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Fix copyright and guidelines test errors for remaining tutorials (#1182)
Fixes #1181 . ### Description - Update the guideline test to avoid counting line returns in non-text outputs (for example, html data) - Add copyright for all remaining scripts and clean replicates of old copyright info in the following notebooks - 2d_classification\mednist_tutorial.ipynb - 2d_registration\registration_mednist.ipynb - 3d_classification\densenet_training_array.ipynb - acceleration\multi_gpu_test.ipynb - acceleration\threadbuffer_performance.ipynb - acceleration\transform_speed.ipynb - modules\TorchIO_MONAI_PyTorch_Lightning.ipynb - modules\UNet_input_size_constrains.ipynb - multimodal\openi_multilabel_classification_transchex\transchex_openi_multilabel_classification.ipynb - experiment_management\spleen_segmentation_aim.ipynb - experiment_management\spleen_segmentation_mlflow.ipynb - Fix markdown cells for notebooks to match guidelines - experiment_management\bundle_integrate_mlflow.ipynb - modules\resample_benchmark.ipynb - Update the outputs in the following notebooks: - 2d_classification\mednist_tutorial.ipynb - 2d_registration\registration_mednist.ipynb - 3d_classification\densenet_training_array.ipynb ### Checks <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [x] Avoid including large-size files in the PR. - [x] Clean up long text outputs from code cells in the notebook. - [x] For security purposes, please check the contents and remove any sensitive info such as user names and private key. - [x] Ensure (1) hyperlinks and markdown anchors are working (2) use relative paths for tutorial repo files (3) put figure and graphs in the `./figure` folder - [ ] Notebook runs automatically `./runner.sh -t <path to .ipynb file>` Signed-off-by: Mingxin Zheng <[email protected]> Signed-off-by: Mingxin Zheng <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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‎2d_classification/mednist_tutorial.ipynb

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‎2d_registration/registration_mednist.ipynb

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‎2d_segmentation/torch/unet_evaluation_array.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎2d_segmentation/torch/unet_evaluation_dict.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎2d_segmentation/torch/unet_training_array.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎2d_segmentation/torch/unet_training_dict.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎3d_classification/densenet_training_array.ipynb

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‎3d_classification/ignite/densenet_evaluation_array.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎3d_classification/ignite/densenet_evaluation_dict.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎3d_classification/ignite/densenet_training_array.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎3d_classification/ignite/densenet_training_dict.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎3d_classification/torch/densenet_evaluation_array.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎3d_classification/torch/densenet_evaluation_dict.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎3d_classification/torch/densenet_training_array.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎3d_classification/torch/densenet_training_dict.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎acceleration/distributed_training/unet_evaluation_ddp.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎acceleration/distributed_training/unet_evaluation_horovod.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎acceleration/distributed_training/unet_evaluation_workflows.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎acceleration/distributed_training/unet_training_ddp.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎acceleration/distributed_training/unet_training_horovod.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎acceleration/distributed_training/unet_training_smartcache.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎acceleration/distributed_training/unet_training_workflows.py

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# Copyright 2020 MONAI Consortium
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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at

‎acceleration/multi_gpu_test.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"Copyright (c) MONAI Consortium \n",
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"Licensed under the Apache License, Version 2.0 (the \"License\"); \n",
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"you may not use this file except in compliance with the License. \n",
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"You may obtain a copy of the License at \n",
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"&nbsp;&nbsp;&nbsp;&nbsp;http://www.apache.org/licenses/LICENSE-2.0 \n",
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"Unless required by applicable law or agreed to in writing, software \n",
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"distributed under the License is distributed on an \"AS IS\" BASIS, \n",
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"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n",
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"See the License for the specific language governing permissions and \n",
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"limitations under the License.\n",
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"\n",
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"# Multi GPU Test\n",
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"\n",
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/acceleration/multi_gpu_test.ipynb)"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"!python -c \"import monai\" || pip install -q \"monai-weekly[ignite]\""
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"cell_type": "code",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"MONAI version: 0.4.0\n",
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"Numpy version: 1.19.1\n",
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"Pytorch version: 1.7.0a0+7036e91\n",
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"MONAI flags: HAS_EXT = False, USE_COMPILED = False\n",
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"MONAI rev id: 0563a4467fa602feca92d91c7f47261868d171a1\n",
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"MONAI version: 1.1.0+2.g97918e46\n",
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"Numpy version: 1.22.2\n",
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"Pytorch version: 1.13.0a0+d0d6b1f\n",
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"MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
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"MONAI rev id: 97918e46e0d2700c050e678d72e3edb35afbd737\n",
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"MONAI __file__: /workspace/monai/monai-in-dev/monai/__init__.py\n",
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"\n",
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"Optional dependencies:\n",
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"Pytorch Ignite version: 0.4.2\n",
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"Nibabel version: 3.2.1\n",
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"scikit-image version: 0.15.0\n",
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"Pillow version: 8.0.1\n",
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"Tensorboard version: 2.2.0\n",
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"gdown version: 3.12.2\n",
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"TorchVision version: 0.8.0a0\n",
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"ITK version: 5.1.2\n",
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"tqdm version: 4.54.1\n",
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"lmdb version: 1.0.0\n",
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"psutil version: 5.7.2\n",
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"Pytorch Ignite version: 0.4.10\n",
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"Nibabel version: 4.0.2\n",
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"scikit-image version: 0.19.3\n",
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"Pillow version: 9.0.1\n",
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"Tensorboard version: 2.10.1\n",
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"gdown version: 4.6.0\n",
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"TorchVision version: 0.14.0a0\n",
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"tqdm version: 4.64.1\n",
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"lmdb version: 1.3.0\n",
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"psutil version: 5.9.2\n",
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"pandas version: 1.4.4\n",
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"einops version: 0.6.0\n",
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"transformers version: 4.21.3\n",
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"mlflow version: 2.0.1\n",
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"pynrrd version: 1.0.0\n",
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"\n",
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"For details about installing the optional dependencies, please visit:\n",
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" https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
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"# Copyright 2020 MONAI Consortium\n",
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"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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"# you may not use this file except in compliance with the License.\n",
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"# You may obtain a copy of the License at\n",
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"# http://www.apache.org/licenses/LICENSE-2.0\n",
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"# Unless required by applicable law or agreed to in writing, software\n",
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"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
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"# See the License for the specific language governing permissions and\n",
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"# limitations under the License.\n",
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"\n",
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"from monai.config import print_config\n",
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"cell_type": "code",
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"name": "stdout",
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"text": [
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"2023-01-20 14:16:00,009 - Engine run starting with max_epochs=2.\n",
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"2023-01-20 14:16:00,189 - Epoch[1] Complete. Time taken: 00:00:00.180\n",
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"2023-01-20 14:16:00,201 - Epoch[2] Complete. Time taken: 00:00:00.011\n",
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"2023-01-20 14:16:00,201 - Engine run complete. Time taken: 00:00:00.192\n"
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"\toutput: 23339.560546875\n",
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"text": [
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"2023-01-20 14:16:00,208 - Engine run starting with max_epochs=2.\n",
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"2023-01-20 14:16:01,364 - Epoch[1] Complete. Time taken: 00:00:01.154\n",
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"2023-01-20 14:16:01,391 - Epoch[2] Complete. Time taken: 00:00:00.026\n",
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"2023-01-20 14:16:01,391 - Engine run complete. Time taken: 00:00:01.181\n"
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"2023-01-20 14:16:01,402 - Engine run starting with max_epochs=2.\n",
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"2023-01-20 14:16:01,475 - Epoch[1] Complete. Time taken: 00:00:00.073\n",
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"2023-01-20 14:16:01,575 - Epoch[2] Complete. Time taken: 00:00:00.100\n",
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"2023-01-20 14:16:01,576 - Engine run complete. Time taken: 00:00:00.174\n"
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"version": "3.8.13"
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"nbformat": 4,

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