A multi-entity Transformer for multi-agent spatiotemporal modeling.

Last update: Nov 15, 2022

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Overview

`baller2vec`

This is the repository for the paper:

Michael A. Alcorn and Anh Nguyen. baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling. arXiv. 2021.


Left: the input for `baller2vec` at each time step t is an unordered set of feature vectors containing information about the identities and locations of NBA players on the court. Right: `baller2vec` generalizes the standard Transformer to the multi-entity setting by employing a novel self-attention mask tensor. The mask is then reshaped into a matrix for compatibility with typical Transformer implementations.

Left: the input for baller2vec at each time step t is an unordered set of feature vectors containing information about the identities and locations of NBA players on the court. Right: baller2vec generalizes the standard Transformer to the multi-entity setting by employing a novel self-attention mask tensor. The mask is then reshaped into a matrix for compatibility with typical Transformer implementations.


By exclusively learning to predict the trajectory of the ball, `baller2vec` was able to infer idiosyncratic player attributes.


Further, nearest neighbors in `baller2vec`'s embedding space are plausible doppelgängers. Credit for the images: Erik Drost, Keith Allison, Jose Garcia, Keith Allison, Verse Photography, and Joe Glorioso.


Additionally, several attention heads in `baller2vec` appear to perform different basketball-relevant functions, such as anticipating passes. Code to generate the GIF was adapted from @linouk23's NBA Player Movement's repository.


Here, a `baller2vec` model trained to simultaneously predict the trajectories of all the players on the court uses both the historical and current context to forecast the target player's trajectory at each time step. The left grid shows the target player's true trajectory at each time step while the right grid shows `baller2vec`'s forecast distribution. The blue-bordered center cell is the "stationary" trajectory.

Here, a baller2vec model trained to simultaneously predict the trajectories of all the players on the court uses both the historical and current context to forecast the target player's trajectory at each time step. The left grid shows the target player's true trajectory at each time step while the right grid shows baller2vec's forecast distribution. The blue-bordered center cell is the "stationary" trajectory.

Citation

If you use this code for your own research, please cite:

@article{alcorn2021baller2vec,
   title={baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling},
   author={Alcorn, Michael A. and Nguyen, Anh},
   journal={arXiv preprint arXiv:1609.03675},
   year={2021}
}

Training `baller2vec`

Setting up `.basketball_profile`

After you've cloned the repository to your desired location, create a file called .basketball_profile in your home directory:

nano ~/.basketball_profile

and copy and paste in the contents of .basketball_profile, replacing each of the variable values with paths relevant to your environment. Next, add the following line to the end of your ~/.bashrc:

source ~/.basketball_profile

and either log out and log back in again or run:

source ~/.bashrc

You should now be able to copy and paste all of the commands in the various instructions sections. For example:

echo ${PROJECT_DIR}

should print the path you set for PROJECT_DIR in .basketball_profile.

Installing the necessary Python packages

cd ${PROJECT_DIR}
pip3 install --upgrade -r requirements.txt

Organizing the play-by-play and tracking data

Copy events.zip (which I acquired from here [mirror here] using https://downgit.github.io) to the DATA_DIR directory and unzip it:

mkdir -p ${DATA_DIR}
cp ${PROJECT_DIR}/events.zip ${DATA_DIR}
cd ${DATA_DIR}
unzip -q events.zip
rm events.zip

Descriptions for the various EVENTMSGTYPEs can be found here (mirror here).

Clone the tracking data from here (mirror here) to the DATA_DIR directory:

cd ${DATA_DIR}
git clone [email protected]:linouk23/NBA-Player-Movements.git

A description of the tracking data can be found here.

Generating the training data

cd ${PROJECT_DIR}
nohup python3 generate_game_numpy_arrays.py > data.log &

You can monitor its progress with:

top

or:

ls -U ${GAMES_DIR} | wc -l

There should be 1,262 NumPy arrays (corresponding to 631 X/y pairs) when finished.

Animating a sequence

If you don't have a display hooked up to your GPU server, you'll need to first clone the repository to your local machine and retrieve certain files from the remote server:

# From your local machine.
mkdir -p ~/scratch
cd ~/scratch

username=michael
server=gpu3.cse.eng.auburn.edu
data_dir=/home/michael/baller2vec_data
scp ${username}@${server}:${data_dir}/baller2vec_config.pydict .

games_dir=${data_dir}/games
gameid=0021500622

scp ${username}@${server}:${games_dir}/\{${gameid}_X.npy,${gameid}_y.npy\} .

You can then run this code in the Python interpreter from within the repository (make sure you source .basketball_profile first if running locally):

import os

from animator import Game
from settings import DATA_DIR, GAMES_DIR

gameid = "0021500622"
try:
    game = Game(DATA_DIR, GAMES_DIR, gameid)
except FileNotFoundError:
    home_dir = os.path.expanduser("~")
    DATA_DIR = f"{home_dir}/scratch"
    GAMES_DIR = f"{home_dir}/scratch"
    game = Game(DATA_DIR, GAMES_DIR, gameid)

# https://youtu.be/FRrh_WkyXko?t=109
start_period = 3
start_time = "1:55"
stop_period = 3
stop_time = "1:51"
game.show_seq(start_period, start_time, stop_period, stop_time)

to generate the following animation:

Running the training script

Run (or copy and paste) the following script, editing the variables as appropriate.

#!/usr/bin/env bash

# Experiment identifier. Output will be saved to ${EXPERIMENTS_DIR}/${JOB}.
JOB=$(date +%Y%m%d%H%M%S)

# Training options.
echo "train:" >> ${JOB}.yaml
task=ball_traj  # ball_traj, ball_loc, event, player_traj, score, or seq2seq.
echo "  task: ${task}" >> ${JOB}.yaml
echo "  min_playing_time: 0" >> ${JOB}.yaml  # 0/13314/39917/1.0e+6 --> 100%/75%/50%/0%.
echo "  train_valid_prop: 0.95" >> ${JOB}.yaml
echo "  train_prop: 0.95" >> ${JOB}.yaml
echo "  train_samples_per_epoch: 20000" >> ${JOB}.yaml
echo "  valid_samples: 1000" >> ${JOB}.yaml
echo "  workers: 10" >> ${JOB}.yaml
echo "  learning_rate: 1.0e-5" >> ${JOB}.yaml
if [[ ("$task" = "event") || ("$task" = "score") ]]
then
    prev_model=False
    echo "  prev_model: ${prev_model}" >> ${JOB}.yaml
    if [[ "$prev_model" != "False" ]]
    then
        echo "  patience: 5" >> ${JOB}.yaml
    fi
fi

# Dataset options.
echo "dataset:" >> ${JOB}.yaml
echo "  hz: 5" >> ${JOB}.yaml
echo "  secs: 4" >> ${JOB}.yaml
echo "  player_traj_n: 11" >> ${JOB}.yaml
echo "  max_player_move: 4.5" >> ${JOB}.yaml
echo "  ball_traj_n: 19" >> ${JOB}.yaml
echo "  max_ball_move: 8.5" >> ${JOB}.yaml
echo "  n_players: 10" >> ${JOB}.yaml
echo "  next_score_change_time_max: 35" >> ${JOB}.yaml
echo "  n_time_to_next_score_change: 36" >> ${JOB}.yaml
echo "  n_ball_loc_x: 95" >> ${JOB}.yaml
echo "  n_ball_loc_y: 51" >> ${JOB}.yaml
echo "  ball_future_secs: 2" >> ${JOB}.yaml

# Model options.
echo "model:" >> ${JOB}.yaml
echo "  embedding_dim: 20" >> ${JOB}.yaml
echo "  sigmoid: none" >> ${JOB}.yaml
echo "  mlp_layers: [128, 256, 512]" >> ${JOB}.yaml
echo "  nhead: 8" >> ${JOB}.yaml
echo "  dim_feedforward: 2048" >> ${JOB}.yaml
echo "  num_layers: 6" >> ${JOB}.yaml
echo "  dropout: 0.0" >> ${JOB}.yaml
if [[ "$task" != "seq2seq" ]]
then
    echo "  use_cls: False" >> ${JOB}.yaml
    echo "  embed_before_mlp: True" >> ${JOB}.yaml
fi

# Save experiment settings.
mkdir -p ${EXPERIMENTS_DIR}/${JOB}
mv ${JOB}.yaml ${EXPERIMENTS_DIR}/${JOB}/

# Start training the model.
gpu=0
cd ${PROJECT_DIR}
nohup python3 train_baller2vec.py ${JOB} ${gpu} > ${EXPERIMENTS_DIR}/${JOB}/train.log &

A multi-entity Transformer for multi-agent spatiotemporal modeling.

Related tags

Overview

`baller2vec`

Citation

Training `baller2vec`

Setting up `.basketball_profile`

Installing the necessary Python packages

Organizing the play-by-play and tracking data

Generating the training data

Animating a sequence

Running the training script

Owner

Michael A. Alcorn

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A multi-entity Transformer for multi-agent spatiotemporal modeling.

Related tags

Overview

baller2vec

Citation

Training baller2vec

Setting up .basketball_profile

Installing the necessary Python packages

Organizing the play-by-play and tracking data

Generating the training data

Animating a sequence

Running the training script

Owner

Michael A. Alcorn

Neural machine translation between the writings of Shakespeare and modern English using TensorFlow

Deep metric learning methods implemented in Chainer

Official code for "End-to-End Optimization of Scene Layout" -- including VAE, Diff Render, SPADE for colorization (CVPR 2020 Oral)

👨‍💻 run nanosaur in simulation with Gazebo/Ingnition

Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models

Python Single Object Tracking Evaluation

My personal Home Assistant configuration.

TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.

Codes for building and training the neural network model described in Domain-informed neural networks for interaction localization within astroparticle experiments.

An Industrial Grade Federated Learning Framework

Pytorch implementation of Value Iteration Networks (NIPS 2016 best paper)

Implementation of a Transformer that Ponders, using the scheme from the PonderNet paper

Pytorch codes for "Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation"

Answer a series of contextually-dependent questions like they may occur in natural human-to-human conversations.

KAPAO is an efficient multi-person human pose estimation model that detects keypoints and poses as objects and fuses the detections to predict human poses.

Code for the Interspeech 2021 paper "AST: Audio Spectrogram Transformer".

Python and Julia in harmony.

A basic reminder tool written in Python.

Codes to pre-train T5 (Text-to-Text Transfer Transformer) models pre-trained on Japanese web texts

Code for "3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop"

`baller2vec`

Training `baller2vec`

Setting up `.basketball_profile`