From c65291e6b987283e4767a8ad2bd2d2433ca3782e Mon Sep 17 00:00:00 2001
From: Daniil Kazantsev <dkazanc@hotmail.com>
Date: Thu, 28 Nov 2019 23:01:03 +0000
Subject: all work completed on gpu version of pdtv

---
 test/test_CPU_regularisers.py | 11 +++++-
 test/test_run_test.py         | 88 +++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 98 insertions(+), 1 deletion(-)

(limited to 'test')

diff --git a/test/test_CPU_regularisers.py b/test/test_CPU_regularisers.py
index 95e2a3f..266ca8a 100644
--- a/test/test_CPU_regularisers.py
+++ b/test/test_CPU_regularisers.py
@@ -3,7 +3,7 @@ import unittest
 import os
 #import timeit
 import numpy as np
-from ccpi.filters.regularisers import FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th, ROF_TV
+from ccpi.filters.regularisers import FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th, ROF_TV, PD_TV
 from testroutines import BinReader, rmse 
 ###############################################################################
 
@@ -39,6 +39,15 @@ class TestRegularisers(unittest.TestCase):
 
         self.assertAlmostEqual(rms,0.02,delta=0.01)
 
+    def test_PD_TV_CPU(self):
+        Im,input,ref = self.getPars()
+
+        pd_cpu,info = PD_TV(input, 0.02, 300, 0.0, 0, 0, 8, 0.0025, 'cpu');
+
+        rms = rmse(Im, pd_cpu)
+        
+        self.assertAlmostEqual(rms,0.02,delta=0.01)
+
     def test_TV_ROF_CPU(self):
         # set parameters
         Im, input,ref = self.getPars()
diff --git a/test/test_run_test.py b/test/test_run_test.py
index e693e03..1707aec 100755
--- a/test/test_run_test.py
+++ b/test/test_run_test.py
@@ -164,6 +164,94 @@ class TestRegularisers(unittest.TestCase):
 
         self.assertLessEqual(diff_im.sum() , 1)
 
+    def test_PD_TV_CPU_vs_GPU(self):
+        print(__name__)
+        #filename = os.path.join("test","lena_gray_512.tif")
+        #plt = TiffReader()
+        filename = os.path.join("test","test_imageLena.bin")
+        plt = BinReader()
+        # read image
+        Im = plt.imread(filename)
+        Im = np.asarray(Im, dtype='float32')
+
+        Im = Im/255
+        perc = 0.05
+        u0 = Im + np.random.normal(loc = 0 ,
+                                          scale = perc * Im ,
+                                          size = np.shape(Im))
+        u_ref = Im + np.random.normal(loc = 0 ,
+                                          scale = 0.01 * Im ,
+                                          size = np.shape(Im))
+
+        # map the u0 u0->u0>0
+        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+        u0 = u0.astype('float32')
+        u_ref = u_ref.astype('float32')
+
+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+        print ("____________PD-TV bench___________________")
+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+
+        pars = {'algorithm' : PD_TV, \
+                'input' : u0,\
+                'regularisation_parameter':0.02, \
+                'number_of_iterations' :1500 ,\
+                'tolerance_constant':0.0,\
+                'methodTV': 0 ,\
+                'nonneg': 0,
+                'lipschitz_const' : 8,
+                'tau' : 0.0025}
+                
+        print ("#############PD TV CPU####################")
+        start_time = timeit.default_timer()
+        (pd_cpu,info_vec_cpu) = PD_TV(pars['input'], 
+                      pars['regularisation_parameter'],
+                      pars['number_of_iterations'],
+                      pars['tolerance_constant'], 
+                      pars['methodTV'],
+                      pars['nonneg'],
+                      pars['lipschitz_const'],
+                      pars['tau'],'cpu')
+
+        rms = rmse(Im, pd_cpu)
+        pars['rmse'] = rms
+
+        txtstr = printParametersToString(pars)
+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+        print (txtstr)
+
+        print ("##############PD TV GPU##################")
+        start_time = timeit.default_timer()
+        try:
+            (pd_gpu,info_vec_gpu) = PD_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['lipschitz_const'],
+              pars['tau'],'gpu')
+
+        except ValueError as ve:
+            self.skipTest("Results not comparable. GPU computing error.")
+
+        rms = rmse(Im, pd_gpu)
+        pars['rmse'] = rms
+        pars['algorithm'] = PD_TV
+        txtstr = printParametersToString(pars)
+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+        print (txtstr)
+
+        print ("--------Compare the results--------")
+        tolerance = 1e-05
+        diff_im = np.zeros(np.shape(pd_cpu))
+        diff_im = abs(pd_cpu - pd_gpu)
+        diff_im[diff_im > tolerance] = 1
+
+        self.assertLessEqual(diff_im.sum() , 1)
+
+
     def test_SB_TV_CPU_vs_GPU(self):
         print(__name__)
         #filename = os.path.join("test","lena_gray_512.tif")
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