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setup.py
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#########################################################################################################
#--------------------------Corresponding setup.py file for nnUNet extensions.---------------------------#
#########################################################################################################
# Includes parts from original nnunet (https://github.com/MIC-DKFZ/nnUNet)
import pathlib
from setuptools import setup, find_namespace_packages
# -- The directory containing this file] -- #
HERE = pathlib.Path(__file__).parent
# -- The text of the README file -- #
README = (HERE / "README_Lifelong-nnUNet.md").read_text()
# -- Setup -- #
setup(name='nnunet_ext',
packages=find_namespace_packages(include=["nnunet_ext", "nnunet_ext.*"]),
#version='1.6.6',
description='Add short description',
long_description=README,
long_description_content_type="text/markdown",
url='Add url', # url to repository
author='Add author',
author_email='Add email address',
license='Apache License Version 2.0, January 2004',
install_requires=[
# Add only the packages that are not in the nnUNet repositories setup.py file!
"tqdm"
],
entry_points={
'console_scripts': [
'nnUNet_dataset_label_mapping = nnunet_ext.experiment_planning.dataset_label_mapping:main',# Use when the labels of the masks need to be changed based on a mapping file
'nnUNet_delete_tasks = nnunet_ext.scripts.delete_specified_task:main', # Use for deleting preprocessed and planned data or after clean up when a test failed
'nnUNet_update_checkpoints = nnunet_ext.scripts.update_checkpoints:main', # Use for modifying the checkpoints
'nnUNet_train_multihead = nnunet_ext.run.run_training:main_multihead', # Use for multi head training
'nnUNet_train_sequential = nnunet_ext.run.run_training:main_sequential', # Use for sequential training --> transfer learning using n tasks
'nnUNet_train_sequential_GIN = nnunet_ext.run.run_training:main_sequential_GIN', # Use for sequential training with GIN data augmentation
'nnUNet_train_FOCUS = nnunet_ext.run.run_training:main_focus',
'nnUNet_train_TED = nnunet_ext.run.run_training:main_ted',
'nnUNet_train_rehearsal = nnunet_ext.run.run_training:main_rehearsal', # Use for rehearsal training
'nnUNet_train_ewc = nnunet_ext.run.run_training:main_ewc', # Use for EWC training
'nnUNet_train_rw = nnunet_ext.run.run_training:main_rw', # Use for RW training
'nnUNet_train_lwf = nnunet_ext.run.run_training:main_lwf', # Use for LWF training
'nnUNet_train_mib = nnunet_ext.run.run_training:main_mib', # Use for MiB training
'nnUNet_train_plop = nnunet_ext.run.run_training:main_plop', # Use for PLOP training
'nnUNet_train_pod = nnunet_ext.run.run_training:main_pod', # Use for POD training
'nnUNet_train_body_froz = nnunet_ext.run.run_training:main_frozen_body_seq', # Use for frozen body sequential training
'nnUNet_evaluate = nnunet_ext.run.run_evaluation:main', # Use for evaluation of any method
'nnUNet_parameter_search = nnunet_ext.run.run_param_search:main', # Use for parameter search for any parameter using extension trainer
## -- Experimental Trainers -- ##
'nnUNet_train_ewc_ln = nnunet_ext.run.run_training:main_ewc_ln', # Use for EWC on LN layers
'nnUNet_train_ewc_unet = nnunet_ext.run.run_training:main_ewc_unet', # Use for EWC on nnUNet layers
'nnUNet_train_ewc_vit = nnunet_ext.run.run_training:main_ewc_vit', # Use for EWC on ViT layers
'nnUNet_train_froz_ewc = nnunet_ext.run.run_training:main_froz_ewc', # Use for EWC and frozen ViT (every 2nd task)
'nnUNet_train_frozen_nonln = nnunet_ext.run.run_training:main_frozen_nonln', # Use for freezing all layers except LN
'nnUNet_train_frozen_unet = nnunet_ext.run.run_training:main_frozen_unet', # Use for freezing all nnUNet layers
'nnUNet_train_frozen_vit = nnunet_ext.run.run_training:main_frozen_vit', # Use for freezing all ViT layers
'nnUNet_train_vae = nnunet_ext.run.run_training:main_vae',# is this even used?
'nnUNet_train_rehearsal_no_skips_frozen = nnunet_ext.run.run_training:main_rehearsal_no_skips_frozen',
'nnUNet_train_feature_rehearsal2 = nnunet_ext.run.run_training:main_feature_rehearsal2',
'nnUNet_train_feature_rehearsal_dm = nnunet_ext.run.run_training:main_feature_rehearsal_dm',
'nnUNet_train_feature_rehearsal_no_skips_dm = nnunet_ext.run.run_training:main_feature_rehearsal_no_skips_dm',
'nnUNet_train_feature_rehearsal_no_skips_dm_GIN = nnunet_ext.run.run_training:main_feature_rehearsal_no_skips_dm_GIN',
'nnUNet_train_feature_rehearsal_dm_GIN = nnunet_ext.run.run_training:main_feature_rehearsal_dm_GIN',
'nnUNet_train_feature_rehearsal_no_freeze = nnunet_ext.run.run_training:main_feature_rehearsal_no_freeze',
'nnUNet_train_feature_rehearsal_no_replay = nnunet_ext.run.run_training:main_feature_rehearsal_no_replay',
'nnUNet_train_sequential_no_skips = nnunet_ext.run.run_training:main_sequential_no_skips',
'nnUNet_train_vae_rehearsal_no_skips = nnunet_ext.run.run_training:main_vae_rehearsal_no_skips',
'nnUNet_train_feature_rehearsal_no_skips = nnunet_ext.run.run_training:main_feature_rehearsal_no_skips',
'nnUNet_train_vae_rehearsal_no_skips_no_conditioning = nnunet_ext.run.run_training:main_vae_rehearsal_no_skips_no_conditioning',
'nnUNet_train_vae_rehearsal_no_skips_larger_vae_force_init = nnunet_ext.run.run_training:main_vae_rehearsal_no_skips_larger_vae_force_init',
'nnUNet_train_vae_rehearsal_no_skips_condition_on_both = nnunet_ext.run.run_training:main_vae_rehearsal_no_skips_condition_on_both',
'nnUNet_train_vae_rehearsal_no_skips_large = nnunet_ext.run.run_training:main_vae_rehearsal_no_skips_large',
'nnUNet_train_vae_rehearsal_no_skips_pipe = nnunet_ext.run.run_training:main_vae_rehearsal_no_skips_pipe',
'nnUNet_train_vae_rehearsal_with_skips = nnunet_ext.run.run_training:main_vae_rehearsal_with_skips',
'nnUNet_train_curl = nnunet_ext.run.run_training:main_curl',
'nnUNet_train_seg_dist = nnunet_ext.run.run_training:main_seg_dist',
'nnUNet_ood_detection_per_slice = nnunet_ext.run.run_ood_detection_per_slice:run_ood_detection_per_slice',
'nnUNet_ood_detection = nnunet_ext.run.run_ood_detection:run_ood_detection',
'nnUNet_inference = nnunet_ext.run.run_inference:main',
'nnUNet_evaluate2 = nnunet_ext.run.run_evaluation:run_evaluation2',
'nnUNet_evaluate_all = nnunet_ext.run.run_evaluation:run_evaluation_all',
],
},
keywords=['deep learning', 'image segmentation', 'medical image analysis',
'medical image segmentation', 'nnU-Net', 'nnunet', 'CL', 'Continual Learning',
'Lifelong Learning', 'Learning without Forgetting', 'nnU-Net extensions']
)