57 lines
2.5 KiB
Markdown
57 lines
2.5 KiB
Markdown
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# Trajectron++: Multi-Agent Generative Trajectory Forecasting With Heterogeneous Data for Control #
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This repository contains the code for [Trajectron++: Multi-Agent Generative Trajectory Forecasting With Heterogeneous Data for Control](https://arxiv.org/abs/2001.03093) by Tim Salzmann\*, Boris Ivanovic\*, Punarjay Chakravarty, and Marco Pavone (\* denotes equal contribution).
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Specifically, this branch is for the Trajectron++ applied to the nuScenes autonomous driving dataset.
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## Installation ##
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### Note about Submodules ###
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When cloning this branch, make sure you clone the submodules as well, with the following command:
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```
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git clone --recurse-submodules <repository cloning URL>
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```
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Alternatively, you can clone the repository as normal and then load submodules later with:
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```
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git submodule init # Initializing our local configuration file
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git submodule update # Fetching all of the data from the submodules at the specified commits
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```
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### Environment Setup ###
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First, we'll create a conda environment to hold the dependencies.
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```
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conda create --name trajectron++ python=3.6 -y
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source activate trajectron++
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pip install -r requirements.txt
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```
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Then, since this project uses IPython notebooks, we'll install this conda environment as a kernel.
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```
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python -m ipykernel install --user --name trajectron++ --display-name "Python 3.6 (Trajectron++)"
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```
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Now, you can start a Jupyter session and view/run all the notebooks in `code/notebooks` with
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```
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jupyter notebook
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```
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When you're done, don't forget to deactivate the conda environment with
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```
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source deactivate
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```
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## Scripts ##
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Run any of these with a `-h` or `--help` flag to see all available command arguments.
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* `code/train.py` - Trains a new Trajectron++ model.
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* `code/notebooks/run_eval.bash` - Evaluates the performance of the Trajectron++. This script mainly collects evaluation data, which can then be visualized with `code/notebooks/NuScenes Quantitative.ipynb`.
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* `data/nuScenes/process_nuScenes.py` - Processes the nuScenes dataset into a format that the Trajectron++ can directly work with, following our internal structures for handling data (see `code/data` for more information).
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* `code/notebooks/NuScenes Qualitative.ipynb` - Visualizes the predictions that the Trajectron++ makes.
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## Datasets ##
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A sample of fully-processed scenes from the nuScenes dataset are available in this repository, in `data/processed`.
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If you want the *original* nuScenes dataset, you can find it here: [nuScenes Dataset](https://www.nuscenes.org/).
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