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

Related tags

Deep Learningballer2vec
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.
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.

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

  1. 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).

  1. 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

  1. 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\} .
  1. 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 &
Owner
Michael A. Alcorn
Brute-forcing my way through life.
Michael A. Alcorn
GitHub Repository
Continuous Security Group Rule Change Detection & Response at scale

Introduction Get notified of Security Group Changes across all AWS Accounts & Regions in an AWS Organization, with the ability to respond/revert those

Raajhesh Kannaa Chidambaram 3 Aug 13, 2022
TensorFlow-based neural network library

Sonnet Documentation | Examples Sonnet is a library built on top of TensorFlow 2 designed to provide simple, composable abstractions for machine learn

DeepMind 9.5k Jan 07, 2023
👨‍💻 run nanosaur in simulation with Gazebo/Ingnition

🦕 👨‍💻 nanosaur_gazebo nanosaur The smallest NVIDIA Jetson dinosaur robot, open-source, fully 3D printable, based on ROS2 & Isaac ROS. Designed & ma

nanosaur 9 Jul 19, 2022
Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite and .pb from .tflite.

tflite2tensorflow Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite and .pb from .tflite. 1. Supported Layers No. TFLite Layer TF

Katsuya Hyodo 214 Dec 29, 2022
3D HourGlass Networks for Human Pose Estimation Through Videos

3D-HourGlass-Network 3D CNN Based Hourglass Network for Human Pose Estimation (3D Human Pose) from videos. This was my summer'18 research project. Dis

Naman Jain 51 Jan 02, 2023
A library for hidden semi-Markov models with explicit durations

hsmmlearn hsmmlearn is a library for unsupervised learning of hidden semi-Markov models with explicit durations. It is a port of the hsmm package for

Joris Vankerschaver 69 Dec 20, 2022
🥈78th place in Riiid Solution🥈

Riiid Answer Correctness Prediction Introduction This repository is the code that placed 78th in Riiid Answer Correctness Prediction competition. Requ

ds wook 14 Apr 26, 2022
PyTorch implementation of paper A Fast Knowledge Distillation Framework for Visual Recognition.

FKD: A Fast Knowledge Distillation Framework for Visual Recognition Official PyTorch implementation of paper A Fast Knowledge Distillation Framework f

Zhiqiang Shen 129 Dec 24, 2022
A curated list of neural rendering resources.

Awesome-of-Neural-Rendering A curated list of neural rendering and related resources. Please feel free to pull requests or open an issue to add papers

Zhiwei ZHANG 43 Dec 09, 2022
Segmentation models with pretrained backbones. PyTorch.

Python library with Neural Networks for Image Segmentation based on PyTorch. The main features of this library are: High level API (just two lines to

Pavel Yakubovskiy 6.6k Jan 06, 2023
The fastest way to visualize GradCAM with your Keras models.

VizGradCAM VizGradCam is the fastest way to visualize GradCAM in Keras models. GradCAM helps with providing visual explainability of trained models an

58 Nov 19, 2022
Recognize Handwritten Digits using Deep Learning on the browser itself.

MNIST on the Web An attempt to predict MNIST handwritten digits from my PyTorch model from the browser (client-side) and not from the server, with the

Harjyot Bagga 7 May 28, 2022
A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

Emma 1 Jan 18, 2022
A simple root calculater for python

Root A simple root calculater Usage/Examples python3 root.py 9 3 4 # Order: number - grid - number of decimals # Output: 2.08

Reza Hosseinzadeh 5 Feb 10, 2022
Deep learning toolbox based on PyTorch for hyperspectral data classification.

Deep learning toolbox based on PyTorch for hyperspectral data classification.

Nicolas 304 Dec 28, 2022
Convex optimization for fun and profit.

CFMM Optimal Routing This repository contains the code needed to generate the figures used in the paper Optimal Routing for Constant Function Market M

Guillermo Angeris 183 Dec 29, 2022
[CVPR 2022] CoTTA Code for our CVPR 2022 paper Continual Test-Time Domain Adaptation

CoTTA Code for our CVPR 2022 paper Continual Test-Time Domain Adaptation Prerequisite Please create and activate the following conda envrionment. To r

Qin Wang 87 Jan 08, 2023
This repository is maintained for the scientific paper tittled " Study of keyword extraction techniques for Electric Double Layer Capacitor domain using text similarity indexes: An experimental analysis "

kwd-extraction-study This repository is maintained for the scientific paper tittled " Study of keyword extraction techniques for Electric Double Layer

ping 543f 1 Dec 05, 2022
HEAM: High-Efficiency Approximate Multiplier Optimization for Deep Neural Networks

Approximate Multiplier by HEAM What's HEAM? HEAM is a general optimization method to generate high-efficiency approximate multipliers for specific app

4 Sep 11, 2022
Code for "ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on", accepted at WACV 2021 Generation of Human Behavior Workshop.

ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on [ Paper ] [ Project Page ] This repository contains the code fo

Andrew Jong 97 Dec 13, 2022