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# -*- coding: utf-8 -*-
# CCP in Tomographic Imaging (CCPi) Core Imaging Library (CIL).
# Copyright 2017 UKRI-STFC
# Copyright 2017 University of Manchester
# 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 ccpi.optimisation.algorithms import Algorithm
from ccpi.framework import ImageData, DataContainer
import numpy as np
import numpy
import time
from ccpi.optimisation.operators import BlockOperator
from ccpi.framework import BlockDataContainer
from ccpi.optimisation.functions import FunctionOperatorComposition
class PDHG(Algorithm):
'''Primal Dual Hybrid Gradient'''
def __init__(self, **kwargs):
super(PDHG, self).__init__(max_iteration=kwargs.get('max_iteration',0))
self.f = kwargs.get('f', None)
self.operator = kwargs.get('operator', None)
self.g = kwargs.get('g', None)
self.tau = kwargs.get('tau', None)
self.sigma = kwargs.get('sigma', 1.)
if self.f is not None and self.operator is not None and \
self.g is not None:
if self.tau is None:
# Compute operator Norm
normK = self.operator.norm()
# Primal & dual stepsizes
self.tau = 1/(self.sigma*normK**2)
print ("Calling from creator")
self.set_up(self.f,
self.g,
self.operator,
self.tau,
self.sigma)
def set_up(self, f, g, operator, tau = None, sigma = None, opt = None, **kwargs):
# algorithmic parameters
self.operator = operator
self.f = f
self.g = g
self.tau = tau
self.sigma = sigma
self.opt = opt
if sigma is None and tau is None:
raise ValueError('Need sigma*tau||K||^2<1')
self.x_old = self.operator.domain_geometry().allocate()
self.x_tmp = self.x_old.copy()
self.x = self.x_old.copy()
self.y_old = self.operator.range_geometry().allocate()
self.y_tmp = self.y_old.copy()
self.y = self.y_old.copy()
self.xbar = self.x_old.copy()
# relaxation parameter
self.theta = 1
self.update_objective()
self.configured = True
def update(self):
# Gradient descent, Dual problem solution
self.operator.direct(self.xbar, out=self.y_tmp)
self.y_tmp *= self.sigma
self.y_tmp += self.y_old
#self.y = self.f.proximal_conjugate(self.y_old, self.sigma)
self.f.proximal_conjugate(self.y_tmp, self.sigma, out=self.y)
# Gradient ascent, Primal problem solution
self.operator.adjoint(self.y, out=self.x_tmp)
self.x_tmp *= -1*self.tau
self.x_tmp += self.x_old
self.g.proximal(self.x_tmp, self.tau, out=self.x)
#Update
self.x.subtract(self.x_old, out=self.xbar)
self.xbar *= self.theta
self.xbar += self.x
self.x_old.fill(self.x)
self.y_old.fill(self.y)
def update_objective(self):
p1 = self.f(self.operator.direct(self.x)) + self.g(self.x)
d1 = -(self.f.convex_conjugate(self.y) + self.g.convex_conjugate(-1*self.operator.adjoint(self.y)))
self.loss.append([p1,d1,p1-d1])
def PDHG_old(f, g, operator, tau = None, sigma = None, opt = None, **kwargs):
# algorithmic parameters
if opt is None:
opt = {'tol': 1e-6, 'niter': 500, 'show_iter': 100, \
'memopt': False}
if sigma is None and tau is None:
raise ValueError('Need sigma*tau||K||^2<1')
niter = opt['niter'] if 'niter' in opt.keys() else 1000
tol = opt['tol'] if 'tol' in opt.keys() else 1e-4
memopt = opt['memopt'] if 'memopt' in opt.keys() else False
show_iter = opt['show_iter'] if 'show_iter' in opt.keys() else False
stop_crit = opt['stop_crit'] if 'stop_crit' in opt.keys() else False
x_old = operator.domain_geometry().allocate()
y_old = operator.range_geometry().allocate()
xbar = x_old.copy()
x_tmp = x_old.copy()
x = x_old.copy()
y_tmp = y_old.copy()
y = y_tmp.copy()
# relaxation parameter
theta = 1
t = time.time()
primal = []
dual = []
pdgap = []
for i in range(niter):
if memopt:
operator.direct(xbar, out = y_tmp)
y_tmp *= sigma
y_tmp += y_old
else:
y_tmp = y_old + sigma * operator.direct(xbar)
f.proximal_conjugate(y_tmp, sigma, out=y)
if memopt:
operator.adjoint(y, out = x_tmp)
x_tmp *= -1*tau
x_tmp += x_old
else:
x_tmp = x_old - tau*operator.adjoint(y)
g.proximal(x_tmp, tau, out=x)
x.subtract(x_old, out=xbar)
xbar *= theta
xbar += x
x_old.fill(x)
y_old.fill(y)
if i%10==0:
p1 = f(operator.direct(x)) + g(x)
d1 = - ( f.convex_conjugate(y) + g.convex_conjugate(-1*operator.adjoint(y)) )
primal.append(p1)
dual.append(d1)
pdgap.append(p1-d1)
print(p1, d1, p1-d1)
t_end = time.time()
return x, t_end - t, primal, dual, pdgap
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