def run_main_iter(self):
"""
Goes through the main iteration for the given configuration.
:return: None
"""
t = self.__t__
Quameasopts = self.Quameasopts
x_rec = copy.deepcopy(self.res)
lambdaForTv = 2 * self.__bm__ * self.__lambda__
for i in range(self.niter):
res_prev = None
if Quameasopts is not None:
res_prev = copy.deepcopy(self.res)
if self.verbose:
if i == 0:
print(str(self.name).upper() +
' ' + "algorithm in progress.")
toc = time.clock()
if i == 1:
tic = time.clock()
print('Esitmated time until completetion (s): ' +
str((self.niter - 1) * (tic - toc)))
getattr(self, self.dataminimizing)()
x_rec_old = copy.deepcopy(x_rec)
x_rec = im3ddenoise(self.res, self.__numiter_tv__, 1. / lambdaForTv)
t_old = t
t = (1 + np.sqrt(1 + 4 * t ** 2)) / 2
self.res = x_rec + (t_old - 1) / t * (x_rec - x_rec_old)
self.error_measurement(res_prev, i)
def run_main_iter(self):
"""
Goes through the main iteration for the given configuration.
:return: None
"""
Quameasopts = self.Quameasopts
lambdaForTv = 2 * self.__bm__ * self.lmbda
for i in range(self.niter):
res_prev = None
if Quameasopts is not None:
res_prev = copy.deepcopy(self.res)
if self.verbose:
if i == 0:
print(str(self.name).upper() +
' ' + "algorithm in progress.")
toc = time.clock()
if i == 1:
tic = time.clock()
print('Esitmated time until completetion (s): ' +
str((self.niter - 1) * (tic - toc)))
getattr(self, self.dataminimizing)()
self.res = im3ddenoise(self.res, 20, 1. / lambdaForTv)
self.error_measurement(res_prev, i)
def run_main_iter(self):
"""
Goes through the main iteration for the given configuration.
:return: None
"""
Quameasopts = self.Quameasopts
for i in range(self.niter):
res_prev = None
if Quameasopts is not None:
res_prev = copy.deepcopy(self.res)
if self.verbose:
if i == 0:
print(
str(self.name).upper() + " " +
"algorithm in progress.")
toc = default_timer()
if i == 1:
tic = default_timer()
print("Esitmated time until completetion (s): " +
str((self.niter - 1) * (tic - toc)))
getattr(self, self.dataminimizing)()
# print("run_main_iter: gpuids = {}", self.gpuids)
self.res = im3ddenoise(self.res, self.tviter, self.tvlambda,
self.gpuids)
self.error_measurement(res_prev, i)
def run_main_iter(self):
"""
Goes through the main iteration for the given configuration.
:return: None
"""
t = self.__t__
Quameasopts = self.Quameasopts
x_rec = copy.deepcopy(self.res)
lambdaForTv = 2 * self.__bm__ * self.__lambda__
for i in range(self.niter):
res_prev = None
if Quameasopts is not None:
res_prev = copy.deepcopy(self.res)
if self.verbose:
self._estimate_time_until_completion(i)
getattr(self, self.dataminimizing)()
x_rec_old = copy.deepcopy(x_rec)
x_rec = im3ddenoise(self.res, self.__numiter_tv__,
1.0 / lambdaForTv, self.gpuids)
t_old = t
t = (1 + np.sqrt(1 + 4 * t**2)) / 2
self.res = x_rec + (t_old - 1) / t * (x_rec - x_rec_old)
self.error_measurement(res_prev, i)
def run_main_iter(self):
"""
Goes through the main iteration for the given configuration.
:return: None
"""
Quameasopts = self.Quameasopts
lambdaForTv = 2 * self.__bm__ * self.lmbda
for i in range(self.niter):
res_prev = copy.deepcopy(self.res) if Quameasopts is not None else None
if self.verbose:
self._estimate_time_until_completion(i)
getattr(self, self.dataminimizing)()
self.res = im3ddenoise(self.res, 20, 1.0 / lambdaForTv, self.gpuids)
if Quameasopts is not None:
self.error_measurement(res_prev, i)
def run_main_iter(self):
"""
Goes through the main iteration for the given configuration.
:return: None
"""
Quameasopts = self.Quameasopts
for i in range(self.niter):
res_prev = None
if Quameasopts is not None:
res_prev = copy.deepcopy(self.res)
if self.verbose:
self._estimate_time_until_completion(i)
getattr(self, self.dataminimizing)()
# print("run_main_iter: gpuids = {}", self.gpuids)
self.res = im3ddenoise(self.res, self.tviter, self.tvlambda,
self.gpuids)
self.error_measurement(res_prev, i)
def run_main_iter(self):
"""
Goes through the main iteration for the given configuration.
:return: None
"""
t = self.__t__
Quameasopts = self.Quameasopts
x_rec = copy.deepcopy(self.res)
lambdaForTv = 2 * self.__bm__ * self.__lambda__
for i in range(self.niter):
res_prev = None
if Quameasopts is not None:
res_prev = copy.deepcopy(self.res)
if self.verbose:
if i == 0:
print(
str(self.name).upper() + " " +
"algorithm in progress.")
toc = default_timer()
if i == 1:
tic = default_timer()
remaining_time = (self.niter - 1) * (tic - toc)
seconds = int(remaining_time)
print("Estimated time until completion : " +
time.strftime("%H:%M:%S", time.gmtime(seconds)))
getattr(self, self.dataminimizing)()
x_rec_old = copy.deepcopy(x_rec)
x_rec = im3ddenoise(self.res, self.__numiter_tv__,
1.0 / lambdaForTv, self.gpuids)
t_old = t
t = (1 + np.sqrt(1 + 4 * t**2)) / 2
self.res = x_rec + (t_old - 1) / t * (x_rec - x_rec_old)
self.error_measurement(res_prev, i)
