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Welcome to InferBiomechanics

This is a small repository with example scripts, code and baselines to train and evaluate models that can infer physics information from pure motion, trained and evaluated on AddBiomechanics data.

Getting The Data

If you are running on the Sherlock cluster, the latest data is already loaded at /home/groups/delp/data.

For local training runs, the latest dataset is available in a Google Drive folder, here.

Please download the data, and place it into the data folder in this repository. When completed, there should be a data/train folder, and a data/dev folder, each with many *.b3d files in them.

Running the Code

First, run pip3 install -r requirements.txt

There are several tasks you might want to run, all of which can be accessed from the command line entrypoint, src/cli/main.py. To run main.py, you'll need to be in the src directory.

Training a Model

To generate model snapshots, run this command:

python3 main.py train ...

We use Weights and Biases to track model training, so you'll need to create an account there, and either:

  1. set the WANDB_API_KEY environment variable to your API key.
  2. run wandb login from the command line, and follow the instructions.

Once that's set up, your runs will automatically log to your account, and you can see them in the web interface. By default, the runs log to a shared academic project, shpd1.

Visualizing a Model

It's often helpful to be able to see how a model is screwing up, and what might be strange in the data.

This command will automatically load the selected model type from the latest checkpoint, run the loaded model on the training set, and visualize the results in the browser:

python3 main.py visualize ...

ANALYTICAL BASELINE: To visualize the results of running an analytical baseline, run with the --model analytical flag.

Evaluating a Model

To get performance numbers for a given model on the whole dataset, even if it hasn't finished training, run:

python3 main.py analyze ...

This will automatically pick up the latest model checkpoint file, and run it on the whole dataset, and print out the results.

ANALYTICAL BASELINE: To evaluate the results of running an analytical baseline on the whole dataset, run with the --model analytical flag.