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)