diff options
Diffstat (limited to 'Wrappers')
| -rwxr-xr-x | Wrappers/Python/conda-recipe/run_test.py | 57 | ||||
| -rw-r--r-- | Wrappers/Python/src/gpu_regularisers.pyx | 156 |
2 files changed, 100 insertions, 113 deletions
diff --git a/Wrappers/Python/conda-recipe/run_test.py b/Wrappers/Python/conda-recipe/run_test.py index 239ec64..abc3e1b 100755 --- a/Wrappers/Python/conda-recipe/run_test.py +++ b/Wrappers/Python/conda-recipe/run_test.py @@ -90,9 +90,6 @@ class TestRegularisers(unittest.TestCase): pars['number_of_iterations'],
pars['time_marching_parameter'],'gpu')
except ValueError as ve:
- self.assertTrue(True)
- return
- except:
self.skipTest("Results not comparable. GPU computing error.")
rms = rmse(Im, rof_gpu)
@@ -106,9 +103,6 @@ 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
- #TODO skip test in case of CUDA error
- if (diff_im.sum()>1):
- self.skipTest("Results not comparable. GPU computing error.")
self.assertLessEqual(diff_im.sum() , 1)
def test_FGP_TV_CPU_vs_GPU(self):
@@ -177,11 +171,8 @@ class TestRegularisers(unittest.TestCase): pars['methodTV'],
pars['nonneg'],
pars['printingOut'],'gpu')
-
+
except ValueError as ve:
- self.assertTrue(True)
- return
- except:
self.skipTest("Results not comparable. GPU computing error.")
rms = rmse(Im, fgp_gpu)
@@ -196,8 +187,6 @@ class TestRegularisers(unittest.TestCase): diff_im = np.zeros(np.shape(fgp_cpu))
diff_im = abs(fgp_cpu - fgp_gpu)
diff_im[diff_im > tolerance] = 1
- if (diff_im.sum()>1):
- self.skipTest("Results not comparable. GPU computing error.")
self.assertLessEqual(diff_im.sum() , 1)
@@ -265,11 +254,8 @@ class TestRegularisers(unittest.TestCase): pars['tolerance_constant'],
pars['methodTV'],
pars['printingOut'],'gpu')
-
+
except ValueError as ve:
- self.assertTrue(True)
- return
- except:
self.skipTest("Results not comparable. GPU computing error.")
rms = rmse(Im, sb_gpu)
@@ -283,8 +269,6 @@ class TestRegularisers(unittest.TestCase): diff_im = np.zeros(np.shape(sb_cpu))
diff_im = abs(sb_cpu - sb_gpu)
diff_im[diff_im > tolerance] = 1
- if (diff_im.sum()>1):
- self.skipTest("Results not comparable. GPU computing error.")
self.assertLessEqual(diff_im.sum(), 1)
def test_TGV_CPU_vs_GPU(self):
@@ -349,11 +333,8 @@ class TestRegularisers(unittest.TestCase): pars['alpha0'],
pars['number_of_iterations'],
pars['LipshitzConstant'],'gpu')
-
+
except ValueError as ve:
- self.assertTrue(True)
- return
- except:
self.skipTest("Results not comparable. GPU computing error.")
rms = rmse(Im, tgv_gpu)
@@ -367,8 +348,6 @@ class TestRegularisers(unittest.TestCase): diff_im = np.zeros(np.shape(tgv_gpu))
diff_im = abs(tgv_cpu - tgv_gpu)
diff_im[diff_im > tolerance] = 1
- if (diff_im.sum()>1):
- self.skipTest("Results not comparable. GPU computing error.")
self.assertLessEqual(diff_im.sum() , 1)
def test_LLT_ROF_CPU_vs_GPU(self):
@@ -431,9 +410,6 @@ class TestRegularisers(unittest.TestCase): pars['time_marching_parameter'],'gpu')
except ValueError as ve:
- self.assertTrue(True)
- return
- except:
self.skipTest("Results not comparable. GPU computing error.")
rms = rmse(Im, lltrof_gpu)
@@ -447,8 +423,6 @@ class TestRegularisers(unittest.TestCase): diff_im = np.zeros(np.shape(lltrof_gpu))
diff_im = abs(lltrof_cpu - lltrof_gpu)
diff_im[diff_im > tolerance] = 1
- if (diff_im.sum()>1):
- self.skipTest("Results not comparable. GPU computing error.")
self.assertLessEqual(diff_im.sum(), 1)
def test_NDF_CPU_vs_GPU(self):
@@ -515,9 +489,6 @@ class TestRegularisers(unittest.TestCase): pars['penalty_type'],'gpu')
except ValueError as ve:
- self.assertTrue(True)
- return
- except:
self.skipTest("Results not comparable. GPU computing error.")
rms = rmse(Im, ndf_gpu)
pars['rmse'] = rms
@@ -530,8 +501,6 @@ class TestRegularisers(unittest.TestCase): diff_im = np.zeros(np.shape(ndf_cpu))
diff_im = abs(ndf_cpu - ndf_gpu)
diff_im[diff_im > tolerance] = 1
- if (diff_im.sum()>1):
- self.skipTest("Results not comparable. GPU computing error.")
self.assertLessEqual(diff_im.sum(), 1)
@@ -593,9 +562,6 @@ class TestRegularisers(unittest.TestCase): pars['time_marching_parameter'], 'gpu')
except ValueError as ve:
- self.assertTrue(True)
- return
- except:
self.skipTest("Results not comparable. GPU computing error.")
rms = rmse(Im, diff4th_gpu)
pars['rmse'] = rms
@@ -608,8 +574,6 @@ class TestRegularisers(unittest.TestCase): diff_im = np.zeros(np.shape(diff4th_cpu))
diff_im = abs(diff4th_cpu - diff4th_gpu)
diff_im[diff_im > tolerance] = 1
- if (diff_im.sum()>1):
- self.skipTest("Results not comparable. GPU computing error.")
self.assertLessEqual(diff_im.sum() , 1)
def test_FDGdTV_CPU_vs_GPU(self):
@@ -683,9 +647,6 @@ class TestRegularisers(unittest.TestCase): pars['nonneg'],
pars['printingOut'],'gpu')
except ValueError as ve:
- self.assertTrue(True)
- return
- except:
self.skipTest("Results not comparable. GPU computing error.")
rms = rmse(Im, fgp_dtv_gpu)
pars['rmse'] = rms
@@ -698,8 +659,6 @@ class TestRegularisers(unittest.TestCase): diff_im = np.zeros(np.shape(fgp_dtv_cpu))
diff_im = abs(fgp_dtv_cpu - fgp_dtv_gpu)
diff_im[diff_im > tolerance] = 1
- if (diff_im.sum()>1):
- self.skipTest("Results not comparable. GPU computing error.")
self.assertLessEqual(diff_im.sum(), 1)
def test_cpu_ROF_TV(self):
@@ -809,15 +768,10 @@ 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
- except:
self.skipTest("Results not comparable. GPU computing error.")
rms_rof = rmse(Im, rof_gpu)
# now compare obtained rms with the expected value
- if (abs(rms_rof-rms_rof_exp)>=tolerance):
- self.skipTest("Results not comparable. GPU computing error.")
self.assertLess(abs(rms_rof-rms_rof_exp) , tolerance)
def test_gpu_FGP(self):
@@ -855,14 +809,9 @@ class TestRegularisers(unittest.TestCase): pars_fgp_tv['nonneg'],
pars_fgp_tv['printingOut'],'gpu')
except ValueError as ve:
- self.assertTrue(True)
- return
- except:
self.skipTest("Results not comparable. GPU computing error.")
rms_fgp = rmse(Im, fgp_gpu)
# now compare obtained rms with the expected value
- if (abs(rms_fgp-rms_fgp_exp) >= tolerance):
- self.skipTest("Results not comparable. GPU computing error.")
self.assertLess(abs(rms_fgp-rms_fgp_exp) , tolerance)
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); + |