def test_FDGdTV_CPU_vs_GPU(self):
#filename = os.path.join("test","lena_gray_512.tif")
#plt = TiffReader()
# read image
filename = os.path.join("test","test_imageLena.bin")
plt = BinReader()
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-dTV bench___________________")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
# set parameters
pars = {'algorithm' : FGP_dTV, \
'input' : u0,\
'refdata' : u_ref,\
'regularisation_parameter':0.02, \
'number_of_iterations' :500 ,\
'tolerance_constant':0.0,\
'eta_const':0.2,\
'methodTV': 0 ,\
'nonneg': 0}
print ("#############FGP dTV CPU####################")
start_time = timeit.default_timer()
(fgp_dtv_cpu,info_vec_cpu) = FGP_dTV(pars['input'],
pars['refdata'],
pars['regularisation_parameter'],
pars['number_of_iterations'],
pars['tolerance_constant'],
pars['eta_const'],
pars['methodTV'],
pars['nonneg'],'cpu')
rms = rmse(Im, fgp_dtv_cpu)
pars['rmse'] = rms
txtstr = printParametersToString(pars)
txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
print (txtstr)
print ("##############FGP dTV GPU##################")
start_time = timeit.default_timer()
try:
(fgp_dtv_gpu,info_vec_gpu) = FGP_dTV(pars['input'],
pars['refdata'],
pars['regularisation_parameter'],
pars['number_of_iterations'],
pars['tolerance_constant'],
pars['eta_const'],
pars['methodTV'],
pars['nonneg'],'gpu')
except ValueError as ve:
self.skipTest("Results not comparable. GPU computing error.")
rms = rmse(Im, fgp_dtv_gpu)
pars['rmse'] = rms
pars['algorithm'] = FGP_dTV
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_dtv_cpu))
diff_im = abs(fgp_dtv_cpu - fgp_dtv_gpu)
diff_im[diff_im > tolerance] = 1
self.assertLessEqual(diff_im.sum(), 1)
def test_Diff4th_CPU_vs_GPU(self):
#filename = os.path.join("test","lena_gray_512.tif")
#plt = TiffReader()
# read image
filename = os.path.join("test","test_imageLena.bin")
plt = BinReader()
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 ("___Anisotropic Diffusion 4th Order (2D)____")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
# set parameters
pars = {'algorithm' : Diff4th, \
'input' : u0,\
'regularisation_parameter':0.8, \
'edge_parameter':0.02,\
'number_of_iterations' :1000 ,\
'time_marching_parameter':0.0001,\
'tolerance_constant':0.0}
print ("#############Diff4th CPU####################")
start_time = timeit.default_timer()
(diff4th_cpu,info_vec_cpu) = Diff4th(pars['input'],
pars['regularisation_parameter'],
pars['edge_parameter'],
pars['number_of_iterations'],
pars['time_marching_parameter'],
pars['tolerance_constant'],'cpu')
rms = rmse(Im, diff4th_cpu)
pars['rmse'] = rms
txtstr = printParametersToString(pars)
txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
print (txtstr)
print ("##############Diff4th GPU##################")
start_time = timeit.default_timer()
try:
(diff4th_gpu,info_vec_gpu) = Diff4th(pars['input'],
pars['regularisation_parameter'],
pars['edge_parameter'],
pars['number_of_iterations'],
pars['time_marching_parameter'],
pars['tolerance_constant'],'gpu')
except ValueError as ve:
self.skipTest("Results not comparable. GPU computing error.")
rms = rmse(Im, diff4th_gpu)
pars['rmse'] = rms
pars['algorithm'] = Diff4th
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(diff4th_cpu))
diff_im = abs(diff4th_cpu - diff4th_gpu)
diff_im[diff_im > tolerance] = 1
self.assertLessEqual(diff_im.sum() , 1)