def test_ifft2(shape, named):
shape = shape + [2]
data = create_input(shape, named=named)
if named:
dim = ("height", "width")
else:
dim = (-2, -1)
out_torch = transforms.ifft2(data, dim=dim).numpy()
out_torch = out_torch[..., 0] + 1j * out_torch[..., 1]
input_numpy = tensor_to_complex_numpy(data)
input_numpy = np.fft.ifftshift(input_numpy, (-2, -1))
out_numpy = np.fft.ifft2(input_numpy, norm="ortho")
out_numpy = np.fft.fftshift(out_numpy, (-2, -1))
assert np.allclose(out_torch, out_numpy)
mul_names = mul.names
mr_forward = torch.where(
sampling_mask.rename(None) == 0,
torch.tensor([0.0], dtype=masked_kspace.dtype).to(masked_kspace.device),
transforms.fft2(mul).rename(None),
)
error = mr_forward - torch.where(
sampling_mask.rename(None) == 0,
torch.tensor([0.0], dtype=masked_kspace.dtype).to(masked_kspace.device),
masked_kspace.rename(None),
)
error = error.refine_names(*mul_names)
mr_backward = transforms.ifft2(error)
out = transforms.complex_multiplication(transforms.conjugate(sensitivity_map),
mr_backward).sum("coil")
# numpy
# mul_numpy = sensitivity_map_numpy * input_image_numpy
# mr_forward_numpy = sampling_mask_numpy * numpy_fft(mul_numpy)
# error_numpy = mr_forward_numpy - sampling_mask_numpy * masked_kspace_numpy
# mr_backward_numpy = numpy_ifft(error_numpy)
# out_numpy = (sensitivity_map_numpy.conjugate() * mr_backward_numpy).sum(1)
# np.allclose(tensor_to_complex_numpy(out), out_numpy)
# numpy 2
mr_backward_numpy = numpy_ifft(