def get_basic_generators(self):
self.load_dataset()
self.do_split()
if self.threeD:
dl_tr = DataLoader3D(self.dataset_tr,
self.basic_generator_patch_size,
self.patch_size,
self.batch_size,
True,
oversample_foreground_percent=self.
oversample_foreground_percent)
dl_val = DataLoader3D(self.dataset_val,
self.patch_size,
self.patch_size,
self.batch_size,
True,
oversample_foreground_percent=self.
oversample_foreground_percent)
else:
raise NotImplementedError
return dl_tr, dl_val
def get_basic_generators(self):
self.load_dataset()
self.do_split()
if self.threeD:
dl_tr = DataLoader3D(self.dataset_tr, self.patch_size, self.patch_size, self.batch_size,
False, oversample_foreground_percent=self.oversample_foreground_percent
, pad_mode="constant", pad_sides=self.pad_all_sides)
dl_val = DataLoader3D(self.dataset_val, self.patch_size, self.patch_size, self.batch_size, False,
oversample_foreground_percent=self.oversample_foreground_percent,
pad_mode="constant", pad_sides=self.pad_all_sides)
else:
dl_tr = DataLoader2D(self.dataset_tr, self.patch_size, self.patch_size, self.batch_size,
transpose=self.plans.get('transpose_forward'),
oversample_foreground_percent=self.oversample_foreground_percent
, pad_mode="constant", pad_sides=self.pad_all_sides)
dl_val = DataLoader2D(self.dataset_val, self.patch_size, self.patch_size, self.batch_size,
transpose=self.plans.get('transpose_forward'),
oversample_foreground_percent=self.oversample_foreground_percent,
pad_mode="constant", pad_sides=self.pad_all_sides)
return dl_tr, dl_val
def get_basic_generators(self):
self.load_dataset()
"""
def load_dataset(folder):
# we don't load the actual data but instead return the filename to the np file. the properties are loaded though
case_identifiers = get_case_identifiers(folder)
case_identifiers.sort()
dataset = OrderedDict()
for c in case_identifiers:
dataset[c] = OrderedDict()
dataset[c]['data_file'] = join(folder, "%s.npz"%c)
with open(join(folder, "%s.pkl"%c), 'rb') as f:
dataset[c]['properties'] = pickle.load(f)
if dataset[c].get('seg_from_prev_stage_file') is not None:
dataset[c]['seg_from_prev_stage_file'] = join(folder, "%s_segs.npz"%c)
return dataset
"""
self.do_split()
if self.threeD:
dl_tr = DataLoader3D(self.dataset_tr, self.basic_generator_patch_size, self.patch_size, self.batch_size,
False, oversample_foreground_percent=self.oversample_foreground_percent,
pad_mode="constant", pad_sides=self.pad_all_sides)
dl_val = DataLoader3D(self.dataset_val, self.patch_size, self.patch_size, self.batch_size, False,
oversample_foreground_percent=self.oversample_foreground_percent,
pad_mode="constant", pad_sides=self.pad_all_sides)
else:
dl_tr = DataLoader2D(self.dataset_tr, self.basic_generator_patch_size, self.patch_size, self.batch_size,
transpose=self.plans.get('transpose_forward'),
oversample_foreground_percent=self.oversample_foreground_percent,
pad_mode="constant", pad_sides=self.pad_all_sides)
dl_val = DataLoader2D(self.dataset_val, self.patch_size, self.patch_size, self.batch_size,
transpose=self.plans.get('transpose_forward'),
oversample_foreground_percent=self.oversample_foreground_percent,
pad_mode="constant", pad_sides=self.pad_all_sides)
return dl_tr, dl_val
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,
np.array(plans['stage_properties'][0].patch_size).astype(int))