def getPars(self):
#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))
u0 = u0.astype('float32')
u_ref = u_ref.astype('float32')
return Im, u0, u_ref
def test_FGP_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("____________FGP-TV bench___________________")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
# set parameters
pars = {'algorithm' : FGP_TV, \
'input' : u0,\
'regularisation_parameter':0.02, \
'number_of_iterations' :400 ,\
'tolerance_constant':0.0,\
'methodTV': 0 ,\
'nonneg': 0}
print("#############FGP TV CPU####################")
start_time = timeit.default_timer()
(fgp_cpu, infocpu) = FGP_TV(pars['input'],
pars['regularisation_parameter'],
pars['number_of_iterations'],
pars['tolerance_constant'],
pars['methodTV'], pars['nonneg'], '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,
infogpu) = FGP_TV(pars['input'], pars['regularisation_parameter'],
pars['number_of_iterations'],
pars['tolerance_constant'], pars['methodTV'],
pars['nonneg'], '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)
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)