def initialize(self, training=True, force_load_plans=False): if not self.was_initialized: os.makedirs(self.output_folder, exist_ok=True) if force_load_plans or (self.plans is None): self.load_plans_file() self.process_plans(self.plans) self.setup_DA_params() ################# Here we wrap the loss for deep supervision ############ # we need to know the number of outputs of the network net_numpool = len(self.net_num_pool_op_kernel_sizes) # we give each output a weight which decreases exponentially (division by 2) as the resolution decreases # this gives higher resolution outputs more weight in the loss weights = np.array([1 / (2 ** i) for i in range(net_numpool)]) # we don't use the lowest 2 outputs. Normalize weights so that they sum to 1 mask = np.array([True if i < net_numpool - 1 else False for i in range(net_numpool)]) weights[~mask] = 0 weights = weights / weights.sum() # now wrap the loss self.loss = MultipleOutputLoss2(self.loss, weights) ################# END ################### self.folder_with_preprocessed_data = join(self.dataset_directory, self.plans['data_identifier'] + "_stage%d" % self.stage) if training: self.dl_tr, self.dl_val = self.get_basic_generators() if self.unpack_data: print("unpacking dataset") unpack_dataset(self.folder_with_preprocessed_data) print("done") else: print( "INFO: Not unpacking data! Training may be slow due to that. Pray you are not using 2d or you " "will wait all winter for your model to finish!") self.tr_gen, self.val_gen = get_no_augmentation(self.dl_tr, self.dl_val, params=self.data_aug_params, deep_supervision_scales=self.deep_supervision_scales, pin_memory=self.pin_memory) self.print_to_log_file("TRAINING KEYS:\n %s" % (str(self.dataset_tr.keys())), also_print_to_console=False) self.print_to_log_file("VALIDATION KEYS:\n %s" % (str(self.dataset_val.keys())), also_print_to_console=False) else: pass self.initialize_network() self.initialize_optimizer_and_scheduler() assert isinstance(self.network, (SegmentationNetwork, nn.DataParallel)) else: self.print_to_log_file('self.was_initialized is True, not running self.initialize again') self.was_initialized = True
def initialize(self, training=True, force_load_plans=False): super().initialize(training, force_load_plans) if training: self.dl_tr, self.dl_val = self.get_basic_generators() self.tr_gen, self.val_gen = get_no_augmentation( self.dl_tr, self.dl_val, params=self.data_aug_params, deep_supervision_scales=self.deep_supervision_scales, pin_memory=self.pin_memory)
def initialize(self, training=True, force_load_plans=False): """ For prediction of test cases just set training=False, this will prevent loading of training data and training batchgenerator initialization :param training: :return: """ os.makedirs(self.output_folder, exist_ok=True) if force_load_plans or (self.plans is None): self.load_plans_file() self.process_plans(self.plans) self.setup_DA_params() self.folder_with_preprocessed_data = join( self.dataset_directory, self.plans['data_identifier'] + "_stage%d" % self.stage) if training: self.dl_tr, self.dl_val = self.get_basic_generators() if self.unpack_data: print("unpacking dataset") unpack_dataset(self.folder_with_preprocessed_data) print("done") else: print( "INFO: Not unpacking data! Training may be slow due to that. Pray you are not using 2d or you " "will wait all winter for your model to finish!") self.tr_gen, self.val_gen = get_no_augmentation( self.dl_tr, self.dl_val, params=self.data_aug_params) self.print_to_log_file("TRAINING KEYS:\n %s" % (str(self.dataset_tr.keys())), also_print_to_console=False) self.print_to_log_file("VALIDATION KEYS:\n %s" % (str(self.dataset_val.keys())), also_print_to_console=False) else: pass self.initialize_network() assert isinstance(self.network, (SegmentationNetwork, nn.DataParallel)) self.was_initialized = True self.data_aug_params['mirror_axes'] = ()