def validate(self,
do_mirroring: bool = True,
use_sliding_window: bool = True,
step_size: float = 0.5,
save_softmax: bool = True,
use_gaussian: bool = True,
overwrite: bool = True,
validation_folder_name: str = 'validation_raw',
debug: bool = False,
all_in_gpu: bool = False,
force_separate_z: bool = None,
interpolation_order: int = 3,
interpolation_order_z=0,
segmentation_export_kwargs: dict = None,
run_postprocessing_on_folds: bool = True):
ds = self.network.decoder.deep_supervision
self.network.decoder.deep_supervision = False
ret = nnUNetTrainer.validate(
self,
do_mirroring=do_mirroring,
use_sliding_window=use_sliding_window,
step_size=step_size,
save_softmax=save_softmax,
use_gaussian=use_gaussian,
overwrite=overwrite,
validation_folder_name=validation_folder_name,
debug=debug,
all_in_gpu=all_in_gpu,
segmentation_export_kwargs=segmentation_export_kwargs,
run_postprocessing_on_folds=run_postprocessing_on_folds)
self.network.decoder.deep_supervision = ds
return ret
def validate(self,
do_mirroring: bool = True,
use_sliding_window: bool = True,
step_size: float = 0.5,
save_softmax: bool = True,
use_gaussian: bool = True,
overwrite: bool = True,
validation_folder_name: str = 'validation_raw',
debug: bool = False,
all_in_gpu: bool = False,
force_separate_z: bool = None,
interpolation_order: int = 3,
interpolation_order_z=0):
ds = self.network.decoder.deep_supervision
self.network.decoder.deep_supervision = False
ret = nnUNetTrainer.validate(
self,
do_mirroring,
use_sliding_window,
step_size,
save_softmax,
use_gaussian,
overwrite,
validation_folder_name,
debug,
all_in_gpu,
force_separate_z=force_separate_z,
interpolation_order=interpolation_order,
interpolation_order_z=interpolation_order_z)
self.network.decoder.deep_supervision = ds
return ret
def validate(self,
do_mirroring: bool = True,
use_sliding_window: bool = True,
step_size: float = 0.5,
save_softmax: bool = True,
use_gaussian: bool = True,
overwrite: bool = True,
validation_folder_name: str = 'validation_raw',
debug: bool = False,
all_in_gpu: bool = False,
segmentation_export_kwargs: dict = None):
if self.local_rank == 0:
if isinstance(self.network, DDP):
net = self.network.module
else:
net = self.network
ds = net.do_ds
net.do_ds = False
ret = nnUNetTrainer.validate(self, do_mirroring,
use_sliding_window, step_size,
save_softmax, use_gaussian, overwrite,
validation_folder_name, debug,
all_in_gpu,
segmentation_export_kwargs)
net.do_ds = ds
return ret
def validate(self, do_mirroring: bool = True, use_sliding_window: bool = True, step_size: float = 0.5,
save_softmax: bool = True, use_gaussian: bool = True, overwrite: bool = True,
validation_folder_name: str = 'validation_raw', debug: bool = False, all_in_gpu: bool = False,
force_separate_z: bool = None, interpolation_order: int = 3, interpolation_order_z=0):
if self.local_rank == 0:
if isinstance(self.network, DDP):
net = self.network.module
else:
net = self.network
ds = net.do_ds
net.do_ds = False
ret = nnUNetTrainer.validate(self, do_mirroring, use_sliding_window, step_size, save_softmax, use_gaussian,
overwrite, validation_folder_name, debug, all_in_gpu,
force_separate_z=force_separate_z, interpolation_order=interpolation_order,
interpolation_order_z=interpolation_order_z)
net.do_ds = ds
return ret
