From bcdf186ccdca54a3df383512ad5a117004500a60 Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Mon, 26 Feb 2018 12:50:58 +0000 Subject: CPU-GPU naming consistency --- Wrappers/Python/src/cpu_regularizers.cpp | 546 ++++++++++----------- Wrappers/Python/src/gpu_regularizers.pyx | 69 ++- Wrappers/Python/test/test_cpu_vs_gpu.py | 10 + .../Python/test/test_cpu_vs_gpu_regularizers.py | 8 +- 4 files changed, 353 insertions(+), 280 deletions(-) create mode 100644 Wrappers/Python/test/test_cpu_vs_gpu.py (limited to 'Wrappers') diff --git a/Wrappers/Python/src/cpu_regularizers.cpp b/Wrappers/Python/src/cpu_regularizers.cpp index e311570..43d5d11 100644 --- a/Wrappers/Python/src/cpu_regularizers.cpp +++ b/Wrappers/Python/src/cpu_regularizers.cpp @@ -27,7 +27,7 @@ limitations under the License. #include "boost/tuple/tuple.hpp" #include "SplitBregman_TV_core.h" -#include "FGP_TV_core.h" +//#include "FGP_TV_core.h" #include "LLT_model_core.h" #include "PatchBased_Regul_core.h" #include "TGV_PD_core.h" @@ -305,289 +305,289 @@ bp::list SplitBregman_TV(np::ndarray input, double d_mu, int iter, double d_epsi -bp::list FGP_TV(np::ndarray input, double d_mu, int iter, double d_epsil, int methTV) { +//bp::list FGP_TV(np::ndarray input, double d_mu, int iter, double d_epsil, int methTV) { - // the result is in the following list - bp::list result; + //// the result is in the following list + //bp::list result; - int number_of_dims, dimX, dimY, dimZ, ll, j, count; - float *A, *D = NULL, *D_old = NULL, *P1 = NULL, *P2 = NULL, *P3 = NULL, *P1_old = NULL, *P2_old = NULL, *P3_old = NULL, *R1 = NULL, *R2 = NULL, *R3 = NULL; - float lambda, tk, tkp1, re, re1, re_old, epsil, funcval; + //int number_of_dims, dimX, dimY, dimZ, ll, j, count; + //float *A, *D = NULL, *D_old = NULL, *P1 = NULL, *P2 = NULL, *P3 = NULL, *P1_old = NULL, *P2_old = NULL, *P3_old = NULL, *R1 = NULL, *R2 = NULL, *R3 = NULL; + //float lambda, tk, tkp1, re, re1, re_old, epsil, funcval; - //number_of_dims = mxGetNumberOfDimensions(prhs[0]); - //dim_array = mxGetDimensions(prhs[0]); - - number_of_dims = input.get_nd(); - int dim_array[3]; + ////number_of_dims = mxGetNumberOfDimensions(prhs[0]); + ////dim_array = mxGetDimensions(prhs[0]); - dim_array[0] = input.shape(0); - dim_array[1] = input.shape(1); - if (number_of_dims == 2) { - dim_array[2] = -1; - } - else { - dim_array[2] = input.shape(2); - } - // Parameter handling is be done in Python - ///*Handling Matlab input data*/ - //if ((nrhs < 2) || (nrhs > 5)) mexErrMsgTxt("At least 2 parameters is required: Image(2D/3D), Regularization parameter. The full list of parameters: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1')"); - - ///*Handling Matlab input data*/ - //A = (float *)mxGetData(prhs[0]); /*noisy image (2D/3D) */ - A = reinterpret_cast(input.get_data()); - - //mu = (float)mxGetScalar(prhs[1]); /* regularization parameter */ - lambda = (float)d_mu; - - //iter = 35; /* default iterations number */ + //number_of_dims = input.get_nd(); + //int dim_array[3]; - //epsil = 0.0001; /* default tolerance constant */ - epsil = (float)d_epsil; - //methTV = 0; /* default isotropic TV penalty */ - //if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5)) iter = (int)mxGetScalar(prhs[2]); /* iterations number */ - //if ((nrhs == 4) || (nrhs == 5)) epsil = (float)mxGetScalar(prhs[3]); /* tolerance constant */ - //if (nrhs == 5) { - // char *penalty_type; - // penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */ - // if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',"); - // if (strcmp(penalty_type, "l1") == 0) methTV = 1; /* enable 'l1' penalty */ - // mxFree(penalty_type); + //dim_array[0] = input.shape(0); + //dim_array[1] = input.shape(1); + //if (number_of_dims == 2) { + //dim_array[2] = -1; //} - //if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input image must be in a single precision"); } - - //plhs[1] = mxCreateNumericMatrix(1, 1, mxSINGLE_CLASS, mxREAL); - bp::tuple shape1 = bp::make_tuple(dim_array[0], dim_array[1]); - np::dtype dtype = np::dtype::get_builtin(); - np::ndarray out1 = np::zeros(shape1, dtype); + //else { + //dim_array[2] = input.shape(2); + //} + //// Parameter handling is be done in Python + /////*Handling Matlab input data*/ + ////if ((nrhs < 2) || (nrhs > 5)) mexErrMsgTxt("At least 2 parameters is required: Image(2D/3D), Regularization parameter. The full list of parameters: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1')"); + + /////*Handling Matlab input data*/ + ////A = (float *)mxGetData(prhs[0]); /*noisy image (2D/3D) */ + //A = reinterpret_cast(input.get_data()); + + ////mu = (float)mxGetScalar(prhs[1]); /* regularization parameter */ + //lambda = (float)d_mu; + + ////iter = 35; /* default iterations number */ + + ////epsil = 0.0001; /* default tolerance constant */ + //epsil = (float)d_epsil; + ////methTV = 0; /* default isotropic TV penalty */ + ////if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5)) iter = (int)mxGetScalar(prhs[2]); /* iterations number */ + ////if ((nrhs == 4) || (nrhs == 5)) epsil = (float)mxGetScalar(prhs[3]); /* tolerance constant */ + ////if (nrhs == 5) { + //// char *penalty_type; + //// penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */ + //// if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',"); + //// if (strcmp(penalty_type, "l1") == 0) methTV = 1; /* enable 'l1' penalty */ + //// mxFree(penalty_type); + ////} + ////if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input image must be in a single precision"); } + + ////plhs[1] = mxCreateNumericMatrix(1, 1, mxSINGLE_CLASS, mxREAL); + //bp::tuple shape1 = bp::make_tuple(dim_array[0], dim_array[1]); + //np::dtype dtype = np::dtype::get_builtin(); + //np::ndarray out1 = np::zeros(shape1, dtype); - //float *funcvalA = (float *)mxGetData(plhs[1]); - float * funcvalA = reinterpret_cast(out1.get_data()); - //if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input image must be in a single precision"); } - - /*Handling Matlab output data*/ - dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2]; - - tk = 1.0f; - tkp1 = 1.0f; - count = 1; - re_old = 0.0f; - - if (number_of_dims == 2) { - dimZ = 1; /*2D case*/ - /*D = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); - D_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); - P1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); - P2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); - P1_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); - P2_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); - R1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); - R2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));*/ - - bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1]); - np::dtype dtype = np::dtype::get_builtin(); - - - np::ndarray npD = np::zeros(shape, dtype); - np::ndarray npD_old = np::zeros(shape, dtype); - np::ndarray npP1 = np::zeros(shape, dtype); - np::ndarray npP2 = np::zeros(shape, dtype); - np::ndarray npP1_old = np::zeros(shape, dtype); - np::ndarray npP2_old = np::zeros(shape, dtype); - np::ndarray npR1 = np::zeros(shape, dtype); - np::ndarray npR2 = np::zeros(shape, dtype); - - D = reinterpret_cast(npD.get_data()); - D_old = reinterpret_cast(npD_old.get_data()); - P1 = reinterpret_cast(npP1.get_data()); - P2 = reinterpret_cast(npP2.get_data()); - P1_old = reinterpret_cast(npP1_old.get_data()); - P2_old = reinterpret_cast(npP2_old.get_data()); - R1 = reinterpret_cast(npR1.get_data()); - R2 = reinterpret_cast(npR2.get_data()); - - /* begin iterations */ - for (ll = 0; ll(out1.get_data()); + ////if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input image must be in a single precision"); } + + ///*Handling Matlab output data*/ + //dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2]; + + //tk = 1.0f; + //tkp1 = 1.0f; + //count = 1; + //re_old = 0.0f; + + //if (number_of_dims == 2) { + //dimZ = 1; /*2D case*/ + ///*D = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + //D_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + //P1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + //P2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + //P1_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + //P2_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + //R1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + //R2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));*/ + + //bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1]); + //np::dtype dtype = np::dtype::get_builtin(); + + + //np::ndarray npD = np::zeros(shape, dtype); + //np::ndarray npD_old = np::zeros(shape, dtype); + //np::ndarray npP1 = np::zeros(shape, dtype); + //np::ndarray npP2 = np::zeros(shape, dtype); + //np::ndarray npP1_old = np::zeros(shape, dtype); + //np::ndarray npP2_old = np::zeros(shape, dtype); + //np::ndarray npR1 = np::zeros(shape, dtype); + //np::ndarray npR2 = np::zeros(shape, dtype); + + //D = reinterpret_cast(npD.get_data()); + //D_old = reinterpret_cast(npD_old.get_data()); + //P1 = reinterpret_cast(npP1.get_data()); + //P2 = reinterpret_cast(npP2.get_data()); + //P1_old = reinterpret_cast(npP1_old.get_data()); + //P2_old = reinterpret_cast(npP2_old.get_data()); + //R1 = reinterpret_cast(npR1.get_data()); + //R2 = reinterpret_cast(npR2.get_data()); + + ///* begin iterations */ + //for (ll = 0; ll 4) { - Obj_func2D(A, D, P1, P2, lambda, dimX, dimY); - funcval = 0.0f; - for (j = 0; j 2) { - if (re > re_old) { - Obj_func2D(A, D, P1, P2, lambda, dimX, dimY); - funcval = 0.0f; - for (j = 0; j(npD); - result.append(out1); - result.append(ll); - } - if (number_of_dims == 3) { - /*D = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); - D_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); - P1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); - P2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); - P3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); - P1_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); - P2_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); - P3_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); - R1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); - R2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); - R3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));*/ - bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1], dim_array[2]); - np::dtype dtype = np::dtype::get_builtin(); + ///*updating R and t*/ + //tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f; + //Rupd_func2D(P1, P1_old, P2, P2_old, R1, R2, tkp1, tk, dimX, dimY); + + ///* calculate norm */ + //re = 0.0f; re1 = 0.0f; + //for (j = 0; j 4) { + //Obj_func2D(A, D, P1, P2, lambda, dimX, dimY); + //funcval = 0.0f; + //for (j = 0; j 2) { + //if (re > re_old) { + //Obj_func2D(A, D, P1, P2, lambda, dimX, dimY); + //funcval = 0.0f; + //for (j = 0; j(npD); + //result.append(out1); + //result.append(ll); + //} + //if (number_of_dims == 3) { + ///*D = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + //D_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + //P1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + //P2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + //P3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + //P1_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + //P2_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + //P3_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + //R1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + //R2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + //R3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));*/ + //bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1], dim_array[2]); + //np::dtype dtype = np::dtype::get_builtin(); - np::ndarray npD = np::zeros(shape, dtype); - np::ndarray npD_old = np::zeros(shape, dtype); - np::ndarray npP1 = np::zeros(shape, dtype); - np::ndarray npP2 = np::zeros(shape, dtype); - np::ndarray npP3 = np::zeros(shape, dtype); - np::ndarray npP1_old = np::zeros(shape, dtype); - np::ndarray npP2_old = np::zeros(shape, dtype); - np::ndarray npP3_old = np::zeros(shape, dtype); - np::ndarray npR1 = np::zeros(shape, dtype); - np::ndarray npR2 = np::zeros(shape, dtype); - np::ndarray npR3 = np::zeros(shape, dtype); - - D = reinterpret_cast(npD.get_data()); - D_old = reinterpret_cast(npD_old.get_data()); - P1 = reinterpret_cast(npP1.get_data()); - P2 = reinterpret_cast(npP2.get_data()); - P3 = reinterpret_cast(npP3.get_data()); - P1_old = reinterpret_cast(npP1_old.get_data()); - P2_old = reinterpret_cast(npP2_old.get_data()); - P3_old = reinterpret_cast(npP3_old.get_data()); - R1 = reinterpret_cast(npR1.get_data()); - R2 = reinterpret_cast(npR2.get_data()); - R3 = reinterpret_cast(npR3.get_data()); - /* begin iterations */ - for (ll = 0; ll 3) { - Obj_func3D(A, D, P1, P2, P3, lambda, dimX, dimY, dimZ); - funcval = 0.0f; - for (j = 0; j 2) { - if (re > re_old) { - Obj_func3D(A, D, P1, P2, P3, lambda, dimX, dimY, dimZ); - funcval = 0.0f; - for (j = 0; j(npD); - result.append(out1); - result.append(ll); - } + //np::ndarray npD = np::zeros(shape, dtype); + //np::ndarray npD_old = np::zeros(shape, dtype); + //np::ndarray npP1 = np::zeros(shape, dtype); + //np::ndarray npP2 = np::zeros(shape, dtype); + //np::ndarray npP3 = np::zeros(shape, dtype); + //np::ndarray npP1_old = np::zeros(shape, dtype); + //np::ndarray npP2_old = np::zeros(shape, dtype); + //np::ndarray npP3_old = np::zeros(shape, dtype); + //np::ndarray npR1 = np::zeros(shape, dtype); + //np::ndarray npR2 = np::zeros(shape, dtype); + //np::ndarray npR3 = np::zeros(shape, dtype); + + //D = reinterpret_cast(npD.get_data()); + //D_old = reinterpret_cast(npD_old.get_data()); + //P1 = reinterpret_cast(npP1.get_data()); + //P2 = reinterpret_cast(npP2.get_data()); + //P3 = reinterpret_cast(npP3.get_data()); + //P1_old = reinterpret_cast(npP1_old.get_data()); + //P2_old = reinterpret_cast(npP2_old.get_data()); + //P3_old = reinterpret_cast(npP3_old.get_data()); + //R1 = reinterpret_cast(npR1.get_data()); + //R2 = reinterpret_cast(npR2.get_data()); + //R3 = reinterpret_cast(npR3.get_data()); + ///* begin iterations */ + //for (ll = 0; ll 3) { + //Obj_func3D(A, D, P1, P2, P3, lambda, dimX, dimY, dimZ); + //funcval = 0.0f; + //for (j = 0; j 2) { + //if (re > re_old) { + //Obj_func3D(A, D, P1, P2, P3, lambda, dimX, dimY, dimZ); + //funcval = 0.0f; + //for (j = 0; j(npD); + //result.append(out1); + //result.append(ll); + //} - return result; -} + //return result; +//} bp::list LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher) { // the result is in the following list diff --git a/Wrappers/Python/src/gpu_regularizers.pyx b/Wrappers/Python/src/gpu_regularizers.pyx index c724471..263fa4a 100644 --- a/Wrappers/Python/src/gpu_regularizers.pyx +++ b/Wrappers/Python/src/gpu_regularizers.pyx @@ -25,7 +25,9 @@ cdef extern void NLM_GPU_kernel(float *A, float* B, float *Eucl_Vec, int N, int M, int Z, int dimension, int SearchW, int SimilW, int SearchW_real, float denh2, float lambdaf); -cdef extern void TV_ROF_GPU_kernel(float* A, float* B, int N, int M, int Z, int iter, float tau, float lambdaf); +cdef extern void TV_ROF_GPU(float* Input, float* Output, int N, int M, int Z, int iter, float tau, float lambdaf); +cdef extern void TV_FGP_GPU(float *Input, float *Output, float lambda, int iter, float epsil, int methodTV, int nonneg, int printM, int N, int M, int Z); + cdef extern float pad_crop(float *A, float *Ap, int OldSizeX, int OldSizeY, int OldSizeZ, int NewSizeX, int NewSizeY, int NewSizeZ, @@ -343,7 +345,7 @@ def ROFTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, np.zeros([dims[0],dims[1]], dtype='float32') # Running CUDA code here - TV_ROF_GPU_kernel( + TV_ROF_GPU( &inputData[0,0], &B[0,0], dims[0], dims[1], 1, iterations , @@ -366,7 +368,7 @@ 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_kernel( + TV_ROF_GPU( &inputData[0,0,0], &B[0,0,0], dims[0], dims[1], dims[2], iterations , @@ -374,3 +376,64 @@ def ROFTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, regularization_parameter); return B + + +def TVFGP2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + float regularization_parameter, + int iterations, + float tolerance_param, + int methodTV, + int nonneg, + int printM): + + cdef long dims[2] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + + cdef np.ndarray[np.float32_t, ndim=2, mode="c"] B = \ + np.zeros([dims[0],dims[1]], dtype='float32') + + # Running CUDA code here + TV_FGP_GPU( + &inputData[0,0], &B[0,0], + regularization_parameter , + iterations, + tolerance_param, + methodTV, + nonneg, + printM, + dims[0], dims[1], 1); + + return B + +def TVFGP3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, + float regularization_parameter, + int iterations, + float tolerance_param, + int methodTV, + int nonneg, + int printM): + + cdef long dims[3] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + dims[2] = inputData.shape[2] + + cdef np.ndarray[np.float32_t, ndim=3, mode="c"] B = \ + np.zeros([dims[0],dims[1],dims[2]], dtype='float32') + + # Running CUDA code here + TV_FGP_GPU( + &inputData[0,0,0], &B[0,0,0], + regularization_parameter , + iterations, + tolerance_param, + methodTV, + nonneg, + printM, + dims[0], dims[1], dims[2]); + + return B + + + diff --git a/Wrappers/Python/test/test_cpu_vs_gpu.py b/Wrappers/Python/test/test_cpu_vs_gpu.py new file mode 100644 index 0000000..74d65dd --- /dev/null +++ b/Wrappers/Python/test/test_cpu_vs_gpu.py @@ -0,0 +1,10 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Feb 21 12:12:22 2018 + +# CPU vs GPU comparison tests + +@author: algol +""" + diff --git a/Wrappers/Python/test/test_cpu_vs_gpu_regularizers.py b/Wrappers/Python/test/test_cpu_vs_gpu_regularizers.py index d742a7f..6344021 100644 --- a/Wrappers/Python/test/test_cpu_vs_gpu_regularizers.py +++ b/Wrappers/Python/test/test_cpu_vs_gpu_regularizers.py @@ -12,8 +12,8 @@ import matplotlib.pyplot as plt import numpy as np import os import timeit -from ccpi.filters.gpu_regularizers import Diff4thHajiaboli, NML, GPU_ROF_TV -from ccpi.filters.cpu_regularizers_cython import ROF_TV +from ccpi.filters.gpu_regularizers import Diff4thHajiaboli, NML, TV_ROF_GPU +from ccpi.filters.cpu_regularizers_cython import TV_ROF_CPU ############################################################################### def printParametersToString(pars): txt = r'' @@ -64,7 +64,7 @@ pars = {'algorithm': ROF_TV , \ } print ("#################ROF TV CPU#####################") start_time = timeit.default_timer() -rof_cpu = ROF_TV(pars['input'], +rof_cpu = TV_ROF_CPU(pars['input'], pars['number_of_iterations'], pars['regularization_parameter'], pars['time_marching_parameter'] @@ -89,7 +89,7 @@ plt.title('{}'.format('CPU results')) print ("#################ROF TV GPU#####################") start_time = timeit.default_timer() -rof_gpu = GPU_ROF_TV(pars['input'], +rof_gpu = TV_ROF_GPU(pars['input'], pars['number_of_iterations'], pars['time_marching_parameter'], pars['regularization_parameter']) -- cgit v1.2.3