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,
pad_mode="constant",
pad_sides=self.pad_all_sides)
dl_val = DataLoader3D(self.dataset_val,
self.patch_size,
self.patch_size,
self.batch_size,
True,
oversample_foreground_percent=self.
oversample_foreground_percent,
pad_mode="constant",
pad_sides=self.pad_all_sides)
else:
raise NotImplementedError("2D has no cascade")
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.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,
memmap_mode='r')
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,
memmap_mode='r')
else:
dl_tr = DataLoader2D(self.dataset_tr,
self.basic_generator_patch_size,
self.patch_size,
self.batch_size,
oversample_foreground_percent=self.
oversample_foreground_percent,
pad_mode="constant",
pad_sides=self.pad_all_sides,
memmap_mode='r')
dl_val = DataLoader2D(self.dataset_val,
self.patch_size,
self.patch_size,
self.batch_size,
oversample_foreground_percent=self.
oversample_foreground_percent,
pad_mode="constant",
pad_sides=self.pad_all_sides,
memmap_mode='r')
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.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
val_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
val_transforms = Compose(val_transforms)
# batchgenerator_val = SingleThreadedAugmenter(dataloader_val, val_transforms)
batchgenerator_val = MultiThreadedAugmenter(dataloader_val, val_transforms, 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 tuframework.training.dataloading.dataset_loading import DataLoader3D, load_dataset
from tuframework.paths import preprocessing_output_dir
import os
import pickle
t = "Task002_Heart"
p = preprocessing_output_dir+"/"+ t
dataset = load_dataset(p, 0)
with open(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))