- Linux (tested on Ubuntu 18.04)
- Python 3.7
- CUDA 10.2
Installation of dependencies is managed with anaconda. For reproducibility, the state of the development environment is captured in the environment.yml file. These dependencies can be installed into a dnbp-env conda environment using the following steps:
conda env create -f environment.yml
conda activate dnbp-env
pip install -e .Once the environment is installed and activated, all dependencies for this repository will be satisfied. Once finished with experiments, you can simply close your terminal or run conda deactivate to exit the conda environment. To remove the environment, run conda remove --name dnbp-env --all.
Alternatively, minimal setup without Anaconda is also possible. For example, by using the venv python module to create a virtual environment containing project dependencies. To setup the project in this fashion, run the following:
python3.7 -m venv .venv/dnbp-env
source .venv/dnbp-env/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
pip install -e .When finished with experiments, run deactivate to exit environment. To remove the environment, remove the .venv/dnbp-env/ directory.
Setup
-
Download dataset from 'pendulum.zip' google drive
-
Extract pendulum dataset to
./data/pendulum
Training
Train DNBP Model:
python experiments/pendulum_tracking/dnbp_train.py ./experiments/pendulum_tracking/dnbp_config_file.json
Train LSTM Model:
python experiments/pendulum_tracking/lstm_train.py ./experiments/pendulum_tracking/lstm_config_file.json
Evaluation
-
Train new models or download pre-trained models from 'pendulum_models.zip' google drive
-
If using pre-trained models, extract compressed .zip files to
./experiments/pendulum_tracking/dnbp_models/and./experiments/pendulum_tracking/lstm_models/ -
To plot pre-computed evaluation results, run:
python ./experiments/pendulum_tracking/helpers/plot_quantitative.py -
To re-test models and plot results, run:
./experiments/pendulum_tracking/run_evaluations.sh -
If using a newly trained model, you may need to adjust the hardcoded
epochvalues in the above evaluation files.
Setup
-
Download dataset from 'spider.zip' google drive
-
Extract spider dataset to
./data/spider
Training
Train DNBP Model:
python ./experiments/spider_tracking/dnbp_train.py ./experiments/spider_tracking/dnbp_config_file.json
Train LSTM Model:
python ./experiments/spider_tracking/lstm_train.py ./experiments/spider_tracking/lstm_config_file.json
Evaluation
-
Train new models or download pre-trained models from 'spider_models.zip' google drive
-
If using pre-trained models, extract compressed .zip files to
./experiments/spider_tracking/dnbp_models/and./experiments/spider_tracking/lstm_models/ -
To plot pre-computed evaluation results, run:
python ./experiments/spider_tracking/helpers/plot_quantitative.py -
To re-test models and plot results, run:
./experiments/spider_tracking/run_evaluations.sh -
If using a newly trained model, you may need to adjust the hardcoded
epochvalues in the above evaluation files.
Setup
-
Download dataset from F-PHAB authors (Garcia-Hernando et al.)
-
Extract F-PHAB dataset to
./data/hand_tracking/F-PHAB
To preprocess dataset (image cropping)
cd ./data/hand_tracking
python preprocess_hands.py
python preprocess_rgb_crops.py
cd ../..
Training
Train DNBP Model:
python ./experiments/hand_tracking/dnbp_train.py ./experiments/hand_tracking/dnbp_config_file.json
Evaluation
-
If using pre-trained models, extract compressed .zip files to
./experiments/hand_tracking/dnbp_models/ -
To plot pre-computed evaluation results, run:
python ./experiments/hand_tracking/helpers/plot_quantitative.py -
To re-test models and plot results, run:
./experiments/hand_tracking/run_evaluations.sh -
If using a newly trained model, you may need to adjust the hardcoded
epochvalues in the above evaluation files.
@article{opipari2021dnbp,
author = {Opipari, Anthony and Chen, Chao and Wang, Shoutian and Pavlasek, Jana and Desingh, Karthik and Jenkins, Odest Chadwicke},
title = {Differentiable Nonparametric Belief Propagation},
year = {2021},
journal={arXiv preprint arXiv:2101.05948}
}
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