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
PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

This is the official implementation of the following paper: Torsten Scholak, Nathan Schucher, Dzmitry Bahdanau. PICARD - Parsing Incrementally for Con

ElementAI 217 Jan 01, 2023
A scikit-learn-compatible module for estimating prediction intervals.

|Anaconda|_ MAPIE - Model Agnostic Prediction Interval Estimator MAPIE allows you to easily estimate prediction intervals using your favourite sklearn

SimAI 584 Dec 27, 2022
A scikit-learn compatible neural network library that wraps PyTorch

A scikit-learn compatible neural network library that wraps PyTorch. Resources Documentation Source Code Examples To see more elaborate examples, look

4.9k Jan 03, 2023
Deep High-Resolution Representation Learning for Human Pose Estimation

Deep High-Resolution Representation Learning for Human Pose Estimation (accepted to CVPR2019) News If you are interested in internship or research pos

HRNet 167 Dec 27, 2022
Python scripts for performing stereo depth estimation using the HITNET Tensorflow model.

HITNET-Stereo-Depth-estimation Python scripts for performing stereo depth estimation using the HITNET Tensorflow model from Google Research. Stereo de

Ibai Gorordo 76 Jan 02, 2023
A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)

SEAL ⠀⠀⠀ A PyTorch implementation of Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019) Abstract Node classification an

Benedek Rozemberczki 202 Dec 27, 2022
PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

Sharpness-aware Quantization for Deep Neural Networks Recent Update 2021.11.23: We release the source code of SAQ. Setup the environments Clone the re

Zhuang AI Group 30 Dec 19, 2022
PyTorch code for 'Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning'

Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning This repository is for EMSRDPN introduced in the foll

7 Feb 10, 2022
Predicting Tweet Sentiment Maching Learning and streamlit

Predicting-Tweet-Sentiment-Maching-Learning-and-streamlit (I prefere using Visual Studio Code ) Open the folder in VS Code Run the first cell in requi

1 Nov 20, 2021
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images

MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images This repository contains the implementation of our paper MetaAvatar: Learni

sfwang 96 Dec 13, 2022
NudeNet: Neural Nets for Nudity Classification, Detection and selective censoring

NudeNet: Neural Nets for Nudity Classification, Detection and selective censoring Uncensored version of the following image can be found at https://i.

notAI.tech 1.1k Dec 29, 2022
Pytorch Lightning code guideline for conferences

Deep learning project seed Use this seed to start new deep learning / ML projects. Built in setup.py Built in requirements Examples with MNIST Badges

Pytorch Lightning 1k Jan 02, 2023
DFFNet: An IoT-perceptive Dual Feature Fusion Network for General Real-time Semantic Segmentation

DFFNet Paper DFFNet: An IoT-perceptive Dual Feature Fusion Network for General Real-time Semantic Segmentation. Xiangyan Tang, Wenxuan Tu, Keqiu Li, J

4 Sep 23, 2022
Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR)

Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR) This is the official implementation of our paper Personalized Tran

Yongchun Zhu 81 Dec 29, 2022
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

OCTIS : Optimizing and Comparing Topic Models is Simple! OCTIS (Optimizing and Comparing Topic models Is Simple) aims at training, analyzing and compa

MIND 478 Jan 01, 2023
Exploring the link between uncertainty estimates obtained via "exact" Bayesian inference and out-of-distribution (OOD) detection.

Uncertainty-based OOD detection Exploring the link between uncertainty estimates obtained by "exact" Bayesian inference and out-of-distribution (OOD)

Christian Henning 1 Nov 05, 2022
[CVPR 2021] Exemplar-Based Open-Set Panoptic Segmentation Network (EOPSN)

EOPSN: Exemplar-Based Open-Set Panoptic Segmentation Network (CVPR 2021) PyTorch implementation for EOPSN. We propose open-set panoptic segmentation t

Jaedong Hwang 49 Dec 30, 2022
Implementation of H-Transformer-1D, Hierarchical Attention for Sequence Learning using 🤗 transformers

hierarchical-transformer-1d Implementation of H-Transformer-1D, Hierarchical Attention for Sequence Learning using 🤗 transformers In Progress!! 2021.

MyungHoon Jin 7 Nov 06, 2022
Implementation of various Vision Transformers I found interesting

Implementation of various Vision Transformers I found interesting

Kim Seonghyeon 78 Dec 06, 2022
Download files from DSpace systems (because for some reason DSpace won't let you)

DSpaceDL A tool for downloading files from DSpace items. For some reason, DSpace systems have a dogshit UI, and Universities absolutely LOOOVE to use

Soumitra Shewale 5 Dec 01, 2022