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Quick Start

This Quick Start contains installation instructions for the Open Dataset codebase. Refer to the Colab tutorial for a quick demo of the installation and data format.

System Requirements

  • g++ 5 or higher.
  • TensorFlow 1.14.0
  • TensorFlow 1.15.0 (the upcoming tf release is supported in r1.0-tf.1.15 branch)

The code has two main parts. One is a utility written in C++ to compute the evaluation metrics. The other part is a set of TensorFlow functions in Python to help with model training.

First, download the code and enter the base directory.

git clone https://github.com/waymo-research/waymo-open-dataset.git waymo-od
cd waymo-od
git checkout remotes/origin/r1.0

We use the Bazel build system. These commands should install it in most cases. Please see these instructions for other ways to install Bazel. We assume you have Python installed.

sudo apt-get install --assume-yes pkg-config zip g++ zlib1g-dev unzip python3 python3-pip
wget https://github.com/bazelbuild/bazel/releases/download/0.28.0/bazel-0.28.0-installer-linux-x86_64.sh
sudo bash ./bazel-0.28.0-installer-linux-x86_64.sh
sudo apt install build-essential

Configure .bazelrc. This command may output some warnings due to the differences between Python 2 and Python 3, which you may safely ignore.

./configure.sh

To delete previous bazel outputs and reset internal caches, run the following command:

bazel clean

Metrics Computation

The core metrics library is written in C++, so it can be wrapped in other languages or frameworks. It can compute detection metrics (mAP) and tracking metrics (MOTA). See more information about the metrics on the website.

We provide command line tools and TensorFlow ops to call the detection metrics library to compute detection metrics. We will provide a similar wrapper for tracking metrics library in the future.

Command Line Tool

This tool does not require TensorFlow.

Run the metrics-related tests to verify they work as expected.

bazel test waymo_open_dataset/metrics:all

This binary computes the metric values given a pair of prediction and ground truth files.

bazel build waymo_open_dataset/metrics/tools/compute_detection_metrics_main
bazel-bin/waymo_open_dataset/metrics/tools/compute_detection_metrics_main waymo_open_dataset/metrics/tools/fake_predictions.bin  waymo_open_dataset/metrics/tools/fake_ground_truths.bin

A TensorFlow Op

A TensorFlow op is defined at metrics/ops/metrics_ops.cc. We provide a Python wrapper of the op at metrics/ops/py_metrics_ops.py, and a tf.metrics like implementation of the op at metrics/python/detection_metrics.py.

Install NumPy and TensorFlow and reconfigure .bazelrc.

pip3 install numpy tensorflow
./configure.sh

We have configured our build system to work with TensorFlow 1.14.0. For a higher version, you might need to update the proto version in WORKSPACE to match your TensorFlow version.

Run TensorFlow metrics op related tests. They can serve as examples for usage.

bazel build waymo_open_dataset/metrics/ops/...
bazel test waymo_open_dataset/metrics/ops/...
bazel test waymo_open_dataset/metrics/python/...

Python Utilities

We provide a set of TensorFlow libraries in the utils directory to help with building models. Refer to the Colab tutorial for examples of their usage.

bazel test waymo_open_dataset/utils/...

Use pre-compiled pip/pip3 packages

We only pre-compiled the package for Python 2.7, 3.5, 3.6. If you need the lib for a different python version, follow steps in pip_pkg_scripts to build pip package on your own.

pip3 install upgrade --pip
pip3 install waymo-open-dataset==1.0.1 --user