From 5ae13b1d55da87f4c3f3908fc91ec2424daaf4b3 Mon Sep 17 00:00:00 2001
From: epapoutsellis <epapoutsellis@gmail.com>
Date: Mon, 29 Apr 2019 09:43:18 +0100
Subject: changes to PD aglo
---
.../ccpi/optimisation/algorithms/Algorithm.py | 22 ++--
.../Python/wip/Demos/PDHG_TV_Denoising_Poisson.py | 127 ++++++++++-----------
2 files changed, 76 insertions(+), 73 deletions(-)
(limited to 'Wrappers')
diff --git a/Wrappers/Python/ccpi/optimisation/algorithms/Algorithm.py b/Wrappers/Python/ccpi/optimisation/algorithms/Algorithm.py
index 47376a5..12cbabc 100755
--- a/Wrappers/Python/ccpi/optimisation/algorithms/Algorithm.py
+++ b/Wrappers/Python/ccpi/optimisation/algorithms/Algorithm.py
@@ -146,12 +146,18 @@ class Algorithm(object):
print ("Stop cryterion has been reached.")
i = 0
- print("Iteration {:<5} Primal {:<5} Dual {:<5} PDgap".format('','',''))
+# print("Iteration {:<5} Primal {:<5} Dual {:<5} PDgap".format('','',''))
for _ in self:
-
+
if self.iteration % self.update_objective_interval == 0:
- if verbose:
+
+ if callback is not None:
+ callback(self.iteration, self.get_last_objective(), self.x)
+
+ else:
+
+ if verbose:
# if verbose and self.iteration % self.update_objective_interval == 0:
#pass
@@ -163,16 +169,16 @@ class Algorithm(object):
# self.get_last_objective()[2]))
- print ("Iteration {}/{}, {}".format(self.iteration,
- self.max_iteration, self.get_last_objective()) )
+ print ("Iteration {}/{}, {}".format(self.iteration,
+ self.max_iteration, self.get_last_objective()) )
#print ("Iteration {}/{}, Primal, Dual, PDgap = {}".format(self.iteration,
# self.max_iteration, self.get_last_objective()) )
- else:
- if callback is not None:
- callback(self.iteration, self.get_last_objective(), self.x)
+# else:
+# if callback is not None:
+# callback(self.iteration, self.get_last_objective(), self.x)
i += 1
if i == iterations:
break
diff --git a/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py b/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py
index 32ab62d..ccdabb2 100644
--- a/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py
+++ b/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py
@@ -88,7 +88,7 @@ pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True)
pdhg.max_iteration = 2000
pdhg.update_objective_interval = 50
-def pdgap_print(niter, objective, solution):
+def pdgap_objectives(niter, objective, solution):
print( "{:04}/{:04} {:<5} {:.4f} {:<5} {:.4f} {:<5} {:.4f}".\
@@ -97,9 +97,7 @@ def pdgap_print(niter, objective, solution):
objective[1],'',\
objective[2]))
-#pdhg.run(2000)
-
-pdhg.run(2000, callback = pdgap_print)
+pdhg.run(2000, callback = pdgap_objectives)
plt.figure(figsize=(15,15))
@@ -124,66 +122,65 @@ plt.title('Middle Line Profiles')
plt.show()
-##%% Check with CVX solution
#%% Check with CVX solution
-from ccpi.optimisation.operators import SparseFiniteDiff
-
-try:
- from cvxpy import *
- cvx_not_installable = True
-except ImportError:
- cvx_not_installable = False
-
-
-if cvx_not_installable:
-
- ##Construct problem
- u1 = Variable(ig.shape)
- q = Variable()
-
- DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann')
- DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann')
-
- # Define Total Variation as a regulariser
- regulariser = alpha * sum(norm(vstack([DX.matrix() * vec(u1), DY.matrix() * vec(u1)]), 2, axis = 0))
-
- fidelity = sum( u1 - multiply(noisy_data.as_array(), log(u1)) )
- constraints = [q>= fidelity, u1>=0]
-
- solver = ECOS
- obj = Minimize( regulariser + q)
- prob = Problem(obj, constraints)
- result = prob.solve(verbose = True, solver = solver)
-
-
- diff_cvx = numpy.abs( pdhg.get_output().as_array() - u1.value )
-
- plt.figure(figsize=(15,15))
- plt.subplot(3,1,1)
- plt.imshow(pdhg.get_output().as_array())
- plt.title('PDHG solution')
- plt.colorbar()
- plt.subplot(3,1,2)
- plt.imshow(u1.value)
- plt.title('CVX solution')
- plt.colorbar()
- plt.subplot(3,1,3)
- plt.imshow(diff_cvx)
- plt.title('Difference')
- plt.colorbar()
- plt.show()
-
- plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'PDHG')
- plt.plot(np.linspace(0,N,N), u1.value[int(N/2),:], label = 'CVX')
- plt.legend()
- plt.title('Middle Line Profiles')
- plt.show()
-
- print('Primal Objective (CVX) {} '.format(obj.value))
- print('Primal Objective (PDHG) {} '.format(pdhg.objective[-1][0]))
-
-
-
-
-
+#from ccpi.optimisation.operators import SparseFiniteDiff
+#
+#try:
+# from cvxpy import *
+# cvx_not_installable = True
+#except ImportError:
+# cvx_not_installable = False
+#
+#
+#if cvx_not_installable:
+#
+# ##Construct problem
+# u1 = Variable(ig.shape)
+# q = Variable()
+#
+# DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann')
+# DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann')
+#
+# # Define Total Variation as a regulariser
+# regulariser = alpha * sum(norm(vstack([DX.matrix() * vec(u1), DY.matrix() * vec(u1)]), 2, axis = 0))
+#
+# fidelity = sum( u1 - multiply(noisy_data.as_array(), log(u1)) )
+# constraints = [q>= fidelity, u1>=0]
+#
+# solver = ECOS
+# obj = Minimize( regulariser + q)
+# prob = Problem(obj, constraints)
+# result = prob.solve(verbose = True, solver = solver)
+#
+#
+# diff_cvx = numpy.abs( pdhg.get_output().as_array() - u1.value )
+#
+# plt.figure(figsize=(15,15))
+# plt.subplot(3,1,1)
+# plt.imshow(pdhg.get_output().as_array())
+# plt.title('PDHG solution')
+# plt.colorbar()
+# plt.subplot(3,1,2)
+# plt.imshow(u1.value)
+# plt.title('CVX solution')
+# plt.colorbar()
+# plt.subplot(3,1,3)
+# plt.imshow(diff_cvx)
+# plt.title('Difference')
+# plt.colorbar()
+# plt.show()
+#
+# plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'PDHG')
+# plt.plot(np.linspace(0,N,N), u1.value[int(N/2),:], label = 'CVX')
+# plt.legend()
+# plt.title('Middle Line Profiles')
+# plt.show()
+#
+# print('Primal Objective (CVX) {} '.format(obj.value))
+# print('Primal Objective (PDHG) {} '.format(pdhg.objective[-1][0]))
+#
+#
+#
+#
+#
--
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