def __call__(self, inputs):
kspace, mask, target = inputs
# pad if necessary
p1 = max(0, self.shape[0] - target.shape[-2])
p2 = max(0, self.shape[1] - target.shape[-1])
target_padded = torch.nn.functional.pad(
target, (p2 // 2, -(-p2 // 2), p1 // 2, -(-p1 // 2)),
)
# crop if necessary
target_cropped = transforms.center_crop(target_padded, self.shape)
# resimulate
kspace_cropped = transforms.fft2(prep_fft_channel(target_cropped))
new_mask = mask(kspace_cropped.shape).expand_as(kspace_cropped)
new_kspace = unprep_fft_channel(kspace_cropped * new_mask)
new_mask = unprep_fft_channel(new_mask)
tcs = target_cropped.shape[-3]
if not tcs == 2:
target_cropped = target_cropped[
..., ((tcs // 2) // 2) * 2 : ((tcs // 2) // 2) * 2 + 2, :, :
]
return new_kspace, new_mask, target_cropped
def __call__(self, inputs):
if self.use_target:
tar = inputs[-1]
else:
tar = unprep_fft_channel(
transforms.ifft2(prep_fft_channel(inputs[0])))
norm = torch.norm(tar, p=self.p)
if self.reduction == "mean" and not self.p == "inf":
norm /= np.prod(tar.shape)**(1 / self.p)
if len(inputs) == 2:
return inputs[0] / norm, inputs[1] / norm
else:
return inputs[0] / norm, inputs[1], inputs[2] / norm
def __call__(self, inputs):
kspace, mask, target = inputs
inv = unprep_fft_channel(transforms.ifft2(prep_fft_channel(kspace)))
return inv, target