nnunet/training/network_training/mynnUNetTrainerv2_noDA.py
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, run_postprocessing_on_folds: bool = True):
        """
        We need to wrap this because we need to enforce self.network.do_ds = False for prediction

        """        
        ds = self.network.do_ds
        if do_mirroring:
            print("WARNING! do_mirroring was True but we cannot do that because we trained without mirroring. "
                  "do_mirroring was set to False")
        do_mirroring = False
        self.network.do_ds = False
        ret = super().validate(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.do_ds = ds
        return ret

        We need to wrap this because we need to enforce self.network.do_ds = False for prediction

        
We need to wrap this because we need to enforce self.network.do_ds = False for prediction
We need to wrap this because we need to enforce self . network . do_ds = False for prediction
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, run_postprocessing_on_folds: bool = True):
        ds = self.network.do_ds
        if do_mirroring:
            print("WARNING! do_mirroring was True but we cannot do that because we trained without mirroring. "
                  "do_mirroring was set to False")
        do_mirroring = False
        self.network.do_ds = False
        ret = super().validate(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.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 , segmentation_export_kwargs : dict = None , run_postprocessing_on_folds : bool = True ) : ds = self . network . do_ds if do_mirroring : print ( "WARNING! do_mirroring was True but we cannot do that because we trained without mirroring. " "do_mirroring was set to False" ) do_mirroring = False self . network . do_ds = False ret = super ( ) . validate ( 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 . do_ds = ds return ret
We need to wrap this because we need to enforce self.network.do_ds = False for prediction
We need to wrap this because we need to enforce self . network . do_ds = False for prediction
"""
        We need to wrap this because we need to enforce self.network.do_ds = False for prediction

        """
self
do_mirroring
bool
use_sliding_window
bool
step_size
float
save_softmax
bool
use_gaussian
bool
overwrite
bool
validation_folder_name
str
debug
bool
all_in_gpu
bool
segmentation_export_kwargs
dict
run_postprocessing_on_folds
bool