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pyproject.toml
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pyproject.toml
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[project]
name = "MultiTalent"
version = "2.0"
requires-python = ">=3.9"
description = "MultiTalent is a framework to combine training with multiple datasets, heavily based on nnU-NetV2"
readme = "readme.md"
license = { file = "LICENSE" }
authors = [
{ name = "Constantin Ulrich", email = '[email protected]' },
{ name = "Fabian Isensee", email = "[email protected]"}]
classifiers = [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"Intended Audience :: Healthcare Industry",
"Programming Language :: Python :: 3",
"License :: OSI Approved :: Apache Software License",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Scientific/Engineering :: Image Recognition",
"Topic :: Scientific/Engineering :: Medical Science Apps.",
]
keywords = [
'deep learning',
'image segmentation',
'semantic segmentation',
'medical image analysis',
'medical image segmentation',
'nnU-Net',
'nnunet'
]
dependencies = [
"torch>=2.1.2",
"acvl-utils>=0.2,<0.3", # 0.3 may bring breaking changes. Careful!
"dynamic-network-architectures>=0.3.1,<0.4", # 0.3.1 and lower are supported, 0.4 may have breaking changes. Let's be careful here
"tqdm",
"dicom2nifti",
"scipy",
"batchgenerators>=0.25",
"numpy>=1.24",
"scikit-learn",
"scikit-image>=0.19.3",
"SimpleITK>=2.2.1",
"pandas",
"graphviz",
'tifffile',
'requests',
"nibabel",
"matplotlib",
"seaborn",
"imagecodecs",
"yacs",
"medpy",
"batchgeneratorsv2>=0.2",
"einops",
"blosc2"
]
[project.urls]
homepage = "https://github.com/MIC-DKFZ/MultiTalent"
repository = "https://github.com/MIC-DKFZ/MultiTalent"
[project.scripts]
multitalent_plan_and_preprocess = "multitalent.experiment_planning.plan_and_preprocess_entrypoints:plan_and_preprocess_entry"
multitalent_extract_fingerprint = "multitalent.experiment_planning.plan_and_preprocess_entrypoints:extract_fingerprint_entry"
multitalent_plan_experiment = "multitalent.experiment_planning.plan_and_preprocess_entrypoints:plan_experiment_entry"
multitalent_preprocess = "multitalent.experiment_planning.plan_and_preprocess_entrypoints:preprocess_entry"
multitalent_train = "multitalent.run.run_training_MT:run_training_entry"
multitalent_predict_from_modelfolder = "multitalent.inference.predict_from_raw_data:predict_entry_point_modelfolder"
multitalent_predict = "multitalent.inference.predict_from_raw_data:predict_entry_point"
nnUNetv2_train = "multitalent.run.run_training_MT:run_training_entry"
nnUNetv2_predict_from_modelfolder = "multitalent.inference.predict_from_raw_data:predict_entry_point_modelfolder"
nnUNetv2_predict = "multitalent.inference.predict_from_raw_data:predict_entry_point"
multitalent_convert_old_nnUNet_dataset = "multitalent.dataset_conversion.convert_raw_dataset_from_old_nnunet_format:convert_entry_point"
multitalent_find_best_configuration = "multitalent.evaluation.find_best_configuration:find_best_configuration_entry_point"
multitalent_determine_postprocessing = "multitalent.postprocessing.remove_connected_components:entry_point_determine_postprocessing_folder"
multitalent_apply_postprocessing = "multitalent.postprocessing.remove_connected_components:entry_point_apply_postprocessing"
multitalent_ensemble = "multitalent.ensembling.ensemble:entry_point_ensemble_folders"
multitalent_accumulate_crossval_results = "multitalent.evaluation.find_best_configuration:accumulate_crossval_results_entry_point"
multitalent_plot_overlay_pngs = "multitalent.utilities.overlay_plots:entry_point_generate_overlay"
multitalent_download_pretrained_model_by_url = "multitalent.model_sharing.entry_points:download_by_url"
multitalent_install_pretrained_model_from_zip = "multitalent.model_sharing.entry_points:install_from_zip_entry_point"
multitalent_export_model_to_zip = "multitalent.model_sharing.entry_points:export_pretrained_model_entry"
multitalent_move_plans_between_datasets = "multitalent.experiment_planning.plans_for_pretraining.move_plans_between_datasets:entry_point_move_plans_between_datasets"
multitalent_evaluate_folder = "multitalent.evaluation.evaluate_predictions:evaluate_folder_entry_point"
multitalent_evaluate_simple = "multitalent.evaluation.evaluate_predictions:evaluate_simple_entry_point"
multitalent_convert_MSD_dataset = "multitalent.dataset_conversion.convert_MSD_dataset:entry_point"
prepare_MT_training = "multitalent.utilities.MultiTalent.create_MT_trainingsjson:prepare_MT_training"
[project.optional-dependencies]
dev = [
"black",
"ruff",
"pre-commit"
]
[build-system]
requires = ["setuptools>=67.8.0"]
build-backend = "setuptools.build_meta"
[tool.codespell]
skip = '.git,*.pdf,*.svg'
#
# ignore-words-list = ''