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-rwxr-xr-xWrappers/Python/ccpi/optimisation/operators/LinearOperator.py61
-rwxr-xr-xWrappers/Python/ccpi/optimisation/ops.py294
-rw-r--r--Wrappers/Python/test/test_Operator.py29
3 files changed, 90 insertions, 294 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/operators/LinearOperator.py b/Wrappers/Python/ccpi/optimisation/operators/LinearOperator.py
index e19304f..8ecefb8 100755
--- a/Wrappers/Python/ccpi/optimisation/operators/LinearOperator.py
+++ b/Wrappers/Python/ccpi/optimisation/operators/LinearOperator.py
@@ -6,6 +6,8 @@ Created on Tue Mar 5 15:57:52 2019
"""
from ccpi.optimisation.operators import Operator
+from ccpi.framework import ImageGeometry
+import numpy
class LinearOperator(Operator):
@@ -20,3 +22,62 @@ class LinearOperator(Operator):
only available to linear operators'''
raise NotImplementedError
+
+ @staticmethod
+ def PowerMethod(operator, iterations, x0=None):
+ '''Power method to calculate iteratively the Lipschitz constant'''
+ # Initialise random
+
+ if x0 is None:
+ #x0 = op.create_image_data()
+ x0 = operator.domain_geometry().allocate(ImageGeometry.RANDOM_INT)
+
+ x1 = operator.domain_geometry().allocate()
+ y_tmp = operator.range_geometry().allocate()
+ s = numpy.zeros(iterations)
+ # Loop
+ for it in numpy.arange(iterations):
+ #x1 = operator.adjoint(operator.direct(x0))
+ operator.direct(x0,out=y_tmp)
+ operator.adjoint(y_tmp,out=x1)
+ x1norm = x1.norm()
+ #s[it] = (x1*x0).sum() / (x0.squared_norm())
+ s[it] = x1.dot(x0) / x0.squared_norm()
+ #x0 = (1.0/x1norm)*x1
+ x1 *= (1.0 / x1norm)
+ x0.fill(x1)
+ return numpy.sqrt(s[-1]), numpy.sqrt(s), x0
+
+ @staticmethod
+ def PowerMethodNonsquare(op,numiters , x0=None):
+ # Initialise random
+ # Jakob's
+ # inputsize , outputsize = op.size()
+ #x0 = ImageContainer(numpy.random.randn(*inputsize)
+ # Edo's
+ #vg = ImageGeometry(voxel_num_x=inputsize[0],
+ # voxel_num_y=inputsize[1],
+ # voxel_num_z=inputsize[2])
+ #
+ #x0 = ImageData(geometry = vg, dimension_labels=['vertical','horizontal_y','horizontal_x'])
+ #print (x0)
+ #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*x1).sum())
+ x1norm = x1.norm()
+ #print ("x0 **********" ,x0)
+ #print ("x1 **********" ,x1)
+ s[it] = (x1*x0).sum() / (x0.squared_norm())
+ x0 = (1.0/x1norm)*x1
+ return numpy.sqrt(s[-1]), numpy.sqrt(s), x0
+
+
diff --git a/Wrappers/Python/ccpi/optimisation/ops.py b/Wrappers/Python/ccpi/optimisation/ops.py
deleted file mode 100755
index fcd0d9e..0000000
--- a/Wrappers/Python/ccpi/optimisation/ops.py
+++ /dev/null
@@ -1,294 +0,0 @@
-# -*- coding: utf-8 -*-
-# This work is part of the Core Imaging Library developed by
-# Visual Analytics and Imaging System Group of the Science Technology
-# Facilities Council, STFC
-
-# Copyright 2018 Jakob Jorgensen, Daniil Kazantsev and 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.
-
-import numpy
-from scipy.sparse.linalg import svds
-from ccpi.framework import DataContainer
-from ccpi.framework import AcquisitionData
-from ccpi.framework import ImageData
-from ccpi.framework import ImageGeometry
-from ccpi.framework import AcquisitionGeometry
-from numbers import Number
-# Maybe operators need to know what types they take as inputs/outputs
-# to not just use generic DataContainer
-
-
-class Operator(object):
- '''Operator that maps from a space X -> Y'''
- def __init__(self, **kwargs):
- self.scalar = 1
- def is_linear(self):
- '''Returns if the operator is linear'''
- return False
- def direct(self,x, out=None):
- raise NotImplementedError
- def size(self):
- # To be defined for specific class
- raise NotImplementedError
- def norm(self):
- raise NotImplementedError
- def allocate_direct(self):
- '''Allocates memory on the Y space'''
- raise NotImplementedError
- def allocate_adjoint(self):
- '''Allocates memory on the X space'''
- raise NotImplementedError
- def range_geometry(self):
- raise NotImplementedError
- def domain_geometry(self):
- raise NotImplementedError
- def __rmul__(self, other):
- '''reverse multiplication of Operator with number sets the variable scalar in the Operator'''
- assert isinstance(other, Number)
- self.scalar = other
- return self
-
-class LinearOperator(Operator):
- '''Operator that maps from a space X -> Y'''
- def is_linear(self):
- '''Returns if the operator is linear'''
- return True
- def adjoint(self,x, out=None):
- raise NotImplementedError
-
-class Identity(Operator):
- def __init__(self):
- self.s1 = 1.0
- self.L = 1
- super(Identity, self).__init__()
-
- def direct(self,x,out=None):
- if out is None:
- return x.copy()
- else:
- out.fill(x)
-
- def adjoint(self,x, out=None):
- if out is None:
- return x.copy()
- else:
- out.fill(x)
-
- def size(self):
- return NotImplemented
-
- def get_max_sing_val(self):
- return self.s1
-
-class TomoIdentity(Operator):
- def __init__(self, geometry, **kwargs):
- super(TomoIdentity, self).__init__()
- self.s1 = 1.0
- self.geometry = geometry
-
- def is_linear(self):
- return True
- def direct(self,x,out=None):
-
- if out is None:
- if self.scalar != 1:
- return x * self.scalar
- return x.copy()
- else:
- if self.scalar != 1:
- out.fill(x * self.scalar)
- return
- out.fill(x)
- return
-
- def adjoint(self,x, out=None):
- return self.direct(x, out)
-
- def norm(self):
- return self.s1
-
- def get_max_sing_val(self):
- return self.s1
- def allocate_direct(self):
- if issubclass(type(self.geometry), ImageGeometry):
- return ImageData(geometry=self.geometry)
- elif issubclass(type(self.geometry), AcquisitionGeometry):
- return AcquisitionData(geometry=self.geometry)
- else:
- raise ValueError("Wrong geometry type: expected ImageGeometry of AcquisitionGeometry, got ", type(self.geometry))
- def allocate_adjoint(self):
- return self.allocate_direct()
- def range_geometry(self):
- return self.geometry
- def domain_geometry(self):
- return self.geometry
-
-
-
-class FiniteDiff2D(Operator):
- def __init__(self):
- self.s1 = 8.0
- super(FiniteDiff2D, self).__init__()
-
- def direct(self,x, out=None):
- '''Forward differences with Neumann BC.'''
- # FIXME this seems to be working only with numpy arrays
-
- d1 = numpy.zeros_like(x.as_array())
- d1[:,:-1] = x.as_array()[:,1:] - x.as_array()[:,:-1]
- d2 = numpy.zeros_like(x.as_array())
- d2[:-1,:] = x.as_array()[1:,:] - x.as_array()[:-1,:]
- d = numpy.stack((d1,d2),0)
- #x.geometry.voxel_num_z = 2
- return type(x)(d,False,geometry=x.geometry)
-
- def adjoint(self,x, out=None):
- '''Backward differences, Neumann BC.'''
- Nrows = x.get_dimension_size('horizontal_x')
- Ncols = x.get_dimension_size('horizontal_y')
- Nchannels = 1
- if len(x.shape) == 4:
- Nchannels = x.get_dimension_size('channel')
- zer = numpy.zeros((Nrows,1))
- xxx = x.as_array()[0,:,:-1]
- #
- h = numpy.concatenate((zer,xxx), 1)
- h -= numpy.concatenate((xxx,zer), 1)
-
- zer = numpy.zeros((1,Ncols))
- xxx = x.as_array()[1,:-1,:]
- #
- v = numpy.concatenate((zer,xxx), 0)
- v -= numpy.concatenate((xxx,zer), 0)
- return type(x)(h + v, False, geometry=x.geometry)
-
- def size(self):
- return NotImplemented
-
- def get_max_sing_val(self):
- return self.s1
-
-def PowerMethodNonsquareOld(op,numiters):
- # Initialise random
- # Jakob's
- #inputsize = op.size()[1]
- #x0 = ImageContainer(numpy.random.randn(*inputsize)
- # Edo's
- #vg = ImageGeometry(voxel_num_x=inputsize[0],
- # voxel_num_y=inputsize[1],
- # voxel_num_z=inputsize[2])
- #
- #x0 = ImageData(geometry = vg, dimension_labels=['vertical','horizontal_y','horizontal_x'])
- #print (x0)
- #x0.fill(numpy.random.randn(*x0.shape))
-
- x0 = op.create_image_data()
-
- s = numpy.zeros(numiters)
- # Loop
- for it in numpy.arange(numiters):
- x1 = op.adjoint(op.direct(x0))
- x1norm = numpy.sqrt((x1**2).sum())
- #print ("x0 **********" ,x0)
- #print ("x1 **********" ,x1)
- s[it] = (x1*x0).sum() / (x0*x0).sum()
- x0 = (1.0/x1norm)*x1
- return numpy.sqrt(s[-1]), numpy.sqrt(s), x0
-
-#def PowerMethod(op,numiters):
-# # Initialise random
-# x0 = np.random.randn(400)
-# s = np.zeros(numiters)
-# # Loop
-# for it in np.arange(numiters):
-# x1 = np.dot(op.transpose(),np.dot(op,x0))
-# x1norm = np.sqrt(np.sum(np.dot(x1,x1)))
-# s[it] = np.dot(x1,x0) / np.dot(x1,x0)
-# x0 = (1.0/x1norm)*x1
-# return s, x0
-
-
-def PowerMethodNonsquare(op,numiters , x0=None):
- # Initialise random
- # Jakob's
- # inputsize , outputsize = op.size()
- #x0 = ImageContainer(numpy.random.randn(*inputsize)
- # Edo's
- #vg = ImageGeometry(voxel_num_x=inputsize[0],
- # voxel_num_y=inputsize[1],
- # voxel_num_z=inputsize[2])
- #
- #x0 = ImageData(geometry = vg, dimension_labels=['vertical','horizontal_y','horizontal_x'])
- #print (x0)
- #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*x1).sum())
- x1norm = x1.norm()
- #print ("x0 **********" ,x0)
- #print ("x1 **********" ,x1)
- s[it] = (x1*x0).sum() / (x0.squared_norm())
- x0 = (1.0/x1norm)*x1
- return numpy.sqrt(s[-1]), numpy.sqrt(s), x0
-
-class LinearOperatorMatrix(Operator):
- def __init__(self,A):
- self.A = A
- self.s1 = None # Largest singular value, initially unknown
- super(LinearOperatorMatrix, self).__init__()
-
- def direct(self,x, out=None):
- if out is None:
- return type(x)(numpy.dot(self.A,x.as_array()))
- else:
- numpy.dot(self.A, x.as_array(), out=out.as_array())
-
-
- def adjoint(self,x, out=None):
- if out is None:
- return type(x)(numpy.dot(self.A.transpose(),x.as_array()))
- else:
- numpy.dot(self.A.transpose(),x.as_array(), out=out.as_array())
-
-
- def size(self):
- return self.A.shape
-
- def get_max_sing_val(self):
- # If unknown, compute and store. If known, simply return it.
- if self.s1 is None:
- self.s1 = svds(self.A,1,return_singular_vectors=False)[0]
- return self.s1
- else:
- return self.s1
- def allocate_direct(self):
- '''allocates the memory to hold the result of adjoint'''
- #numpy.dot(self.A.transpose(),x.as_array())
- M_A, N_A = self.A.shape
- out = numpy.zeros((N_A,1))
- return DataContainer(out)
- def allocate_adjoint(self):
- '''allocate the memory to hold the result of direct'''
- #numpy.dot(self.A.transpose(),x.as_array())
- M_A, N_A = self.A.shape
- out = numpy.zeros((M_A,1))
- return DataContainer(out)
diff --git a/Wrappers/Python/test/test_Operator.py b/Wrappers/Python/test/test_Operator.py
index 293fb43..70a1cd6 100644
--- a/Wrappers/Python/test/test_Operator.py
+++ b/Wrappers/Python/test/test_Operator.py
@@ -8,6 +8,7 @@ import numpy
from timeit import default_timer as timer
from ccpi.framework import ImageGeometry
from ccpi.optimisation.operators import Gradient, Identity, SparseFiniteDiff
+from ccpi.optimisation.operators import LinearOperator
def dt(steps):
return steps[-1] - steps[-2]
@@ -97,7 +98,35 @@ class TestOperator(CCPiTestClass):
G.adjoint(u, out=res)
w = G.adjoint(u)
self.assertNumpyArrayEqual(res.as_array(), w.as_array())
+ def test_PowerMethod(self):
+ N, M = 2, 3
+
+ ig = ImageGeometry(N, M)
+ Id = Identity(ig)
+
+ G = Gradient(ig)
+
+ uid = Id.domain_geometry().allocate(ImageGeometry.RANDOM_INT)
+
+ a = LinearOperator.PowerMethod(Id, 10, uid)
+ b = LinearOperator.PowerMethodNonsquare(Id, 10, uid)
+
+ print ("Edo impl", a[0])
+ print ("old impl", b[0])
+
+ #self.assertAlmostEqual(a[0], b[0])
+ self.assertNumpyArrayAlmostEqual(a[0],b[0],decimal=6)
+
+ a = LinearOperator.PowerMethod(G, 10, uid)
+ b = LinearOperator.PowerMethodNonsquare(G, 10, uid)
+
+ print ("Edo impl", a[0])
+ print ("old impl", b[0])
+ self.assertNumpyArrayAlmostEqual(a[0],b[0],decimal=2)
+ #self.assertAlmostEqual(a[0], b[0])
+
+
generated by cgit v1.2.3 (git 2.25.1) at 2024-11-15 19:37:15 +0000