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-rw-r--r--Wrappers/Matlab/mex_compile/compileGPU_mex.m24
-rw-r--r--Wrappers/Python/conda-recipe/build.sh2
-rw-r--r--Wrappers/Python/conda-recipe/meta.yaml2
-rwxr-xr-xWrappers/Python/conda-recipe/run_test.py67
-rw-r--r--Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py4
-rw-r--r--Wrappers/Python/src/gpu_regularisers.pyx156
6 files changed, 148 insertions, 107 deletions
diff --git a/Wrappers/Matlab/mex_compile/compileGPU_mex.m b/Wrappers/Matlab/mex_compile/compileGPU_mex.m
index e0311ea..dd1475c 100644
--- a/Wrappers/Matlab/mex_compile/compileGPU_mex.m
+++ b/Wrappers/Matlab/mex_compile/compileGPU_mex.m
@@ -7,11 +7,10 @@
% In the code bellow we provide a full explicit path to nvcc compiler
% ! paths to matlab and CUDA sdk can be different, modify accordingly !
-% Tested on Ubuntu 16.04/MATLAB 2016b/cuda7.5/gcc4.9
-
-% Installation HAS NOT been tested on Windows, please contact me if you'll be able to
-% install software on Windows and I gratefully include it into the master release.
+% Tested on Ubuntu 18.04/MATLAB 2016b/cuda10.0/gcc7.3
+% Installation HAS NOT been tested on Windows, please you Cmake build or
+% modify the code bellow accordingly
fsep = '/';
pathcopyFrom = sprintf(['..' fsep '..' fsep '..' fsep 'Core' fsep 'regularisers_GPU'], 1i);
@@ -28,44 +27,45 @@ fprintf('%s \n', '<<<<<<<<<<<Compiling GPU regularisers (CUDA)>>>>>>>>>>>>>');
fprintf('%s \n', 'Compiling ROF-TV...');
!/usr/local/cuda/bin/nvcc -O0 -c TV_ROF_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
-mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu ROF_TV_GPU.cpp TV_ROF_GPU_core.o
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu ROF_TV_GPU.cpp TV_ROF_GPU_core.o
movefile('ROF_TV_GPU.mex*',Pathmove);
fprintf('%s \n', 'Compiling FGP-TV...');
!/usr/local/cuda/bin/nvcc -O0 -c TV_FGP_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
-mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu FGP_TV_GPU.cpp TV_FGP_GPU_core.o
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu FGP_TV_GPU.cpp TV_FGP_GPU_core.o
movefile('FGP_TV_GPU.mex*',Pathmove);
fprintf('%s \n', 'Compiling SB-TV...');
!/usr/local/cuda/bin/nvcc -O0 -c TV_SB_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
-mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu SB_TV_GPU.cpp TV_SB_GPU_core.o
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu SB_TV_GPU.cpp TV_SB_GPU_core.o
movefile('SB_TV_GPU.mex*',Pathmove);
fprintf('%s \n', 'Compiling TGV...');
!/usr/local/cuda/bin/nvcc -O0 -c TGV_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
-mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu TGV_GPU.cpp TGV_GPU_core.o
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu TGV_GPU.cpp TGV_GPU_core.o
movefile('TGV_GPU.mex*',Pathmove);
fprintf('%s \n', 'Compiling dFGP-TV...');
!/usr/local/cuda/bin/nvcc -O0 -c dTV_FGP_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
-mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu FGP_dTV_GPU.cpp dTV_FGP_GPU_core.o
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu FGP_dTV_GPU.cpp dTV_FGP_GPU_core.o
movefile('FGP_dTV_GPU.mex*',Pathmove);
fprintf('%s \n', 'Compiling NonLinear Diffusion...');
!/usr/local/cuda/bin/nvcc -O0 -c NonlDiff_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
-mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu NonlDiff_GPU.cpp NonlDiff_GPU_core.o
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu NonlDiff_GPU.cpp NonlDiff_GPU_core.o
movefile('NonlDiff_GPU.mex*',Pathmove);
fprintf('%s \n', 'Compiling Anisotropic diffusion of higher order...');
!/usr/local/cuda/bin/nvcc -O0 -c Diffus_4thO_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
-mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu Diffusion_4thO_GPU.cpp Diffus_4thO_GPU_core.o
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu Diffusion_4thO_GPU.cpp Diffus_4thO_GPU_core.o
movefile('Diffusion_4thO_GPU.mex*',Pathmove);
fprintf('%s \n', 'Compiling ROF-LLT...');
!/usr/local/cuda/bin/nvcc -O0 -c LLT_ROF_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
-mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu LLT_ROF_GPU.cpp LLT_ROF_GPU_core.o
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu LLT_ROF_GPU.cpp LLT_ROF_GPU_core.o
movefile('LLT_ROF_GPU.mex*',Pathmove);
+
delete TV_ROF_GPU_core* TV_FGP_GPU_core* TV_SB_GPU_core* dTV_FGP_GPU_core* NonlDiff_GPU_core* Diffus_4thO_GPU_core* TGV_GPU_core* LLT_ROF_GPU_core* CCPiDefines.h
fprintf('%s \n', 'All successfully compiled!');
diff --git a/Wrappers/Python/conda-recipe/build.sh b/Wrappers/Python/conda-recipe/build.sh
index 54bc8e2..eec7c2f 100644
--- a/Wrappers/Python/conda-recipe/build.sh
+++ b/Wrappers/Python/conda-recipe/build.sh
@@ -4,7 +4,7 @@ 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
diff --git a/Wrappers/Python/conda-recipe/meta.yaml b/Wrappers/Python/conda-recipe/meta.yaml
index ed73165..808493e 100644
--- a/Wrappers/Python/conda-recipe/meta.yaml
+++ b/Wrappers/Python/conda-recipe/meta.yaml
@@ -1,6 +1,6 @@
package:
name: ccpi-regulariser
- version: 0.10.2
+ version: 0.10.3
build:
diff --git a/Wrappers/Python/conda-recipe/run_test.py b/Wrappers/Python/conda-recipe/run_test.py
index 499ae7f..cfb3f53 100755
--- a/Wrappers/Python/conda-recipe/run_test.py
+++ b/Wrappers/Python/conda-recipe/run_test.py
@@ -2,7 +2,7 @@ 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 ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, DIFF4th
from PIL import Image
class TiffReader(object):
@@ -37,6 +37,8 @@ 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
@@ -63,11 +65,11 @@ class TestRegularisers(unittest.TestCase):
# set parameters
pars = {'algorithm': ROF_TV, \
- 'input' : u0,\
- 'regularisation_parameter':0.04,\
- 'number_of_iterations': 1000,\
- 'time_marching_parameter': 0.0001
- }
+ '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'],
@@ -88,8 +90,8 @@ class TestRegularisers(unittest.TestCase):
pars['number_of_iterations'],
pars['time_marching_parameter'],'gpu')
except ValueError as ve:
- self.assertTrue(True)
- return
+ self.skipTest("Results not comparable. GPU computing error.")
+
rms = rmse(Im, rof_gpu)
pars['rmse'] = rms
pars['algorithm'] = ROF_TV
@@ -101,10 +103,10 @@ class TestRegularisers(unittest.TestCase):
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
@@ -169,10 +171,10 @@ class TestRegularisers(unittest.TestCase):
pars['methodTV'],
pars['nonneg'],
pars['printingOut'],'gpu')
-
+
except ValueError as ve:
- self.assertTrue(True)
- return
+ self.skipTest("Results not comparable. GPU computing error.")
+
rms = rmse(Im, fgp_gpu)
pars['rmse'] = rms
pars['algorithm'] = FGP_TV
@@ -189,6 +191,7 @@ class TestRegularisers(unittest.TestCase):
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
@@ -251,10 +254,10 @@ class TestRegularisers(unittest.TestCase):
pars['tolerance_constant'],
pars['methodTV'],
pars['printingOut'],'gpu')
-
+
except ValueError as ve:
- self.assertTrue(True)
- return
+ self.skipTest("Results not comparable. GPU computing error.")
+
rms = rmse(Im, sb_gpu)
pars['rmse'] = rms
pars['algorithm'] = SB_TV
@@ -269,6 +272,7 @@ class TestRegularisers(unittest.TestCase):
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
@@ -329,10 +333,10 @@ class TestRegularisers(unittest.TestCase):
pars['alpha0'],
pars['number_of_iterations'],
pars['LipshitzConstant'],'gpu')
-
+
except ValueError as ve:
- self.assertTrue(True)
- return
+ self.skipTest("Results not comparable. GPU computing error.")
+
rms = rmse(Im, tgv_gpu)
pars['rmse'] = rms
pars['algorithm'] = TGV
@@ -347,6 +351,7 @@ class TestRegularisers(unittest.TestCase):
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
@@ -405,8 +410,8 @@ class TestRegularisers(unittest.TestCase):
pars['time_marching_parameter'],'gpu')
except ValueError as ve:
- self.assertTrue(True)
- return
+ self.skipTest("Results not comparable. GPU computing error.")
+
rms = rmse(Im, lltrof_gpu)
pars['rmse'] = rms
pars['algorithm'] = LLT_ROF
@@ -421,6 +426,7 @@ class TestRegularisers(unittest.TestCase):
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
@@ -483,8 +489,7 @@ class TestRegularisers(unittest.TestCase):
pars['penalty_type'],'gpu')
except ValueError as ve:
- self.assertTrue(True)
- return
+ self.skipTest("Results not comparable. GPU computing error.")
rms = rmse(Im, ndf_gpu)
pars['rmse'] = rms
pars['algorithm'] = NDF
@@ -557,8 +562,7 @@ class TestRegularisers(unittest.TestCase):
pars['time_marching_parameter'], 'gpu')
except ValueError as ve:
- self.assertTrue(True)
- return
+ self.skipTest("Results not comparable. GPU computing error.")
rms = rmse(Im, diff4th_gpu)
pars['rmse'] = rms
pars['algorithm'] = DIFF4th
@@ -603,8 +607,8 @@ class TestRegularisers(unittest.TestCase):
'input' : u0,\
'refdata' : u_ref,\
'regularisation_parameter':0.04, \
- 'number_of_iterations' :2000 ,\
- 'tolerance_constant':1e-06,\
+ 'number_of_iterations' :1000 ,\
+ 'tolerance_constant':1e-07,\
'eta_const':0.2,\
'methodTV': 0 ,\
'nonneg': 0 ,\
@@ -643,8 +647,7 @@ class TestRegularisers(unittest.TestCase):
pars['nonneg'],
pars['printingOut'],'gpu')
except ValueError as ve:
- self.assertTrue(True)
- return
+ self.skipTest("Results not comparable. GPU computing error.")
rms = rmse(Im, fgp_dtv_gpu)
pars['rmse'] = rms
pars['algorithm'] = FGP_dTV
@@ -765,8 +768,8 @@ class TestRegularisers(unittest.TestCase):
pars_rof_tv['number_of_iterations'],
pars_rof_tv['time_marching_parameter'],'gpu')
except ValueError as ve:
- self.assertTrue(True)
- return
+ 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)
@@ -806,10 +809,10 @@ class TestRegularisers(unittest.TestCase):
pars_fgp_tv['nonneg'],
pars_fgp_tv['printingOut'],'gpu')
except ValueError as ve:
- self.assertTrue(True)
- return
+ 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__':
diff --git a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
index 616eab0..6529b5c 100644
--- a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
@@ -656,8 +656,8 @@ pars = {'algorithm' : FGP_dTV, \
'input' : u0,\
'refdata' : u_ref,\
'regularisation_parameter':0.04, \
- 'number_of_iterations' :2000 ,\
- 'tolerance_constant':1e-06,\
+ 'number_of_iterations' :1000 ,\
+ 'tolerance_constant':1e-07,\
'eta_const':0.2,\
'methodTV': 0 ,\
'nonneg': 0 ,\
diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx
index 302727e..2b97865 100644
--- a/Wrappers/Python/src/gpu_regularisers.pyx
+++ b/Wrappers/Python/src/gpu_regularisers.pyx
@@ -18,15 +18,17 @@ import cython
import numpy as np
cimport numpy as np
-cdef extern void TV_ROF_GPU_main(float* Input, float* Output, float lambdaPar, int iter, float tau, int N, int M, int Z);
-cdef extern void TV_FGP_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int N, int M, int Z);
-cdef extern void TV_SB_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int printM, int N, int M, int Z);
-cdef extern void TGV_GPU_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY);
-cdef extern void LLT_ROF_GPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int N, int M, int Z);
-cdef extern void NonlDiff_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int N, int M, int Z);
-cdef extern void dTV_FGP_GPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int N, int M, int Z);
-cdef extern void Diffus4th_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int N, int M, int Z);
-cdef extern void PatchSelect_GPU_main(float *Input, unsigned short *H_i, unsigned short *H_j, float *Weights, int N, int M, int SearchWindow, int SimilarWin, int NumNeighb, float h);
+CUDAErrorMessage = 'CUDA error'
+
+cdef extern int TV_ROF_GPU_main(float* Input, float* Output, float lambdaPar, int iter, float tau, int N, int M, int Z);
+cdef extern int TV_FGP_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int N, int M, int Z);
+cdef extern int TV_SB_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int printM, int N, int M, int Z);
+cdef extern int TGV_GPU_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY);
+cdef extern int LLT_ROF_GPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int N, int M, int Z);
+cdef extern int NonlDiff_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int N, int M, int Z);
+cdef extern int dTV_FGP_GPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int N, int M, int Z);
+cdef extern int Diffus4th_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int N, int M, int Z);
+cdef extern int PatchSelect_GPU_main(float *Input, unsigned short *H_i, unsigned short *H_j, float *Weights, int N, int M, int SearchWindow, int SimilarWin, int NumNeighb, float h);
# Total-variation Rudin-Osher-Fatemi (ROF)
def TV_ROF_GPU(inputData,
@@ -186,15 +188,16 @@ def ROFTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
np.zeros([dims[0],dims[1]], dtype='float32')
- # Running CUDA code here
- TV_ROF_GPU_main(
+ # Running CUDA code here
+ if (TV_ROF_GPU_main(
&inputData[0,0], &outputData[0,0],
regularisation_parameter,
iterations ,
time_marching_parameter,
- dims[1], dims[0], 1);
-
- return outputData
+ dims[1], dims[0], 1)==0):
+ return outputData;
+ else:
+ raise ValueError(CUDAErrorMessage);
def ROFTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
float regularisation_parameter,
@@ -210,14 +213,15 @@ def ROFTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
# Running CUDA code here
- TV_ROF_GPU_main(
+ if (TV_ROF_GPU_main(
&inputData[0,0,0], &outputData[0,0,0],
regularisation_parameter,
iterations ,
time_marching_parameter,
- dims[2], dims[1], dims[0]);
-
- return outputData
+ dims[2], dims[1], dims[0])==0):
+ return outputData;
+ else:
+ raise ValueError(CUDAErrorMessage);
#****************************************************************#
#********************** Total-variation FGP *********************#
#****************************************************************#
@@ -238,16 +242,18 @@ def FGPTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
np.zeros([dims[0],dims[1]], dtype='float32')
# Running CUDA code here
- TV_FGP_GPU_main(&inputData[0,0], &outputData[0,0],
+ if (TV_FGP_GPU_main(&inputData[0,0], &outputData[0,0],
regularisation_parameter,
iterations,
tolerance_param,
methodTV,
nonneg,
printM,
- dims[1], dims[0], 1);
-
- return outputData
+ dims[1], dims[0], 1)==0):
+ return outputData;
+ else:
+ raise ValueError(CUDAErrorMessage);
+
def FGPTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
float regularisation_parameter,
@@ -266,16 +272,18 @@ def FGPTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
# Running CUDA code here
- TV_FGP_GPU_main(&inputData[0,0,0], &outputData[0,0,0],
+ if (TV_FGP_GPU_main(&inputData[0,0,0], &outputData[0,0,0],
regularisation_parameter ,
iterations,
tolerance_param,
methodTV,
nonneg,
printM,
- dims[2], dims[1], dims[0]);
-
- return outputData
+ dims[2], dims[1], dims[0])==0):
+ return outputData;
+ else:
+ raise ValueError(CUDAErrorMessage);
+
#***************************************************************#
#********************** Total-variation SB *********************#
#***************************************************************#
@@ -295,15 +303,17 @@ def SBTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
np.zeros([dims[0],dims[1]], dtype='float32')
# Running CUDA code here
- TV_SB_GPU_main(&inputData[0,0], &outputData[0,0],
+ if (TV_SB_GPU_main(&inputData[0,0], &outputData[0,0],
regularisation_parameter,
iterations,
tolerance_param,
methodTV,
printM,
- dims[1], dims[0], 1);
-
- return outputData
+ dims[1], dims[0], 1)==0):
+ return outputData;
+ else:
+ raise ValueError(CUDAErrorMessage);
+
def SBTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
float regularisation_parameter,
@@ -321,15 +331,17 @@ def SBTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
# Running CUDA code here
- TV_SB_GPU_main(&inputData[0,0,0], &outputData[0,0,0],
+ if (TV_SB_GPU_main(&inputData[0,0,0], &outputData[0,0,0],
regularisation_parameter ,
iterations,
tolerance_param,
methodTV,
printM,
- dims[2], dims[1], dims[0]);
-
- return outputData
+ dims[2], dims[1], dims[0])==0):
+ return outputData;
+ else:
+ raise ValueError(CUDAErrorMessage);
+
#***************************************************************#
#************************ LLT-ROF model ************************#
@@ -349,8 +361,11 @@ def LLT_ROF_GPU2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
np.zeros([dims[0],dims[1]], dtype='float32')
# Running CUDA code here
- LLT_ROF_GPU_main(&inputData[0,0], &outputData[0,0],regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[1],dims[0],1);
- return outputData
+ if (LLT_ROF_GPU_main(&inputData[0,0], &outputData[0,0],regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[1],dims[0],1)==0):
+ return outputData;
+ else:
+ raise ValueError(CUDAErrorMessage);
+
def LLT_ROF_GPU3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
float regularisation_parameterROF,
@@ -367,8 +382,11 @@ def LLT_ROF_GPU3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
# Running CUDA code here
- LLT_ROF_GPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[2], dims[1], dims[0]);
- return outputData
+ if (LLT_ROF_GPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[2], dims[1], dims[0])==0):
+ return outputData;
+ else:
+ raise ValueError(CUDAErrorMessage);
+
#***************************************************************#
@@ -389,13 +407,16 @@ def TGV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
np.zeros([dims[0],dims[1]], dtype='float32')
#/* Run TGV iterations for 2D data */
- TGV_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter,
+ if (TGV_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter,
alpha1,
alpha0,
iterationsNumb,
LipshitzConst,
- dims[1],dims[0])
- return outputData
+ dims[1],dims[0])==0):
+ return outputData
+ else:
+ raise ValueError(CUDAErrorMessage);
+
#****************************************************************#
#**************Directional Total-variation FGP ******************#
@@ -419,7 +440,7 @@ def FGPdTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
np.zeros([dims[0],dims[1]], dtype='float32')
# Running CUDA code here
- dTV_FGP_GPU_main(&inputData[0,0], &refdata[0,0], &outputData[0,0],
+ if (dTV_FGP_GPU_main(&inputData[0,0], &refdata[0,0], &outputData[0,0],
regularisation_parameter,
iterations,
tolerance_param,
@@ -427,9 +448,11 @@ def FGPdTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
methodTV,
nonneg,
printM,
- dims[1], dims[0], 1);
-
- return outputData
+ dims[1], dims[0], 1)==0):
+ return outputData
+ else:
+ raise ValueError(CUDAErrorMessage);
+
def FGPdTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
np.ndarray[np.float32_t, ndim=3, mode="c"] refdata,
@@ -450,7 +473,7 @@ def FGPdTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
# Running CUDA code here
- dTV_FGP_GPU_main(&inputData[0,0,0], &refdata[0,0,0], &outputData[0,0,0],
+ if (dTV_FGP_GPU_main(&inputData[0,0,0], &refdata[0,0,0], &outputData[0,0,0],
regularisation_parameter ,
iterations,
tolerance_param,
@@ -458,8 +481,11 @@ def FGPdTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
methodTV,
nonneg,
printM,
- dims[2], dims[1], dims[0]);
- return outputData
+ dims[2], dims[1], dims[0])==0):
+ return outputData;
+ else:
+ raise ValueError(CUDAErrorMessage);
+
#****************************************************************#
#***************Nonlinear (Isotropic) Diffusion******************#
@@ -483,8 +509,11 @@ def NDF_GPU_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
# Run Nonlinear Diffusion iterations for 2D data
# Running CUDA code here
- NonlDiff_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[1], dims[0], 1)
- return outputData
+ if (NonlDiff_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[1], dims[0], 1)==0):
+ return outputData;
+ else:
+ raise ValueError(CUDAErrorMessage);
+
def NDF_GPU_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
float regularisation_parameter,
@@ -502,9 +531,11 @@ def NDF_GPU_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
# Run Nonlinear Diffusion iterations for 3D data
# Running CUDA code here
- NonlDiff_GPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0])
+ if (NonlDiff_GPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0])==0):
+ return outputData;
+ else:
+ raise ValueError(CUDAErrorMessage);
- return outputData
#****************************************************************#
#************Anisotropic Fourth-Order diffusion******************#
#****************************************************************#
@@ -522,8 +553,11 @@ def Diff4th_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
# Run Anisotropic Fourth-Order diffusion for 2D data
# Running CUDA code here
- Diffus4th_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, dims[1], dims[0], 1)
- return outputData
+ if (Diffus4th_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, dims[1], dims[0], 1)==0):
+ return outputData
+ else:
+ raise ValueError(CUDAErrorMessage);
+
def Diff4th_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
float regularisation_parameter,
@@ -540,9 +574,11 @@ def Diff4th_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
# Run Anisotropic Fourth-Order diffusion for 3D data
# Running CUDA code here
- Diffus4th_GPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, dims[2], dims[1], dims[0])
+ if (Diffus4th_GPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, dims[2], dims[1], dims[0])==0):
+ return outputData;
+ else:
+ raise ValueError(CUDAErrorMessage);
- return outputData
#****************************************************************#
#************Patch-based weights pre-selection******************#
#****************************************************************#
@@ -571,6 +607,8 @@ def PatchSel_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
np.zeros([dims[0], dims[1],dims[2]], dtype='uint16')
# Run patch-based weight selection function
- PatchSelect_GPU_main(&inputData[0,0], &H_j[0,0,0], &H_i[0,0,0], &Weights[0,0,0], dims[2], dims[1], searchwindow, patchwindow, neighbours, edge_parameter)
-
- return H_i, H_j, Weights
+ if (PatchSelect_GPU_main(&inputData[0,0], &H_j[0,0,0], &H_i[0,0,0], &Weights[0,0,0], dims[2], dims[1], searchwindow, patchwindow, neighbours, edge_parameter)==0):
+ return H_i, H_j, Weights;
+ else:
+ raise ValueError(CUDAErrorMessage);
+
generated by cgit v1.2.3 (git 2.39.1) at 2025-11-04 12:45:39 +0000