def forward(self, x, input):
s = x.size(2)
mask = torch.zeros_like(x)
for j in range(s):
lf = input['num_lf'][j]
mask[:, :, j, ...] = T.mask_center(x[:, :, j, ...], lf)
return mask
def forward(self, masked_kspace: torch.Tensor,
mask: torch.Tensor) -> torch.Tensor:
# get low frequency line locations and mask them out
cent = mask.shape[-2] // 2
left = torch.nonzero(mask.squeeze()[:cent] == 0)[-1]
right = torch.nonzero(mask.squeeze()[cent:] == 0)[0] + cent
num_low_freqs = right - left
pad = (mask.shape[-2] - num_low_freqs + 1) // 2
x = transforms.mask_center(masked_kspace, pad, pad + num_low_freqs)
# convert to image space
x = fastmri.ifft2c(x)
x, b = self.chans_to_batch_dim(x)
# estimate sensitivities
x = self.norm_unet(x)
x = self.batch_chans_to_chan_dim(x, b)
x = self.divide_root_sum_of_squares(x)
return x
def forward(self, masked_kspace, mask):
def get_low_frequency_lines(mask):
l = r = mask.shape[-2] // 2
while mask[..., r, :]:
r += 1
while mask[..., l, :]:
l -= 1
return l + 1, r
l, r = get_low_frequency_lines(mask)
num_low_freqs = r - l
pad = (mask.shape[-2] - num_low_freqs + 1) // 2
x = T.mask_center(masked_kspace, pad, pad + num_low_freqs)
x = fastmri.ifft2c(x)
x, b = self.chans_to_batch_dim(x)
x = self.norm_unet(x)
x = self.batch_chans_to_chan_dim(x, b)
x = self.divide_root_sum_of_squares(x)
return x