reconstruction_datasets = list()
iSEG_train = None
iSEG_CSV = None
MRBrainS_train = None
MRBrainS_CSV = None
ABIDE_train = None
ABIDE_CSV = None
iSEG_augmentation_strategy = None
MRBrainS_augmentation_strategy = None
ABIDE_augmentation_strategy = None
# Initialize the model trainers
model_trainer_factory = ModelTrainerFactory(
model_factory=CustomModelFactory(),
criterion_factory=CustomCriterionFactory())
model_trainers = model_trainer_factory.create(model_trainer_configs)
if not isinstance(model_trainers, list):
model_trainers = [model_trainers]
# Create datasets
if dataset_configs.get("iSEG", None) is not None:
iSEG_train, iSEG_valid, iSEG_test, iSEG_reconstruction = iSEGSliceDatasetFactory.create_train_valid_test(
source_dir=dataset_configs["iSEG"].path,
modalities=dataset_configs["iSEG"].modalities,
dataset_id=ISEG_ID,
test_size=dataset_configs["iSEG"].validation_split,
max_subjects=dataset_configs["iSEG"].max_subjects,
max_num_patches=dataset_configs["iSEG"].max_num_patches,
augment=dataset_configs["iSEG"].augment,
gt_reconstructors = list()
augmented_input_reconstructors = list()
iSEG_train = None
iSEG_CSV = None
MRBrainS_train = None
MRBrainS_CSV = None
ABIDE_train = None
ABIDE_CSV = None
iSEG_augmentation_strategy = None
MRBrainS_augmentation_strategy = None
ABIDE_augmentation_strategy = None
# Initialize the model trainers
model_trainer_factory = ModelTrainerFactory(model_factory=CustomModelFactory(),
criterion_factory=CustomCriterionFactory())
model_trainers = model_trainer_factory.create(model_trainer_configs)
if not isinstance(model_trainers, list):
model_trainers = [model_trainers]
# Create datasets
if dataset_configs.get("iSEG", None) is not None:
if dataset_configs["iSEG"].hist_shift_augmentation:
iSEG_augmentation_strategy = AugmentInput(
Compose([ShiftHistogram(exec_probability=0.50, min_lambda=-5, max_lambda=5)]))
iSEG_train, iSEG_valid, iSEG_test, iSEG_reconstruction = iSEGSliceDatasetFactory.create_train_valid_test(
source_dir=dataset_configs["iSEG"].path,
modalities=dataset_configs["iSEG"].modalities,
dataset_id=ISEG_ID,
test_size=dataset_configs["iSEG"].validation_split,