def test_adjoint_and_gradients(im_size, batch_size):
tf.random.set_seed(0)
grid_size = tuple(np.array(im_size) * 2)
im_rank = len(im_size)
M = im_size[0] * 2**im_rank
nufft_ob = KbNufftModule(im_size=im_size,
grid_size=grid_size,
norm='ortho',
grad_traj=True)
# Generate Trajectory
ktraj_ori = tf.Variable(
tf.random.uniform(
(batch_size, im_rank, M), minval=-1 / 2, maxval=1 / 2) * 2 * np.pi)
# Have a random signal
signal = tf.Variable(
tf.cast(tf.random.uniform((batch_size, 1, *im_size)), tf.complex64))
kdata = tf.Variable(
kbnufft_forward(nufft_ob._extract_nufft_interpob())(signal, ktraj_ori))
Idata = tf.Variable(
kbnufft_adjoint(nufft_ob._extract_nufft_interpob())(kdata, ktraj_ori))
ktraj_noise = np.copy(ktraj_ori)
ktraj_noise += 0.01 * tf.Variable(
tf.random.uniform(
(batch_size, im_rank, M), minval=-1 / 2, maxval=1 / 2) * 2 * np.pi)
ktraj = tf.Variable(ktraj_noise)
with tf.GradientTape(persistent=True) as g:
I_nufft = kbnufft_adjoint(nufft_ob._extract_nufft_interpob())(kdata,
ktraj)
A = get_fourier_matrix(ktraj, im_size, im_rank, do_ifft=True)
I_ndft = tf.reshape(tf.transpose(tf.matmul(kdata, A), [0, 1, 2]),
(batch_size, 1, *im_size))
loss_nufft = tf.math.reduce_mean(tf.abs(Idata - I_nufft)**2)
loss_ndft = tf.math.reduce_mean(tf.abs(Idata - I_ndft)**2)
tf_test = tf.test.TestCase()
# Test if the NUFFT and NDFT operation is same
tf_test.assertAllClose(I_nufft, I_ndft, atol=2e-3)
# Test gradients with respect to kdata
gradient_ndft_kdata = g.gradient(I_ndft, kdata)[0]
gradient_nufft_kdata = g.gradient(I_nufft, kdata)[0]
tf_test.assertAllClose(gradient_ndft_kdata,
gradient_nufft_kdata,
atol=6e-3)
# Test gradients with respect to trajectory location
gradient_ndft_traj = g.gradient(I_ndft, ktraj)[0]
gradient_nufft_traj = g.gradient(I_nufft, ktraj)[0]
tf_test.assertAllClose(gradient_ndft_traj, gradient_nufft_traj, atol=6e-3)
# Test gradients in chain rule with respect to ktraj
gradient_ndft_loss = g.gradient(loss_ndft, ktraj)[0]
gradient_nufft_loss = g.gradient(loss_nufft, ktraj)[0]
tf_test.assertAllClose(gradient_ndft_loss, gradient_nufft_loss, atol=5e-4)
def test_forward_gradient():
traj = ktraj_function()
image = tf.zeros([1, 1, *image_shape], dtype=tf.complex64)
nufft_ob = KbNufftModule(
im_size=(640, 400),
grid_size=None,
norm='ortho',
)
forward_op = kbnufft_forward(nufft_ob._extract_nufft_interpob())
with tf.GradientTape() as tape:
tape.watch(image)
res = forward_op(image, traj)
grad = tape.gradient(res, image)
tf_test = tf.test.TestCase()
tf_test.assertEqual(grad.shape, image.shape)
def test_adjoint_gradient():
traj = ktraj_function()
kspace = tf.zeros([1, 1, kspace_shape], dtype=tf.complex64)
nufft_ob = KbNufftModule(
im_size=(640, 400),
grid_size=None,
norm='ortho',
)
backward_op = kbnufft_adjoint(nufft_ob._extract_nufft_interpob())
with tf.GradientTape() as tape:
tape.watch(kspace)
res = backward_op(kspace, traj)
grad = tape.gradient(res, kspace)
tf_test = tf.test.TestCase()
tf_test.assertEqual(grad.shape, kspace.shape)
def test_ncpdnet_init_and_call_3d(dcomp, volume_shape):
model = NCPDNet(
n_iter=1,
n_primal=2,
n_filters=2,
multicoil=False,
im_size=volume_shape,
three_d=True,
dcomp=dcomp,
fastmri=False,
)
af = 16
traj = get_stacks_of_radial_trajectory(volume_shape, af=af)
spokelength = volume_shape[-2]
nspokes = volume_shape[-1] // af
nstacks = volume_shape[0]
kspace_shape = nspokes * spokelength * nstacks
extra_args = (tf.constant([volume_shape]), )
if dcomp:
nufft_ob = KbNufftModule(
im_size=volume_shape,
grid_size=None,
norm='ortho',
)
interpob = nufft_ob._extract_nufft_interpob()
nufftob_forw = kbnufft_forward(interpob)
nufftob_back = kbnufft_adjoint(interpob)
dcomp = calculate_radial_dcomp_tf(
interpob,
nufftob_forw,
nufftob_back,
traj[0],
stacks=True,
)
dcomp = tf.ones([1, tf.shape(dcomp)[0]],
dtype=dcomp.dtype) * dcomp[None, :]
extra_args += (dcomp, )
res = model([
tf.zeros([1, 1, kspace_shape, 1], dtype=tf.complex64),
traj,
extra_args,
])
assert res.shape[1:4] == volume_shape
def profile_tfkbnufft(
image,
ktraj,
im_size,
device,
):
if device == 'CPU':
num_nuffts = 20
else:
num_nuffts = 50
print(f'Using {device}')
device_name = f'/{device}:0'
with tf.device(device_name):
image = tf.constant(image)
if device == 'GPU':
image = tf.cast(image, tf.complex64)
ktraj = tf.constant(ktraj)
nufft_ob = KbNufftModule(im_size=im_size, grid_size=None, norm='ortho')
forward_op = kbnufft_forward(nufft_ob._extract_nufft_interpob())
adjoint_op = kbnufft_adjoint(nufft_ob._extract_nufft_interpob())
# warm-up computation
for _ in range(2):
y = forward_op(image, ktraj)
start_time = time.perf_counter()
for _ in range(num_nuffts):
y = forward_op(image, ktraj)
end_time = time.perf_counter()
avg_time = (end_time - start_time) / num_nuffts
print('forward average time: {}'.format(avg_time))
# warm-up computation
for _ in range(2):
x = adjoint_op(y, ktraj)
# run the adjoint speed tests
start_time = time.perf_counter()
for _ in range(num_nuffts):
x = adjoint_op(y, ktraj)
end_time = time.perf_counter()
avg_time = (end_time - start_time) / num_nuffts
print('backward average time: {}'.format(avg_time))
class NFFTBase(Layer):
def __init__(self,
multicoil=False,
im_size=(640, 472),
density_compensation=False,
**kwargs):
super(NFFTBase, self).__init__(**kwargs)
self.multicoil = multicoil
self.im_size = im_size
self.nufft_ob = KbNufftModule(
im_size=im_size,
grid_size=None,
norm='ortho',
)
self.density_compensation = density_compensation
self.forward_op = kbnufft_forward(
self.nufft_ob._extract_nufft_interpob())
self.backward_op = kbnufft_adjoint(
self.nufft_ob._extract_nufft_interpob())
def pad_for_nufft(self, image):
return _pad_for_nufft(image, self.im_size)
def crop_for_pad(self, image, shape):
return _crop_for_pad(image, shape, self.im_size)
def crop_for_nufft(self, image):
return _crop_for_nufft(image, self.im_size)
def op(self, inputs):
if self.multicoil:
image, ktraj, smaps = inputs
else:
image, ktraj = inputs
# for tfkbnufft we need a coil dimension even if there is none
image = image[:, None, ..., 0]
if self.multicoil:
image = image * smaps
kspace = nufft(self.nufft_ob, image, ktraj, image_size=self.im_size)
# TODO: get rid of shape return as not needed in the end.
# shape is computed once in the preprocessing and passed on as is.
shape = tf.ones([tf.shape(image)[0]],
dtype=tf.int32) * tf.shape(image)[-1]
return kspace[..., None], [shape]
def adj_op(self, inputs):
if self.multicoil:
if self.density_compensation:
kspace, ktraj, smaps, shape, dcomp, = inputs
else:
kspace, ktraj, smaps, shape = inputs
else:
if self.density_compensation:
kspace, ktraj, shape, dcomp = inputs
else:
kspace, ktraj, shape = inputs
if self.density_compensation:
kspace = tf.cast(dcomp, kspace.dtype) * kspace[..., 0]
else:
kspace = kspace[..., 0]
image = self.backward_op(kspace, ktraj)
## image resizing
if len(self.im_size) < 3:
# NOTE: for now very ugly way to deal with this condition
shape = tf.reshape(shape[0], [])
reshaping_condition = tf.math.less(shape, self.im_size[-1])
else:
shape = shape[0]
reshaping_condition = tf.reduce_any(
tf.math.less(shape, self.im_size))
image_reshaped = tf.cond(
pred=reshaping_condition,
true_fn=lambda: self.crop_for_pad(image, shape),
false_fn=lambda: image,
)
if self.multicoil:
image = tf.reduce_sum(image_reshaped * tf.math.conj(smaps), axis=1)
else:
image = image_reshaped[:, 0]
image = image[..., None]
return image
class NFFTBase(Layer):
def __init__(self, multicoil=False, im_size=(640, 472), density_compensation=False, **kwargs):
super(NFFTBase, self).__init__(**kwargs)
self.multicoil = multicoil
self.im_size = im_size
self.nufft_ob = KbNufftModule(
im_size=im_size,
grid_size=None,
norm='ortho',
)
self.density_compensation = density_compensation
self.forward_op = kbnufft_forward(self.nufft_ob._extract_nufft_interpob())
self.backward_op = kbnufft_adjoint(self.nufft_ob._extract_nufft_interpob())
def pad_for_nufft(self, image):
return _pad_for_nufft(image, self.im_size)
def crop_for_pad(self, image, shape):
return _crop_for_pad(image, shape, self.im_size)
def crop_for_nufft(self, image):
return _crop_for_nufft(image, self.im_size)
def op(self, inputs):
if self.multicoil:
image, ktraj, smaps = inputs
else:
image, ktraj = inputs
# for tfkbnufft we need a coil dimension even if there is none
image = image[:, None, ..., 0]
if self.multicoil:
image = image * smaps
kspace = nufft(self.nufft_ob, image, ktraj, image_size=self.im_size)
shape = tf.ones([tf.shape(image)[0]], dtype=tf.int32) * tf.shape(image)[-1]
return kspace[..., None], [shape]
def adj_op(self, inputs):
if self.multicoil:
if self.density_compensation:
kspace, ktraj, smaps, shape, dcomp, = inputs
else:
kspace, ktraj, smaps, shape = inputs
else:
if self.density_compensation:
kspace, ktraj, shape, dcomp = inputs
else:
kspace, ktraj, shape = inputs
shape = tf.reshape(shape[0], [])
if self.density_compensation:
kspace = tf.cast(dcomp, kspace.dtype) * kspace[..., 0]
else:
kspace = kspace[..., 0]
image = self.backward_op(kspace, ktraj)
image_reshaped = tf.cond(
tf.math.greater_equal(shape, self.im_size[-1]),
lambda: image,
lambda: self.crop_for_pad(image, shape),
)
if self.multicoil:
image = tf.reduce_sum(image_reshaped * tf.math.conj(smaps), axis=1)
else:
image = image_reshaped[:, 0]
image = image[..., None]
return image
class NonCartesianFastMRIDatasetBuilder(FastMRIDatasetBuilder):
def __init__(
self,
image_size=IMAGE_SIZE,
acq_type='radial',
dcomp=True,
scale_factor=1e6,
traj=None,
crop_image_data=False,
**kwargs,
):
self.image_size = image_size
self.acq_type = acq_type
self.traj = traj
self._check_acq_type()
self.dcomp = dcomp
self.scale_factor = scale_factor
self.crop_image_data = crop_image_data
self.nufft_obj = KbNufftModule(
im_size=self.image_size,
grid_size=None,
norm='ortho',
)
super(NonCartesianFastMRIDatasetBuilder, self).__init__(
**kwargs,
)
if self.brain:
raise ValueError(
'Currently the non cartesian data works only with knee data.')
self._check_mode()
self._check_dcomp_multicoil()
def _check_acq_type(self,):
if self.acq_type not in ['spiral', 'radial', 'cartesian_debug', 'other']:
raise ValueError(
f'acq_type must be spiral, radial or cartesian_debug but is {self.acq_type}'
)
if self.acq_type == 'other' and self.traj is None:
raise ValueError(
f'Please provide a trajectory as input in case `acq_type` is `other`'
)
def _check_mode(self,):
if self.mode == 'test':
raise ValueError('NonCartesian dataset cannot be used in test mode')
def _check_dcomp_multicoil(self,):
if self.multicoil and not self.dcomp:
raise ValueError('You must use density compensation when in multicoil')
def generate_trajectory(self,):
if self.acq_type == 'radial':
traj = get_radial_trajectory(self.image_size, af=self.af)
elif self.acq_type == 'cartesian':
traj = get_debugging_cartesian_trajectory()
elif self.acq_type == 'spiral':
traj = get_spiral_trajectory(self.image_size, af=self.af)
elif self.acq_type == 'other':
traj = self.traj
return traj
def preprocessing(self, image, kspace):
traj = self.generate_trajectory()
interpob = self.nufft_obj._extract_nufft_interpob()
nufftob_forw = kbnufft_forward(interpob, multiprocessing=True)
nufftob_back = kbnufft_adjoint(interpob, multiprocessing=True)
if self.dcomp:
dcomp = calculate_density_compensator(
interpob,
nufftob_forw,
nufftob_back,
traj[0],
)
traj = tf.repeat(traj, tf.shape(image)[0], axis=0)
orig_image_channels = ortho_ifft2d(kspace)
if self.crop_image_data:
image = adjust_image_size(image, self.image_size)
nc_kspace = nufft(self.nufft_obj, orig_image_channels, traj, self.image_size, multicoil=self.multicoil)
nc_kspace, image = scale_tensors(nc_kspace, image, scale_factor=self.scale_factor)
image = image[..., None]
nc_kspaces_channeled = nc_kspace[..., None]
orig_shape = tf.ones([tf.shape(kspace)[0]], dtype=tf.int32) * self.image_size[-1]
if not self.crop_image_data:
output_shape = tf.shape(image)[1:][None, :]
output_shape = tf.tile(output_shape, [tf.shape(image)[0], 1])
extra_args = (orig_shape,)
if self.dcomp:
dcomp = tf.ones(
[tf.shape(kspace)[0], tf.shape(dcomp)[0]],
dtype=dcomp.dtype,
) * dcomp[None, :]
extra_args += (dcomp,)
model_inputs = (nc_kspaces_channeled, traj)
if self.multicoil:
smaps = non_cartesian_extract_smaps(nc_kspace, traj, dcomp, nufftob_back, self.image_size)
model_inputs += (smaps,)
if not self.crop_image_data:
model_inputs += (output_shape,)
model_inputs += (extra_args,)
return model_inputs, image
