Offical code for the paper: "Growing 3D Artefacts and Functional Machines with Neural Cellular Automata" https://arxiv.org/abs/2103.08737

Overview

Growing 3D Artefacts and Functional Machines with Neural Cellular Automata

Paper

alt text

Video of more results: https://www.youtube.com/watch?v=-EzztzKoPeo


Requirements

Installation

For general installation

python setup.py install

For ray tune + mlflow

python -m pip install -r ray-requirements.txt
python setup.py install

Usage

Make sure an evocraft-py server is running, either with test-evocraft-py --interactive or by following the steps in https://github.com/real-itu/Evocraft-py.

Configs

Each nca is trained on a specific structure w/ hyperparams and configurations defined in yaml config, which we use with hydra to create the NCA trainer class.

Example Config for generating a "PlainBlacksmith" Minecraft Structure:

trainer:
    name: PlainBlacksmith
    min_steps: 48
    max_steps: 64
    visualize_output: true
    device_id: 0
    use_cuda: true
    num_hidden_channels: 10
    epochs: 20000
    batch_size: 5
    model_config:
        normal_std: 0.1
        update_net_channel_dims: [32, 32]
    optimizer_config:
        lr: 0.002
    dataset_config:
        nbt_path: artefact_nca/data/structs_dataset/nbts/village/plain_village_blacksmith.nbt

defaults:
  - voxel

Generation and Training

See generation notebook for ways to load in a pretrained nca and generate a structure in minecraft

See training notebook for ways to train an nca

CLI training

python artefact_nca/train.py config={path to yaml config} trainer.dataset_config.nbt_path={absolute path to nbt file to use}

Example:

python artefact_nca/train.py config=pretrained_models/PlainBlacksmith/plain_blacksmith.yaml trainer.dataset_config.nbt_path=/home/shyam/Code/3d-artefacts-nca/artefact_nca/data/structs_dataset/nbts/village/plain_village_blacksmith.nbt

Spawning in minecraft

See generation notebook for more details

Example spawning the oak tree

  1. Load in a trainer
from artefact_nca.trainer.voxel_ca_trainer import VoxelCATrainer

nbt_path = {path to repo}/artefact_nca/data/structs_dataset/nbts/village/Extra_dark_oak.nbt
ct = VoxelCATrainer.from_config(
                    "{path to repo}/pretrained_models/Extra_dark_oak/extra_dark_oak.yaml",
                    config={
                        "pretrained_path":"{path to repo}/pretrained_models/Extra_dark_oak/Extra_dark_oak.pt",
                        "dataset_config":{"nbt_path":nbt_path},
                        "use_cuda":False
                    }
                )
  1. Create MinecraftClient to view the growth of the structure in Minecraft at position (-10, 10, 10) (x, y, z)
from artefact_nca.utils.minecraft import MinecraftClient
m = MinecraftClient(ct, (-10, 10, 10))
  1. Spawn 100 iterations and display progress every 5 time steps
m.spawn(100)

Output should look like this:

alt text

Structures

see data directory. To view structures and spawn in minecraft see generation notebook. An example of spawning and viewing the Tree:

import matplotlib.pyplot as plt
from artefact_nca.utils.minecraft import MinecraftClient

base_nbt_path = {path to nbts}
nbt_path = "{}/village/Extra_dark_oak.nbt".format(base_nbt_path)

 # spawn at coords (50, 10, 10)
blocks, unique_vals, target, color_dict, unique_val_dict = MinecraftClient.load_entity("Extra_dark_oak", nbt_path=nbt_path, load_coord=(50,10,10))

color_arr = convert_to_color(target, color_dict)

fig = plt.figure()
ax = fig.gca(projection='3d')
ax.voxels(color_arr, facecolors=color_arr, edgecolor='k')

plt.show()

This should spawn and display:

alt text alt text

Authors

Shyam Sudhakaran [email protected], https://github.com/shyamsn97

Djordje Grbic [email protected], https://github.com/djole

Siyan Li [email protected], https://github.com/sli613

Adam Katona [email protected], https://github.com/adam-katona

Elias Najarro https://github.com/enajx

Claire Glanois https://github.com/claireaoi

Sebastian Risi [email protected], https://github.com/sebastianrisi

Citation

If you use the code for academic or commecial use, please cite the associated paper:

@inproceedings{Sudhakaran2021,
   title = {Growing 3D Artefacts and Functional Machines with Neural Cellular Automata}, 
   author = {Shyam Sudhakaran and Djordje Grbic and Siyan Li and Adam Katona and Elias Najarro and Claire Glanois and Sebastian Risi},
   booktitle = {2021 Conference on Artificial Life},
   year = {2021},
   url = {https://arxiv.org/abs/2103.08737}
}
Owner
Robotics Evolution and Art Lab
Robotics Evolution and Art Lab
EdMIPS: Rethinking Differentiable Search for Mixed-Precision Neural Networks

EdMIPS is an efficient algorithm to search the optimal mixed-precision neural network directly without proxy task on ImageNet given computation budgets. It can be applied to many popular network arch

Zhaowei Cai 47 Dec 30, 2022
Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.

Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.

Nikolas Petrou 1 Jan 13, 2022
A Demo server serving Bert through ONNX with GPU written in Rust with <3

Demo BERT ONNX server written in rust This demo showcase the use of onnxruntime-rs on BERT with a GPU on CUDA 11 served by actix-web and tokenized wit

Xavier Tao 28 Jan 01, 2023
Fight Recognition from Still Images in the Wild @ WACVW2022, Real-world Surveillance Workshop

Fight Detection from Still Images in the Wild Detecting fights from still images is an important task required to limit the distribution of social med

Şeymanur Aktı 10 Nov 09, 2022
For medical image segmentation

LeViT_UNet For medical image segmentation Our model is based on LeViT (https://github.com/facebookresearch/LeViT). You'd better gitclone its codes. Th

13 Dec 24, 2022
This is the workbook I created while I was studying for the Qiskit Associate Developer exam. I hope this becomes useful to others as it was for me :)

A Workbook for the Qiskit Developer Certification Exam Hello everyone! This is Bartu, a fellow Qiskitter. I have recently taken the Certification exam

Bartu Bisgin 66 Dec 10, 2022
[ICCV2021] 3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds

3DVG-Transformer This repository is for the ICCV 2021 paper "3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds" Our method "3DV

22 Dec 11, 2022
PyTorch Implement of Context Encoders: Feature Learning by Inpainting

Context Encoders: Feature Learning by Inpainting This is the Pytorch implement of CVPR 2016 paper on Context Encoders 1) Semantic Inpainting Demo Inst

321 Dec 25, 2022
A collection of papers about Transformer in the field of medical image analysis.

A collection of papers about Transformer in the field of medical image analysis.

Junyu Chen 377 Jan 05, 2023
To SMOTE, or not to SMOTE?

To SMOTE, or not to SMOTE? This package includes the code required to repeat the experiments in the paper and to analyze the results. To SMOTE, or not

Amazon Web Services 1 Jan 03, 2022
A PyTorch implementation: "LASAFT-Net-v2: Listen, Attend and Separate by Attentively aggregating Frequency Transformation"

LASAFT-Net-v2 Listen, Attend and Separate by Attentively aggregating Frequency Transformation Woosung Choi, Yeong-Seok Jeong, Jinsung Kim, Jaehwa Chun

Woosung Choi 29 Jun 04, 2022
An experimental technique for efficiently exploring neural architectures.

SMASH: One-Shot Model Architecture Search through HyperNetworks An experimental technique for efficiently exploring neural architectures. This reposit

Andy Brock 478 Aug 04, 2022
VLG-Net: Video-Language Graph Matching Networks for Video Grounding

VLG-Net: Video-Language Graph Matching Networks for Video Grounding Introduction Official repository for VLG-Net: Video-Language Graph Matching Networ

Mattia Soldan 25 Dec 04, 2022
AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation

AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation

Frank Liu 26 Oct 13, 2022
Code for Massive-scale Decoding for Text Generation using Lattices

Massive-scale Decoding for Text Generation using Lattices Jiacheng Xu, Greg Durrett TL;DR: a new search algorithm to construct lattices encoding many

Jiacheng Xu 37 Dec 18, 2022
A curated list of automated deep learning (including neural architecture search and hyper-parameter optimization) resources.

Awesome AutoDL A curated list of automated deep learning related resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awe

D-X-Y 2k Dec 30, 2022
Source code for Acorn, the precision farming rover by Twisted Fields

Acorn precision farming rover This is the software repository for Acorn, the precision farming rover by Twisted Fields. For more information see twist

Twisted Fields 198 Jan 02, 2023
Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond

CRF - Conditional Random Fields A library for dense conditional random fields (CRFs). This is the official accompanying code for the paper Regularized

Đ.Khuê Lê-Huu 21 Nov 26, 2022
A comprehensive list of published machine learning applications to cosmology

ml-in-cosmology This github attempts to maintain a comprehensive list of published machine learning applications to cosmology, organized by subject ma

George Stein 290 Dec 29, 2022
The source code of CVPR17 'Generative Face Completion'.

GenerativeFaceCompletion Matcaffe implementation of our CVPR17 paper on face completion. In each panel from left to right: original face, masked input

Yijun Li 313 Oct 18, 2022