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Advanced Installation

We provide step-by-step instructions to install our devkit. These instructions are based on the nuscenes-devkit installation instructions.

Download

Download the devkit to your home directory using:

cd && git clone https://github.com/tudelft-iv/vod-devkit.git

Install Python

The devkit is tested for Python 3.8 onwards, but we recommend to use Python 3.8. For Ubuntu: If the right Python version is not already installed on your system, install it by running:

sudo apt install python-pip
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt-get update
sudo apt-get install python3.8
sudo apt-get install python3.8-dev

For Mac OS download and install from https://www.python.org/downloads/mac-osx/.

Setup a Conda environment

Next we setup a Conda environment. An alternative to Conda is to use virtualenvwrapper, as described below.

Install miniconda

See the official Miniconda page.

Setup a Conda environment

We create a new Conda environment named vod-devkit.

conda create --name vod-devkit python=3.8

Activate the environment

If you are inside the virtual environment, your shell prompt should look like: (vod-devkit) user@computer:~$ If that is not the case, you can enable the virtual environment using:

conda activate vod-devkit

To deactivate the virtual environment, use:

source deactivate

Setup a virtualenvwrapper environment

Another option for setting up a new virtual environment is to use virtualenvwrapper. Skip these steps if you have already setup a Conda environment. Follow these instructions to setup your environment.

Install virtualenvwrapper

To install virtualenvwrapper, run:

pip install virtualenvwrapper

Add the following two lines to ~/.bashrc (~/.bash_profile on MAC OS) to set the location where the virtual environments should live and the location of the script installed with this package:

export WORKON_HOME=$HOME/.virtualenvs
source [VIRTUAL_ENV_LOCATION]

Replace [VIRTUAL_ENV_LOCATION] with either /usr/local/bin/virtualenvwrapper.sh or ~/.local/bin/virtualenvwrapper.sh depending on where it is installed on your system. After editing it, reload the shell startup file by running e.g. source ~/.bashrc.

Note: If you are facing dependency issues with the PIP package, you can also install the devkit as a Conda package. For more details, see this nuscenes-devkit issue.

Create the virtual environment

We create a new virtual environment named vod-devkit.

mkvirtualenv vod-devkit --python=python3.8

Activate the virtual environment

If you are inside the virtual environment, your shell prompt should look like: (vod-devkit) user@computer:~$ If that is not the case, you can enable the virtual environment using:

workon vod-devkit

To deactivate the virtual environment, use:

deactivate

Setup PYTHONPATH

Add the src directory to your PYTHONPATH environmental variable, by adding the following to your ~/.bashrc (for virtualenvwrapper, you could alternatively add it in ~/.virtualenvs/vod/bin/postactivate):

export PYTHONPATH="${PYTHONPATH}:$HOME/vod-devkit/src"

Install required packages

To install the required packages, run the following command in your favourite virtual environment:

pip install -r setup/requirements.txt

Note: Unlike the nuscenes-devkit, all of the requirements (base, prediction, tracking) are installed by default. Comment out the appropriate lines in the setup/requirements.txt file to only install a subset of the requirements, or install only the specific requirements as follows (e.g. for prediction):

pip install -r setup/requirements/requirements_prediction.txt

Setup environment variable

Finally, if you want to run the unit tests you need to point the devkit to the vod dataset folder on your disk. Set the VOD environment variable to point to your data folder:

export VOD="/data/sets/vod"

Setup Matplotlib backend

When using Matplotlib, it is generally recommended to define the backend used for rendering:

  1. Under Ubuntu the default backend Agg results in any plot not being rendered by default. This does not apply inside Jupyter notebooks.
  2. Under MacOSX a call to plt.plot() may fail with the following error (see here for more details): 
    libc++abi.dylib: terminating with uncaught exception of type NSException

To set the backend, add the following to your ~/.matplotlib/matplotlibrc file, which needs to be created if it does not exist yet:

backend: TKAgg

Verify install

To verify your environment run python -m unittest in the src folder (TODO make test work; this command will result in failed tests as-is).

You can also run assert_download.py in the src/vod/tests folders to verify that all files are in the right place.

That's it, you should be good to go!