def soft_dc(self, x, input):
if self.space == 'img-space':
x = T.fft2(sens_expand(x, input['sens_maps']))
x = torch.where(input['mask'], x - input['kspace'], self.zero)
if self.space == 'img-space':
x = sens_reduce(T.ifft2(x), input['sens_maps'])
return self.lambda_ * x
def forward(self, x):
# if torch.any(torch.isnan(x)):
# pdb.set_trace()
y = T.fft2(x)
# if torch.any(torch.isnan(y)):
# pdb.set_trace()
return y
def net_forward(self, x, input):
if self.space == 'k-space':
x = sens_reduce(T.ifft2(x), input['sens_maps'])
x = merge_multi_slice(x, cat_dim=-2).unsqueeze(1).contiguous()
x = self.net(x)
x = unmerge_multi_slice(x, 2).contiguous()
if self.space == 'k-space':
x = T.fft2(sens_expand(x, input['sens_maps']))
return x
def test_fft2(shape):
shape = shape + [2]
input = create_input(shape)
out_torch = transforms.fft2(input).numpy()
out_torch = out_torch[..., 0] + 1j * out_torch[..., 1]
input_numpy = utils.tensor_to_complex_np(input)
input_numpy = np.fft.ifftshift(input_numpy, (-2, -1))
out_numpy = np.fft.fft2(input_numpy, norm='ortho')
out_numpy = np.fft.fftshift(out_numpy, (-2, -1))
assert np.allclose(out_torch, out_numpy)
def soft_dc(self, x, input):
# if torch.any(torch.isnan(x)):
# pdb.set_trace()
if self.space == 'img-space':
x = T.fft2(sens_expand(x, input['sens_maps']))
# if torch.any(torch.isnan(x)):
# pdb.set_trace()
x = torch.where(input['mask'], x - input['kspace'], self.zero)
if self.space == 'img-space':
x = sens_reduce(T.ifft2(x), input['sens_maps'])
# if torch.any(torch.isnan(x)):
# pdb.set_trace()
return self.lambda_ * x
def net_forward(self, x, input):
xinitial = x
# if torch.any(torch.isnan(x)):
# pdb.set_trace()
if self.space == 'k-space':
x = sens_reduce(T.ifft2(x), input['sens_maps'])
# if torch.any(torch.isnan(x)):
# pdb.set_trace()
x = merge_multi_slice(x, cat_dim=-2).unsqueeze(1).contiguous()
x = self.net(x)
# if torch.any(torch.isnan(x)):
# pdb.set_trace()
x = unmerge_multi_slice(x, 2).contiguous()
if self.space == 'k-space':
x = T.fft2(sens_expand(x, input['sens_maps']))
# if torch.any(torch.isnan(x)):
# pdb.set_trace()
return x
def forward(self, x):
return T.fft2(x)