diff options
| -rwxr-xr-x | Wrappers/Python/ccpi/optimisation/algs.py | 6 | ||||
| -rwxr-xr-x | Wrappers/Python/ccpi/optimisation/ops.py | 14 | ||||
| -rwxr-xr-x | Wrappers/Python/ccpi/optimisation/spdhg.py | 338 |
3 files changed, 350 insertions, 8 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/algs.py b/Wrappers/Python/ccpi/optimisation/algs.py index 24ed6e9..6890e3b 100755 --- a/Wrappers/Python/ccpi/optimisation/algs.py +++ b/Wrappers/Python/ccpi/optimisation/algs.py @@ -22,7 +22,11 @@ import time from ccpi.optimisation.funcs import Function from ccpi.optimisation.funcs import ZeroFun -from ccpi.framework import ImageData, AcquisitionData +from ccpi.framework import ImageData +from ccpi.framework import AcquisitionData +from ccpi.framework.optimisation.spdhg import spdhg +from ccpi.framework.optimisation.spdhg import KullbackLeibler +from ccpi.framework.optimisation.spdhg import KullbackLeiblerConvexConjugate def FISTA(x_init, f=None, g=None, opt=None): '''Fast Iterative Shrinkage-Thresholding Algorithm diff --git a/Wrappers/Python/ccpi/optimisation/ops.py b/Wrappers/Python/ccpi/optimisation/ops.py index 96f85d8..450b084 100755 --- a/Wrappers/Python/ccpi/optimisation/ops.py +++ b/Wrappers/Python/ccpi/optimisation/ops.py @@ -186,10 +186,10 @@ def PowerMethodNonsquareOld(op,numiters): # return s, x0 -def PowerMethodNonsquare(op,numiters): +def PowerMethodNonsquare(op,numiters , x0=None): # Initialise random # Jakob's - inputsize , outputsize = op.size() + # inputsize , outputsize = op.size() #x0 = ImageContainer(numpy.random.randn(*inputsize) # Edo's #vg = ImageGeometry(voxel_num_x=inputsize[0], @@ -200,16 +200,16 @@ def PowerMethodNonsquare(op,numiters): #print (x0) #x0.fill(numpy.random.randn(*x0.shape)) - - #x0 = op.create_image_data() - x0 = op.allocate_direct() - x0.fill(numpy.random.randn(*x0.shape)) + if x0 is None: + #x0 = op.create_image_data() + x0 = op.allocate_direct() + x0.fill(numpy.random.randn(*x0.shape)) s = numpy.zeros(numiters) # Loop for it in numpy.arange(numiters): x1 = op.adjoint(op.direct(x0)) - x1norm = numpy.sqrt((x1**2).sum()) + x1norm = numpy.sqrt((x1*x1).sum()) #print ("x0 **********" ,x0) #print ("x1 **********" ,x1) s[it] = (x1*x0).sum() / (x0*x0).sum() diff --git a/Wrappers/Python/ccpi/optimisation/spdhg.py b/Wrappers/Python/ccpi/optimisation/spdhg.py new file mode 100755 index 0000000..263a7cd --- /dev/null +++ b/Wrappers/Python/ccpi/optimisation/spdhg.py @@ -0,0 +1,338 @@ +# Copyright 2018 Matthias Ehrhardt, Edoardo Pasca
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+# http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy
+
+from ccpi.optimisation.funcs import Function
+from ccpi.framework import ImageData
+from ccpi.framework import AcquisitionData
+
+
+class spdhg():
+ """Computes a saddle point with a stochastic PDHG.
+
+ This means, a solution (x*, y*), y* = (y*_1, ..., y*_n) such that
+
+ (x*, y*) in arg min_x max_y sum_i=1^n <y_i, A_i> - f*[i](y_i) + g(x)
+
+ where g : X -> IR_infty and f[i] : Y[i] -> IR_infty are convex, l.s.c. and
+ proper functionals. For this algorithm, they all may be non-smooth and no
+ strong convexity is assumed.
+
+ Parameters
+ ----------
+ f : list of functions
+ Functionals Y[i] -> IR_infty that all have a convex conjugate with a
+ proximal operator, i.e.
+ f[i].convex_conj.prox(sigma[i]) : Y[i] -> Y[i].
+ g : function
+ Functional X -> IR_infty that has a proximal operator, i.e.
+ g.prox(tau) : X -> X.
+ A : list of functions
+ Operators A[i] : X -> Y[i] that possess adjoints: A[i].adjoint
+ x : primal variable, optional
+ By default equals 0.
+ y : dual variable, optional
+ Part of a product space. By default equals 0.
+ z : variable, optional
+ Adjoint of dual variable, z = A^* y. By default equals 0 if y = 0.
+ tau : scalar / vector / matrix, optional
+ Step size for primal variable. Note that the proximal operator of g
+ has to be well-defined for this input.
+ sigma : scalar, optional
+ Scalar / vector / matrix used as step size for dual variable. Note that
+ the proximal operator related to f (see above) has to be well-defined
+ for this input.
+ prob : list of scalars, optional
+ Probabilities prob[i] that a subset i is selected in each iteration.
+ If fun_select is not given, then the sum of all probabilities must
+ equal 1.
+ A_norms : list of scalars, optional
+ Norms of the operators in A. Can be used to determine the step sizes
+ tau and sigma and the probabilities prob.
+ fun_select : function, optional
+ Function that selects blocks at every iteration IN -> {1,...,n}. By
+ default this is serial sampling, fun_select(k) selects an index
+ i \in {1,...,n} with probability prob[i].
+
+ References
+ ----------
+ [CERS2018] A. Chambolle, M. J. Ehrhardt, P. Richtarik and C.-B. Schoenlieb,
+ *Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling
+ and Imaging Applications*. SIAM Journal on Optimization, 28(4), 2783-2808
+ (2018) http://doi.org/10.1007/s10851-010-0251-1
+
+ [E+2017] M. J. Ehrhardt, P. J. Markiewicz, P. Richtarik, J. Schott,
+ A. Chambolle and C.-B. Schoenlieb, *Faster PET reconstruction with a
+ stochastic primal-dual hybrid gradient method*. Wavelets and Sparsity XVII,
+ 58 (2017) http://doi.org/10.1117/12.2272946.
+
+ [EMS2018] M. J. Ehrhardt, P. J. Markiewicz and C.-B. Schoenlieb, *Faster
+ PET Reconstruction with Non-Smooth Priors by Randomization and
+ Preconditioning*. (2018) ArXiv: http://arxiv.org/abs/1808.07150
+ """
+
+ def __init__(self, f, g, A, x=None, y=None, z=None, tau=None, sigma=None,
+ prob=None, A_norms=None, fun_select=None):
+ # fun_select is optional and by default performs serial sampling
+
+ if x is None:
+ x = A[0].allocate_direct(0)
+
+ if y is None:
+ if z is not None:
+ raise ValueError('y and z have to be defaulted together')
+
+ y = [Ai.allocate_adjoint(0) for Ai in A]
+ z = 0 * x.copy()
+
+ else:
+ if z is None:
+ raise ValueError('y and z have to be defaulted together')
+
+ if A_norms is not None:
+ if tau is not None or sigma is not None or prob is not None:
+ raise ValueError('Either A_norms or (tau, sigma, prob) must '
+ 'be given')
+
+ tau = 1 / sum(A_norms)
+ sigma = [1 / nA for nA in A_norms]
+ prob = [nA / sum(A_norms) for nA in A_norms]
+
+ #uniform prob, needs different sigma and tau
+ #n = len(A)
+ #prob = [1./n] * n
+
+ if fun_select is None:
+ if prob is None:
+ raise ValueError('prob was not determined')
+
+ def fun_select(k):
+ return [int(numpy.random.choice(len(A), 1, p=prob))]
+
+ self.iter = 0
+ self.x = x
+
+ self.y = y
+ self.z = z
+
+ self.f = f
+ self.g = g
+ self.A = A
+ self.tau = tau
+ self.sigma = sigma
+ self.prob = prob
+ self.fun_select = fun_select
+
+ # Initialize variables
+ self.z_relax = z.copy()
+ self.tmp = self.x.copy()
+
+ def update(self):
+ # select block
+ selected = self.fun_select(self.iter)
+
+ # update primal variable
+ #tmp = (self.x - self.tau * self.z_relax).as_array()
+ #self.x.fill(self.g.prox(tmp, self.tau))
+ self.tmp = - self.tau * self.z_relax
+ self.tmp += self.x
+ self.x = self.g.prox(self.tmp, self.tau)
+
+ # update dual variable and z, z_relax
+ self.z_relax = self.z.copy()
+ for i in selected:
+ # save old yi
+ y_old = self.y[i].copy()
+
+ # y[i]= prox(tmp)
+ tmp = y_old + self.sigma[i] * self.A[i].direct(self.x)
+ self.y[i] = self.f[i].convex_conj.prox(tmp, self.sigma[i])
+
+ # update adjoint of dual variable
+ dz = self.A[i].adjoint(self.y[i] - y_old)
+ self.z += dz
+
+ # compute extrapolation
+ self.z_relax += (1 + 1 / self.prob[i]) * dz
+
+ self.iter += 1
+
+
+## Functions
+
+class KullbackLeibler(Function):
+ def __init__(self, data, background):
+ self.data = data
+ self.background = background
+ self.__offset = None
+
+ def __call__(self, x):
+ """Return the KL-diveregnce in the point ``x``.
+
+ If any components of ``x`` is non-positive, the value is positive
+ infinity.
+
+ Needs one extra array of memory of the size of `prior`.
+ """
+
+ # define short variable names
+ y = self.data
+ r = self.background
+
+ # Compute
+ # sum(x + r - y + y * log(y / (x + r)))
+ # = sum(x - y * log(x + r)) + self.offset
+ # Assume that
+ # x + r > 0
+
+ # sum the result up
+ obj = numpy.sum(x - y * numpy.log(x + r)) + self.offset()
+
+ if numpy.isnan(obj):
+ # In this case, some element was less than or equal to zero
+ return numpy.inf
+ else:
+ return obj
+
+ @property
+ def convex_conj(self):
+ """The convex conjugate functional of the KL-functional."""
+ return KullbackLeiblerConvexConjugate(self.data, self.background)
+
+ def offset(self):
+ """The offset which is independent of the unknown."""
+
+ if self.__offset is None:
+ tmp = self.domain.element()
+
+ # define short variable names
+ y = self.data
+ r = self.background
+
+ tmp = self.domain.element(numpy.maximum(y, 1))
+ tmp = r - y + y * numpy.log(tmp)
+
+ # sum the result up
+ self.__offset = numpy.sum(tmp)
+
+ return self.__offset
+
+# def __repr__(self):
+# """to be added???"""
+# """Return ``repr(self)``."""
+ # return '{}({!r}, {!r}, {!r})'.format(self.__class__.__name__,
+ ## self.domain, self.data,
+ # self.background)
+
+
+class KullbackLeiblerConvexConjugate(Function):
+ """The convex conjugate of Kullback-Leibler divergence functional.
+
+ Notes
+ -----
+ The functional :math:`F^*` with prior :math:`g>0` is given by:
+
+ .. math::
+ F^*(x)
+ =
+ \\begin{cases}
+ \\sum_{i} \left( -g_i \ln(1 - x_i) \\right)
+ & \\text{if } x_i < 1 \\forall i
+ \\\\
+ +\\infty & \\text{else}
+ \\end{cases}
+
+ See Also
+ --------
+ KullbackLeibler : convex conjugate functional
+ """
+
+ def __init__(self, data, background):
+ self.data = data
+ self.background = background
+
+ def __call__(self, x):
+ y = self.data
+ r = self.background
+
+ tmp = numpy.sum(- x * r - y * numpy.log(1 - x))
+
+ if numpy.isnan(tmp):
+ # In this case, some element was larger than or equal to one
+ return numpy.inf
+ else:
+ return tmp
+
+
+ def prox(self, x, tau, out=None):
+ # Let y = data, r = background, z = x + tau * r
+ # Compute 0.5 * (z + 1 - sqrt((z - 1)**2 + 4 * tau * y))
+ # Currently it needs 3 extra copies of memory.
+
+ if out is None:
+ out = x.copy()
+
+ # define short variable names
+ try: # this should be standard SIRF/CIL mode
+ y = self.data.as_array()
+ r = self.background.as_array()
+ x = x.as_array()
+
+ try:
+ taua = tau.as_array()
+ except:
+ taua = tau
+
+ z = x + tau * r
+
+ out.fill(0.5 * (z + 1 - numpy.sqrt((z - 1) ** 2 + 4 * taua * y)))
+
+ return out
+
+ except: # e.g. for NumPy
+ y = self.data
+ r = self.background
+
+ try:
+ taua = tau.as_array()
+ except:
+ taua = tau
+
+ z = x + tau * r
+
+ out[:] = 0.5 * (z + 1 - numpy.sqrt((z - 1) ** 2 + 4 * taua * y))
+
+ return out
+
+ @property
+ def convex_conj(self):
+ return KullbackLeibler(self.data, self.background)
+
+
+def mult(x, y):
+ try:
+ xa = x.as_array()
+ except:
+ xa = x
+
+ out = y.clone()
+ out.fill(xa * y.as_array())
+
+ return out
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