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