diff options
4 files changed, 58 insertions, 34 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py b/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py index 1946d67..6d3bf86 100644 --- a/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py +++ b/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py @@ -93,12 +93,12 @@ class L2NormSquared(Function): if self.b is None: return x/(1+2*tau) else: - tmp = x - tmp -= self.b - tmp /= (1+2*tau) - tmp += self.b - return tmp -# return (x-self.b)/(1+2*tau) + self.b +# tmp = x +# tmp -= self.b +# tmp /= (1+2*tau) +# tmp += self.b +# return tmp + return (x-self.b)/(1+2*tau) + self.b # if self.b is not None: # out=x diff --git a/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py b/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py index 7e6b6e7..2004e5f 100755 --- a/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py +++ b/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py @@ -96,7 +96,8 @@ class MixedL21Norm(Function): tmp = [ el*el for el in x.containers] res = sum(tmp).sqrt().maximum(1.0) frac = [el/res for el in x.containers] - return BlockDataContainer(*frac) + return BlockDataContainer(*frac) + #TODO this is slow, why??? # return x.divide(x.pnorm().maximum(1.0)) diff --git a/Wrappers/Python/wip/pdhg_TV_denoising.py b/Wrappers/Python/wip/pdhg_TV_denoising.py index a5cd1bf..e6d38e9 100755 --- a/Wrappers/Python/wip/pdhg_TV_denoising.py +++ b/Wrappers/Python/wip/pdhg_TV_denoising.py @@ -20,10 +20,10 @@ from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, \ from skimage.util import random_noise from timeit import default_timer as timer -def dt(steps): - return steps[-1] - steps[-2] +#def dt(steps): +# return steps[-1] - steps[-2] -# Create phantom for TV denoising +# Create phantom for TV Gaussian denoising N = 100 diff --git a/Wrappers/Python/wip/pdhg_tv_denoising_poisson.py b/Wrappers/Python/wip/pdhg_tv_denoising_poisson.py index 9fad6f8..fbe0d9b 100644 --- a/Wrappers/Python/wip/pdhg_tv_denoising_poisson.py +++ b/Wrappers/Python/wip/pdhg_tv_denoising_poisson.py @@ -6,24 +6,25 @@ Created on Fri Feb 22 14:53:03 2019 @author: evangelos """ -from ccpi.framework import ImageData, ImageGeometry, BlockDataContainer - import numpy as np import matplotlib.pyplot as plt +from ccpi.framework import ImageData, ImageGeometry + from ccpi.optimisation.algorithms import PDHG, PDHG_old from ccpi.optimisation.operators import BlockOperator, Identity, Gradient -from ccpi.optimisation.functions import ZeroFun, KullbackLeibler, \ - MixedL21Norm, FunctionOperatorComposition, BlockFunction +from ccpi.optimisation.functions import ZeroFunction, KullbackLeibler, \ + MixedL21Norm, BlockFunction from skimage.util import random_noise +from timeit import default_timer as timer # ############################################################################ -# Create phantom for TV denoising +# Create phantom for TV Poisson denoising N = 100 data = np.zeros((N,N)) @@ -41,13 +42,11 @@ plt.imshow(noisy_data.as_array()) plt.colorbar() plt.show() -#%% - # Regularisation Parameter -alpha = 10 +alpha = 2 #method = input("Enter structure of PDHG (0=Composite or 1=NotComposite): ") -method = '1' +method = '0' if method == '0': # Create operators @@ -57,15 +56,11 @@ if method == '0': # Form Composite Operator operator = BlockOperator(op1, op2, shape=(2,1) ) - #### Create functions -# f = FunctionComposition_new(operator, mixed_L12Norm(alpha), \ -# L2NormSq(0.5, b = noisy_data) ) - f1 = alpha * MixedL21Norm() - f2 = KullbackLeibler(b = noisy_data) + f2 = KullbackLeibler(noisy_data) f = BlockFunction(f1, f2 ) - g = ZeroFun() + g = ZeroFunction() else: @@ -73,29 +68,57 @@ else: # No Composite # ########################################################################### operator = Gradient(ig) - f = alpha * FunctionOperatorComposition(operator, MixedL21Norm()) + f = alpha * MixedL21Norm() g = KullbackLeibler(noisy_data) ########################################################################### #%% # Compute operator Norm normK = operator.norm() -print ("normK", normK) -# Primal & dual stepsizes -#sigma = 1 -#tau = 1/(sigma*normK**2) -sigma = 1/normK -tau = 1/normK +# Primal & dual stepsizes +sigma = 1 +tau = 1/(sigma*normK**2) opt = {'niter':2000} +opt1 = {'niter':2000, 'memopt': True} +t1 = timer() res, time, primal, dual, pdgap = PDHG_old(f, g, operator, tau = tau, sigma = sigma, opt = opt) - -plt.figure(figsize=(5,5)) +t2 = timer() + +print(" Run memopt") + +t3 = timer() +res1, time1, primal1, dual1, pdgap1 = PDHG_old(f, g, operator, tau = tau, sigma = sigma, opt = opt1) +t4 = timer() + +#%% +plt.figure(figsize=(15,15)) +plt.subplot(3,1,1) plt.imshow(res.as_array()) +plt.title('no memopt') +plt.colorbar() +plt.subplot(3,1,2) +plt.imshow(res1.as_array()) +plt.title('memopt') +plt.colorbar() +plt.subplot(3,1,3) +plt.imshow((res1 - res).abs().as_array()) +plt.title('diff') plt.colorbar() plt.show() +# +plt.plot(np.linspace(0,N,N), res1.as_array()[int(N/2),:], label = 'memopt') +plt.plot(np.linspace(0,N,N), res.as_array()[int(N/2),:], label = 'no memopt') +plt.legend() +plt.show() + +print ("Time: No memopt in {}s, \n Time: Memopt in {}s ".format(t2-t1, t4 -t3)) +diff = (res1 - res).abs().as_array().max() + +print(" Max of abs difference is {}".format(diff)) + #pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma) #pdhg.max_iteration = 2000 |