def test_Wavelet3D_PyWt(self):
"""Test the adjoint operator for the 3D Wavelet transform
"""
for ch in self.num_channels:
print("Testing with Num Channels : " + str(ch))
for i in range(self.max_iter):
print("Process Wavelet3D PyWt test '{0}'...", i)
wavelet_op_adj = WaveletN(
wavelet_name="sym8",
nb_scale=4,
dim=3,
padding_mode='periodization',
n_coils=ch,
n_jobs=-1,
)
Img = np.squeeze(
np.random.randn(ch, self.N, self.N, self.N) +
1j * np.random.randn(ch, self.N, self.N, self.N)
)
f_p = wavelet_op_adj.op(Img)
f = (np.random.randn(*f_p.shape) +
1j * np.random.randn(*f_p.shape))
I_p = wavelet_op_adj.adj_op(f)
x_d = np.vdot(Img, I_p)
x_ad = np.vdot(f_p, f)
np.testing.assert_allclose(x_d, x_ad, rtol=1e-5)
print(" Wavelet3 adjoint test passes")
def test_weighted_sparse_threshold_weights(self):
# Test the weighted sparse threshold operator
num_scales = 3
linear_op = WaveletN('sym8', nb_scales=num_scales)
coeff = linear_op.op(np.zeros((128, 128)))
coeffs_shape = linear_op.coeffs_shape
scales_shape = np.unique(coeffs_shape, axis=0)
constant_weights = WeightedSparseThreshold(
weights=1e-10,
coeffs_shape=coeffs_shape,
)
out = constant_weights.op(np.random.random(coeff.shape))
assert np.all(constant_weights.weights[:np.prod(coeffs_shape[0])] == 0)
assert np.all(
constant_weights.weights[np.prod(coeffs_shape[0]):] == 1e-10
)
# Scale weights
custom_scale_weights = np.arange(num_scales + 1)
scale_based = WeightedSparseThreshold(
weights=custom_scale_weights,
coeffs_shape=coeffs_shape,
weight_type='scale_based',
zero_weight_coarse=False,
)
out = scale_based.op(np.random.random(coeff.shape))
start = 0
for i, scale_shape in enumerate(scales_shape):
scale_sz = np.prod(scale_shape)
stop = start + scale_sz * np.sum(scale_shape == coeffs_shape)
np.testing.assert_equal(
scale_based.weights[start:stop],
custom_scale_weights[i],
)
start = stop
# Custom Weights
custom_weights = np.random.random(coeff.shape)
custom = WeightedSparseThreshold(
weights=custom_weights,
coeffs_shape=coeffs_shape,
weight_type='custom',
)
out = custom.op(np.random.random(coeff.shape))
assert np.all(custom.weights[:np.prod(coeffs_shape[0])] == 0)
np.testing.assert_equal(
custom.weights[np.prod(coeffs_shape[0]):],
custom_weights[np.prod(coeffs_shape[0]):],
)
def test_Wavelet2D_PyWt(self):
"""Test the adjoint operator for the 2D Wavelet transform
"""
for i in range(self.max_iter):
print("Process Wavelet2D PyWt test '{0}'...", i)
wavelet_op_adj = WaveletN(wavelet_name="sym8", nb_scale=4)
Img = (np.random.randn(self.N, self.N) +
1j * np.random.randn(self.N, self.N))
f_p = wavelet_op_adj.op(Img)
f = (np.random.randn(*f_p.shape) +
1j * np.random.randn(*f_p.shape))
I_p = wavelet_op_adj.adj_op(f)
x_d = np.vdot(Img, I_p)
x_ad = np.vdot(f_p, f)
np.testing.assert_allclose(x_d, x_ad, rtol=1e-6)
print(" Wavelet2 adjoint test passes")
def get_operators(
kspace_data,
loc,
mask,
fourier_type=1,
max_iter=80,
regularisation=None,
linear=None,
):
"""Create the various operators from the config file."""
n_coils = 1 if kspace_data.ndim == 2 else kspace_data.shape[0]
shape = kspace_data.shape[-2:]
if fourier_type == 0: # offline reconstruction
kspace_generator = KspaceGeneratorBase(full_kspace=kspace_data,
mask=mask,
max_iter=max_iter)
fourier_op = FFT(shape=shape, n_coils=n_coils, mask=mask)
elif fourier_type == 1: # online type I reconstruction
kspace_generator = Column2DKspaceGenerator(full_kspace=kspace_data,
mask_cols=loc)
fourier_op = FFT(shape=shape, n_coils=n_coils, mask=mask)
elif fourier_type == 2: # online type II reconstruction
kspace_generator = DataOnlyKspaceGenerator(full_kspace=kspace_data,
mask_cols=loc)
fourier_op = ColumnFFT(shape=shape, n_coils=n_coils)
else:
raise NotImplementedError
if linear is None:
linear_op = Identity()
else:
lin_cls = linear.pop("class", None)
if lin_cls == "WaveletN":
linear_op = WaveletN(n_coils=n_coils, n_jobs=4, **linear)
linear_op.op(np.zeros_like(kspace_data))
elif lin_cls == "Identity":
linear_op = Identity()
else:
raise NotImplementedError
prox_op = IdentityProx()
if regularisation is not None:
reg_cls = regularisation.pop("class")
if reg_cls == "LASSO":
prox_op = LASSO(weights=regularisation["weights"])
if reg_cls == "GroupLASSO":
prox_op = GroupLASSO(weights=regularisation["weights"])
elif reg_cls == "OWL":
prox_op = OWL(**regularisation,
n_coils=n_coils,
bands_shape=linear_op.coeffs_shape)
elif reg_cls == "IdentityProx":
prox_op = IdentityProx()
linear_op = Identity()
return kspace_generator, fourier_op, linear_op, prox_op
def test_Wavelet2D_ISAP(self):
"""Test the adjoint operator for the 2D Wavelet transform
"""
for ch in self.num_channels:
print("Testing with Num Channels : " + str(ch))
for i in range(self.max_iter):
print("Process Wavelet2D_ISAP test '{0}'...", i)
wavelet_op_adj = WaveletN(wavelet_name="HaarWaveletTransform",
nb_scale=4,
n_coils=ch,
n_jobs=2)
Img = np.squeeze(
np.random.randn(ch, self.N, self.N) +
1j * np.random.randn(ch, self.N, self.N))
f_p = wavelet_op_adj.op(Img)
f = (np.random.randn(*f_p.shape) +
1j * np.random.randn(*f_p.shape))
I_p = wavelet_op_adj.adj_op(f)
x_d = np.vdot(Img, I_p)
x_ad = np.vdot(f_p, f)
np.testing.assert_allclose(x_d, x_ad, rtol=1e-5)
print(" Wavelet2 adjoint test passes")
# Calculate SSIM
base_ssim = ssim(image_rec0, image)
print(base_ssim)
#############################################################################
# FISTA optimization
# ------------------
#
# We now want to refine the zero order solution using a FISTA optimization.
# The cost function is set to Proximity Cost + Gradient Cost
# Setup the operators
linear_op = WaveletN(
wavelet_name="sym8",
nb_scales=4,
dim=3,
padding_mode="periodization",
)
regularizer_op = SparseThreshold(Identity(), 2 * 1e-11, thresh_type="soft")
# Setup Reconstructor
reconstructor = SingleChannelReconstructor(
fourier_op=fourier_op,
linear_op=linear_op,
regularizer_op=regularizer_op,
gradient_formulation='synthesis',
verbose=1,
)
# Start Reconstruction
x_final, costs, metrics = reconstructor.reconstruct(
kspace_data=kspace_data,
optimization_alg='fista',
def E(**kwargs):
# --
# -- Computes the cost of a given mask or parametrisation of mask --
# --
# INPUTS: images: list of all images used to evaluate the reconstruction
# kspace_data: list of noised kspace data associated to these images
# param: list of lower and upper level parameters. Must contain:
# - epsilon: weight of L2 norm in lower level reconstruction
# - gamma: parameter for approximation of L1 norm
# - c: weight of L for upper level cost function
# - beta: weight of P for upper level cost function
# Remark: You should choose these parameters to have E(pk)~1 at the beginning (otherwire, L-BFGS-B may stop too early)
# fourier_op: Fourier operator for lower level reconstruction
# linear_op: Linear operator for lower level reconstruction
# mask_type (optional): learn cartesian mask if mask_type="cartesian", otherwise each point is independant.
# Other parametrisations may be implemented later.
# pk: initial mask. Mandatory if mask_type!="cartesian"
# lk: initial mask parametrisation. Mandatory if mask_type="cartesian"
# verbose (optional)
# Getting parameters
images = kwargs.get("images", None)
kspace_data = kwargs.get("kspace_data", None)
param = kwargs.get("param", None)
samples = kwargs.get("samples", [])
fourier_op = NonCartesianFFT(samples=samples,
shape=images[0].shape,
implementation='cpu')
wavelet_name = kwargs.get("wavelet_name", "")
wavelet_scale = kwargs.get("wavelet_scale", 1)
linear_op = WaveletN(wavelet_name=wavelet_name,
nb_scale=wavelet_scale,
padding_mode="periodization")
parallel = kwargs.get("parallel", False)
mask_type = kwargs.get("mask_type", "")
lk = kwargs.get("lk", None)
pk = kwargs.get("pk", None)
verbose = kwargs.get("verbose", 0)
if verbose >= 0: print("\n\nEVALUATING E(p)")
# Checking inputs errors
if images is None or len(images) < 1:
raise ValueError("At least one image is needed")
if param is None: raise ValueError("Lower level parameters must be given")
if len(images) != len(kspace_data):
raise ValueError("Need as many images and kspace data")
#Compute P(pk/lk)
Ep = 0
if mask_type == "cartesian":
pk = pcart(lk)
Ep = P(lk, param["beta"])
elif mask_type == "radial_CO":
n_rad = kwargs.get("n_rad")
pk = pradCO(lk, n_rad)
Ep = P(lk, param["beta"])
else:
Ep = P(pk, param["beta"])
#Compute L(pk/lk)
Nimages = len(images)
if parallel:
uk_list = Parallel(n_jobs=-1, verbose=0)(
delayed(pdhg)(kspace_data[i],
pk,
samples=samples,
shape=images[0].shape,
wavelet_name=wavelet_name,
wavelet_scale=wavelet_scale,
param=param,
mask_type=mask_type,
const=kwargs.get("const", {}),
verbose=-1) for i in range(Nimages))
Ep += np.sum(
[L(uk_list[i][0], images[i], param["c"])
for i in range(Nimages)]) / Nimages
else:
for i in range(Nimages):
if verbose >= 0: print(f"\nImage {i+1}:")
u0_mat, y = images[i], kspace_data[i]
if verbose > 0: print("\nStarting PDHG")
uk, _ = pdhg(y,
pk,
maxit=50,
fourier_op=fourier_op,
linear_op=linear_op,
**kwargs)
Ep += L(uk, u0_mat, param["c"]) / Nimages
return Ep
def grad_L(**kwargs):
# --
# -- Compute gradient of L with respect to p
# --
# INPUTS: pk: Point where we want to compute the gradient
# u0_mat: Ground_truth image
# y: kspace data associated to u0_mat
# param: list of lower and upper level parameters. Must contain:
# - epsilon: weight of L2 norm in lower level reconstruction
# - gamma: parameter for approximation of L1 norm
# - c: weight of L for upper level cost function
# - beta: weight of P for upper level cost function
# Remark: You should choose these parameters to have E(pk)~1 at the beginning (otherwire, L-BFGS-B may stop too early)
# fourier_op: Fourier operator for lower level reconstruction
# linear_op: Linear operator for lower level reconstruction
#
# max_cgiter (optional): maximum number of Conjugate Gradient iterations (default: 3000)
# cgtol (optional): tolerance of Conjugate Gradient iterations (default: 1e-6)
# compute_conv (optional): plot convergence if True (default: False)
# verbose (optional)
# -- Getting parameters
max_cgiter = kwargs.get("max_cgiter", 4000)
cgtol = kwargs.get("cgtol", 1e-6)
compute_conv = kwargs.get("compute_conv", False)
mask_type = kwargs.get("mask_type", "")
learn_mask = kwargs.get("learn_mask", True)
learn_alpha = kwargs.get("learn_alpha", True)
u0_mat = kwargs.get("u0_mat", None)
param = kwargs.get("param", None)
y = kwargs.get("y", None)
pk = kwargs.get("pk", None)
samples = kwargs.get("samples", [])
fourier_op = NonCartesianFFT(samples=samples,
shape=u0_mat.shape,
implementation='cpu')
wavelet_name = kwargs.get("wavelet_name", "")
wavelet_scale = kwargs.get("wavelet_scale", 1)
linear_op = WaveletN(wavelet_name=wavelet_name,
nb_scale=wavelet_scale,
padding_mode="periodization")
const = kwargs.get("const", {})
verbose = kwargs.get("verbose", 0)
cg_conv = []
if u0_mat is None:
raise ValueError("A ground truth image u0_mat is needed")
if y is None: raise ValueError("kspace data y are needed")
n = len(u0_mat)
# -- Compute uk from pk with lower level solver if not given
if verbose >= 0: print("\nStarting PDHG")
uk, _ = pdhg(y,
pk,
mask_type=mask_type,
fourier_op=fourier_op,
linear_op=linear_op,
param=param,
maxit=50,
verbose=verbose,
const=const)
# -- Defining linear operator from pk and uk
def mv(w):
w_complex = np.reshape(w[:n**2] + 1j * w[n**2:], (n, n))
fx = np.reshape(
Du2_Etot(uk,
pk,
w_complex,
eps=param["epsilon"],
fourier_op=fourier_op,
y=y,
linear_op=linear_op,
gamma=param["gamma"]), (n**2, ))
return np.concatenate([np.real(fx), np.imag(fx)])
lin = LinearOperator((2 * n**2, 2 * n**2), matvec=mv)
if verbose >= 0: print("\nStarting Conjugate Gradient method")
t1 = time.time()
B = np.reshape(Du_L(uk, u0_mat, param["c"]), (n**2, ))
B_real = np.concatenate([np.real(B), np.imag(B)])
def cgcall(x):
#CG callback function to plot convergence
if compute_conv:
cg_conv.append(
np.linalg.norm(lin(x) - B_real) / np.linalg.norm(B_real))
x_inter, _ = cg(lin,
B_real,
tol=cgtol,
maxiter=max_cgiter,
callback=cgcall)
if verbose >= 0:
print(
f"Finished in {time.time()-t1}s - ||Ax-b||/||b||: {np.linalg.norm(lin(x_inter)-B_real)/np.linalg.norm(B_real)}"
)
# -- Plotting
if compute_conv:
plt.plot(cg_conv)
plt.yscale("log")
plt.title("Convergence of the conjugate gradient")
plt.xlabel("Number of iterations")
plt.ylabel("||Ax-b||/||b||")
#plt.savefig("Upper Level/CG_conv.png")
if np.linalg.norm(lin(x_inter) - B_real) / np.linalg.norm(B_real) > 1e-3:
return np.zeros(pk.shape)
else:
return -Dpu_Etot(uk,
pk,
np.reshape(x_inter[:n**2] + 1j * x_inter[n**2:],
(n, n)),
eps=param["epsilon"],
fourier_op=fourier_op,
y=y,
linear_op=linear_op,
gamma=param["gamma"],
learn_mask=learn_mask,
learn_alpha=learn_alpha)
zero_filled = fourier_op.adj_op(kspace_obs)
image_rec0 = pysap.Image(data=np.sqrt(np.sum(np.abs(zero_filled)**2, axis=0)))
# image_rec0.show()
base_ssim = ssim(image_rec0, image)
print('The Base SSIM is : ' + str(base_ssim))
#############################################################################
# FISTA optimization
# ------------------
#
# We now want to refine the zero order solution using a FISTA optimization.
# The cost function is set to Proximity Cost + Gradient Cost
# Setup the operators
linear_op = WaveletN(
wavelet_name='sym8',
nb_scale=4,
)
regularizer_op = SparseThreshold(Identity(), 1.5e-8, thresh_type="soft")
# Setup Reconstructor
reconstructor = SelfCalibrationReconstructor(
fourier_op=fourier_op,
linear_op=linear_op,
regularizer_op=regularizer_op,
gradient_formulation='synthesis',
kspace_portion=0.01,
verbose=1,
)
x_final, costs, metrics = reconstructor.reconstruct(
kspace_data=kspace_obs,
optimization_alg='fista',
num_iterations=10,
def pdhg(data, p, **kwargs):
# --
# -- MAIN LOWER LEVEL FUNCTION
# --
# INPUTS: - data: kspace measurements
# - p: p[:-1]=subsampling mask S(p), p[-1]=regularisation parameter alpha(p)
# So len(p)=len(data)+1
# - fourier_op: fourier operator from a full mask of same shape as the final image.
# - linear_op: linear operator used in regularisation functions
# For the moment, only use waveletN.
# - param: lower level energy parameters
# Must contain parameters keys "epsilon" and "gamma".
# mask_type (optional): type of mask used ("cartesian", "radial"). Assume a cartesian mask if not given.
# --
# OPTIONAL INPUTS:
# - const: algorithm constants if we already know the values we want to use for tau and sigma
# If not given, will compute them according to what is said in the article.
# - compute_energy: bool, we compute and return energy over iterations if True (default: False)
# - ground_truth: matrix representing the true image the data come from (default: None). If not None, we compute the ssim over iterations.
# - maxit,tol: We stop the algorithm when the norm of the difference between two steps
# is smaller than tol or after maxit iterations (default: 200, 1e-4)
# --
# OUTPUTS: - uk: final image
# - norms(, energy, ssims): evolution of stopping criterion (and energy if compute_energy is True / ssims if ground_truth not None)
fourier_op = kwargs.get("fourier_op", None)
linear_op = kwargs.get("linear_op", None)
param = kwargs.get("param", None)
# Create fourier_op and linear_op if not given for multithreading
if fourier_op is None:
samples = kwargs.get("samples", [])
shape = kwargs.get("shape", ())
if samples is not None:
fourier_op = NonCartesianFFT(samples=samples,
shape=shape,
implementation='cpu')
if fourier_op is None:
raise ValueError("A fourier operator fourier_op must be given")
if linear_op is None:
wavelet_name = kwargs.get("wavelet_name", "")
wavelet_scale = kwargs.get("wavelet_scale", 1)
if wavelet_name != "":
linear_op = WaveletN(wavelet_name=wavelet_name,
nb_scale=wavelet_scale,
padding_mode="periodization")
if linear_op is None:
raise ValueError("A linear operator linear_op must be given")
if param is None: raise ValueError("Lower level parameters must be given")
mask_type = kwargs.get("mask_type", "")
const = kwargs.get("const", {})
compute_energy = kwargs.get("compute_energy", False)
ground_truth = kwargs.get("ground_truth", None)
maxit = kwargs.get("maxit", 200)
tol = kwargs.get("tol", 1e-6)
verbose = kwargs.get("verbose", 1)
#Global parameters
p, pn1 = p[:-1], p[-1]
epsilon = param["epsilon"]
gamma = param["gamma"]
n_iter = 0
#Algorithm constants
const = compute_constants(param, const, p)
if verbose >= 0: print("Sigma:", const["sigma"], "\nTau:", const["tau"])
#Initializing
uk = fourier_op.adj_op(p * data)
vk = np.copy(uk)
wk = linear_op.op(uk)
uk_bar = np.copy(uk)
norm = 2 * tol
#For plots
if compute_energy:
energy = []
if ground_truth is not None:
ssims = []
norms = []
#Main loop
t1 = time.time()
while n_iter < maxit and norm > tol:
uk, vk, wk, uk_bar, norm = step(uk, vk, wk, uk_bar, const, p, pn1,
data, param, linear_op, fourier_op,
mask_type)
n_iter += 1
#Saving informations
norms.append(norm)
if compute_energy:
energy.append(
energy_wavelet(uk, p, pn1, data, gamma, epsilon, linear_op,
fourier_op))
if ground_truth is not None:
ssims.append(ssim(uk, ground_truth))
#Printing
if n_iter % 10 == 0 and verbose > 0:
if compute_energy:
print(n_iter, " iterations:\nCost:", energy[-1], "\nNorm:",
norm, "\n")
else:
print(n_iter, " iterations:\nNorm:", norm, "\n")
if verbose >= 0:
print("Finished in", time.time() - t1, "seconds.")
#Return
if compute_energy and ground_truth is not None:
return uk, norms, energy, ssims
elif ground_truth is not None:
return uk, norms, ssims
elif compute_energy:
return uk, norms, energy
else:
return uk, norms
image_rec0 = pysap.Image(data=grid_soln)
# image_rec0.show()
base_ssim = ssim(image_rec0, image)
print('The Base SSIM is : ' + str(base_ssim))
#############################################################################
# FISTA optimization
# ------------------
#
# We now want to refine the zero order solution using a FISTA optimization.
# The cost function is set to Proximity Cost + Gradient Cost
# TODO get the right mu operator
# Setup the operators
linear_op = WaveletN(wavelet_name="sym8", nb_scales=4, dim=3)
regularizer_op = SparseThreshold(Identity(), 6 * 1e-9, thresh_type="soft")
# Setup Reconstructor
reconstructor = SingleChannelReconstructor(
fourier_op=fourier_op,
linear_op=linear_op,
regularizer_op=regularizer_op,
gradient_formulation='synthesis',
verbose=1,
)
# Start Reconstruction
x_final, costs, metrics = reconstructor.reconstruct(
kspace_data=kspace_obs,
optimization_alg='fista',
num_iterations=10,
)
image_rec0 = pysap.Image(data=np.sqrt(np.sum(np.abs(zero_filled)**2, axis=0)))
# image_rec0.show()
base_ssim = ssim(image_rec0, image)
print('The Base SSIM is : ' + str(base_ssim))
#############################################################################
# FISTA optimization
# ------------------
#
# We now want to refine the zero order solution using a FISTA optimization.
# The cost function is set to Proximity Cost + Gradient Cost
# Setup the operators
linear_op = WaveletN(
wavelet_name='sym8',
nb_scale=4,
n_coils=cartesian_ref_image.shape[0],
)
regularizer_op = GroupLASSO(weights=6e-8)
# Setup Reconstructor
reconstructor = CalibrationlessReconstructor(
fourier_op=fourier_op,
linear_op=linear_op,
regularizer_op=regularizer_op,
gradient_formulation='synthesis',
verbose=1,
)
x_final, costs, metrics = reconstructor.reconstruct(
kspace_data=kspace_obs,
optimization_alg='fista',
num_iterations=300,
