Wheelodex » Projects » nnunetv2

nnunetv2

View on PyPI — Reverse Dependencies (16)

2.2.2 nnunetv2-2.2.2-py3-none-any.whl

Wheel Details

Project: nnunetv2
Version: 2.2.2
Filename: nnunetv2-2.2.2-py3-none-any.whl
Download: [link]
Size: 237459
MD5: 60b2dc016741a131c5321b64ff4369be
SHA256: a6b66a8260f57e564390565321b8140eb77eb902ed54253a677700f12124c8b8
Uploaded: 2024-02-21 09:15:42 +0000

dist-info

METADATA · WHEEL · RECORD · top_level.txt · entry_points.txt

METADATA

Metadata-Version: 2.1
Name: nnunetv2
Version: 2.2.2
Summary: nnU-Net is a framework for out-of-the box image segmentation.
Author: Helmholtz Imaging Applied Computer Vision Lab
Author-Email: Fabian Isensee <f.isensee[at]dkfz-heidelberg.de>
Project-Url: homepage, https://github.com/MIC-DKFZ/nnUNet
Project-Url: repository, https://github.com/MIC-DKFZ/nnUNet
License: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [2019] [Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Keywords: deep learning,image segmentation,semantic segmentation,medical image analysis,medical image segmentation,nnU-Net,nnunet
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Healthcare Industry
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.9
Requires-Dist: torch (>=2.0.0)
Requires-Dist: acvl-utils (>=0.2)
Requires-Dist: dynamic-network-architectures (==0.2)
Requires-Dist: tqdm
Requires-Dist: dicom2nifti
Requires-Dist: scipy
Requires-Dist: batchgenerators (>=0.25)
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: scikit-image (>=0.19.3)
Requires-Dist: SimpleITK (>=2.2.1)
Requires-Dist: pandas
Requires-Dist: graphviz
Requires-Dist: tifffile
Requires-Dist: requests
Requires-Dist: nibabel
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: imagecodecs
Requires-Dist: yacs
Requires-Dist: black; extra == "dev"
Requires-Dist: ruff; extra == "dev"
Requires-Dist: pre-commit; extra == "dev"
Provides-Extra: dev
Description-Content-Type: text/markdown
License-File: LICENSE
[Description omitted; length: 9991 characters]

WHEEL

Wheel-Version: 1.0
Generator: bdist_wheel (0.38.4)
Root-Is-Purelib: true
Tag: py3-none-any

RECORD

Path Digest Size
nnunetv2/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/configuration.py sha256=xi9zsbwREbC8nD3-XI0aSBBHu7LBKLYBft6X2irxe-s 416
nnunetv2/paths.py sha256=uDGitS-iLo0z_svWnsR6DbFQtKpyJuZ5NnL0bpUk024 1865
nnunetv2/batch_running/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/batch_running/collect_results_custom_Decathlon.py sha256=GrF8x5qbtcoXqdGrdFA7dp6uuMXZBEAvZo8i_ihOako 5696
nnunetv2/batch_running/collect_results_custom_Decathlon_2d.py sha256=1L8i8__shXi9AFHAS1sE0uNZl8BB1gGh6M5JNqMGD4U 708
nnunetv2/batch_running/generate_lsf_runs_customDecathlon.py sha256=oihFYEMxWdrVQp0wyBZE5XzMmQ827BUrnc_fhMdzR1g 3603
nnunetv2/batch_running/benchmarking/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/batch_running/benchmarking/generate_benchmarking_commands.py sha256=FKux606WbprJeJADrWtwVIl4Bh0QAX5gn5Ggzw64iXQ 2041
nnunetv2/batch_running/benchmarking/summarize_benchmark_results.py sha256=JBgJ9cwO9tFsPGzBbOK2mlHJaVUctLionVtERVieuwU 3272
nnunetv2/batch_running/release_trainings/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/batch_running/release_trainings/nnunetv2_v1/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/batch_running/release_trainings/nnunetv2_v1/collect_results.py sha256=CwQgaGOy52LoABHGsdhVfPwOTP1d58Y8MJtgLKSe27A 5662
nnunetv2/batch_running/release_trainings/nnunetv2_v1/generate_lsf_commands.py sha256=AuLIJhPkqoECleiwlAYFIxt_9se440-yvxJFiC9iJes 3833
nnunetv2/dataset_conversion/Dataset027_ACDC.py sha256=blfpi4RvEWGFB2_vXCpA3xlvAP-gND7maXgn3jwCFc8 3194
nnunetv2/dataset_conversion/Dataset073_Fluo_C3DH_A549_SIM.py sha256=g0F5Kf64fvSDSIpAI7rDQ_CX9gv3azpiXq50AsrDnJE 4162
nnunetv2/dataset_conversion/Dataset114_MNMs.py sha256=0axA4VgydMrfG4jWiZxALwGwjGH-whTVCDR8sr0oB6U 9018
nnunetv2/dataset_conversion/Dataset115_EMIDEC.py sha256=QSCbRc4uwFduFyApxaQyh68xR11A7-S4yxSeDLd7AIk 2406
nnunetv2/dataset_conversion/Dataset120_RoadSegmentation.py sha256=o2nNvmzcuVw1BkuFxEbBzV3cfmiRsJZUjSy1Ul_DXQc 3430
nnunetv2/dataset_conversion/Dataset137_BraTS21.py sha256=zQeiEWTkWF2XSvtWQsYKCqM63to6ldEgqmboAMRA23M 3994
nnunetv2/dataset_conversion/Dataset218_Amos2022_task1.py sha256=EF9X9HgIpnxXfQOj0qG-Wg_YXzbWdl7ZZzVovEl2nc8 3742
nnunetv2/dataset_conversion/Dataset219_Amos2022_task2.py sha256=EHS2JC34HdY8ogfwiOCjdS4i7rJaUqv2rgn4CLZ-jlo 3555
nnunetv2/dataset_conversion/Dataset220_KiTS2023.py sha256=kg0SM0cBt2yAWaaZ2Vy8ywnd3ntE6DBwZj_56Xko4E4 2085
nnunetv2/dataset_conversion/Dataset221_AutoPETII_2023.py sha256=rbJGViM6SsAcCnOYB2wsjfQwgraeH6yxwczpI0dq_-U 3167
nnunetv2/dataset_conversion/Dataset988_dummyDataset4.py sha256=tqL75R2QB9CRfKxM5tqH3QhQeYBSqUeY2MgjNci-_hE 1369
nnunetv2/dataset_conversion/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/dataset_conversion/convert_MSD_dataset.py sha256=RJu-3HT7GEAQ9Ua6hYe859Tol1fiKgACHhbHivHTfS8 6074
nnunetv2/dataset_conversion/convert_raw_dataset_from_old_nnunet_format.py sha256=oAfcDPbT44Pp-8CtMA56C0XOlXEayWnsBvhHaZuxzaY 2930
nnunetv2/dataset_conversion/generate_dataset_json.py sha256=ok_aPol_1uAQ2hdYw6ELvXCuV-Gh-bsIqOmx664d0ec 4045
nnunetv2/dataset_conversion/datasets_for_integration_tests/Dataset996_IntegrationTest_Hippocampus_regions_ignore.py sha256=u9RgWyTZe6Sa_qw3ZHF4Mwu-1SRQvMUC-iaTJrjJ5-o 3250
nnunetv2/dataset_conversion/datasets_for_integration_tests/Dataset997_IntegrationTest_Hippocampus_regions.py sha256=D9wumdg1sz2NsfiZKhPWBhmlh80vGzIw8f6i0OR48No 1468
nnunetv2/dataset_conversion/datasets_for_integration_tests/Dataset998_IntegrationTest_Hippocampus_ignore.py sha256=r496AYmuBpoRsKk_3I4Gz_cJEcxHiWgYwEPWPDuUhEk 1364
nnunetv2/dataset_conversion/datasets_for_integration_tests/Dataset999_IntegrationTest_Hippocampus.py sha256=QlrTV31YplkmDJu-9VWorhmmOLWQXe2x1vhjKirmVOU 1085
nnunetv2/dataset_conversion/datasets_for_integration_tests/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/ensembling/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/ensembling/ensemble.py sha256=Km6oJYenw5_7TCnUpnZZMYcaS5vHLS02S7HoNYAlExc 10027
nnunetv2/evaluation/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/evaluation/accumulate_cv_results.py sha256=uvomryk1Zz4XQqpQrPg3H9nggkD8f_azZvy1fxYOXng 3273
nnunetv2/evaluation/evaluate_predictions.py sha256=3BnkL0rMo9Buh5zxRXl-88gSBG0A24i4uvZhlKRHv3Q 12563
nnunetv2/evaluation/find_best_configuration.py sha256=c1u0CwCmmDivJvy9ZCqRUFHtutbrkzS9mi4ZEakhkMQ 18653
nnunetv2/experiment_planning/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/experiment_planning/plan_and_preprocess_api.py sha256=8OSHdh0P0fQqRRjnNV-WryrdXGjF6R0o_ag5xdZAev4 7894
nnunetv2/experiment_planning/plan_and_preprocess_entrypoints.py sha256=CfMDH0oNN1IBtq9FMoeAXJ_BSTgKwj9RtMKpNVR9V60 16463
nnunetv2/experiment_planning/verify_dataset_integrity.py sha256=0ZLkQcNGD0kSmvxIiYFlpGUzfjs3WLSeNNSY8Bj_tic 12227
nnunetv2/experiment_planning/dataset_fingerprint/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/experiment_planning/dataset_fingerprint/fingerprint_extractor.py sha256=QnKmzV5jzQFfyrreYr4dvuL7sJVUqofS_hPZxzYdzXw 11563
nnunetv2/experiment_planning/experiment_planners/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/experiment_planning/experiment_planners/default_experiment_planner.py sha256=g1GOSSSZIt0WGCTyFpQ6aXT5Miu1ts3-UBMW1wn5rmY 31121
nnunetv2/experiment_planning/experiment_planners/network_topology.py sha256=6rRPrMIvwMIRx7LALxiQVG5c01HzAozULnziCU5Mzyk 3796
nnunetv2/experiment_planning/experiment_planners/resencUNet_planner.py sha256=sroY-uuPzTiZ8zsWuHU1zYrt7bsiGQ5wf-Xlql4n5hA 3224
nnunetv2/experiment_planning/plans_for_pretraining/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/experiment_planning/plans_for_pretraining/move_plans_between_datasets.py sha256=TJKUJJRFqwVOBnDS8ohWpxWLR7U4c9sIg4LM1w_vkZE 4469
nnunetv2/imageio/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/imageio/base_reader_writer.py sha256=g9pjEwC0Sd9IwL2UDTYsNzXXx1bQHFw_LQgbsPxk9kw 5626
nnunetv2/imageio/natural_image_reager_writer.py sha256=YHTfSSI4TQQftncw5qOIz63ld9GwTCrX3G1sQAR-HLc 3344
nnunetv2/imageio/nibabel_reader_writer.py sha256=MaGK1H1cuV50PMByjyT8Ev7Mq5y4VEEyQpzibYF2zhM 8631
nnunetv2/imageio/reader_writer_registry.py sha256=NyMAMDoAWAZFN8gZLcNDpHKCh5RKVXbpfXxwv9rCQao 3865
nnunetv2/imageio/simpleitk_reader_writer.py sha256=18E_BSrJyBc66ySzMQUudbqq4uXklZMre7rGc0S_N4k 5661
nnunetv2/imageio/tif_reader_writer.py sha256=Dmic8muo5ZzcyzUcBzysV6c3eot03XVoT_nxXqOSsGo 4731
nnunetv2/inference/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/inference/data_iterators.py sha256=1_1hO2qkg6gqjOmt7cihP4MCCn6Q1NDEfjKsDCR5pi4 16204
nnunetv2/inference/examples.py sha256=RwCUXM2ZaQ0SEwD9ho4clv6w1_FfuZHPjHdTmUvc9Ns 6485
nnunetv2/inference/export_prediction.py sha256=p8ngYudX7jRGlC9ZPLt6s7jB8gffrugZc1lS7CICD2Y 8259
nnunetv2/inference/predict_from_raw_data.py sha256=fkNl8ZXGgZI84u9CDbNlRp5UVWbUqJoE5eTjhC2cS6Y 55750
nnunetv2/inference/sliding_window_prediction.py sha256=k0wQRDoM53v7_QkUxdfgF5wLa5o5ms1zW1uw5fv6rig 2928
nnunetv2/model_sharing/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/model_sharing/entry_points.py sha256=8gi8ftR7aj2uRXiKgVPIoI0VB5d9fc_tFSABQskxruA 3528
nnunetv2/model_sharing/model_download.py sha256=E8qwI0PMPHoc2sVHtw_n2dEkFsiR9tniKn1sejenjJo 1856
nnunetv2/model_sharing/model_export.py sha256=n7t1UKRPQJv5erg3Ww75JohS6cvPTo0a59GerU7lmkI 6830
nnunetv2/model_sharing/model_import.py sha256=R4VvUj4Kzcx5NRQtlNIs5LzIRj1sYfApn1iLETLy0fo 202
nnunetv2/postprocessing/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/postprocessing/remove_connected_components.py sha256=C8Zmu6q77a_VR78rOJu9ZH7-zHcdvuoM2FL8ges5pOQ 20695
nnunetv2/preprocessing/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/preprocessing/cropping/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/preprocessing/cropping/cropping.py sha256=Hg7srx5awI1_4eIhibjYb3ogsGzvcrQf46pvIg6U5EQ 1511
nnunetv2/preprocessing/normalization/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/preprocessing/normalization/default_normalization_schemes.py sha256=In3y0AIReEEKzkey06-ha95voJCDkbFWnFk3piBN5b8 4086
nnunetv2/preprocessing/normalization/map_channel_name_to_normalization.py sha256=pT-KRxSiIbzr2LcTi8JKbtJ1N7Z7hIw8dOqa4c6BvRA 965
nnunetv2/preprocessing/preprocessors/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/preprocessing/preprocessors/default_preprocessor.py sha256=c3qmlY7-UXi-NWT3IieacTKVHPQgY9yj_AfVmi-Sda0 16203
nnunetv2/preprocessing/resampling/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/preprocessing/resampling/default_resampling.py sha256=_r1-sscQGPUeTOelYHK0BcUAP_3x_z0-0JKGHXPRlVc 9660
nnunetv2/preprocessing/resampling/utils.py sha256=f1zO3blKAolE9kB0r0HcIC0-y6VuXAjcfiAi4W7h0UE 713
nnunetv2/run/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/run/load_pretrained_weights.py sha256=n-aK0535-mVWtnJ_V2wLLEieqZsGSAvmU6KJRH-cx0E 3176
nnunetv2/run/run_training.py sha256=Tyjn0MhBwhWtV0_nXUBKKGvcUnQWc6SUzhyXdKrAcyg 13994
nnunetv2/tests/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/tests/integration_tests/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/tests/integration_tests/add_lowres_and_cascade.py sha256=xo0-ME5cvtw8PzvqEzq2ppiASOXVoNiIz88oR9_D00A 1550
nnunetv2/tests/integration_tests/cleanup_integration_test.py sha256=Xg2724mnphiD3PHwFHxS4RqrubeeVSVnFwzmo_8emmg 659
nnunetv2/tests/integration_tests/run_integration_test_bestconfig_inference.py sha256=rPv7p5qUMAPR1AsMFA8nkLF5SNysL0yzIqQ_JT2TYJ4 4244
nnunetv2/training/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/data_augmentation/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/data_augmentation/compute_initial_patch_size.py sha256=Taik-7TbW9WkKKWWeTwL1oXTYOyLJL1L50lf2lk1Lp8 1157
nnunetv2/training/data_augmentation/custom_transforms/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/data_augmentation/custom_transforms/cascade_transforms.py sha256=JG8FHFB0kvaGGuUFU1sT-h4hcvE-RAc2QhLfLI2K4uc 7310
nnunetv2/training/data_augmentation/custom_transforms/deep_supervision_donwsampling.py sha256=8vhbigkuT-4HscZ2PdRFZlkqGngKdM7CD36Hf7HUups 2601
nnunetv2/training/data_augmentation/custom_transforms/limited_length_multithreaded_augmenter.py sha256=0LkLIn_00alkDgIgMHSE1Le6t-brx0g9VEnRIKuV1oo 349
nnunetv2/training/data_augmentation/custom_transforms/manipulating_data_dict.py sha256=rEv65xaHTH7IKk0vuYnL9HIFSC4O21rC9kEFzM6Tdpc 326
nnunetv2/training/data_augmentation/custom_transforms/masking.py sha256=4KZI14da5EwX_7mvIAqw4dqyY8TylngHXbemgZokMDE 893
nnunetv2/training/data_augmentation/custom_transforms/region_based_training.py sha256=WxNxbnqqjkm4oyXaUtitNDsujozCoB1AptAFzp5uaRQ 1665
nnunetv2/training/data_augmentation/custom_transforms/transforms_for_dummy_2d.py sha256=Y0Q_fnaYHuTu1ZZJq4ts7wm93WPajZzKzl2DavuCUy8 2455
nnunetv2/training/dataloading/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/dataloading/base_data_loader.py sha256=6MotWAy-NoOtw_D61z2Se2HUZOc17_1j8HHcrj6XP_E 8320
nnunetv2/training/dataloading/data_loader_2d.py sha256=BRN0lAYkQAqqAnH2mM5XpqbEtLQTQR0EcGYbYT5GTmA 5638
nnunetv2/training/dataloading/data_loader_3d.py sha256=bqJjIO2y2jBebO6vIm8IZACJ5-E-P8Koak4aVOepVog 3147
nnunetv2/training/dataloading/nnunet_dataset.py sha256=H32IV9CU5-2AnM0mSR1CtJYBTfmqB--we5ziEqEAa6c 6510
nnunetv2/training/dataloading/utils.py sha256=O1fTR55UsxM_2t9myZHLKlgQ9QKqa6U8qEZZ-k5mZaY 2035
nnunetv2/training/logging/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/logging/nnunet_logger.py sha256=7XnavNAvqxn-k035wBwNQf6dQc2RDFBLkGHrxJ3ZY6I 4718
nnunetv2/training/loss/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/loss/compound_losses.py sha256=Rd7NuLwruoKWUdzzvGCUcBO_4TeaCyz6M7zMGvXUNQU 5988
nnunetv2/training/loss/deep_supervision.py sha256=7aP2Iuci_z0_p2p-ELOlE17QyQ5i114AHi0V_iBqU04 1507
nnunetv2/training/loss/dice.py sha256=lZBeLl6uz6Xr55avpapns2bxh7LbwpP7z2vkd6Ay5A4 6743
nnunetv2/training/loss/robust_ce_loss.py sha256=s388znWar2-PC1OebLrAfaA43nWDL2Uw5BX_LoV5P4U 1166
nnunetv2/training/lr_scheduler/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/lr_scheduler/polylr.py sha256=tY6y2DwPPGxyUD19ZYcOiIk3Mh47L8hiZukiNtIy6SM 803
nnunetv2/training/nnUNetTrainer/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py sha256=vLTQ41IQIN4pvAqxCbWp9gqd6R0szuY4f0gfvWr-1x8 67776
nnunetv2/training/nnUNetTrainer/variants/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/nnUNetTrainer/variants/benchmarking/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/nnUNetTrainer/variants/benchmarking/nnUNetTrainerBenchmark_5epochs.py sha256=pzsVo4A8EtB4oQZHEM2Itw-NEjMO8s97ZlXHstVpvNg 2858
nnunetv2/training/nnUNetTrainer/variants/benchmarking/nnUNetTrainerBenchmark_5epochs_noDataLoading.py sha256=W5rhVRVh93v_R8awrD4f7xmDUU1HAzR3LBDle8OnF5I 2361
nnunetv2/training/nnUNetTrainer/variants/data_augmentation/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainerDA5.py sha256=eHmZfxLqeiyglzQmmH2PX-0GbYucFLFwAB-rTqBe83Y 20092
nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainerDAOrd0.py sha256=tFejTgLoFZbXshH2R9S89WSYTOuAjMMIQH-uOL0NOMk 5951
nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainerNoDA.py sha256=JyrjoJ50IH3ZrLRjuQqlniIkbTLl4RUhnLn_yS3kbt8 2099
nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainerNoMirroring.py sha256=z1Ppld-XHOpU1KDEdxvuMC_jKaWGqohho0VwDGNucD4 1230
nnunetv2/training/nnUNetTrainer/variants/loss/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/nnUNetTrainer/variants/loss/nnUNetTrainerCELoss.py sha256=k9Mkt0lhaWzH7slKt9HQqFy0mCpqMZcsrTlKF6r3BLs 1594
nnunetv2/training/nnUNetTrainer/variants/loss/nnUNetTrainerDiceLoss.py sha256=2gbV5APZMKv5lY8lBEguol7aFnunPexQZUYJa1Y4F58 2897
nnunetv2/training/nnUNetTrainer/variants/loss/nnUNetTrainerTopkLoss.py sha256=4DKDb82FEeqO6anb8bCo8K4Xj2am2dkz67PH9Oq_1j0 3319
nnunetv2/training/nnUNetTrainer/variants/lr_schedule/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/nnUNetTrainer/variants/lr_schedule/nnUNetTrainerCosAnneal.py sha256=pg2zBiJgoL36qzbi9m7e0NqrB2a0o4TAqX2YMP4AfFI 517
nnunetv2/training/nnUNetTrainer/variants/network_architecture/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/nnUNetTrainer/variants/network_architecture/nnUNetTrainerBN.py sha256=VNunhwflELc6FIj9_MT2nzo4BtMfnvNabU_8uLpHAYA 3894
nnunetv2/training/nnUNetTrainer/variants/network_architecture/nnUNetTrainerNoDeepSupervision.py sha256=hqzXVJALd1CLOcqzHC29p8_mKtcSmZDnxSWQsCu_-O8 5642
nnunetv2/training/nnUNetTrainer/variants/optimizer/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/nnUNetTrainer/variants/optimizer/nnUNetTrainerAdam.py sha256=bWx-zM4hyjN1d1KyGjY3Is1oCT_bWqHUCsR6N2eGQLQ 2882
nnunetv2/training/nnUNetTrainer/variants/optimizer/nnUNetTrainerAdan.py sha256=u1YGo6WwePQuaJFWOByH2M5fCiShj4JzOO6AfuMLj5o 3259
nnunetv2/training/nnUNetTrainer/variants/sampling/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/nnUNetTrainer/variants/sampling/nnUNetTrainer_probabilisticOversampling.py sha256=9E3c37Lj-F7pUt23fCDM5aNi01MyM-W5YmpD-s3ei28 4623
nnunetv2/training/nnUNetTrainer/variants/training_length/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/training/nnUNetTrainer/variants/training_length/nnUNetTrainer_Xepochs.py sha256=KvCCs4wcXEq53Jf8MAEYCNPhGeZM5TaYz3NjUrSH8WI 3690
nnunetv2/training/nnUNetTrainer/variants/training_length/nnUNetTrainer_Xepochs_NoMirroring.py sha256=f5of9sA0FcV3pCUYfd90xF2F15O095uJUPyAcGQK5q4 3157
nnunetv2/utilities/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/utilities/collate_outputs.py sha256=9zY7zhsB5L5jtbWsIVUJ7mfV35hjrgrEM78tRXMijaA 906
nnunetv2/utilities/dataset_name_id_conversion.py sha256=LvYvAsMCfpU3D4HdHAoLC0UVtR77BfDRpotu6zZ-kf8 3925
nnunetv2/utilities/ddp_allgather.py sha256=sjwLfKwiPaKF3oBGGHMZbi5gwODdzndvhYzEsHdwWa8 1748
nnunetv2/utilities/default_n_proc_DA.py sha256=dfLAZhrIVFavhnzLpTqsM7wf3fuaC8O15u_ojodEA9w 1854
nnunetv2/utilities/file_path_utilities.py sha256=wdNQHqagWxfP3hZm5xTNDbvOx3EM6sQ0MEdMxmYXD0I 5545
nnunetv2/utilities/find_class_by_name.py sha256=e-Lg6fw23glvnalZpxRpVeIN0pik3M6PcVnHxe5lw8U 868
nnunetv2/utilities/get_network_from_plans.py sha256=I2QnWAZ-wbpOpEuwGCwoP94r83uu4MAGUUH3xvjpJcQ 3843
nnunetv2/utilities/helpers.py sha256=tZqbcaMouasJ_P0ttJA7YUUTC-bS2N7Vzvo8L7wF6Ds 543
nnunetv2/utilities/json_export.py sha256=OD9cHL60qfvyXkHnTMid4l8cE5HAjkvH-T57oF_bK7g 2375
nnunetv2/utilities/network_initialization.py sha256=Mqk4YQ_-kn5uyUNPqHYEKxVYfGyk2Em6CX49EbMuaME 509
nnunetv2/utilities/overlay_plots.py sha256=c47Sq5IZS1SDjCQ6ARaJTON5ql7SEqaYF68V92TvnBU 11953
nnunetv2/utilities/utils.py sha256=nC1UzKfZdPWW7xWYVmLu1izW5wieZo3fWSVwnuQ3-NQ 3139
nnunetv2/utilities/label_handling/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/utilities/label_handling/label_handling.py sha256=9uXhIl2tDdNqWFsMsVWr3VPvkG4H9eQyWLi_NA8PiKs 14925
nnunetv2/utilities/plans_handling/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
nnunetv2/utilities/plans_handling/plans_handler.py sha256=-yQv4V-qrTj4LS_qFhdP1-TG9t_p2jot-hBPCZ13GKg 12470
nnunetv2-2.2.2.dist-info/LICENSE sha256=fany3e2oNqiR9TcQRTAjCnsjGPYepMV7BePdRqmuJ_0 11427
nnunetv2-2.2.2.dist-info/METADATA sha256=dzJXCAf-Yc1UQyyKcyXi5CThvxeOe2wPfSQoT-RI7YI 24787
nnunetv2-2.2.2.dist-info/WHEEL sha256=2wepM1nk4DS4eFpYrW1TTqPcoGNfHhhO_i5m4cOimbo 92
nnunetv2-2.2.2.dist-info/entry_points.txt sha256=lqUDaGZPYtqMdIKwDMHLwG5bLoHOY1iKmMHxy2C_O5w 2244
nnunetv2-2.2.2.dist-info/top_level.txt sha256=aShFDgvUc6v2e9lDh4GJ7rhTMgqL3-lRanmxAKy3uUk 9
nnunetv2-2.2.2.dist-info/RECORD — —

top_level.txt

nnunetv2

entry_points.txt

[console_scripts]
nnUNetv2_accumulate_crossval_results = nnunetv2.evaluation.find_best_configuration:accumulate_crossval_results_entry_point
nnUNetv2_apply_postprocessing = nnunetv2.postprocessing.remove_connected_components:entry_point_apply_postprocessing
nnUNetv2_convert_MSD_dataset = nnunetv2.dataset_conversion.convert_MSD_dataset:entry_point
nnUNetv2_convert_old_nnUNet_dataset = nnunetv2.dataset_conversion.convert_raw_dataset_from_old_nnunet_format:convert_entry_point
nnUNetv2_determine_postprocessing = nnunetv2.postprocessing.remove_connected_components:entry_point_determine_postprocessing_folder
nnUNetv2_download_pretrained_model_by_url = nnunetv2.model_sharing.entry_points:download_by_url
nnUNetv2_ensemble = nnunetv2.ensembling.ensemble:entry_point_ensemble_folders
nnUNetv2_evaluate_folder = nnunetv2.evaluation.evaluate_predictions:evaluate_folder_entry_point
nnUNetv2_evaluate_simple = nnunetv2.evaluation.evaluate_predictions:evaluate_simple_entry_point
nnUNetv2_export_model_to_zip = nnunetv2.model_sharing.entry_points:export_pretrained_model_entry
nnUNetv2_extract_fingerprint = nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:extract_fingerprint_entry
nnUNetv2_find_best_configuration = nnunetv2.evaluation.find_best_configuration:find_best_configuration_entry_point
nnUNetv2_install_pretrained_model_from_zip = nnunetv2.model_sharing.entry_points:install_from_zip_entry_point
nnUNetv2_move_plans_between_datasets = nnunetv2.experiment_planning.plans_for_pretraining.move_plans_between_datasets:entry_point_move_plans_between_datasets
nnUNetv2_plan_and_preprocess = nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:plan_and_preprocess_entry
nnUNetv2_plan_experiment = nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:plan_experiment_entry
nnUNetv2_plot_overlay_pngs = nnunetv2.utilities.overlay_plots:entry_point_generate_overlay
nnUNetv2_predict = nnunetv2.inference.predict_from_raw_data:predict_entry_point
nnUNetv2_predict_from_modelfolder = nnunetv2.inference.predict_from_raw_data:predict_entry_point_modelfolder
nnUNetv2_preprocess = nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:preprocess_entry
nnUNetv2_train = nnunetv2.run.run_training:run_training_entry