r_nonneg = 1
r_printing = 0
g_CCPI_reg_toolkit = CCPiReg_FGP_TV(r_alpha, r_iterations, r_tolerance, r_iso, r_nonneg, r_printing, 'cpu')
t2 = timer()
res2 = g_CCPI_reg_toolkit.proximal(noisy_data, 1.)
t3 = timer()
print(t3-t2)
plt.figure()
plt.imshow(np.abs(res1.as_array()-res2.as_array()))
plt.colorbar()
plt.title("Difference CIL_FGP_TV vs CCPi_FGP_TV")
plt.show()
print("MAE" , mae(res1, res2))
np.testing.assert_array_almost_equal(res1.as_array(), res2.as_array(), decimal=3)
###################################################################
###################################################################
###################################################################
###################################################################
print("Compare CIL_FGP_TV vs CCPiReg_FGP_TV no tolerance (3D)")
print ("Building 3D phantom using TomoPhantom software")
model = 13 # select a model number from the library
N_size = 64 # Define phantom dimensions using a scalar value (cubic phantom)
path = os.path.dirname(tomophantom.__file__)
path_library3D = os.path.join(path, "Phantom3DLibrary.dat")
#This will generate a N_size x N_size x N_size phantom (3D)
phantom_tm = TomoP3D.Model(model, N_size, path_library3D)
def test_SPDHG_vs_SPDHG_explicit_axpby(self):
data = dataexample.SIMPLE_PHANTOM_2D.get(size=(128, 128))
if debug_print:
print("test_SPDHG_vs_SPDHG_explicit_axpby here")
ig = data.geometry
ig.voxel_size_x = 0.1
ig.voxel_size_y = 0.1
detectors = ig.shape[0]
angles = np.linspace(0, np.pi, 180)
ag = AcquisitionGeometry('parallel',
'2D',
angles,
detectors,
pixel_size_h=0.1,
angle_unit='radian')
# Select device
# device = input('Available device: GPU==1 / CPU==0 ')
# if device=='1':
# dev = 'gpu'
# else:
# dev = 'cpu'
dev = 'cpu'
Aop = AstraProjectorSimple(ig, ag, dev)
sin = Aop.direct(data)
# Create noisy data. Apply Gaussian noise
noises = ['gaussian', 'poisson']
noise = noises[1]
if noise == 'poisson':
np.random.seed(10)
scale = 5
eta = 0
noisy_data = AcquisitionData(
np.random.poisson(scale * (eta + sin.as_array())) / scale,
geometry=ag)
elif noise == 'gaussian':
np.random.seed(10)
n1 = np.random.normal(0, 0.1, size=ag.shape)
noisy_data = AcquisitionData(n1 + sin.as_array(), geometry=ag)
else:
raise ValueError('Unsupported Noise ', noise)
#%% 'explicit' SPDHG, scalar step-sizes
subsets = 10
size_of_subsets = int(len(angles) / subsets)
# create GradientOperator operator
op1 = GradientOperator(ig)
# take angles and create uniform subsets in uniform+sequential setting
list_angles = [
angles[i:i + size_of_subsets]
for i in range(0, len(angles), size_of_subsets)
]
# create acquisitioin geometries for each the interval of splitting angles
list_geoms = [
AcquisitionGeometry('parallel',
'2D',
list_angles[i],
detectors,
pixel_size_h=0.1,
angle_unit='radian')
for i in range(len(list_angles))
]
# create with operators as many as the subsets
A = BlockOperator(*[
AstraProjectorSimple(ig, list_geoms[i], dev)
for i in range(subsets)
] + [op1])
## number of subsets
#(sub2ind, ind2sub) = divide_1Darray_equally(range(len(A)), subsets)
#
## acquisisiton data
## acquisisiton data
AD_list = []
for sub_num in range(subsets):
for i in range(0, len(angles), size_of_subsets):
arr = noisy_data.as_array()[i:i + size_of_subsets, :]
AD_list.append(
AcquisitionData(arr, geometry=list_geoms[sub_num]))
g = BlockDataContainer(*AD_list)
alpha = 0.5
## block function
F = BlockFunction(*[
*[KullbackLeibler(b=g[i])
for i in range(subsets)] + [alpha * MixedL21Norm()]
])
G = IndicatorBox(lower=0)
prob = [1 / (2 * subsets)] * (len(A) - 1) + [1 / 2]
algos = []
algos.append(
SPDHG(f=F,
g=G,
operator=A,
max_iteration=1000,
update_objective_interval=200,
prob=prob.copy(),
use_axpby=True))
algos[0].run(1000, verbose=0)
algos.append(
SPDHG(f=F,
g=G,
operator=A,
max_iteration=1000,
update_objective_interval=200,
prob=prob.copy(),
use_axpby=False))
algos[1].run(1000, verbose=0)
# np.testing.assert_array_almost_equal(algos[0].get_output().as_array(), algos[1].get_output().as_array())
from cil.utilities.quality_measures import mae, mse, psnr
qm = (mae(algos[0].get_output(), algos[1].get_output()),
mse(algos[0].get_output(), algos[1].get_output()),
psnr(algos[0].get_output(), algos[1].get_output()))
if debug_print:
print("Quality measures", qm)
assert qm[0] < 0.005
assert qm[1] < 3.e-05
def test_SPDHG_vs_PDHG_explicit(self):
data = dataexample.SIMPLE_PHANTOM_2D.get(size=(128, 128))
ig = data.geometry
ig.voxel_size_x = 0.1
ig.voxel_size_y = 0.1
detectors = ig.shape[0]
angles = np.linspace(0, np.pi, 180)
ag = AcquisitionGeometry('parallel',
'2D',
angles,
detectors,
pixel_size_h=0.1,
angle_unit='radian')
# Select device
dev = 'cpu'
Aop = AstraProjectorSimple(ig, ag, dev)
sin = Aop.direct(data)
# Create noisy data. Apply Gaussian noise
noises = ['gaussian', 'poisson']
noise = noises[1]
if noise == 'poisson':
scale = 5
noisy_data = scale * applynoise.poisson(sin / scale, seed=10)
# np.random.seed(10)
# scale = 5
# eta = 0
# noisy_data = AcquisitionData(np.random.poisson( scale * (eta + sin.as_array()))/scale, ag)
elif noise == 'gaussian':
noisy_data = noise.gaussian(sin, var=0.1, seed=10)
# np.random.seed(10)
# n1 = np.random.normal(0, 0.1, size = ag.shape)
# noisy_data = AcquisitionData(n1 + sin.as_array(), ag)
else:
raise ValueError('Unsupported Noise ', noise)
#%% 'explicit' SPDHG, scalar step-sizes
subsets = 10
size_of_subsets = int(len(angles) / subsets)
# create Gradient operator
op1 = GradientOperator(ig)
# take angles and create uniform subsets in uniform+sequential setting
list_angles = [
angles[i:i + size_of_subsets]
for i in range(0, len(angles), size_of_subsets)
]
# create acquisitioin geometries for each the interval of splitting angles
list_geoms = [
AcquisitionGeometry('parallel',
'2D',
list_angles[i],
detectors,
pixel_size_h=0.1,
angle_unit='radian')
for i in range(len(list_angles))
]
# create with operators as many as the subsets
A = BlockOperator(*[
AstraProjectorSimple(ig, list_geoms[i], dev)
for i in range(subsets)
] + [op1])
## number of subsets
#(sub2ind, ind2sub) = divide_1Darray_equally(range(len(A)), subsets)
#
## acquisisiton data
## acquisisiton data
AD_list = []
for sub_num in range(subsets):
for i in range(0, len(angles), size_of_subsets):
arr = noisy_data.as_array()[i:i + size_of_subsets, :]
AD_list.append(
AcquisitionData(arr, geometry=list_geoms[sub_num]))
g = BlockDataContainer(*AD_list)
alpha = 0.5
## block function
F = BlockFunction(*[
*[KullbackLeibler(b=g[i])
for i in range(subsets)] + [alpha * MixedL21Norm()]
])
G = IndicatorBox(lower=0)
prob = [1 / (2 * subsets)] * (len(A) - 1) + [1 / 2]
spdhg = SPDHG(f=F,
g=G,
operator=A,
max_iteration=1000,
update_objective_interval=200,
prob=prob)
spdhg.run(1000, verbose=0)
#%% 'explicit' PDHG, scalar step-sizes
op1 = GradientOperator(ig)
op2 = Aop
# Create BlockOperator
operator = BlockOperator(op1, op2, shape=(2, 1))
f2 = KullbackLeibler(b=noisy_data)
g = IndicatorBox(lower=0)
normK = operator.norm()
sigma = 1 / normK
tau = 1 / normK
f1 = alpha * MixedL21Norm()
f = BlockFunction(f1, f2)
# Setup and run the PDHG algorithm
pdhg = PDHG(f=f, g=g, operator=operator, tau=tau, sigma=sigma)
pdhg.max_iteration = 1000
pdhg.update_objective_interval = 200
pdhg.run(1000, verbose=0)
#%% show diff between PDHG and SPDHG
# plt.imshow(spdhg.get_output().as_array() -pdhg.get_output().as_array())
# plt.colorbar()
# plt.show()
from cil.utilities.quality_measures import mae, mse, psnr
qm = (mae(spdhg.get_output(),
pdhg.get_output()), mse(spdhg.get_output(),
pdhg.get_output()),
psnr(spdhg.get_output(), pdhg.get_output()))
if debug_print:
print("Quality measures", qm)
np.testing.assert_almost_equal(mae(spdhg.get_output(),
pdhg.get_output()),
0.00150,
decimal=3)
np.testing.assert_almost_equal(mse(spdhg.get_output(),
pdhg.get_output()),
1.68590e-05,
decimal=3)
def test_SPDHG_vs_PDHG_implicit(self):
data = dataexample.SIMPLE_PHANTOM_2D.get(size=(128, 128))
ig = data.geometry
ig.voxel_size_x = 0.1
ig.voxel_size_y = 0.1
detectors = ig.shape[0]
angles = np.linspace(0, np.pi, 90)
ag = AcquisitionGeometry('parallel',
'2D',
angles,
detectors,
pixel_size_h=0.1,
angle_unit='radian')
# Select device
dev = 'cpu'
Aop = AstraProjectorSimple(ig, ag, dev)
sin = Aop.direct(data)
# Create noisy data. Apply Gaussian noise
noises = ['gaussian', 'poisson']
noise = noises[1]
noisy_data = ag.allocate()
if noise == 'poisson':
np.random.seed(10)
scale = 20
eta = 0
noisy_data.fill(
np.random.poisson(scale * (eta + sin.as_array())) / scale)
elif noise == 'gaussian':
np.random.seed(10)
n1 = np.random.normal(0, 0.1, size=ag.shape)
noisy_data.fill(n1 + sin.as_array())
else:
raise ValueError('Unsupported Noise ', noise)
# Create BlockOperator
operator = Aop
f = KullbackLeibler(b=noisy_data)
alpha = 0.005
g = alpha * TotalVariation(50, 1e-4, lower=0)
normK = operator.norm()
#% 'implicit' PDHG, preconditioned step-sizes
tau_tmp = 1.
sigma_tmp = 1.
tau = sigma_tmp / operator.adjoint(
tau_tmp * operator.range_geometry().allocate(1.))
sigma = tau_tmp / operator.direct(
sigma_tmp * operator.domain_geometry().allocate(1.))
# initial = operator.domain_geometry().allocate()
# # Setup and run the PDHG algorithm
pdhg = PDHG(f=f,
g=g,
operator=operator,
tau=tau,
sigma=sigma,
max_iteration=1000,
update_objective_interval=500)
pdhg.run(verbose=0)
subsets = 10
size_of_subsets = int(len(angles) / subsets)
# take angles and create uniform subsets in uniform+sequential setting
list_angles = [
angles[i:i + size_of_subsets]
for i in range(0, len(angles), size_of_subsets)
]
# create acquisitioin geometries for each the interval of splitting angles
list_geoms = [
AcquisitionGeometry('parallel',
'2D',
list_angles[i],
detectors,
pixel_size_h=0.1,
angle_unit='radian')
for i in range(len(list_angles))
]
# create with operators as many as the subsets
A = BlockOperator(*[
AstraProjectorSimple(ig, list_geoms[i], dev)
for i in range(subsets)
])
## number of subsets
#(sub2ind, ind2sub) = divide_1Darray_equally(range(len(A)), subsets)
#
## acquisisiton data
AD_list = []
for sub_num in range(subsets):
for i in range(0, len(angles), size_of_subsets):
arr = noisy_data.as_array()[i:i + size_of_subsets, :]
AD_list.append(
AcquisitionData(arr, geometry=list_geoms[sub_num]))
g = BlockDataContainer(*AD_list)
## block function
F = BlockFunction(*[KullbackLeibler(b=g[i]) for i in range(subsets)])
G = alpha * TotalVariation(50, 1e-4, lower=0)
prob = [1 / len(A)] * len(A)
spdhg = SPDHG(f=F,
g=G,
operator=A,
max_iteration=1000,
update_objective_interval=200,
prob=prob)
spdhg.run(1000, verbose=0)
from cil.utilities.quality_measures import mae, mse, psnr
qm = (mae(spdhg.get_output(),
pdhg.get_output()), mse(spdhg.get_output(),
pdhg.get_output()),
psnr(spdhg.get_output(), pdhg.get_output()))
if debug_print:
print("Quality measures", qm)
np.testing.assert_almost_equal(mae(spdhg.get_output(),
pdhg.get_output()),
0.000335,
decimal=3)
np.testing.assert_almost_equal(mse(spdhg.get_output(),
pdhg.get_output()),
5.51141e-06,
decimal=3)
def test_PDHG_vs_PDHG_explicit_axpby(self):
data = dataexample.SIMPLE_PHANTOM_2D.get(size=(128, 128))
if debug_print:
print("test_PDHG_vs_PDHG_explicit_axpby here")
ig = data.geometry
ig.voxel_size_x = 0.1
ig.voxel_size_y = 0.1
detectors = ig.shape[0]
angles = np.linspace(0, np.pi, 180)
ag = AcquisitionGeometry('parallel',
'2D',
angles,
detectors,
pixel_size_h=0.1,
angle_unit='radian')
dev = 'cpu'
Aop = AstraProjectorSimple(ig, ag, dev)
sin = Aop.direct(data)
# Create noisy data. Apply Gaussian noise
noises = ['gaussian', 'poisson']
noise = noises[1]
if noise == 'poisson':
np.random.seed(10)
scale = 5
eta = 0
noisy_data = AcquisitionData(
np.random.poisson(scale * (eta + sin.as_array())) / scale,
geometry=ag)
elif noise == 'gaussian':
np.random.seed(10)
n1 = np.random.normal(0, 0.1, size=ag.shape)
noisy_data = AcquisitionData(n1 + sin.as_array(), geometry=ag)
else:
raise ValueError('Unsupported Noise ', noise)
alpha = 0.5
op1 = GradientOperator(ig)
op2 = Aop
# Create BlockOperator
operator = BlockOperator(op1, op2, shape=(2, 1))
f2 = KullbackLeibler(b=noisy_data)
g = IndicatorBox(lower=0)
normK = operator.norm()
sigma = 1. / normK
tau = 1. / normK
f1 = alpha * MixedL21Norm()
f = BlockFunction(f1, f2)
# Setup and run the PDHG algorithm
algos = []
algos.append(
PDHG(f=f,
g=g,
operator=operator,
tau=tau,
sigma=sigma,
max_iteration=1000,
update_objective_interval=200,
use_axpby=True))
algos[0].run(1000, verbose=0)
algos.append(
PDHG(f=f,
g=g,
operator=operator,
tau=tau,
sigma=sigma,
max_iteration=1000,
update_objective_interval=200,
use_axpby=False))
algos[1].run(1000, verbose=0)
from cil.utilities.quality_measures import mae, mse, psnr
qm = (mae(algos[0].get_output(), algos[1].get_output()),
mse(algos[0].get_output(), algos[1].get_output()),
psnr(algos[0].get_output(), algos[1].get_output()))
if debug_print:
print("Quality measures", qm)
np.testing.assert_array_less(qm[0], 0.005)
np.testing.assert_array_less(qm[1], 3e-05)
def test_compare_regularisation_toolkit(self):
print("Compare CIL_FGP_TV vs CCPiReg_FGP_TV no tolerance (2D)")
data = dataexample.SHAPES.get()
ig = data.geometry
ag = ig
# Create noisy data.
n1 = np.random.normal(0, 0.1, size=ig.shape)
noisy_data = ig.allocate()
noisy_data.fill(n1 + data.as_array())
alpha = 0.1
iters = 1000
# CIL_FGP_TV no tolerance
g_CIL = alpha * TotalVariation(
iters, tolerance=None, lower=0, info=True)
t0 = timer()
res1 = g_CIL.proximal(noisy_data, 1.)
t1 = timer()
print(t1 - t0)
# CCPi Regularisation toolkit high tolerance
r_alpha = alpha
r_iterations = iters
r_tolerance = 1e-9
r_iso = 0
r_nonneg = 1
r_printing = 0
g_CCPI_reg_toolkit = CCPiReg_FGP_TV(r_alpha, r_iterations, r_tolerance,
r_iso, r_nonneg, r_printing, 'cpu')
t2 = timer()
res2 = g_CCPI_reg_toolkit.proximal(noisy_data, 1.)
t3 = timer()
print(t3 - t1)
np.testing.assert_array_almost_equal(res1.as_array(),
res2.as_array(),
decimal=4)
###################################################################
###################################################################
###################################################################
###################################################################
print("Compare CIL_FGP_TV vs CCPiReg_FGP_TV with iterations.")
iters = 408
# CIL_FGP_TV no tolerance
g_CIL = alpha * TotalVariation(iters, tolerance=1e-9, lower=0.)
t0 = timer()
res1 = g_CIL.proximal(noisy_data, 1.)
t1 = timer()
print(t1 - t0)
# CCPi Regularisation toolkit high tolerance
r_alpha = alpha
r_iterations = iters
r_tolerance = 1e-9
r_iso = 0
r_nonneg = 1
r_printing = 0
g_CCPI_reg_toolkit = CCPiReg_FGP_TV(r_alpha, r_iterations, r_tolerance,
r_iso, r_nonneg, r_printing, 'cpu')
t2 = timer()
res2 = g_CCPI_reg_toolkit.proximal(noisy_data, 1.)
t3 = timer()
print(t3 - t2)
print(mae(res1, res2))
np.testing.assert_array_almost_equal(res1.as_array(),
res2.as_array(),
decimal=3)