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:
"""
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:
# copy segs from prev stage to separate folder and extract them
# If we don't do this then we need to make sure to manually delete the folder if we want to update
# segs_from_prev_stage. I will probably forget to do so, so I leave this in as a safeguard
if isdir(self.folder_with_segs_from_prev_stage_for_train):
shutil.rmtree(self.folder_with_segs_from_prev_stage_for_train)
maybe_mkdir_p(self.folder_with_segs_from_prev_stage_for_train)
segs_from_prev_stage_files = subfiles(self.folder_with_segs_from_prev_stage, suffix='.npz')
for s in segs_from_prev_stage_files:
shutil.copy(s, self.folder_with_segs_from_prev_stage_for_train)
# if we don't do this then performance is shit
if self.unpack_data:
unpack_dataset(self.folder_with_segs_from_prev_stage_for_train)
self.folder_with_segs_from_prev_stage = self.folder_with_segs_from_prev_stage_for_train
self.setup_DA_params()
if self.folder_with_preprocessed_data is not None:
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_default_augmentation(self.dl_tr, self.dl_val,
self.data_aug_params['patch_size_for_spatialtransform'],
self.data_aug_params)
self.print_to_log_file("TRAINING KEYS:\n %s" % (str(self.dataset_tr.keys())))
self.print_to_log_file("VALIDATION KEYS:\n %s" % (str(self.dataset_val.keys())))
else:
pass
self.initialize_network_optimizer_and_scheduler()
assert isinstance(self.network, SegmentationNetwork)
self.was_initialized = True
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:
"""
maybe_mkdir_p(self.output_folder)
if force_load_plans or (self.plans is None):
self.load_plans_file()
self.process_plans(self.plans)
self.setup_DA_params()
self.AutoAugment = AutoAugment(
self.data_aug_params['patch_size_for_spatialtransform'],
self.data_aug_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:
self.print_to_log_file("unpacking dataset")
unpack_dataset(self.folder_with_preprocessed_data)
self.print_to_log_file("done")
else:
self.print_to_log_file(
"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_default_augmentation(
self.dl_tr, self.dl_val,
self.data_aug_params['patch_size_for_spatialtransform'],
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_optimizer_and_scheduler()
# assert isinstance(self.network, (SegmentationNetwork, nn.DataParallel))
self.was_initialized = True
