From 3caa686662f7d937cf7eb852dde437cd66e79a6e Mon Sep 17 00:00:00 2001 From: Tomas Kulhanek Date: Thu, 21 Feb 2019 02:10:14 -0500 Subject: restructured sources --- Wrappers/Python/conda-recipe/bld.bat | 20 - Wrappers/Python/conda-recipe/build.sh | 17 - .../Python/conda-recipe/conda_build_config.yaml | 9 - Wrappers/Python/conda-recipe/meta.yaml | 40 - Wrappers/Python/conda-recipe/run_test.py | 819 --------------------- 5 files changed, 905 deletions(-) delete mode 100644 Wrappers/Python/conda-recipe/bld.bat delete mode 100644 Wrappers/Python/conda-recipe/build.sh delete mode 100644 Wrappers/Python/conda-recipe/conda_build_config.yaml delete mode 100644 Wrappers/Python/conda-recipe/meta.yaml delete mode 100755 Wrappers/Python/conda-recipe/run_test.py (limited to 'Wrappers/Python/conda-recipe') diff --git a/Wrappers/Python/conda-recipe/bld.bat b/Wrappers/Python/conda-recipe/bld.bat deleted file mode 100644 index 6c84355..0000000 --- a/Wrappers/Python/conda-recipe/bld.bat +++ /dev/null @@ -1,20 +0,0 @@ -IF NOT DEFINED CIL_VERSION ( -ECHO CIL_VERSION Not Defined. -exit 1 -) - -mkdir "%SRC_DIR%\ccpi" -ROBOCOPY /E "%RECIPE_DIR%\..\.." "%SRC_DIR%\ccpi" -ROBOCOPY /E "%RECIPE_DIR%\..\..\..\Core" "%SRC_DIR%\Core" -::cd %SRC_DIR%\ccpi\Python -cd %SRC_DIR% - -:: issue cmake to create setup.py -cmake -G "NMake Makefiles" %RECIPE_DIR%\..\..\..\ -DBUILD_PYTHON_WRAPPERS=ON -DCONDA_BUILD=ON -DBUILD_CUDA=OFF -DCMAKE_BUILD_TYPE="Release" -DLIBRARY_LIB="%CONDA_PREFIX%\lib" -DLIBRARY_INC="%CONDA_PREFIX%" -DCMAKE_INSTALL_PREFIX="%PREFIX%\Library" - -::%PYTHON% setup-regularisers.py build_ext -::if errorlevel 1 exit 1 -::%PYTHON% setup-regularisers.py install -::if errorlevel 1 exit 1 -nmake install -if errorlevel 1 exit 1 \ No newline at end of file diff --git a/Wrappers/Python/conda-recipe/build.sh b/Wrappers/Python/conda-recipe/build.sh deleted file mode 100644 index 39c0f2c..0000000 --- a/Wrappers/Python/conda-recipe/build.sh +++ /dev/null @@ -1,17 +0,0 @@ - -mkdir "$SRC_DIR/ccpi" -cp -rv "$RECIPE_DIR/../.." "$SRC_DIR/ccpi" -cp -rv "$RECIPE_DIR/../../../Core" "$SRC_DIR/Core" - -cd $SRC_DIR -##cuda=off - -cmake -G "Unix Makefiles" $RECIPE_DIR/../../../ -DBUILD_PYTHON_WRAPPER=ON -DCONDA_BUILD=ON -DBUILD_CUDA=ON -DCMAKE_BUILD_TYPE="Release" -DLIBRARY_LIB=$CONDA_PREFIX/lib -DLIBRARY_INC=$CONDA_PREFIX -DCMAKE_INSTALL_PREFIX=$PREFIX - - -make install - -#$PYTHON setup-regularisers.py build_ext -#$PYTHON setup-regularisers.py install - - diff --git a/Wrappers/Python/conda-recipe/conda_build_config.yaml b/Wrappers/Python/conda-recipe/conda_build_config.yaml deleted file mode 100644 index fbe82dc..0000000 --- a/Wrappers/Python/conda-recipe/conda_build_config.yaml +++ /dev/null @@ -1,9 +0,0 @@ -python: - - 2.7 # [not win] - - 3.5 - - 3.6 -# - 3.7 -numpy: - - 1.12 - - 1.14 - - 1.15 diff --git a/Wrappers/Python/conda-recipe/meta.yaml b/Wrappers/Python/conda-recipe/meta.yaml deleted file mode 100644 index 7435b2b..0000000 --- a/Wrappers/Python/conda-recipe/meta.yaml +++ /dev/null @@ -1,40 +0,0 @@ -package: - name: ccpi-regulariser - version: {{CIL_VERSION}} - -build: - preserve_egg_dir: False - number: 0 - script_env: - - CIL_VERSION - -test: - files: - - lena_gray_512.tif - requires: - - pillow=4.1.1 - -requirements: - build: - - python - - numpy {{ numpy }} - - setuptools - - cython - - vc 14 # [win and py36] - - vc 14 # [win and py35] - - vc 9 # [win and py27] - - cmake - - run: - - {{ pin_compatible('numpy', max_pin='x.x') }} - - python - - numpy - - vc 14 # [win and py36] - - vc 14 # [win and py35] - - vc 9 # [win and py27] - - libgcc-ng - -about: - home: http://www.ccpi.ac.uk - license: BSD license - summary: 'CCPi Core Imaging Library Quantification Toolbox' diff --git a/Wrappers/Python/conda-recipe/run_test.py b/Wrappers/Python/conda-recipe/run_test.py deleted file mode 100755 index 21f3216..0000000 --- a/Wrappers/Python/conda-recipe/run_test.py +++ /dev/null @@ -1,819 +0,0 @@ -import unittest -import numpy as np -import os -import timeit -from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th -from PIL import Image - -class TiffReader(object): - def imread(self, filename): - return np.asarray(Image.open(filename)) -############################################################################### -def printParametersToString(pars): - txt = r'' - for key, value in pars.items(): - if key== 'algorithm' : - txt += "{0} = {1}".format(key, value.__name__) - elif key == 'input': - txt += "{0} = {1}".format(key, np.shape(value)) - elif key == 'refdata': - txt += "{0} = {1}".format(key, np.shape(value)) - else: - txt += "{0} = {1}".format(key, value) - txt += '\n' - return txt -def nrmse(im1, im2): - rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(im1.size)) - max_val = max(np.max(im1), np.max(im2)) - min_val = min(np.min(im1), np.min(im2)) - return 1 - (rmse / (max_val - min_val)) - -def rmse(im1, im2): - rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(im1.size)) - return rmse -############################################################################### - -class TestRegularisers(unittest.TestCase): - - - def test_ROF_TV_CPU_vs_GPU(self): - #print ("tomas debug test function") - print(__name__) - filename = os.path.join("lena_gray_512.tif") - plt = TiffReader() - # read image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - - Im = Im/255 - perc = 0.05 - u0 = Im + np.random.normal(loc = 0 , - scale = perc * Im , - size = np.shape(Im)) - u_ref = Im + np.random.normal(loc = 0 , - scale = 0.01 * Im , - size = np.shape(Im)) - - # map the u0 u0->u0>0 - # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) - u0 = u0.astype('float32') - u_ref = u_ref.astype('float32') - - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("____________ROF-TV bench___________________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - - # set parameters - pars = {'algorithm': ROF_TV, \ - 'input' : u0,\ - 'regularisation_parameter':0.04,\ - 'number_of_iterations': 2500,\ - 'time_marching_parameter': 0.00002 - } - print ("#############ROF TV CPU####################") - start_time = timeit.default_timer() - rof_cpu = ROF_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'cpu') - rms = rmse(Im, rof_cpu) - pars['rmse'] = rms - - txtstr = printParametersToString(pars) - txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) - print (txtstr) - print ("##############ROF TV GPU##################") - start_time = timeit.default_timer() - try: - rof_gpu = ROF_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'gpu') - except ValueError as ve: - self.skipTest("Results not comparable. GPU computing error.") - - rms = rmse(Im, rof_gpu) - pars['rmse'] = rms - pars['algorithm'] = ROF_TV - txtstr = printParametersToString(pars) - txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) - print (txtstr) - print ("--------Compare the results--------") - tolerance = 1e-04 - diff_im = np.zeros(np.shape(rof_cpu)) - diff_im = abs(rof_cpu - rof_gpu) - diff_im[diff_im > tolerance] = 1 - self.assertLessEqual(diff_im.sum() , 1) - - def test_FGP_TV_CPU_vs_GPU(self): - print(__name__) - filename = os.path.join("lena_gray_512.tif") - plt = TiffReader() - # read image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - - Im = Im/255 - perc = 0.05 - u0 = Im + np.random.normal(loc = 0 , - scale = perc * Im , - size = np.shape(Im)) - u_ref = Im + np.random.normal(loc = 0 , - scale = 0.01 * Im , - size = np.shape(Im)) - - # map the u0 u0->u0>0 - # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) - u0 = u0.astype('float32') - u_ref = u_ref.astype('float32') - - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("____________FGP-TV bench___________________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - - - # set parameters - pars = {'algorithm' : FGP_TV, \ - 'input' : u0,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :1200 ,\ - 'tolerance_constant':0.00001,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - - print ("#############FGP TV CPU####################") - start_time = timeit.default_timer() - fgp_cpu = FGP_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'cpu') - - - rms = rmse(Im, fgp_cpu) - pars['rmse'] = rms - - txtstr = printParametersToString(pars) - txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) - print (txtstr) - - print ("##############FGP TV GPU##################") - start_time = timeit.default_timer() - try: - fgp_gpu = FGP_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'gpu') - - except ValueError as ve: - self.skipTest("Results not comparable. GPU computing error.") - - rms = rmse(Im, fgp_gpu) - pars['rmse'] = rms - pars['algorithm'] = FGP_TV - txtstr = printParametersToString(pars) - txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) - print (txtstr) - - print ("--------Compare the results--------") - tolerance = 1e-05 - diff_im = np.zeros(np.shape(fgp_cpu)) - diff_im = abs(fgp_cpu - fgp_gpu) - diff_im[diff_im > tolerance] = 1 - - self.assertLessEqual(diff_im.sum() , 1) - - def test_SB_TV_CPU_vs_GPU(self): - print(__name__) - filename = os.path.join("lena_gray_512.tif") - plt = TiffReader() - # read image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - - Im = Im/255 - perc = 0.05 - u0 = Im + np.random.normal(loc = 0 , - scale = perc * Im , - size = np.shape(Im)) - u_ref = Im + np.random.normal(loc = 0 , - scale = 0.01 * Im , - size = np.shape(Im)) - - # map the u0 u0->u0>0 - # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) - u0 = u0.astype('float32') - u_ref = u_ref.astype('float32') - - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("____________SB-TV bench___________________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - - - # set parameters - pars = {'algorithm' : SB_TV, \ - 'input' : u0,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :150 ,\ - 'tolerance_constant':1e-05,\ - 'methodTV': 0 ,\ - 'printingOut': 0 - } - - print ("#############SB-TV CPU####################") - start_time = timeit.default_timer() - sb_cpu = SB_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['methodTV'], - pars['printingOut'],'cpu') - - - rms = rmse(Im, sb_cpu) - pars['rmse'] = rms - - txtstr = printParametersToString(pars) - txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) - print (txtstr) - - print ("##############SB TV GPU##################") - start_time = timeit.default_timer() - try: - - sb_gpu = SB_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['methodTV'], - pars['printingOut'],'gpu') - - except ValueError as ve: - self.skipTest("Results not comparable. GPU computing error.") - - rms = rmse(Im, sb_gpu) - pars['rmse'] = rms - pars['algorithm'] = SB_TV - txtstr = printParametersToString(pars) - txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) - print (txtstr) - print ("--------Compare the results--------") - tolerance = 1e-05 - diff_im = np.zeros(np.shape(sb_cpu)) - diff_im = abs(sb_cpu - sb_gpu) - diff_im[diff_im > tolerance] = 1 - self.assertLessEqual(diff_im.sum(), 1) - - def test_TGV_CPU_vs_GPU(self): - print(__name__) - filename = os.path.join("lena_gray_512.tif") - plt = TiffReader() - # read image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - - Im = Im/255 - perc = 0.05 - u0 = Im + np.random.normal(loc = 0 , - scale = perc * Im , - size = np.shape(Im)) - u_ref = Im + np.random.normal(loc = 0 , - scale = 0.01 * Im , - size = np.shape(Im)) - - # map the u0 u0->u0>0 - # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) - u0 = u0.astype('float32') - u_ref = u_ref.astype('float32') - - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("____________TGV bench___________________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - - - # set parameters - pars = {'algorithm' : TGV, \ - 'input' : u0,\ - 'regularisation_parameter':0.04, \ - 'alpha1':1.0,\ - 'alpha0':2.0,\ - 'number_of_iterations' :250 ,\ - 'LipshitzConstant' :12 ,\ - } - - print ("#############TGV CPU####################") - start_time = timeit.default_timer() - tgv_cpu = TGV(pars['input'], - pars['regularisation_parameter'], - pars['alpha1'], - pars['alpha0'], - pars['number_of_iterations'], - pars['LipshitzConstant'],'cpu') - - rms = rmse(Im, tgv_cpu) - pars['rmse'] = rms - - txtstr = printParametersToString(pars) - txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) - print (txtstr) - - print ("##############TGV GPU##################") - start_time = timeit.default_timer() - try: - tgv_gpu = TGV(pars['input'], - pars['regularisation_parameter'], - pars['alpha1'], - pars['alpha0'], - pars['number_of_iterations'], - pars['LipshitzConstant'],'gpu') - - except ValueError as ve: - self.skipTest("Results not comparable. GPU computing error.") - - rms = rmse(Im, tgv_gpu) - pars['rmse'] = rms - pars['algorithm'] = TGV - txtstr = printParametersToString(pars) - txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) - print (txtstr) - print ("--------Compare the results--------") - tolerance = 1e-05 - diff_im = np.zeros(np.shape(tgv_gpu)) - diff_im = abs(tgv_cpu - tgv_gpu) - diff_im[diff_im > tolerance] = 1 - self.assertLessEqual(diff_im.sum() , 1) - - def test_LLT_ROF_CPU_vs_GPU(self): - print(__name__) - filename = os.path.join("lena_gray_512.tif") - plt = TiffReader() - # read image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - - Im = Im/255 - perc = 0.05 - u0 = Im + np.random.normal(loc = 0 , - scale = perc * Im , - size = np.shape(Im)) - u_ref = Im + np.random.normal(loc = 0 , - scale = 0.01 * Im , - size = np.shape(Im)) - - # map the u0 u0->u0>0 - # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) - u0 = u0.astype('float32') - u_ref = u_ref.astype('float32') - - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("____________LLT-ROF bench___________________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - - - # set parameters - pars = {'algorithm' : LLT_ROF, \ - 'input' : u0,\ - 'regularisation_parameterROF':0.04, \ - 'regularisation_parameterLLT':0.01, \ - 'number_of_iterations' :1000 ,\ - 'time_marching_parameter' :0.0001 ,\ - } - - print ("#############LLT- ROF CPU####################") - start_time = timeit.default_timer() - lltrof_cpu = LLT_ROF(pars['input'], - pars['regularisation_parameterROF'], - pars['regularisation_parameterLLT'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'cpu') - - rms = rmse(Im, lltrof_cpu) - pars['rmse'] = rms - - txtstr = printParametersToString(pars) - txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) - print (txtstr) - print ("#############LLT- ROF GPU####################") - start_time = timeit.default_timer() - try: - lltrof_gpu = LLT_ROF(pars['input'], - pars['regularisation_parameterROF'], - pars['regularisation_parameterLLT'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'gpu') - - except ValueError as ve: - self.skipTest("Results not comparable. GPU computing error.") - - rms = rmse(Im, lltrof_gpu) - pars['rmse'] = rms - pars['algorithm'] = LLT_ROF - txtstr = printParametersToString(pars) - txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) - print (txtstr) - print ("--------Compare the results--------") - tolerance = 1e-04 - diff_im = np.zeros(np.shape(lltrof_gpu)) - diff_im = abs(lltrof_cpu - lltrof_gpu) - diff_im[diff_im > tolerance] = 1 - self.assertLessEqual(diff_im.sum(), 1) - - def test_NDF_CPU_vs_GPU(self): - print(__name__) - filename = os.path.join("lena_gray_512.tif") - plt = TiffReader() - # read image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - - Im = Im/255 - perc = 0.05 - u0 = Im + np.random.normal(loc = 0 , - scale = perc * Im , - size = np.shape(Im)) - u_ref = Im + np.random.normal(loc = 0 , - scale = 0.01 * Im , - size = np.shape(Im)) - - # map the u0 u0->u0>0 - # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) - u0 = u0.astype('float32') - u_ref = u_ref.astype('float32') - - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("_______________NDF bench___________________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - - - # 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 - } - - print ("#############NDF CPU####################") - start_time = timeit.default_timer() - ndf_cpu = NDF(pars['input'], - pars['regularisation_parameter'], - pars['edge_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'], - pars['penalty_type'],'cpu') - - rms = rmse(Im, ndf_cpu) - pars['rmse'] = rms - - txtstr = printParametersToString(pars) - txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) - print (txtstr) - - print ("##############NDF GPU##################") - start_time = timeit.default_timer() - try: - ndf_gpu = NDF(pars['input'], - pars['regularisation_parameter'], - pars['edge_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'], - pars['penalty_type'],'gpu') - - except ValueError as ve: - self.skipTest("Results not comparable. GPU computing error.") - rms = rmse(Im, ndf_gpu) - pars['rmse'] = rms - pars['algorithm'] = NDF - txtstr = printParametersToString(pars) - txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) - print (txtstr) - print ("--------Compare the results--------") - tolerance = 1e-05 - diff_im = np.zeros(np.shape(ndf_cpu)) - diff_im = abs(ndf_cpu - ndf_gpu) - diff_im[diff_im > tolerance] = 1 - self.assertLessEqual(diff_im.sum(), 1) - - - def test_Diff4th_CPU_vs_GPU(self): - filename = os.path.join("lena_gray_512.tif") - plt = TiffReader() - # read image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - - Im = Im/255 - perc = 0.05 - u0 = Im + np.random.normal(loc = 0 , - scale = perc * Im , - size = np.shape(Im)) - u_ref = Im + np.random.normal(loc = 0 , - scale = 0.01 * Im , - size = np.shape(Im)) - - # map the u0 u0->u0>0 - # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) - u0 = u0.astype('float32') - u_ref = u_ref.astype('float32') - - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("___Anisotropic Diffusion 4th Order (2D)____") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - - # set parameters - pars = {'algorithm' : Diff4th, \ - 'input' : u0,\ - 'regularisation_parameter':3.5, \ - 'edge_parameter':0.02,\ - 'number_of_iterations' :500 ,\ - 'time_marching_parameter':0.001 - } - - print ("#############Diff4th CPU####################") - start_time = timeit.default_timer() - diff4th_cpu = Diff4th(pars['input'], - pars['regularisation_parameter'], - pars['edge_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'cpu') - - rms = rmse(Im, diff4th_cpu) - pars['rmse'] = rms - - txtstr = printParametersToString(pars) - txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) - print (txtstr) - print ("##############Diff4th GPU##################") - start_time = timeit.default_timer() - try: - diff4th_gpu = Diff4th(pars['input'], - pars['regularisation_parameter'], - pars['edge_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'], 'gpu') - - except ValueError as ve: - self.skipTest("Results not comparable. GPU computing error.") - rms = rmse(Im, diff4th_gpu) - pars['rmse'] = rms - pars['algorithm'] = Diff4th - txtstr = printParametersToString(pars) - txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) - print (txtstr) - print ("--------Compare the results--------") - tolerance = 1e-05 - diff_im = np.zeros(np.shape(diff4th_cpu)) - diff_im = abs(diff4th_cpu - diff4th_gpu) - diff_im[diff_im > tolerance] = 1 - self.assertLessEqual(diff_im.sum() , 1) - - def test_FDGdTV_CPU_vs_GPU(self): - filename = os.path.join("lena_gray_512.tif") - plt = TiffReader() - # read image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - - Im = Im/255 - perc = 0.05 - u0 = Im + np.random.normal(loc = 0 , - scale = perc * Im , - size = np.shape(Im)) - u_ref = Im + np.random.normal(loc = 0 , - scale = 0.01 * Im , - size = np.shape(Im)) - - # map the u0 u0->u0>0 - # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) - u0 = u0.astype('float32') - u_ref = u_ref.astype('float32') - - - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("____________FGP-dTV bench___________________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - - # set parameters - pars = {'algorithm' : FGP_dTV, \ - 'input' : u0,\ - 'refdata' : u_ref,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :1000 ,\ - 'tolerance_constant':1e-07,\ - 'eta_const':0.2,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - - print ("#############FGP dTV CPU####################") - start_time = timeit.default_timer() - fgp_dtv_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') - - - rms = rmse(Im, fgp_dtv_cpu) - pars['rmse'] = rms - - txtstr = printParametersToString(pars) - txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) - print (txtstr) - print ("##############FGP dTV GPU##################") - start_time = timeit.default_timer() - try: - fgp_dtv_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') - except ValueError as ve: - self.skipTest("Results not comparable. GPU computing error.") - rms = rmse(Im, fgp_dtv_gpu) - pars['rmse'] = rms - pars['algorithm'] = FGP_dTV - txtstr = printParametersToString(pars) - txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) - print (txtstr) - print ("--------Compare the results--------") - tolerance = 1e-05 - diff_im = np.zeros(np.shape(fgp_dtv_cpu)) - diff_im = abs(fgp_dtv_cpu - fgp_dtv_gpu) - diff_im[diff_im > tolerance] = 1 - self.assertLessEqual(diff_im.sum(), 1) - - def test_cpu_ROF_TV(self): - #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy") - - filename = os.path.join("lena_gray_512.tif") - - plt = TiffReader() - # read image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - Im = Im/255 - - """ - # read noiseless image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - """ - tolerance = 1e-05 - rms_rof_exp = 8.313131464999238e-05 #expected value for ROF model - - # set parameters for ROF-TV - pars_rof_tv = {'algorithm': ROF_TV, \ - 'input' : Im,\ - 'regularisation_parameter':0.04,\ - 'number_of_iterations': 50,\ - 'time_marching_parameter': 0.00001 - } - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("_________testing ROF-TV (2D, CPU)__________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - rof_cpu = ROF_TV(pars_rof_tv['input'], - pars_rof_tv['regularisation_parameter'], - pars_rof_tv['number_of_iterations'], - pars_rof_tv['time_marching_parameter'],'cpu') - rms_rof = rmse(Im, rof_cpu) - - # now compare obtained rms with the expected value - self.assertLess(abs(rms_rof-rms_rof_exp) , tolerance) - def test_cpu_FGP_TV(self): - #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy") - - filename = os.path.join("lena_gray_512.tif") - - plt = TiffReader() - # read image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - Im = Im/255 - """ - # read noiseless image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - """ - tolerance = 1e-05 - rms_fgp_exp = 0.019152347 #expected value for FGP model - - pars_fgp_tv = {'algorithm' : FGP_TV, \ - 'input' : Im,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :50 ,\ - 'tolerance_constant':1e-06,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("_________testing FGP-TV (2D, CPU)__________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - fgp_cpu = FGP_TV(pars_fgp_tv['input'], - pars_fgp_tv['regularisation_parameter'], - pars_fgp_tv['number_of_iterations'], - pars_fgp_tv['tolerance_constant'], - pars_fgp_tv['methodTV'], - pars_fgp_tv['nonneg'], - pars_fgp_tv['printingOut'],'cpu') - rms_fgp = rmse(Im, fgp_cpu) - # now compare obtained rms with the expected value - self.assertLess(abs(rms_fgp-rms_fgp_exp) , tolerance) - - def test_gpu_ROF(self): - #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy") - filename = os.path.join("lena_gray_512.tif") - - plt = TiffReader() - # read image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - Im = Im/255 - - tolerance = 1e-05 - rms_rof_exp = 8.313131464999238e-05 #expected value for ROF model - - # set parameters for ROF-TV - pars_rof_tv = {'algorithm': ROF_TV, \ - 'input' : Im,\ - 'regularisation_parameter':0.04,\ - 'number_of_iterations': 50,\ - 'time_marching_parameter': 0.00001 - } - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("_________testing ROF-TV (2D, GPU)__________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - try: - rof_gpu = ROF_TV(pars_rof_tv['input'], - pars_rof_tv['regularisation_parameter'], - pars_rof_tv['number_of_iterations'], - pars_rof_tv['time_marching_parameter'],'gpu') - except ValueError as ve: - self.skipTest("Results not comparable. GPU computing error.") - - rms_rof = rmse(Im, rof_gpu) - # now compare obtained rms with the expected value - self.assertLess(abs(rms_rof-rms_rof_exp) , tolerance) - - def test_gpu_FGP(self): - #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy") - filename = os.path.join("lena_gray_512.tif") - - plt = TiffReader() - # read image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - Im = Im/255 - tolerance = 1e-05 - - rms_fgp_exp = 0.019152347 #expected value for FGP model - - # set parameters for FGP-TV - pars_fgp_tv = {'algorithm' : FGP_TV, \ - 'input' : Im,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :50 ,\ - 'tolerance_constant':1e-06,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("_________testing FGP-TV (2D, GPU)__________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - try: - fgp_gpu = FGP_TV(pars_fgp_tv['input'], - pars_fgp_tv['regularisation_parameter'], - pars_fgp_tv['number_of_iterations'], - pars_fgp_tv['tolerance_constant'], - pars_fgp_tv['methodTV'], - pars_fgp_tv['nonneg'], - pars_fgp_tv['printingOut'],'gpu') - except ValueError as ve: - self.skipTest("Results not comparable. GPU computing error.") - rms_fgp = rmse(Im, fgp_gpu) - # now compare obtained rms with the expected value - - self.assertLess(abs(rms_fgp-rms_fgp_exp) , tolerance) - -if __name__ == '__main__': - unittest.main() -- cgit v1.2.3