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Merge pull request #2613 from DeepLabCut/pytorch_dlc
🔥 New main backend Deep Learning Library (Engine) for DeepLabCut: PyTorch. Voilà.
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‎.circleci/config.yml‎

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build-and-test:
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working_directory: ~/circleci-demo-python-django
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docker:
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- image: circleci/python:3.8 # primary container for the build job
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- image: circleci/python:3.10 # primary container for the build job
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auth:
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username: mydockerhub-user
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password: $DOCKERHUB_PASSWORD # context / project UI env-var reference

‎.github/workflows/publish-book.yml‎

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steps:
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- uses: actions/checkout@v4
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- name: Set up Python 3.9
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- name: Set up Python 3.10
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uses: actions/setup-python@v4
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with:
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python-version: 3.9
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python-version: 3.10
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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python -m pip install .[tf,docs]
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python -m pip install .[docs]
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pip install jupyter-book sphinxcontrib-mermaid
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- name: Build the book

‎.github/workflows/python-package.yml‎

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pip install git+https://github.com/${{ github.repository }}.git@${{ github.sha }}
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python examples/testscript.py
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python examples/testscript_multianimal.py
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python examples/testscript_pytorch_single_animal.py
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python examples/testscript_pytorch_multi_animal.py

‎.gitignore‎

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/examples/3D*
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/examples/m3*
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/examples/OUT
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/examples/pretrained*
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.local
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.DS_Store
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examples/.DS_Store
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*.ckpt
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snapshot-*
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# Modelzoo checkpoints
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deeplabcut/modelzoo/checkpoints/
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# PyTorch backbone weights
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deeplabcut/pose_estimation_pytorch/models/backbones/pretrained_weights/
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# Wandb files
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wandb/
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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# Spyder project settings
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.spyderproject
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.spyproject
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# IDEs configurations
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.vscode/*
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.idea/*
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# Rope project settings
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.ropeproject

‎.pre-commit-config.yaml‎

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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.4.0
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hooks:
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- id: check-added-large-files
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- id: check-yaml
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- id: end-of-file-fixer
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- id: name-tests-test
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- id: trailing-whitespace
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- repo: https://github.com/PyCQA/isort
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rev: 5.12.0
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hooks:
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- id: isort
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- repo: https://github.com/psf/black
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rev: 22.3.0
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hooks:
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- id: black
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language_version: python3

‎NOTICE.yml‎

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https://github.com/DeepLabCut/DeepLabCut
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Please see AUTHORS for contributors.
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https://github.com/DeepLabCut/DeepLabCut/blob/master/AUTHORS
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https://github.com/DeepLabCut/DeepLabCut/blob/main/AUTHORS
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Licensed under GNU Lesser General Public License v3.0
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include:
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# License for files adapted from DeeperCut by Eldar Insafutdinov
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# https://github.com/eldar/pose-tensorflow
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# Applies to most files in deeplabcut.pose_estimation_tensorflow
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- header: |
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DeepLabCut Toolbox (deeplabcut.org)
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include:
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- deeplabcut/pose_tracking_pytorch/solver/scheduler_factory.py
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- deeplabcut/pose_tracking_pytorch/model/backones/vit_pytorch.py
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# PyTorch license
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- header: |
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See https://github.com/pytorch/pytorch/blob/main/LICENSE

‎README.md‎

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Please click the link above for all the information you need to get started! Please note that currently we support only Python 3.10+ (see conda files for guidance).
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Developers Stable Release:
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- Very quick start: You need to have TensorFlow installed (up to v2.10 supported across platforms) `pip install "deeplabcut[gui,tf]"` that includes all functions plus GUIs, or `pip install deeplabcut[tf]` (headless version with PyTorch and TensorFlow).
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Developers Alpha Release:
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- We also have an alpha release of PyTorch DeepLabCut available! [Please see here for instructions and information](https://github.com/DeepLabCut/DeepLabCut/blob/pytorch_docs/docs/pytorch/user_guide.md).
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Developers Stable Release: very quick start (Python 3.10+ required) to install
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DeepLabCut with the PyTorch engine
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- [Install PyTorch](https://pytorch.org/get-started/locally/) (**select the desired
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CUDA version if you want to use a GPU**): `pip install torch torchvision`
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- Then, [install `pytables`](https://www.pytables.org/usersguide/installation.html): `conda install -c conda-forge pytables==3.8.0`
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- Finally, install `DeepLabCut` (with all functions + the GUI):
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`pip install --pre "deeplabcut[gui]"` or `pip install --pre "deeplabcut"` (headless
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version with PyTorch)!
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To use the TensorFlow engine (requires Python 3.10; TF up to v2.10 supported on Windows,
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up to v2.12 on other platforms): you'll need to run `pip install "deeplabcut[gui,tf]"`
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(which includes all functions plus GUIs) or `pip install "deeplabcut[tf]"` (headless
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version with PyTorch and TensorFlow).
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We recommend using our conda file, see [here](https://github.com/DeepLabCut/DeepLabCut/blob/main/conda-environments/README.md) or the new [`deeplabcut-docker` package](https://github.com/DeepLabCut/DeepLabCut/tree/main/docker).
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# [Documentation: The DeepLabCut Process](https://deeplabcut.github.io/DeepLabCut)
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# [Documentation: The DeepLabCut Process](https://deeplabcut.github.io/DeepLabCut/README.html)
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Our docs walk you through using DeepLabCut, and key API points. For an overview of the toolbox and workflow for project management, see our step-by-step at [Nature Protocols paper](https://doi.org/10.1038/s41596-019-0176-0).
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🐭 pose tracking of single animals demo [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DeepLabCut/DeepLabCut/blob/master/examples/COLAB/COLAB_DEMO_mouse_openfield.ipynb)
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🐭🐭🐭 pose tracking of multiple animals demo [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DeepLabCut/DeepLabCut/blob/master/examples/COLAB/COLAB_3miceDemo.ipynb)
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- See [more demos here](https://github.com/DeepLabCut/DeepLabCut/blob/main/examples/README.md). We provide data and several Jupyter Notebooks: one that walks you through a demo dataset to test your installation, and another Notebook to run DeepLabCut from the beginning on your own data. We also show you how to use the code in Docker, and on Google Colab.
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See [more demos here](https://github.com/DeepLabCut/DeepLabCut/blob/main/examples/README.md). We provide data and several Jupyter Notebooks: one that walks you through a demo dataset to test your installation, and another Notebook to run DeepLabCut from the beginning on your own data. We also show you how to use the code in Docker, and on Google Colab.
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# Why use DeepLabCut?
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‎_config.yml‎

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autodoc_mock_imports: ["wx"]
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autodoc_mock_imports: ["wx", "matplotlib", "qtpy", "PySide6", "napari", "shiboken6"]
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mermaid_output_format: raw
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extra_extensions:
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- numpydoc

‎_toc.yml‎

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- file: docs/ModelZoo
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- file: docs/recipes/MegaDetectorDLCLive
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- caption: 🧑‍🍳 Cookbook (detailed helper guides)
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- file: docs/tutorial
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- file: docs/convert_maDLC
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- file: docs/recipes/OtherData
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- file: docs/recipes/io

‎conda-environments/DEEPLABCUT.yaml‎

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#Licensed under GNU Lesser General Public License v3.0
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#
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# DeepLabCut environment
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# FIRST: INSTALL CORRECT DRIVER for GPU, see https://stackoverflow.com/questions/30820513/what-is-the-correct-version-of-cuda-for-my-nvidia-driver/30820690
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#
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# AFTER THIS FILE IS INSTALLED, if you have a GPU be sure to install cudnn from conda-forge: conda install cudnn -c conda-forge
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# FIRST: If you have an NVIDIA GPU and want to use it, check that you have drivers installed!
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# To check if your GPUs are visible to PyTorch (and thus DeepLabCut), run:
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# >>> python -c "import torch; print(torch.cuda.is_available())"
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#
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# If "False" is printed, PyTorch (and thus DeepLabCut) cannot access your GPU. For
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# more information, see: https://pytorch.org/get-started/locally/
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#
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# update: conda env update -f DEEPLABCUT.yaml
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- torch
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- torchvision
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- "git+https://github.com/DeepLabCut/DeepLabCut.git@pytorch_dlc#egg=deeplabcut[gui,modelzoo,wandb]"

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