diff options
Diffstat (limited to 'Wrappers')
| -rwxr-xr-x | Wrappers/Python/ccpi/optimisation/algorithms/Algorithm.py | 22 | ||||
| -rw-r--r-- | Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py | 127 |
2 files changed, 76 insertions, 73 deletions
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])) +# +# +# +# +# |