Merge pull request #98 from vais-ral/TGV3D

TGV 3D CPU/GPU

8 files changed, 164 insertions, 29 deletions

diff --git a/Wrappers/Python/conda-recipe/run_test.py b/Wrappers/Python/conda-recipe/run_test.py
index 37b9dcc..21f3216 100755
--- a/Wrappers/Python/conda-recipe/run_test.py
+++ b/Wrappers/Python/conda-recipe/run_test.py
@@ -303,7 +303,7 @@ class TestRegularisers(unittest.TestCase):
                 'input' : u0,\

                 'regularisation_parameter':0.04, \

                 'alpha1':1.0,\

-                'alpha0':0.7,\

+                'alpha0':2.0,\

                 'number_of_iterations' :250 ,\

                 'LipshitzConstant' :12 ,\

                 }

diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py
index 859b633..e6befa9 100644
--- a/Wrappers/Python/demos/demo_cpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_regularisers.py
@@ -225,8 +225,8 @@ pars = {'algorithm' : TGV, \
         'input' : u0,\
         'regularisation_parameter':0.04, \
         'alpha1':1.0,\
-        'alpha0':0.7,\
-        'number_of_iterations' :250 ,\
+        'alpha0':2.0,\
+        'number_of_iterations' :1350 ,\
         'LipshitzConstant' :12 ,\
         }
         
diff --git a/Wrappers/Python/demos/demo_cpu_regularisers3D.py b/Wrappers/Python/demos/demo_cpu_regularisers3D.py
index c42c37b..2d2fc22 100644
--- a/Wrappers/Python/demos/demo_cpu_regularisers3D.py
+++ b/Wrappers/Python/demos/demo_cpu_regularisers3D.py
@@ -12,7 +12,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import os
 import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, 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 qualitymetrics import rmse
 ###############################################################################
 def printParametersToString(pars):
@@ -68,7 +68,7 @@ Im2[:,0:M] = Im[:,0:M]
 Im = Im2
 del Im2
 """
-slices = 20
+slices = 15
 
 noisyVol = np.zeros((slices,N,M),dtype='float32')
 noisyRef = np.zeros((slices,N,M),dtype='float32')
@@ -96,7 +96,7 @@ pars = {'algorithm': ROF_TV, \
         'input' : noisyVol,\
         'regularisation_parameter':0.04,\
         'number_of_iterations': 500,\
-        'time_marching_parameter': 0.0025        
+        'time_marching_parameter': 0.0025
         }
 print ("#############ROF TV CPU####################")
 start_time = timeit.default_timer()
@@ -264,6 +264,54 @@ plt.title('{}'.format('Recovered volume on the CPU using LLT-ROF'))
 
 #%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________TGV (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of TGV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : TGV, \
+        'input' : noisyVol,\
+        '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_cpu3D = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'cpu')
+             
+
+rms = rmse(idealVol, tgv_cpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tgv_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using TGV'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("________________NDF (3D)___________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
diff --git a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
index 275e844..230a761 100644
--- a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
@@ -323,8 +323,8 @@ pars = {'algorithm' : TGV, \
         'input' : u0,\
         'regularisation_parameter':0.04, \
         'alpha1':1.0,\
-        'alpha0':0.7,\
-        'number_of_iterations' :250 ,\
+        'alpha0':2.0,\
+        'number_of_iterations' :400 ,\
         'LipshitzConstant' :12 ,\
         }
         
diff --git a/Wrappers/Python/demos/demo_gpu_regularisers.py b/Wrappers/Python/demos/demo_gpu_regularisers.py
index 9115494..e1c6575 100644
--- a/Wrappers/Python/demos/demo_gpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_gpu_regularisers.py
@@ -223,8 +223,8 @@ pars = {'algorithm' : TGV, \
         'input' : u0,\
         'regularisation_parameter':0.04, \
         'alpha1':1.0,\
-        'alpha0':0.7,\
-        'number_of_iterations' :250 ,\
+        'alpha0':2.0,\
+        'number_of_iterations' :1250 ,\
         'LipshitzConstant' :12 ,\
         }
         
diff --git a/Wrappers/Python/demos/demo_gpu_regularisers3D.py b/Wrappers/Python/demos/demo_gpu_regularisers3D.py
index cda2847..b6058d2 100644
--- a/Wrappers/Python/demos/demo_gpu_regularisers3D.py
+++ b/Wrappers/Python/demos/demo_gpu_regularisers3D.py
@@ -12,7 +12,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import os
 import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, 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 qualitymetrics import rmse
 ###############################################################################
 def printParametersToString(pars):
@@ -67,7 +67,7 @@ Im = Im2
 del Im2
 """
 
-#%%
+
 slices = 20
 
 filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
@@ -268,6 +268,53 @@ plt.title('{}'.format('Recovered volume on the GPU using LLT-ROF'))
 
 #%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________TGV (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of TGV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : TGV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04, \
+        'alpha1':1.0,\
+        'alpha0':2.0,\
+        'number_of_iterations' :600 ,\
+        'LipshitzConstant' :12 ,\
+        }
+
+print ("#############TGV GPU####################")
+start_time = timeit.default_timer()
+tgv_gpu3D = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'gpu')
+             
+
+rms = rmse(idealVol, tgv_gpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tgv_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using TGV'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("_______________NDF-TV (3D)_________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 7d57ed1..11a0617 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -22,7 +22,7 @@ cdef extern float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar,
 cdef extern float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
 cdef extern float SB_TV_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int printM, int dimX, int dimY, int dimZ);
 cdef extern float LLT_ROF_CPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
-cdef extern float TGV_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY);
+cdef extern float TGV_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY, int dimZ);
 cdef extern float Diffusion_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ);
 cdef extern float Diffus4th_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
 cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxIter, float tol, int dimX, int dimY, int dimZ);
@@ -202,12 +202,8 @@ def TGV_CPU(inputData, regularisation_parameter, alpha1, alpha0, iterations, Lip
         return TGV_2D(inputData, regularisation_parameter, alpha1, alpha0, 
                       iterations, LipshitzConst)
     elif inputData.ndim == 3:
-        shape = inputData.shape
-        out = inputData.copy()
-        for i in range(shape[0]):
-            out[i,:,:] = TGV_2D(inputData[i,:,:], regularisation_parameter, 
-               alpha1, alpha0, iterations, LipshitzConst)
-        return out
+        return TGV_3D(inputData, regularisation_parameter, alpha1, alpha0, 
+                      iterations, LipshitzConst)
 
 def TGV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
                      float regularisation_parameter,
@@ -229,7 +225,30 @@ def TGV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                        alpha0,
                        iterationsNumb, 
                        LipshitzConst,
-                       dims[1],dims[0])                           
+                       dims[1],dims[0],1)
+    return outputData
+def TGV_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     float alpha1,
+                     float alpha0,
+                     int iterationsNumb, 
+                     float LipshitzConst):
+                         
+    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"] outputData = \
+            np.zeros([dims[0], dims[1], dims[2]], dtype='float32')
+                   
+    #/* Run TGV iterations for 3D data */
+    TGV_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, 
+                       alpha1,
+                       alpha0,
+                       iterationsNumb, 
+                       LipshitzConst,
+                       dims[2], dims[1], dims[0])
     return outputData
 
 #***************************************************************#
diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx
index 47a6149..b52f669 100644
--- a/Wrappers/Python/src/gpu_regularisers.pyx
+++ b/Wrappers/Python/src/gpu_regularisers.pyx
@@ -23,7 +23,7 @@ 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 TGV_GPU_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY, int dimZ);
 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);
@@ -102,12 +102,7 @@ def TGV_GPU(inputData, regularisation_parameter, alpha1, alpha0, iterations, Lip
     if inputData.ndim == 2:
         return TGV2D(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst)
     elif inputData.ndim == 3:
-        shape = inputData.shape
-        out = inputData.copy()
-        for i in range(shape[0]):
-            out[i,:,:] = TGV2D(inputData[i,:,:], regularisation_parameter, 
-               alpha1, alpha0, iterations, LipshitzConst)
-        return out
+        return TGV3D(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst)
 # Directional Total-variation Fast-Gradient-Projection (FGP)
 def dTV_FGP_GPU(inputData,
                      refdata,
@@ -393,7 +388,6 @@ def LLT_ROF_GPU3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
         raise ValueError(CUDAErrorMessage);
 
 
-
 #***************************************************************#
 #***************** Total Generalised Variation *****************#
 #***************************************************************#
@@ -417,11 +411,38 @@ def TGV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                        alpha0,
                        iterationsNumb, 
                        LipshitzConst,
-                       dims[1],dims[0])==0):
+                       dims[1],dims[0], 1)==0):
         return outputData
     else:
         raise ValueError(CUDAErrorMessage);
 
+def TGV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     float alpha1,
+                     float alpha0,
+                     int iterationsNumb, 
+                     float LipshitzConst):
+    
+    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"] outputData = \
+		    np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+          
+    # Running CUDA code here    
+    if (TGV_GPU_main(
+            &inputData[0,0,0], &outputData[0,0,0], regularisation_parameter,
+                       alpha1,
+                       alpha0,
+                       iterationsNumb, 
+                       LipshitzConst,
+                       dims[2], dims[1], dims[0])==0):
+        return outputData;
+    else:
+        raise ValueError(CUDAErrorMessage);
+
 
 #****************************************************************#
 #**************Directional Total-variation FGP ******************#