diff options
Diffstat (limited to 'demos')
| -rw-r--r-- | demos/SoftwareX_supp/Demo_VolumeDenoise.py | 55 | ||||
| -rw-r--r-- | demos/demoMatlab_denoise.m | 39 | ||||
| -rw-r--r-- | demos/demo_cpu_regularisers.py | 66 | ||||
| -rw-r--r-- | demos/demo_cpu_regularisers3D.py | 60 | ||||
| -rw-r--r-- | demos/demo_cpu_vs_gpu_regularisers.py | 250 | ||||
| -rw-r--r-- | demos/demo_gpu_regularisers.py | 56 | ||||
| -rw-r--r-- | demos/demo_gpu_regularisers3D.py | 58 |
7 files changed, 298 insertions, 286 deletions
diff --git a/demos/SoftwareX_supp/Demo_VolumeDenoise.py b/demos/SoftwareX_supp/Demo_VolumeDenoise.py index 6e7ea46..07e3133 100644 --- a/demos/SoftwareX_supp/Demo_VolumeDenoise.py +++ b/demos/SoftwareX_supp/Demo_VolumeDenoise.py @@ -29,7 +29,7 @@ from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, LLT_ROF, NDF, Diff4 #%% print ("Building 3D phantom using TomoPhantom software") tic=timeit.default_timer() -model = 9 # select a model number from the library +model = 16 # select a model number from the library N_size = 256 # Define phantom dimensions using a scalar value (cubic phantom) path = os.path.dirname(tomophantom.__file__) path_library3D = os.path.join(path, "Phantom3DLibrary.dat") @@ -66,16 +66,18 @@ print ("#############ROF TV CPU####################") # set parameters pars = {'algorithm': ROF_TV, \ 'input' : phantom_noise,\ - 'regularisation_parameter':0.04,\ - 'number_of_iterations': 600,\ - 'time_marching_parameter': 0.0025 - } + 'regularisation_parameter':0.02,\ + 'number_of_iterations': 1000,\ + 'time_marching_parameter': 0.001,\ + 'tolerance_constant':0.0} tic=timeit.default_timer() -rof_cpu3D = ROF_TV(pars['input'], +(rof_cpu3D, infcpu) = ROF_TV(pars['input'], pars['regularisation_parameter'], pars['number_of_iterations'], - pars['time_marching_parameter'],'cpu') + pars['time_marching_parameter'], + pars['tolerance_constant'],'cpu') + toc=timeit.default_timer() Run_time_rof = toc - tic @@ -94,28 +96,47 @@ print ("#############ROF TV GPU####################") # set parameters pars = {'algorithm': ROF_TV, \ 'input' : phantom_noise,\ - 'regularisation_parameter':0.04,\ - 'number_of_iterations': 600,\ - 'time_marching_parameter': 0.0025 - } + 'regularisation_parameter':0.06,\ + 'number_of_iterations': 10000,\ + 'time_marching_parameter': 0.00025,\ + 'tolerance_constant':1e-06} tic=timeit.default_timer() -rof_gpu3D = ROF_TV(pars['input'], +(rof_gpu3D, infogpu) = ROF_TV(pars['input'], pars['regularisation_parameter'], pars['number_of_iterations'], - pars['time_marching_parameter'],'gpu') + pars['time_marching_parameter'], + pars['tolerance_constant'],'gpu') + toc=timeit.default_timer() Run_time_rof = toc - tic Qtools = QualityTools(phantom_tm, rof_gpu3D) RMSE_rof = Qtools.rmse() +sliceNo = 128 # SSIM measure -Qtools = QualityTools(phantom_tm[128,:,:]*255, rof_gpu3D[128,:,:]*235) +Qtools = QualityTools(phantom_tm[sliceNo,:,:]*255, rof_gpu3D[sliceNo,:,:]*235) win = np.array([gaussian(11, 1.5)]) win2d = win * (win.T) ssim_rof = Qtools.ssim(win2d) +sliceSel = int(0.5*N_size) +#plt.gray() +plt.figure() +plt.subplot(131) +plt.imshow(rof_gpu3D[sliceSel,:,:],vmin=0, vmax=1.4) +plt.title('3D ROF-TV, axial view') + +plt.subplot(132) +plt.imshow(rof_gpu3D[:,sliceSel,:],vmin=0, vmax=1.4) +plt.title('3D ROF-TV, coronal view') + +plt.subplot(133) +plt.imshow(rof_gpu3D[:,:,sliceSel],vmin=0, vmax=1.4) +plt.title('3D ROF-TV, sagittal view') +plt.show() + print("ROF-TV (gpu) ____ RMSE: {}, MMSIM: {}, run time: {} sec".format(RMSE_rof,ssim_rof[0],Run_time_rof)) #%% print ("#############FGP TV CPU####################") @@ -154,13 +175,13 @@ print ("#############FGP TV GPU####################") pars = {'algorithm' : FGP_TV, \ 'input' : phantom_noise,\ 'regularisation_parameter':0.05, \ - 'number_of_iterations' :80 ,\ - 'tolerance_constant':1e-04,\ + 'number_of_iterations' :1500 ,\ + 'tolerance_constant':1e-06,\ 'methodTV': 0 ,\ 'nonneg': 0} tic=timeit.default_timer() -(fgp_gpu3D) = FGP_TV(pars['input'], +(fgp_gpu3D,infogpu) = FGP_TV(pars['input'], pars['regularisation_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], diff --git a/demos/demoMatlab_denoise.m b/demos/demoMatlab_denoise.m index fa81f6d..a22b40a 100644 --- a/demos/demoMatlab_denoise.m +++ b/demos/demoMatlab_denoise.m @@ -2,11 +2,9 @@ clear; close all fsep = '/'; -%Path1 = sprintf(['..' fsep 'src' fsep 'Matlab' fsep 'mex_compile' fsep 'installed'], 1i); -Path1 = ('/home/kjy41806/Documents/SOFT/CCPi-Regularisation-Toolkit/src/Matlab/mex_compile/installed'); +Path1 = sprintf(['..' fsep 'src' fsep 'Matlab' fsep 'mex_compile' fsep 'installed'], 1i); Path2 = sprintf(['data' fsep], 1i); -%Path3 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep 'supp'], 1i); -Path3 = '/home/kjy41806/Documents/SOFT/CCPi-Regularisation-Toolkit/src/Matlab/supp'; +Path3 = sprintf(['..' fsep 'src' fsep 'Matlab' fsep 'supp'], 1i); addpath(Path1); addpath(Path2); addpath(Path3); @@ -14,14 +12,14 @@ addpath(Path3); Im = double(imread('lena_gray_512.tif'))/255; % loading image u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0; figure; imshow(u0, [0 1]); title('Noisy image'); - %% fprintf('Denoise using the ROF-TV model (CPU) \n'); -lambda_reg = 0.017; % regularsation parameter for all methods -tau_rof = 0.0025; % time-marching constant -iter_rof = 1200; % number of ROF iterations -tic; u_rof = ROF_TV(single(u0), lambda_reg, iter_rof, tau_rof); toc; -energyfunc_val_rof = TV_energy(single(u_rof),single(u0),lambda_reg, 1); % get energy function value +lambda_reg = 0.02; % regularsation parameter for all methods +iter_rof = 2000; % number of ROF iterations +tau_rof = 0.001; % time-marching constant +epsil_tol = 0.0; % tolerance +tic; [u_rof,infovec] = ROF_TV(single(u0), lambda_reg, iter_rof, tau_rof, epsil_tol); toc; +%energyfunc_val_rof = TV_energy(single(u_rof),single(u0),lambda_reg, 1); % get energy function value rmseROF = (RMSE(u_rof(:),Im(:))); fprintf('%s %f \n', 'RMSE error for ROF-TV is:', rmseROF); [ssimval] = ssim(u_rof*255,single(Im)*255); @@ -29,16 +27,14 @@ fprintf('%s %f \n', 'MSSIM error for ROF-TV is:', ssimval); figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (CPU)'); %% % fprintf('Denoise using the ROF-TV model (GPU) \n'); -% tau_rof = 0.0025; % time-marching constant -% iter_rof = 1200; % number of ROF iterations -% tic; u_rofG = ROF_TV_GPU(single(u0), lambda_reg, iter_rof, tau_rof); toc; +% tic; u_rofG = ROF_TV_GPU(single(u0), lambda_reg, iter_rof, tau_rof, epsil_tol); toc; % figure; imshow(u_rofG, [0 1]); title('ROF-TV denoised image (GPU)'); %% fprintf('Denoise using the FGP-TV model (CPU) \n'); -lambda_reg = 0.033; -iter_fgp = 200; % number of FGP iterations -epsil_tol = 1.0e-05; % tolerance -tic; u_fgp = FGP_TV(single(u0), lambda_reg, iter_fgp, epsil_tol); toc; +lambda_reg = 0.02; +iter_fgp = 500; % number of FGP iterations +epsil_tol = 1.0e-06; % tolerance +tic; [u_fgp,infovec] = FGP_TV(single(u0), lambda_reg, iter_fgp, epsil_tol); toc; energyfunc_val_fgp = TV_energy(single(u_fgp),single(u0),lambda_reg, 1); % get energy function value rmseFGP = (RMSE(u_fgp(:),Im(:))); fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmseFGP); @@ -47,15 +43,14 @@ fprintf('%s %f \n', 'MSSIM error for FGP-TV is:', ssimval); figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)'); %% % fprintf('Denoise using the FGP-TV model (GPU) \n'); -% iter_fgp = 300; % number of FGP iterations -% epsil_tol = 1.0e-09; % tolerance % tic; u_fgpG = FGP_TV_GPU(single(u0), lambda_reg, iter_fgp, epsil_tol); toc; % figure; imshow(u_fgpG, [0 1]); title('FGP-TV denoised image (GPU)'); %% fprintf('Denoise using the SB-TV model (CPU) \n'); -iter_sb = 80; % number of SB iterations -epsil_tol = 1.0e-08; % tolerance -tic; u_sb = SB_TV(single(u0), lambda_reg, iter_sb, epsil_tol); toc; +lambda_reg = 0.03; +iter_sb = 300; % number of SB iterations +epsil_tol = 1.0e-06; % tolerance +tic; [u_sb,infovec] = SB_TV(single(u0), lambda_reg, iter_sb, epsil_tol); toc; energyfunc_val_sb = TV_energy(single(u_sb),single(u0),lambda_reg, 1); % get energy function value rmseSB = (RMSE(u_sb(:),Im(:))); fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmseSB); diff --git a/demos/demo_cpu_regularisers.py b/demos/demo_cpu_regularisers.py index f2d2f33..8655623 100644 --- a/demos/demo_cpu_regularisers.py +++ b/demos/demo_cpu_regularisers.py @@ -31,8 +31,7 @@ def printParametersToString(pars): return txt ############################################################################### -#filename = os.path.join( "data" ,"lena_gray_512.tif") -filename = "/home/algol/Documents/DEV/CCPi-Regularisation-Toolkit/test/lena_gray_512.tif" +filename = os.path.join( "data" ,"lena_gray_512.tif") # read image Im = plt.imread(filename) @@ -86,7 +85,7 @@ imgplot = plt.imshow(u0,cmap="gray") pars = {'algorithm': ROF_TV, \ 'input' : u0,\ 'regularisation_parameter':0.02,\ - 'number_of_iterations': 1000,\ + 'number_of_iterations': 4000,\ 'time_marching_parameter': 0.001,\ 'tolerance_constant':1e-06} @@ -265,23 +264,23 @@ imgplot = plt.imshow(u0,cmap="gray") # set parameters pars = {'algorithm' : TGV, \ 'input' : u0,\ - 'regularisation_parameter':0.04, \ + 'regularisation_parameter':0.02, \ 'alpha1':1.0,\ 'alpha0':2.0,\ - 'number_of_iterations' :1350 ,\ + 'number_of_iterations' :1000 ,\ 'LipshitzConstant' :12 ,\ - } - + 'tolerance_constant':1e-06} + print ("#############TGV CPU####################") start_time = timeit.default_timer() -tgv_cpu = TGV(pars['input'], +(tgv_cpu,info_vec_cpu) = TGV(pars['input'], pars['regularisation_parameter'], pars['alpha1'], pars['alpha0'], pars['number_of_iterations'], - pars['LipshitzConstant'],'cpu') - - + pars['LipshitzConstant'], + pars['tolerance_constant'], 'cpu') + Qtools = QualityTools(Im, tgv_cpu) pars['rmse'] = Qtools.rmse() @@ -299,8 +298,6 @@ imgplot = plt.imshow(tgv_cpu, cmap="gray") plt.title('{}'.format('CPU results')) #%% - - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("________________NDF (2D)___________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") @@ -315,21 +312,22 @@ imgplot = plt.imshow(u0,cmap="gray") # set parameters pars = {'algorithm' : NDF, \ 'input' : u0,\ - 'regularisation_parameter':0.025, \ - 'edge_parameter':0.015,\ - 'number_of_iterations' :500 ,\ - 'time_marching_parameter':0.025,\ - 'penalty_type':1 - } + 'regularisation_parameter':0.02, \ + 'edge_parameter':0.017,\ + 'number_of_iterations' :1500 ,\ + 'time_marching_parameter':0.01,\ + 'penalty_type':1,\ + 'tolerance_constant':1e-06} print ("#############NDF CPU################") start_time = timeit.default_timer() -ndf_cpu = NDF(pars['input'], +(ndf_cpu,info_vec_cpu) = NDF(pars['input'], pars['regularisation_parameter'], pars['edge_parameter'], pars['number_of_iterations'], pars['time_marching_parameter'], - pars['penalty_type'],'cpu') + pars['penalty_type'], + pars['tolerance_constant'],'cpu') Qtools = QualityTools(Im, ndf_cpu) pars['rmse'] = Qtools.rmse() @@ -362,19 +360,20 @@ imgplot = plt.imshow(u0,cmap="gray") # set parameters pars = {'algorithm' : Diff4th, \ 'input' : u0,\ - 'regularisation_parameter':3.5, \ + 'regularisation_parameter':0.8, \ 'edge_parameter':0.02,\ - 'number_of_iterations' :500 ,\ - 'time_marching_parameter':0.0015 - } + 'number_of_iterations' :5500 ,\ + 'time_marching_parameter':0.001,\ + 'tolerance_constant':1e-06} print ("#############Diff4th CPU################") start_time = timeit.default_timer() -diff4_cpu = Diff4th(pars['input'], +(diff4_cpu,info_vec_cpu) = Diff4th(pars['input'], pars['regularisation_parameter'], pars['edge_parameter'], pars['number_of_iterations'], - pars['time_marching_parameter'],'cpu') + pars['time_marching_parameter'], + pars['tolerance_constant'],'cpu') Qtools = QualityTools(Im, diff4_cpu) pars['rmse'] = Qtools.rmse() @@ -480,26 +479,23 @@ imgplot = plt.imshow(u0,cmap="gray") pars = {'algorithm' : FGP_dTV, \ 'input' : u0,\ 'refdata' : u_ref,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :2000 ,\ + 'regularisation_parameter':0.02, \ + 'number_of_iterations' :500 ,\ 'tolerance_constant':1e-06,\ 'eta_const':0.2,\ 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } + 'nonneg': 0} print ("#############FGP dTV CPU####################") start_time = timeit.default_timer() -fgp_dtv_cpu = FGP_dTV(pars['input'], +(fgp_dtv_cpu,info_vec_cpu) = FGP_dTV(pars['input'], pars['refdata'], pars['regularisation_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], pars['eta_const'], pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'cpu') + pars['nonneg'],'cpu') Qtools = QualityTools(Im, fgp_dtv_cpu) pars['rmse'] = Qtools.rmse() diff --git a/demos/demo_cpu_regularisers3D.py b/demos/demo_cpu_regularisers3D.py index 0f9cd1a..fc1e8e6 100644 --- a/demos/demo_cpu_regularisers3D.py +++ b/demos/demo_cpu_regularisers3D.py @@ -277,22 +277,23 @@ imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") # set parameters pars = {'algorithm' : TGV, \ 'input' : noisyVol,\ - 'regularisation_parameter':0.04, \ + 'regularisation_parameter':0.02, \ 'alpha1':1.0,\ 'alpha0':2.0,\ - 'number_of_iterations' :250 ,\ + 'number_of_iterations' :500 ,\ 'LipshitzConstant' :12 ,\ - } + 'tolerance_constant':1e-06} print ("#############TGV CPU####################") start_time = timeit.default_timer() -tgv_cpu3D = TGV(pars['input'], +(tgv_cpu3D,info_vec_cpu) = TGV(pars['input'], pars['regularisation_parameter'], pars['alpha1'], pars['alpha0'], pars['number_of_iterations'], - pars['LipshitzConstant'],'cpu') - + pars['LipshitzConstant'], + pars['tolerance_constant'],'cpu') + Qtools = QualityTools(idealVol, tgv_cpu3D) pars['rmse'] = Qtools.rmse() @@ -325,21 +326,22 @@ imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") # set parameters pars = {'algorithm' : NDF, \ 'input' : noisyVol,\ - 'regularisation_parameter':0.025, \ + 'regularisation_parameter':0.02, \ 'edge_parameter':0.015,\ - 'number_of_iterations' :500 ,\ - 'time_marching_parameter':0.025,\ - 'penalty_type': 1 - } - + 'number_of_iterations' :700 ,\ + 'time_marching_parameter':0.01,\ + 'penalty_type': 1,\ + 'tolerance_constant':1e-06} + print ("#############NDF CPU################") start_time = timeit.default_timer() -ndf_cpu3D = NDF(pars['input'], +(ndf_cpu3D,info_vec_cpu) = NDF(pars['input'], pars['regularisation_parameter'], pars['edge_parameter'], pars['number_of_iterations'], pars['time_marching_parameter'], - pars['penalty_type']) + pars['penalty_type'], + pars['tolerance_constant'], 'cpu') Qtools = QualityTools(idealVol, ndf_cpu3D) @@ -373,19 +375,20 @@ imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") # set parameters pars = {'algorithm' : Diff4th, \ 'input' : noisyVol,\ - 'regularisation_parameter':3.5, \ + 'regularisation_parameter':0.8, \ 'edge_parameter':0.02,\ - 'number_of_iterations' :300 ,\ - 'time_marching_parameter':0.0015 - } - + 'number_of_iterations' :500 ,\ + 'time_marching_parameter':0.001,\ + 'tolerance_constant':1e-06} + print ("#############Diff4th CPU################") start_time = timeit.default_timer() -diff4th_cpu3D = Diff4th(pars['input'], +(diff4th_cpu3D,info_vec_cpu) = Diff4th(pars['input'], pars['regularisation_parameter'], pars['edge_parameter'], pars['number_of_iterations'], - pars['time_marching_parameter']) + pars['time_marching_parameter'], + pars['tolerance_constant'],'cpu') Qtools = QualityTools(idealVol, diff4th_cpu3D) @@ -420,26 +423,23 @@ imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") pars = {'algorithm' : FGP_dTV,\ 'input' : noisyVol,\ 'refdata' : noisyRef,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :300 ,\ - 'tolerance_constant':0.00001,\ + 'regularisation_parameter':0.02, \ + 'number_of_iterations' :500 ,\ + 'tolerance_constant':1e-06,\ 'eta_const':0.2,\ 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } + 'nonneg': 0} print ("#############FGP dTV CPU####################") start_time = timeit.default_timer() -fgp_dTV_cpu3D = FGP_dTV(pars['input'], +(fgp_dTV_cpu3D,info_vec_cpu) = FGP_dTV(pars['input'], pars['refdata'], pars['regularisation_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], pars['eta_const'], pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'cpu') + pars['nonneg'],'cpu') Qtools = QualityTools(idealVol, fgp_dTV_cpu3D) diff --git a/demos/demo_cpu_vs_gpu_regularisers.py b/demos/demo_cpu_vs_gpu_regularisers.py index e1eb91f..21e3899 100644 --- a/demos/demo_cpu_vs_gpu_regularisers.py +++ b/demos/demo_cpu_vs_gpu_regularisers.py @@ -66,16 +66,18 @@ imgplot = plt.imshow(u0,cmap="gray") # set parameters pars = {'algorithm': ROF_TV, \ 'input' : u0,\ - 'regularisation_parameter':0.04,\ - 'number_of_iterations': 4500,\ - 'time_marching_parameter': 0.00002 - } + 'regularisation_parameter':0.02,\ + 'number_of_iterations': 1000,\ + 'time_marching_parameter': 0.001,\ + 'tolerance_constant':0.0} + print ("#############ROF TV CPU####################") start_time = timeit.default_timer() -rof_cpu = ROF_TV(pars['input'], +(rof_cpu, infocpu) = ROF_TV(pars['input'], pars['regularisation_parameter'], pars['number_of_iterations'], - pars['time_marching_parameter'],'cpu') + pars['time_marching_parameter'], + pars['tolerance_constant'],'cpu') Qtools = QualityTools(Im, rof_cpu) pars['rmse'] = Qtools.rmse() @@ -95,10 +97,11 @@ plt.title('{}'.format('CPU results')) print ("##############ROF TV GPU##################") start_time = timeit.default_timer() -rof_gpu = ROF_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'gpu') +(rof_gpu, infgpu) = ROF_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'], + pars['tolerance_constant'],'gpu') Qtools = QualityTools(Im, rof_gpu) pars['rmse'] = Qtools.rmse() @@ -130,7 +133,6 @@ if (diff_im.sum() > 1): print ("Arrays do not match!") else: print ("Arrays match") - #%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("____________FGP-TV bench___________________") @@ -146,24 +148,20 @@ imgplot = plt.imshow(u0,cmap="gray") # set parameters pars = {'algorithm' : FGP_TV, \ 'input' : u0,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :1200 ,\ - 'tolerance_constant':0.00001,\ + 'regularisation_parameter':0.02, \ + 'number_of_iterations' :400 ,\ + 'tolerance_constant':0.0,\ 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } + 'nonneg': 0} print ("#############FGP TV CPU####################") start_time = timeit.default_timer() -fgp_cpu = FGP_TV(pars['input'], +(fgp_cpu,infocpu) = FGP_TV(pars['input'], pars['regularisation_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'cpu') - + pars['nonneg'],'cpu') Qtools = QualityTools(Im, fgp_cpu) pars['rmse'] = Qtools.rmse() @@ -184,13 +182,12 @@ plt.title('{}'.format('CPU results')) print ("##############FGP TV GPU##################") start_time = timeit.default_timer() -fgp_gpu = FGP_TV(pars['input'], +(fgp_gpu,infogpu) = FGP_TV(pars['input'], pars['regularisation_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'gpu') + pars['nonneg'],'gpu') Qtools = QualityTools(Im, fgp_gpu) pars['rmse'] = Qtools.rmse() @@ -238,21 +235,18 @@ imgplot = plt.imshow(u0,cmap="gray") # set parameters pars = {'algorithm' : SB_TV, \ 'input' : u0,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :150 ,\ - 'tolerance_constant':1e-05,\ - 'methodTV': 0 ,\ - 'printingOut': 0 - } + 'regularisation_parameter':0.02, \ + 'number_of_iterations' :250 ,\ + 'tolerance_constant':0.0,\ + 'methodTV': 0} print ("#############SB-TV CPU####################") start_time = timeit.default_timer() -sb_cpu = SB_TV(pars['input'], +(sb_cpu, info_vec_cpu) = SB_TV(pars['input'], pars['regularisation_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], - pars['methodTV'], - pars['printingOut'],'cpu') + pars['methodTV'], 'cpu') Qtools = QualityTools(Im, sb_cpu) @@ -274,12 +268,11 @@ plt.title('{}'.format('CPU results')) print ("##############SB TV GPU##################") start_time = timeit.default_timer() -sb_gpu = SB_TV(pars['input'], +(sb_gpu, info_vec_gpu) = SB_TV(pars['input'], pars['regularisation_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], - pars['methodTV'], - pars['printingOut'],'gpu') + pars['methodTV'], 'gpu') Qtools = QualityTools(Im, sb_gpu) pars['rmse'] = Qtools.rmse() @@ -311,36 +304,36 @@ else: print ("Arrays match") #%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("____________TGV bench___________________") +print ("____________LLT-ROF bench___________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot fig = plt.figure() -plt.suptitle('Comparison of TGV regulariser using CPU and GPU implementations') +plt.suptitle('Comparison of LLT-ROF regulariser using CPU and GPU implementations') a=fig.add_subplot(1,4,1) a.set_title('Noisy Image') imgplot = plt.imshow(u0,cmap="gray") # set parameters -pars = {'algorithm' : TGV, \ +pars = {'algorithm' : LLT_ROF, \ 'input' : u0,\ - 'regularisation_parameter':0.04, \ - 'alpha1':1.0,\ - 'alpha0':2.0,\ - 'number_of_iterations' :400 ,\ - 'LipshitzConstant' :12 ,\ - } - -print ("#############TGV CPU####################") + 'regularisation_parameterROF':0.01, \ + 'regularisation_parameterLLT':0.0085, \ + 'number_of_iterations' : 1000 ,\ + 'time_marching_parameter' :0.0001 ,\ + 'tolerance_constant':0.0} + + +print ("#############LLT- ROF CPU####################") start_time = timeit.default_timer() -tgv_cpu = TGV(pars['input'], - pars['regularisation_parameter'], - pars['alpha1'], - pars['alpha0'], +(lltrof_cpu, info_vec_cpu) = LLT_ROF(pars['input'], + pars['regularisation_parameterROF'], + pars['regularisation_parameterLLT'], pars['number_of_iterations'], - pars['LipshitzConstant'],'cpu') - -Qtools = QualityTools(Im, tgv_cpu) + pars['time_marching_parameter'], + pars['tolerance_constant'], 'cpu') + +Qtools = QualityTools(Im, lltrof_cpu) pars['rmse'] = Qtools.rmse() txtstr = printParametersToString(pars) @@ -353,21 +346,22 @@ props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) # place a text box in upper left in axes coords a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) -imgplot = plt.imshow(tgv_cpu, cmap="gray") +imgplot = plt.imshow(lltrof_cpu, cmap="gray") plt.title('{}'.format('CPU results')) -print ("##############TGV GPU##################") +print ("#############LLT- ROF GPU####################") start_time = timeit.default_timer() -tgv_gpu = TGV(pars['input'], - pars['regularisation_parameter'], - pars['alpha1'], - pars['alpha0'], +(lltrof_gpu, info_vec_gpu) = LLT_ROF(pars['input'], + pars['regularisation_parameterROF'], + pars['regularisation_parameterLLT'], pars['number_of_iterations'], - pars['LipshitzConstant'],'gpu') - -Qtools = QualityTools(Im, tgv_gpu) + pars['time_marching_parameter'], + pars['tolerance_constant'], 'gpu') + +Qtools = QualityTools(Im, lltrof_gpu) pars['rmse'] = Qtools.rmse() -pars['algorithm'] = TGV + +pars['algorithm'] = LLT_ROF txtstr = printParametersToString(pars) txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) print (txtstr) @@ -378,13 +372,13 @@ props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) # place a text box in upper left in axes coords a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) -imgplot = plt.imshow(tgv_gpu, cmap="gray") +imgplot = plt.imshow(lltrof_gpu, cmap="gray") plt.title('{}'.format('GPU results')) print ("--------Compare the results--------") tolerance = 1e-05 -diff_im = np.zeros(np.shape(tgv_gpu)) -diff_im = abs(tgv_cpu - tgv_gpu) +diff_im = np.zeros(np.shape(lltrof_gpu)) +diff_im = abs(lltrof_cpu - lltrof_gpu) diff_im[diff_im > tolerance] = 1 a=fig.add_subplot(1,4,4) imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray") @@ -395,34 +389,37 @@ else: print ("Arrays match") #%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("____________LLT-ROF bench___________________") +print ("____________TGV bench___________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot fig = plt.figure() -plt.suptitle('Comparison of LLT-ROF regulariser using CPU and GPU implementations') +plt.suptitle('Comparison of TGV regulariser using CPU and GPU implementations') a=fig.add_subplot(1,4,1) a.set_title('Noisy Image') imgplot = plt.imshow(u0,cmap="gray") # set parameters -pars = {'algorithm' : LLT_ROF, \ +pars = {'algorithm' : TGV, \ 'input' : u0,\ - 'regularisation_parameterROF':0.04, \ - 'regularisation_parameterLLT':0.01, \ - 'number_of_iterations' :4500 ,\ - 'time_marching_parameter' :0.00002 ,\ - } + 'regularisation_parameter':0.02, \ + 'alpha1':1.0,\ + 'alpha0':2.0,\ + 'number_of_iterations' :1000 ,\ + 'LipshitzConstant' :12 ,\ + 'tolerance_constant':0.0} -print ("#############LLT- ROF CPU####################") +print ("#############TGV CPU####################") start_time = timeit.default_timer() -lltrof_cpu = LLT_ROF(pars['input'], - pars['regularisation_parameterROF'], - pars['regularisation_parameterLLT'], +(tgv_cpu, info_vec_cpu) = TGV(pars['input'], + pars['regularisation_parameter'], + pars['alpha1'], + pars['alpha0'], pars['number_of_iterations'], - pars['time_marching_parameter'],'cpu') - -Qtools = QualityTools(Im, lltrof_cpu) + pars['LipshitzConstant'], + pars['tolerance_constant'],'cpu') + +Qtools = QualityTools(Im, tgv_cpu) pars['rmse'] = Qtools.rmse() txtstr = printParametersToString(pars) @@ -435,21 +432,22 @@ props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) # place a text box in upper left in axes coords a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) -imgplot = plt.imshow(lltrof_cpu, cmap="gray") +imgplot = plt.imshow(tgv_cpu, cmap="gray") plt.title('{}'.format('CPU results')) -print ("#############LLT- ROF GPU####################") +print ("##############TGV GPU##################") start_time = timeit.default_timer() -lltrof_gpu = LLT_ROF(pars['input'], - pars['regularisation_parameterROF'], - pars['regularisation_parameterLLT'], +(tgv_gpu, info_vec_gpu) = TGV(pars['input'], + pars['regularisation_parameter'], + pars['alpha1'], + pars['alpha0'], pars['number_of_iterations'], - pars['time_marching_parameter'],'gpu') - -Qtools = QualityTools(Im, lltrof_gpu) + pars['LipshitzConstant'], + pars['tolerance_constant'],'gpu') + +Qtools = QualityTools(Im, tgv_gpu) pars['rmse'] = Qtools.rmse() - -pars['algorithm'] = LLT_ROF +pars['algorithm'] = TGV txtstr = printParametersToString(pars) txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) print (txtstr) @@ -460,13 +458,13 @@ props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) # place a text box in upper left in axes coords a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) -imgplot = plt.imshow(lltrof_gpu, cmap="gray") +imgplot = plt.imshow(tgv_gpu, cmap="gray") plt.title('{}'.format('GPU results')) print ("--------Compare the results--------") tolerance = 1e-05 -diff_im = np.zeros(np.shape(lltrof_gpu)) -diff_im = abs(lltrof_cpu - lltrof_gpu) +diff_im = np.zeros(np.shape(tgv_gpu)) +diff_im = abs(tgv_cpu - tgv_gpu) diff_im[diff_im > tolerance] = 1 a=fig.add_subplot(1,4,4) imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray") @@ -490,21 +488,22 @@ imgplot = plt.imshow(u0,cmap="gray") # set parameters pars = {'algorithm' : NDF, \ 'input' : u0,\ - 'regularisation_parameter':0.06, \ - 'edge_parameter':0.04,\ - 'number_of_iterations' :1000 ,\ - 'time_marching_parameter':0.025,\ - 'penalty_type': 1 - } - + 'regularisation_parameter':0.02, \ + 'edge_parameter':0.017,\ + 'number_of_iterations' :1500 ,\ + 'time_marching_parameter':0.01,\ + 'penalty_type':1,\ + 'tolerance_constant':0.0} + print ("#############NDF CPU####################") start_time = timeit.default_timer() -ndf_cpu = NDF(pars['input'], +(ndf_cpu,info_vec_cpu) = NDF(pars['input'], pars['regularisation_parameter'], pars['edge_parameter'], pars['number_of_iterations'], pars['time_marching_parameter'], - pars['penalty_type'],'cpu') + pars['penalty_type'], + pars['tolerance_constant'],'cpu') Qtools = QualityTools(Im, ndf_cpu) pars['rmse'] = Qtools.rmse() @@ -525,12 +524,13 @@ plt.title('{}'.format('CPU results')) print ("##############NDF GPU##################") start_time = timeit.default_timer() -ndf_gpu = NDF(pars['input'], +(ndf_gpu,info_vec_gpu) = NDF(pars['input'], pars['regularisation_parameter'], pars['edge_parameter'], pars['number_of_iterations'], pars['time_marching_parameter'], - pars['penalty_type'],'gpu') + pars['penalty_type'], + pars['tolerance_constant'],'gpu') Qtools = QualityTools(Im, ndf_gpu) pars['rmse'] = Qtools.rmse() @@ -576,19 +576,20 @@ imgplot = plt.imshow(u0,cmap="gray") # set parameters pars = {'algorithm' : Diff4th, \ 'input' : u0,\ - 'regularisation_parameter':3.5, \ + 'regularisation_parameter':0.8, \ 'edge_parameter':0.02,\ - 'number_of_iterations' :500 ,\ - 'time_marching_parameter':0.001 - } + 'number_of_iterations' :1500 ,\ + 'time_marching_parameter':0.001,\ + 'tolerance_constant':0.0} print ("#############Diff4th CPU####################") start_time = timeit.default_timer() -diff4th_cpu = Diff4th(pars['input'], +(diff4th_cpu,info_vec_cpu) = Diff4th(pars['input'], pars['regularisation_parameter'], pars['edge_parameter'], pars['number_of_iterations'], - pars['time_marching_parameter'],'cpu') + pars['time_marching_parameter'], + pars['tolerance_constant'],'cpu') Qtools = QualityTools(Im, diff4th_cpu) pars['rmse'] = Qtools.rmse() @@ -608,11 +609,12 @@ plt.title('{}'.format('CPU results')) print ("##############Diff4th GPU##################") start_time = timeit.default_timer() -diff4th_gpu = Diff4th(pars['input'], +(diff4th_gpu,info_vec_gpu) = Diff4th(pars['input'], pars['regularisation_parameter'], pars['edge_parameter'], pars['number_of_iterations'], - pars['time_marching_parameter'], 'gpu') + pars['time_marching_parameter'], + pars['tolerance_constant'],'gpu') Qtools = QualityTools(Im, diff4th_gpu) pars['rmse'] = Qtools.rmse() @@ -659,26 +661,23 @@ imgplot = plt.imshow(u0,cmap="gray") pars = {'algorithm' : FGP_dTV, \ 'input' : u0,\ 'refdata' : u_ref,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :1000 ,\ - 'tolerance_constant':1e-07,\ + 'regularisation_parameter':0.02, \ + 'number_of_iterations' :500 ,\ + 'tolerance_constant':0.0,\ 'eta_const':0.2,\ 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } + 'nonneg': 0} print ("#############FGP dTV CPU####################") start_time = timeit.default_timer() -fgp_dtv_cpu = FGP_dTV(pars['input'], +(fgp_dtv_cpu,info_vec_cpu) = FGP_dTV(pars['input'], pars['refdata'], pars['regularisation_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], pars['eta_const'], pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'cpu') + pars['nonneg'],'cpu') Qtools = QualityTools(Im, fgp_dtv_cpu) pars['rmse'] = Qtools.rmse() @@ -699,15 +698,14 @@ plt.title('{}'.format('CPU results')) print ("##############FGP dTV GPU##################") start_time = timeit.default_timer() -fgp_dtv_gpu = FGP_dTV(pars['input'], +(fgp_dtv_gpu,info_vec_gpu) = FGP_dTV(pars['input'], pars['refdata'], pars['regularisation_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], pars['eta_const'], pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'gpu') + pars['nonneg'],'gpu') Qtools = QualityTools(Im, fgp_dtv_gpu) pars['rmse'] = Qtools.rmse() pars['algorithm'] = FGP_dTV diff --git a/demos/demo_gpu_regularisers.py b/demos/demo_gpu_regularisers.py index 6aec283..3efcfce 100644 --- a/demos/demo_gpu_regularisers.py +++ b/demos/demo_gpu_regularisers.py @@ -84,7 +84,7 @@ imgplot = plt.imshow(u0,cmap="gray") pars = {'algorithm': ROF_TV, \ 'input' : u0,\ 'regularisation_parameter':0.02,\ - 'number_of_iterations': 6000,\ + 'number_of_iterations': 4000,\ 'time_marching_parameter': 0.001,\ 'tolerance_constant':1e-06} @@ -261,21 +261,22 @@ imgplot = plt.imshow(u0,cmap="gray") # set parameters pars = {'algorithm' : TGV, \ 'input' : u0,\ - 'regularisation_parameter':0.04, \ + 'regularisation_parameter':0.02, \ 'alpha1':1.0,\ 'alpha0':2.0,\ - 'number_of_iterations' :1250 ,\ + 'number_of_iterations' :1000 ,\ 'LipshitzConstant' :12 ,\ - } + 'tolerance_constant':1e-06} print ("#############TGV CPU####################") start_time = timeit.default_timer() -tgv_gpu = TGV(pars['input'], +(tgv_gpu, info_vec_gpu) = TGV(pars['input'], pars['regularisation_parameter'], pars['alpha1'], pars['alpha0'], pars['number_of_iterations'], - pars['LipshitzConstant'],'gpu') + pars['LipshitzConstant'], + pars['tolerance_constant'],'gpu') Qtools = QualityTools(Im, tgv_gpu) pars['rmse'] = Qtools.rmse() @@ -307,21 +308,22 @@ imgplot = plt.imshow(u0,cmap="gray") # set parameters pars = {'algorithm' : NDF, \ 'input' : u0,\ - 'regularisation_parameter':0.025, \ - 'edge_parameter':0.015,\ - 'number_of_iterations' :500 ,\ - 'time_marching_parameter':0.025,\ - 'penalty_type': 1 - } + 'regularisation_parameter':0.02, \ + 'edge_parameter':0.017,\ + 'number_of_iterations' :1500 ,\ + 'time_marching_parameter':0.01,\ + 'penalty_type':1,\ + 'tolerance_constant':1e-06} print ("##############NDF GPU##################") start_time = timeit.default_timer() -ndf_gpu = NDF(pars['input'], +(ndf_gpu,info_vec_gpu) = NDF(pars['input'], pars['regularisation_parameter'], pars['edge_parameter'], pars['number_of_iterations'], pars['time_marching_parameter'], - pars['penalty_type'],'gpu') + pars['penalty_type'], + pars['tolerance_constant'],'gpu') Qtools = QualityTools(Im, ndf_gpu) pars['rmse'] = Qtools.rmse() @@ -354,19 +356,20 @@ imgplot = plt.imshow(u0,cmap="gray") # set parameters pars = {'algorithm' : Diff4th, \ 'input' : u0,\ - 'regularisation_parameter':3.5, \ + 'regularisation_parameter':0.8, \ 'edge_parameter':0.02,\ - 'number_of_iterations' :500 ,\ - 'time_marching_parameter':0.0015 - } + 'number_of_iterations' :5500 ,\ + 'time_marching_parameter':0.001,\ + 'tolerance_constant':1e-06} print ("#############DIFF4th CPU################") start_time = timeit.default_timer() -diff4_gpu = Diff4th(pars['input'], +(diff4_gpu,info_vec_gpu) = Diff4th(pars['input'], pars['regularisation_parameter'], pars['edge_parameter'], pars['number_of_iterations'], - pars['time_marching_parameter'],'gpu') + pars['time_marching_parameter'], + pars['tolerance_constant'],'gpu') Qtools = QualityTools(Im, diff4_gpu) pars['algorithm'] = Diff4th @@ -470,26 +473,23 @@ imgplot = plt.imshow(u0,cmap="gray") pars = {'algorithm' : FGP_dTV, \ 'input' : u0,\ 'refdata' : u_ref,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :2000 ,\ + 'regularisation_parameter':0.02, \ + 'number_of_iterations' :500 ,\ 'tolerance_constant':1e-06,\ 'eta_const':0.2,\ 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } + 'nonneg': 0} print ("##############FGP dTV GPU##################") start_time = timeit.default_timer() -fgp_dtv_gpu = FGP_dTV(pars['input'], +(fgp_dtv_gpu,info_vec_gpu) = FGP_dTV(pars['input'], pars['refdata'], pars['regularisation_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], pars['eta_const'], pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'gpu') + pars['nonneg'],'gpu') Qtools = QualityTools(Im, fgp_dtv_gpu) pars['rmse'] = Qtools.rmse() diff --git a/demos/demo_gpu_regularisers3D.py b/demos/demo_gpu_regularisers3D.py index 1a13c86..ccf9694 100644 --- a/demos/demo_gpu_regularisers3D.py +++ b/demos/demo_gpu_regularisers3D.py @@ -277,21 +277,22 @@ imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") # set parameters pars = {'algorithm' : TGV, \ 'input' : noisyVol,\ - 'regularisation_parameter':0.04, \ + 'regularisation_parameter':0.02, \ 'alpha1':1.0,\ 'alpha0':2.0,\ - 'number_of_iterations' :600 ,\ + 'number_of_iterations' :500 ,\ 'LipshitzConstant' :12 ,\ - } + 'tolerance_constant':1e-06} print ("#############TGV GPU####################") start_time = timeit.default_timer() -tgv_gpu3D = TGV(pars['input'], +(tgv_gpu3D,info_vec_gpu) = TGV(pars['input'], pars['regularisation_parameter'], pars['alpha1'], pars['alpha0'], pars['number_of_iterations'], - pars['LipshitzConstant'],'gpu') + pars['LipshitzConstant'], + pars['tolerance_constant'],'gpu') Qtools = QualityTools(idealVol, tgv_gpu3D) pars['rmse'] = Qtools.rmse() @@ -322,21 +323,23 @@ imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") # set parameters pars = {'algorithm' : NDF, \ 'input' : noisyVol,\ - 'regularisation_parameter':0.025, \ + 'regularisation_parameter':0.02, \ 'edge_parameter':0.015,\ - 'number_of_iterations' :500 ,\ - 'time_marching_parameter':0.025,\ - 'penalty_type': 1 - } + 'number_of_iterations' :700 ,\ + 'time_marching_parameter':0.01,\ + 'penalty_type': 1,\ + 'tolerance_constant':1e-06} + print ("#############NDF GPU####################") start_time = timeit.default_timer() -ndf_gpu3D = NDF(pars['input'], +(ndf_gpu3D,info_vec_gpu) = NDF(pars['input'], pars['regularisation_parameter'], pars['edge_parameter'], pars['number_of_iterations'], pars['time_marching_parameter'], - pars['penalty_type'],'gpu') + pars['penalty_type'], + pars['tolerance_constant'], 'gpu') Qtools = QualityTools(idealVol, ndf_gpu3D) pars['rmse'] = Qtools.rmse() @@ -368,19 +371,20 @@ imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") # set parameters pars = {'algorithm' : Diff4th, \ 'input' : noisyVol,\ - 'regularisation_parameter':3.5, \ + 'regularisation_parameter':0.8, \ 'edge_parameter':0.02,\ - 'number_of_iterations' :300 ,\ - 'time_marching_parameter':0.0015 - } + 'number_of_iterations' :500 ,\ + 'time_marching_parameter':0.001,\ + 'tolerance_constant':1e-06} print ("#############DIFF4th CPU################") start_time = timeit.default_timer() -diff4_gpu3D = Diff4th(pars['input'], +(diff4_gpu3D,info_vec_gpu) = Diff4th(pars['input'], pars['regularisation_parameter'], pars['edge_parameter'], pars['number_of_iterations'], - pars['time_marching_parameter'],'gpu') + pars['time_marching_parameter'], + pars['tolerance_constant'],'gpu') Qtools = QualityTools(idealVol, diff4_gpu3D) pars['rmse'] = Qtools.rmse() @@ -410,29 +414,27 @@ a.set_title('Noisy Image') imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") # set parameters -pars = {'algorithm' : FGP_dTV, \ +pars = {'algorithm' : FGP_dTV,\ 'input' : noisyVol,\ 'refdata' : noisyRef,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :300 ,\ - 'tolerance_constant':0.00001,\ + 'regularisation_parameter':0.02, + 'number_of_iterations' :500 ,\ + 'tolerance_constant':1e-06,\ 'eta_const':0.2,\ 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } + 'nonneg': 0} print ("#############FGP TV GPU####################") start_time = timeit.default_timer() -fgp_dTV_gpu3D = FGP_dTV(pars['input'], +(fgp_dTV_gpu3D,info_vec_gpu) = FGP_dTV(pars['input'], pars['refdata'], pars['regularisation_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], pars['eta_const'], pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'gpu') + pars['nonneg'],'gpu') + Qtools = QualityTools(idealVol, fgp_dTV_gpu3D) pars['rmse'] = Qtools.rmse() |