a b/setup.py 

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from setuptools import setup, find_namespace_packages





  
  

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setup(name='nnunet',





  
  

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      packages=find_namespace_packages(include=["nnunet", "nnunet.*"]),





  
  

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      version='1.6.6',





  
  

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      description='nnU-Net. Framework for out-of-the box biomedical image segmentation.',





  
  

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      url='https://github.com/MIC-DKFZ/nnUNet',





  
  

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      author='Division of Medical Image Computing, German Cancer Research Center',





  
  

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      author_email='f.isensee@dkfz-heidelberg.de',





  
  

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      license='Apache License Version 2.0, January 2004',





  
  

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      install_requires=[





  
  

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            "torch>=1.6.0a",





  
  

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            "tqdm",





  
  

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            "dicom2nifti",





  
  

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            "scikit-image>=0.14",





  
  

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            "medpy",





  
  

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            "scipy",





  
  

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            "batchgenerators==0.21",





  
  

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            "numpy",





  
  

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            "sklearn",





  
  

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            "SimpleITK",





  
  

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            "pandas",





  
  

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            "requests",





  
  

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            "nibabel", 'tifffile','axial_attention'





  
  

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      ],





  
  

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      entry_points={





  
  

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          'console_scripts': [





  
  

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              'nnUNet_convert_decathlon_task = nnunet.experiment_planning.nnUNet_convert_decathlon_task:main',





  
  

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              'nnUNet_plan_and_preprocess = nnunet.experiment_planning.nnUNet_plan_and_preprocess:main',





  
  

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              'nnUNet_train = nnunet.run.run_training:main',





  
  

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              'nnUNet_train_DP = nnunet.run.run_training_DP:main',





  
  

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              'nnUNet_train_DDP = nnunet.run.run_training_DDP:main',





  
  

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              'nnUNet_predict = nnunet.inference.predict_simple:main',





  
  

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              'nnUNet_ensemble = nnunet.inference.ensemble_predictions:main',





  
  

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              'nnUNet_find_best_configuration = nnunet.evaluation.model_selection.figure_out_what_to_submit:main',





  
  

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              'nnUNet_print_available_pretrained_models = nnunet.inference.pretrained_models.download_pretrained_model:print_available_pretrained_models',





  
  

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              'nnUNet_print_pretrained_model_info = nnunet.inference.pretrained_models.download_pretrained_model:print_pretrained_model_requirements',





  
  

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              'nnUNet_download_pretrained_model = nnunet.inference.pretrained_models.download_pretrained_model:download_by_name',





  
  

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              'nnUNet_download_pretrained_model_by_url = nnunet.inference.pretrained_models.download_pretrained_model:download_by_url',





  
  

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              'nnUNet_determine_postprocessing = nnunet.postprocessing.consolidate_postprocessing_simple:main',





  
  

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              'nnUNet_export_model_to_zip = nnunet.inference.pretrained_models.collect_pretrained_models:export_entry_point',





  
  

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              'nnUNet_install_pretrained_model_from_zip = nnunet.inference.pretrained_models.download_pretrained_model:install_from_zip_entry_point',





  
  

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              'nnUNet_change_trainer_class = nnunet.inference.change_trainer:main',





  
  

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              'nnUNet_evaluate_folder = nnunet.evaluation.evaluator:nnunet_evaluate_folder',





  
  

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              'nnUNet_plot_task_pngs = nnunet.utilities.overlay_plots:entry_point_generate_overlay',





  
  

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          ],





  
  

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      },





  
  

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      keywords=['deep learning', 'image segmentation', 'medical image analysis',





  
  

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                'medical image segmentation', 'nnU-Net', 'nnunet']





  
  

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      )