Saves a checkpoint every save_ever epochs. :return:

if epoch is not None we overwrite epoch. Else we use epoch = self.epoch + 1 (maybe_update_lr is called in on_epoch_end which is call

if epoch is not None we overwrite epoch. Else we use epoch = self.epoch + 1 (maybe_update_lr is called in on_epoch_end which is call

overwrite patient-based early stopping. Always run to 1000 epochs :return:

can be implemented (see nnUNetTrainer) but does not have to. Not implemented here because it imposes stronger assumptions on the pres

Should probably by improved :return:

:param segmentation_export_kwargs: :param model: folder where the model is saved, must contain fold_x subfolders :param list_of_lists: [[

changes best checkpoint pickle nnunettrainer class name to nnUNetTrainer :param experiments: :return:

TP / (TP + FN)

removes all but the largest connected component, individually for each class :param image: :param for_which_classes: can be None. Should

also copied ground truth nifti segmentation into the preprocessed folder so that we can use them for evaluation on the cluster

due to deep supervision the return value and the reference are now lists of tensors. We only need the full resolution output because

Can be implemented, does not have to :param output_torch: :param target_npy: :return:

due to deep supervision the return value and the reference are now lists of tensors. We only need the full resolution output because

due to deep supervision the return value and the reference are now lists of tensors. We only need the full resolution output because

- we increase roation angle from [-15, 15] to [-30, 30] - scale range is now (0.7, 1.4), was (0.85, 1.25) - we don't do elast

- we increase roation angle from [-15, 15] to [-30, 30] - scale range is now (0.7, 1.4), was (0.85, 1.25) - we don't do elast

we leave out the creation of self.deep_supervision_scales, so it remains None :return:

net_num_pool_op_kernel_sizes is different in resunet

Return result as numpy array (labels x metrics).

If self.all_val_eval_metrics is unused (len=0) then we fall back to using -self.all_val_losses for the MA to determine early stopping

disable nnunet postprocessing. this would just waste computation time and does not benefit brats !!!We run this with use_sliding_win

This should run after cropping :param folder: :return:

folder needs the imagesTr, imagesTs and labelsTr subfolders. There also needs to be a dataset.json checks if all training cases and labels ar

:param folder_with_cropped_data: :param overwrite: If True then precomputed values will not be used and instead recomputed from the d