def load_dataset(self, task=None):
if task is None:
self.dataset = load_dataset(
self.folder_with_preprocessed_data[self.tasks[0]])
else:
self.dataset = load_dataset(
self.folder_with_preprocessed_data[task])
def load_dataset(self):
self.dataset = load_dataset(self.folder_with_preprocessed_data)
max(params.get('num_threads') // 2, 1),
params.get("num_cached_per_thread"),
seeds=seeds_val,
pin_memory=pin_memory)
return batchgenerator_train, batchgenerator_val
if __name__ == "__main__":
from nnunet.training.dataloading.dataset_loading import DataLoader3D, load_dataset
from nnunet.paths import preprocessing_output_dir
import os
import pickle
t = "Task002_Heart"
p = os.path.join(preprocessing_output_dir, t)
dataset = load_dataset(p, 0)
with open(os.path.join(p, "plans.pkl"), 'rb') as f:
plans = pickle.load(f)
basic_patch_size = get_patch_size(
np.array(plans['stage_properties'][0].patch_size),
default_3D_augmentation_params['rotation_x'],
default_3D_augmentation_params['rotation_y'],
default_3D_augmentation_params['rotation_z'],
default_3D_augmentation_params['scale_range'])
dl = DataLoader3D(
dataset, basic_patch_size,
np.array(plans['stage_properties'][0].patch_size).astype(int), 1)
tr, val = get_default_augmentation(
dl, dl,
