Pose estimation models implemented in Pytorch Lightning, supporting massively accelerated training on unlabeled videos using NVIDIA DALI. The whole thing is orchestrated by Hydra. Models can be diagnosed with TensorBoard, FiftyOne, and Streamlit.

Preprint: Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools

Try our demo!

Train a network on a toy dataset and visualize the results in Google Colab.

Community

Lightning Pose is primarily maintained by Dan Biderman (Columbia University) and Matt Whiteway (Columbia University). Come chat with us in Discord.

Requirements

Your (potentially remote) machine has a Linux operating system, at least one GPU and CUDA 11.0-12.x installed. This is a requirement for NVIDIA DALI.

Installation

First create a Conda environment in which this package and its dependencies will be installed. As you would do for any other repository --

Create a conda environment:

foo@bar:~$ conda create --name <YOUR_ENVIRONMENT_NAME> python=3.8

and activate it:

foo@bar:~$ conda activate <YOUR_ENVIRONMENT_NAME>

Move into the folder where you want to place the repository folder, and then download it from GitHub:

foo@bar:~$ cd <SOME_FOLDER>
foo@bar:~$ git clone https://github.com/danbider/lightning-pose.git

Then move into the newly-created repository folder, and install dependencies:

foo@bar:~$ cd lightning-pose
foo@bar:~$ pip install -e .

This installation might take between 3-10 minutes, depending on your machine and internet connection.

Now you should be ready to go! You may verify that all the unit tests are passing on your machine by running

foo@bar:~$ pytest

Docker users

Use the appropriate Dockerfiles in this directory to build a Docker image:

docker build -f Dockerfile.cuda11 -t my-image:cuda11 .

docker build -f Dockerfile.cuda12 -t my-image:cuda12 .

Run code inside a container (following this tutorial):

docker run -it --rm --gpus all my-image:cuda11

docker run -it --rm --gpus all --shm-size 256m my-image:cuda12

For a g4dn.xlarge AWS EC2 instance adding the flag --shm-size=256m will provide the necessary memory to execute. The '--gpus all' flag is necessary to allow Docker to access the required drivers for Nvidia DALI to work properly.

Working with hydra

For all of the scripts in our scripts folder, we rely on hydra to manage arguments in config files. You have two options: directly edit the config file, or override it from the command line.

• Edit the hydra config, that is, any of the parameters in scripts/configs/config.yaml, and save it. Then run the script without arguments, e.g.,:

foo@bar:~$ python scripts/train_hydra.py

• Override the argument from the command line; for example, if you want to use a maximum of 11 epochs instead of the default number (not recommended):

foo@bar:~$ python scripts/train_hydra.py training.max_epochs=11

See more documentation on the arguments here.

Training

foo@bar:~$ python scripts/train_hydra.py

In case your config file isn't located in lightning-pose/scripts/configs, which is common if you have multiple projects, run:

foo@bar:~$ python scripts/train_hydra.py \
  --config-path="<PATH/TO/YOUR/CONFIGS/DIR>" \
  --config-name="<CONFIG_NAME.yaml>"

Logs and saved models

The outputs of the training script, namely the model checkpoints and Tensorboard logs, will be saved at the lightning-pose/outputs/YYYY-MM-DD/HH-MM-SS/tb_logs directory. (Note: this behavior can be changed by updating hydra.run.dir in the config yaml to an absolute path of your choosing.)

To view the logged losses with tensorboard in your browser, in the command line, run:

foo@bar:~$ tensorboard --logdir outputs/YYYY-MM-DD/

where you use the date in which you ran the model. Click on the provided link in the terminal, which will look something like http://localhost:6006/. Note that if you save the model at a different directory, just use that directory after --logdir.

Predict keypoints on new videos

With a trained model and a path to a new video, you can generate predictions for each frame and save it as a .csv or .h5 file. To do so for the example dataset, run:

foo@bar:~$ python scripts/predict_new_vids.py eval.hydra_paths=["YYYY-MM-DD/HH-MM-SS/"]

using the same hydra path as before.

In order to use this script more generally, you need to specify several paths:

1. eval.hydra_paths: path to models to use for prediction
2. eval.test_videos_directory: path to a folder with new videos (not a single video)
3. eval.saved_vid_preds_dir: optional path specifying where to save prediction csv files. If null, the predictions will be saved in eval.test_videos_directory.

As above, you could directly edit scripts/configs/config.yaml and run

foo@bar:~$ python scripts/predict_new_vids.py

or override these arguments in the command line.

foo@bar:~$ python scripts/predict_new_vids.py eval.hydra_paths=["2022-01-18/01-03-45"] \
eval.test_videos_directory="/absolute/path/to/unlabeled_videos" \
eval.saved_vid_preds_dir="/absolute/path/to/dir"

Diagnostics

Beyond providing access to loss values throughout training with Tensorboard, the Lightning Pose package also offers several diagnostic tools to compare the performance of trained models on labeled frames and unlabeled videos.

Fiftyone

This component provides tools for visualizing the predictions of one or more trained models on labeled frames or on test videos.

See the documentation here.

Streamlit

This component provides tools for quantifying model performance across a range of metrics for both labeled frames and unlabeled videos:

• Pixel error (labeled data only)
• Temporal norm (unlabeled data only)
• Pose PCA error (if data.columns_for_singleview_pca is not null in the config file)
• Multi-view consistency error (if data.mirrored_column_matches is not null in the config file)

See the documentation here.