Merge pull request #62 from vais-ral/demo_tests

fixes tests/demos and closes #43

4 files changed, 22 insertions, 22 deletions

diff --git a/Core/regularisers_CPU/FGP_TV_core.c b/Core/regularisers_CPU/FGP_TV_core.c
index d828d48..68d58b7 100644
--- a/Core/regularisers_CPU/FGP_TV_core.c
+++ b/Core/regularisers_CPU/FGP_TV_core.c
@@ -55,7 +55,7 @@ float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iteratio
         P1 = calloc(DimTotal, sizeof(float));
         P2 = calloc(DimTotal, sizeof(float));
         P1_prev = calloc(DimTotal, sizeof(float));
-        P2_prev = calloc(DimTotal, sizeof(float));        
+        P2_prev = calloc(DimTotal, sizeof(float));
         R1 = calloc(DimTotal, sizeof(float));
         R2 = calloc(DimTotal, sizeof(float)); 
 		
@@ -63,13 +63,13 @@ float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iteratio
         for(ll=0; ll<iterationsNumb; ll++) {
             
             /* computing the gradient of the objective function */
-            Obj_func2D(Input, Output, R1, R2, lambdaPar, (long)(dimY), (long)(dimZ));
+            Obj_func2D(Input, Output, R1, R2, lambdaPar, (long)(dimX), (long)(dimY));
             
             /* apply nonnegativity */
-            if (nonneg == 1) for(j=0; j<DimTotal; j++) {if (Output[j] < 0.0f) Output[j] = 0.0f;}            
+            if (nonneg == 1) for(j=0; j<DimTotal; j++) {if (Output[j] < 0.0f) Output[j] = 0.0f;}
             
             /*Taking a step towards minus of the gradient*/
-            Grad_func2D(P1, P2, Output, R1, R2, lambdaPar, (long)(dimY), (long)(dimZ));
+            Grad_func2D(P1, P2, Output, R1, R2, lambdaPar, (long)(dimX), (long)(dimY));
             
             /* projection step */
             Proj_func2D(P1, P2, methodTV, DimTotal);
@@ -90,9 +90,9 @@ float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iteratio
 				if (count > 4) break;
             
             /*storing old values*/
-            copyIm(Output, Output_prev, (long)(dimY), (long)(dimZ), 1l);
-            copyIm(P1, P1_prev, (long)(dimY), (long)(dimZ), 1l);
-            copyIm(P2, P2_prev, (long)(dimY), (long)(dimZ), 1l);
+            copyIm(Output, Output_prev, (long)(dimX), (long)(dimY), 1l);
+            copyIm(P1, P1_prev, (long)(dimX), (long)(dimY), 1l);
+            copyIm(P2, P2_prev, (long)(dimX), (long)(dimY), 1l);
             tk = tkp1;
         }
         if (printM == 1) printf("FGP-TV iterations stopped at iteration %i \n", ll);   
diff --git a/Wrappers/Python/conda-recipe/run_test.py b/Wrappers/Python/conda-recipe/run_test.py
index 398ef60..86013a3 100755
--- a/Wrappers/Python/conda-recipe/run_test.py
+++ b/Wrappers/Python/conda-recipe/run_test.py
@@ -65,8 +65,8 @@ class TestRegularisers(unittest.TestCase):
         pars = {'algorithm': ROF_TV, \

                 'input' : u0,\

                 'regularisation_parameter':0.04,\

-                'number_of_iterations': 1200,\

-                'time_marching_parameter': 0.0025        

+                'number_of_iterations': 1000,\

+                'time_marching_parameter': 0.0001

                 }

         print ("#############ROF TV CPU####################")

         start_time = timeit.default_timer()

@@ -94,7 +94,7 @@ class TestRegularisers(unittest.TestCase):
         txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

         print (txtstr)

         print ("--------Compare the results--------")

-        tolerance = 1e-05

+        tolerance = 1e-04

         diff_im = np.zeros(np.shape(rof_cpu))

         diff_im = abs(rof_cpu - rof_gpu)

         diff_im[diff_im > tolerance] = 1

@@ -361,8 +361,8 @@ class TestRegularisers(unittest.TestCase):
                 'input' : u0,\

                 'regularisation_parameterROF':0.04, \

                 'regularisation_parameterLLT':0.01, \

-                'number_of_iterations' :500 ,\

-                'time_marching_parameter' :0.0025 ,\

+                'number_of_iterations' :1000 ,\

+                'time_marching_parameter' :0.0001 ,\

                 }

                 

         print ("#############LLT- ROF CPU####################")

@@ -394,7 +394,7 @@ class TestRegularisers(unittest.TestCase):
         txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

         print (txtstr)

         print ("--------Compare the results--------")

-        tolerance = 1e-05

+        tolerance = 1e-04

         diff_im = np.zeros(np.shape(lltrof_gpu))

         diff_im = abs(lltrof_cpu - lltrof_gpu)

         diff_im[diff_im > tolerance] = 1

@@ -643,14 +643,14 @@ class TestRegularisers(unittest.TestCase):
         Im = np.asarray(Im, dtype='float32')

         """

         tolerance = 1e-05

-        rms_rof_exp = 0.006812507 #expected value for ROF model

+        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': 0.0025

+                            'time_marching_parameter': 0.00001

                             }

         print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

         print ("_________testing ROF-TV (2D, CPU)__________")

@@ -715,14 +715,14 @@ class TestRegularisers(unittest.TestCase):
         Im = Im/255

         

         tolerance = 1e-05

-        rms_rof_exp = 0.006812507 #expected value for ROF model

+        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': 0.0025

+                            'time_marching_parameter': 0.00001

                             }

         print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

         print ("_________testing ROF-TV (2D, GPU)__________")

diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py
index b94f11c..e99b271 100644
--- a/Wrappers/Python/demos/demo_cpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_regularisers.py
@@ -36,7 +36,7 @@ filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
 Im = plt.imread(filename)
 Im = np.asarray(Im, dtype='float32')
 
-Im = Im/255
+Im = Im/255.0
 perc = 0.05
 u0 = Im + np.random.normal(loc = 0 ,
                                   scale = perc * Im , 
diff --git a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
index e45dc40..3d6e92f 100644
--- a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
@@ -66,8 +66,8 @@ imgplot = plt.imshow(u0,cmap="gray")
 pars = {'algorithm': ROF_TV, \
         'input' : u0,\
         'regularisation_parameter':0.04,\
-        'number_of_iterations': 1200,\
-        'time_marching_parameter': 0.0025        
+        'number_of_iterations': 4500,\
+        'time_marching_parameter': 0.00002
         }
 print ("#############ROF TV CPU####################")
 start_time = timeit.default_timer()
@@ -406,8 +406,8 @@ pars = {'algorithm' : LLT_ROF, \
         'input' : u0,\
         'regularisation_parameterROF':0.04, \
         'regularisation_parameterLLT':0.01, \
-        'number_of_iterations' :500 ,\
-        'time_marching_parameter' :0.0025 ,\
+        'number_of_iterations' :4500 ,\
+        'time_marching_parameter' :0.00002 ,\
         }
         
 print ("#############LLT- ROF CPU####################")