import utils
caching = None
restart_from_save = None
rng = subconfig().rng
patch_size = subconfig().patch_size
train_transformation_params = subconfig().train_transformation_params
valid_transformation_params = subconfig().valid_transformation_params
test_transformation_params = subconfig().test_transformation_params
batch_size = 8
nbatches_chunk = 2
chunk_size = batch_size * nbatches_chunk
train_valid_ids = utils.get_train_valid_split(PKL_TRAIN_DATA_PATH)
train_data_iterator = data_iterators.PatientsDataGenerator(data_path=PKL_TRAIN_DATA_PATH,
batch_size=chunk_size,
transform_params=train_transformation_params,
patient_ids=train_valid_ids['train'],
labels_path=TRAIN_LABELS_PATH,
slice2roi_path='pkl_train_slice2roi.pkl',
full_batch=True, random=True, infinite=True, min_slices=5)
valid_data_iterator = data_iterators.PatientsDataGenerator(data_path=PKL_TRAIN_DATA_PATH,
batch_size=chunk_size,
transform_params=valid_transformation_params,
patient_ids=train_valid_ids['valid'],
labels_path=TRAIN_LABELS_PATH,
slice2roi_path='pkl_train_slice2roi.pkl',
'translation_range_x': (-5, 10),
'translation_range_y': (-10, 10),
'shear_range': (0, 0),
'roi_scale_range': (0.95, 1.3),
'zoom_range': (1., 1.),
'do_flip': True,
'sequence_shift': False
}
data_prep_fun = data.transform_norm_rescale_after
batch_size = 32
nbatches_chunk = 16
chunk_size = batch_size * nbatches_chunk
train_valid_ids = utils.get_train_valid_split(PKL_TRAIN_DATA_PATH)
train_data_iterator = data_iterators.SliceNormRescaleDataGenerator(data_path=PKL_TRAIN_DATA_PATH,
batch_size=chunk_size,
transform_params=train_transformation_params,
patient_ids=train_valid_ids['train'],
labels_path=TRAIN_LABELS_PATH,
slice2roi_path='pkl_train_slice2roi_10.pkl',
full_batch=True, random=True, infinite=True,
data_prep_fun=data_prep_fun)
valid_data_iterator = data_iterators.SliceNormRescaleDataGenerator(data_path=PKL_TRAIN_DATA_PATH,
batch_size=chunk_size,
transform_params=valid_transformation_params,
patient_ids=train_valid_ids['valid'],
labels_path=TRAIN_LABELS_PATH,
