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, 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