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-rw-r--r--test/test_CPU_regularisers.py91
-rw-r--r--test/test_FGP_TV.py152
-rw-r--r--test/test_ROF_TV.py3
3 files changed, 243 insertions, 3 deletions
diff --git a/test/test_CPU_regularisers.py b/test/test_CPU_regularisers.py
new file mode 100644
index 0000000..42e4735
--- /dev/null
+++ b/test/test_CPU_regularisers.py
@@ -0,0 +1,91 @@
+import unittest
+import math
+import os
+import timeit
+from ccpi.filters.regularisers import FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th, ROF_TV
+from testroutines import *
+
+###############################################################################
+
+class TestRegularisers(unittest.TestCase):
+
+ def getPars(self,alg,noi=1200):
+ filename = os.path.join("lena_gray_512.tif")
+ plt = TiffReader()
+ # 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')
+ # set parameters
+ pars = {'algorithm': alg, \
+ 'input': u0, \
+ 'regularisation_parameter': 0.04, \
+ 'number_of_iterations': noi, \
+ 'tolerance_constant': 0.00001, \
+ 'methodTV': 0, \
+ 'nonneg': 0, \
+ 'printingOut': 0, \
+ 'time_marching_parameter': 0.00002
+ }
+ return Im, pars
+
+
+ def test_FGP_TV_CPU(self):
+ Im, pars = self.getPars(FGP_TV)
+
+ fgp_cpu = FGP_TV(pars['input'],
+ pars['regularisation_parameter'],
+ pars['number_of_iterations'],
+ pars['tolerance_constant'],
+ pars['methodTV'],
+ pars['nonneg'],
+ pars['printingOut'], 'cpu')
+
+ rms = rmse(Im, fgp_cpu)
+ pars['rmse'] = rms
+ self.assertAlmostEqual(rms,0.02,delta=0.01)
+
+ def test_TV_ROF_CPU(self):
+ # set parameters
+ Im, pars = self.getPars(ROF_TV)
+ # call routine
+ fgp_cpu = ROF_TV(pars['input'],
+ pars['regularisation_parameter'],
+ pars['number_of_iterations'],
+ pars['time_marching_parameter'], 'cpu')
+
+ rms = rmse(Im, fgp_cpu)
+ pars['rmse'] = rms
+
+ #txtstr = printParametersToString(pars)
+ #print(txtstr)
+ # now test that it generates some expected output
+ self.assertAlmostEqual(rms,0.02,delta=0.01)
+
+ def test_SB_TV_CPU(self):
+ # set parameters
+ Im, pars = self.getPars(SB_TV)
+ # call routine
+ fgp_cpu = SB_TV(pars['input'],
+ pars['regularisation_parameter'],
+ pars['number_of_iterations'],
+ pars['time_marching_parameter'], 'cpu')
+
+ rms = rmse(Im, fgp_cpu)
+ pars['rmse'] = rms
+
+ #txtstr = printParametersToString(pars)
+ #print(txtstr)
+ # now test that it generates some expected output
+ self.assertAlmostEqual(rms,0.02,delta=0.01)
diff --git a/test/test_FGP_TV.py b/test/test_FGP_TV.py
new file mode 100644
index 0000000..f0dc540
--- /dev/null
+++ b/test/test_FGP_TV.py
@@ -0,0 +1,152 @@
+import unittest
+import math
+import os
+import timeit
+from ccpi.filters.regularisers import FGP_TV
+#, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
+from testroutines import *
+
+###############################################################################
+
+class TestRegularisers(unittest.TestCase):
+
+ def test_FGP_TV_CPU(self):
+ print(__name__)
+ filename = os.path.join("lena_gray_512.tif")
+ plt = TiffReader()
+ # 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("____________FGP-TV bench___________________")
+ print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+ # set parameters
+ pars = {'algorithm': FGP_TV, \
+ 'input': u0, \
+ 'regularisation_parameter': 0.04, \
+ 'number_of_iterations': 1200, \
+ 'tolerance_constant': 0.00001, \
+ 'methodTV': 0, \
+ 'nonneg': 0, \
+ 'printingOut': 0
+ }
+
+ print("#############FGP TV CPU####################")
+ start_time = timeit.default_timer()
+ fgp_cpu = FGP_TV(pars['input'],
+ pars['regularisation_parameter'],
+ pars['number_of_iterations'],
+ pars['tolerance_constant'],
+ pars['methodTV'],
+ pars['nonneg'],
+ pars['printingOut'], 'cpu')
+
+ rms = rmse(Im, fgp_cpu)
+ pars['rmse'] = rms
+
+ txtstr = printParametersToString(pars)
+ txtstr += "%s = %.3fs" % ('elapsed time', timeit.default_timer() - start_time)
+ print(txtstr)
+ self.assertTrue(math.isclose(rms,0.02,rel_tol=1e-1))
+
+ def test_FGP_TV_CPU_vs_GPU(self):
+ print(__name__)
+ filename = os.path.join("lena_gray_512.tif")
+ plt = TiffReader()
+ # 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("____________FGP-TV bench___________________")
+ print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+ # set parameters
+ pars = {'algorithm': FGP_TV, \
+ 'input': u0, \
+ 'regularisation_parameter': 0.04, \
+ 'number_of_iterations': 1200, \
+ 'tolerance_constant': 0.00001, \
+ 'methodTV': 0, \
+ 'nonneg': 0, \
+ 'printingOut': 0
+ }
+
+ print("#############FGP TV CPU####################")
+ start_time = timeit.default_timer()
+ fgp_cpu = FGP_TV(pars['input'],
+ pars['regularisation_parameter'],
+ pars['number_of_iterations'],
+ pars['tolerance_constant'],
+ pars['methodTV'],
+ pars['nonneg'],
+ pars['printingOut'], 'cpu')
+
+ rms = rmse(Im, fgp_cpu)
+ pars['rmse'] = rms
+
+ txtstr = printParametersToString(pars)
+ txtstr += "%s = %.3fs" % ('elapsed time', timeit.default_timer() - start_time)
+ print(txtstr)
+
+ print("##############FGP TV GPU##################")
+ start_time = timeit.default_timer()
+ try:
+ fgp_gpu = FGP_TV(pars['input'],
+ pars['regularisation_parameter'],
+ pars['number_of_iterations'],
+ pars['tolerance_constant'],
+ pars['methodTV'],
+ pars['nonneg'],
+ pars['printingOut'], 'gpu')
+
+ except ValueError as ve:
+ self.skipTest("Results not comparable. GPU computing error.")
+
+ rms = rmse(Im, fgp_gpu)
+ pars['rmse'] = rms
+ pars['algorithm'] = FGP_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(fgp_cpu))
+ diff_im = abs(fgp_cpu - fgp_gpu)
+ diff_im[diff_im > tolerance] = 1
+
+ self.assertLessEqual(diff_im.sum(), 1)
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/test/test_ROF_TV.py b/test/test_ROF_TV.py
index dda38b7..fa35680 100644
--- a/test/test_ROF_TV.py
+++ b/test/test_ROF_TV.py
@@ -53,9 +53,6 @@ class TestRegularisers(unittest.TestCase):
def test_ROF_TV_CPU_vs_GPU(self):
- # print ("tomas debug test function")
- print(__name__)
- self.fail("testfail2")
filename = os.path.join("lena_gray_512.tif")
plt = TiffReader()
# read image
generated by cgit v1.2.3 (git 2.25.1) at 2024-11-05 13:54:11 +0000