch.stack(residuals, dim=-1).sum(dim=-1)
return _reduce(l, self.reduction)
def forward(
self,
input: torch.Tensor,
target: torch.Tensor,
) -> torch.Tensor:
r"""Defines the computation performed at every call.
"""
_assert_type(
[input, target],
device=self.kernel.device,
dim_range=(4, 4),
n_channels=3,
value_range=(0., self.value_range),
)
# RGB to Y
input = self.convert(input)
target = self.convert(target)
# MS-GMSD
l = ms_gmsd(
input,
target,
kernel=self.kernel,
weights=self.weights,
**self.kwargs,
)
return _reduce(l, self.reduction)
def forward(
self,
input: torch.Tensor,
target: torch.Tensor,
) -> torch.Tensor:
r"""Defines the computation performed at every call.
"""
_assert_type(
[input, target],
device=self.kernel.device,
dim_range=(4, 4),
n_channels=3,
value_range=(0., self.value_range),
)
# Downsample
input = F.avg_pool2d(input, 2, ceil_mode=True)
target = F.avg_pool2d(target, 2, ceil_mode=True)
# RGB to Y
input = self.convert(input)
target = self.convert(target)
# GMSD
l = gmsd(input, target, kernel=self.kernel, **self.kwargs)
return _reduce(l, self.reduction)
def forward(self, input: torch.Tensor) -> torch.Tensor:
r"""Defines the computation performed at every call.
"""
_assert_type([input], device=input.device, dim_range=(3, -1))
l = tv(input, **self.kwargs)
return _reduce(l, self.reduction)
def forward(
self,
input: torch.Tensor,
target: torch.Tensor,
) -> torch.Tensor:
r"""Defines the computation performed at every call.
"""
_assert_type(
[input, target],
device=input.device,
dim_range=(1, -1),
value_range=(0., self.value_range),
)
l = psnr(input, target, **self.kwargs)
return _reduce(l, self.reduction)