used for if the checkpoint is already in ram :param checkpoint: :param train: :return:

used for if the checkpoint is already in ram :param checkpoint: :param train: :return:

Create the network and start the training.

Create the network and start the training.

Create the network and start the training.

Create the network and start the training.

Create the network and start the training.

Create the network and start the training.

Saves a checkpoint every save_ever epochs. :return:

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

Pad an image up to the target size.

Pad an image up to the target size.

Pad an image up to the target size.

Pad an image up to the target size.

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

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:

Should probably by improved :return:

Use this function to predict a 3D image. It does not matter whether the network is a 2D or 3D U-Net, it will detect that automaticall

Use this function to predict a 3D image. It does not matter whether the network is a 2D or 3D U-Net, it will detect that automaticall

Used to predict new unseen data. Not used for the preprocessing of the training/test data :param input_files: :return:

Used to predict new unseen data. Not used for the preprocessing of the training/test data :param input_files: :return:

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

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

if we run with -c then we need to set the correct lr for the first epoch, otherwise it will run the first continued epoch with self.i

if we run with -c then we need to set the correct lr for the first epoch, otherwise it will run the first continued epoch with self.i

- 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

This function disables printing when not in master process

This function disables printing when not in master process

This function disables printing when not in master process

This function disables printing when not in master process

This function disables printing when not in master process

This function disables printing when not in master process