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

Related tags

Deep Learning3d-artefacts-nca
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
GitHub Repository
Efficient Conformer: Progressive Downsampling and Grouped Attention for Automatic Speech Recognition

Efficient Conformer: Progressive Downsampling and Grouped Attention for Automatic Speech Recognition Official implementation of the Efficient Conforme

Maxime Burchi 145 Dec 30, 2022
Demo notebooks for Qiskit application modules demo sessions (Oct 8 & 15):

qiskit-application-modules-demo-sessions This repo hosts demo notebooks for the Qiskit application modules demo sessions hosted on Qiskit YouTube. Par

Qiskit Community 46 Nov 24, 2022
Code for our CVPR 2021 Paper "Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes".

Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes (CVPR 2021) Project page | Paper | Colab | Colab for Drawing App Rethinking Style

CompVis Heidelberg 153 Jan 04, 2023
Simple tools for logging and visualizing, loading and training

TNT TNT is a library providing powerful dataloading, logging and visualization utilities for Python. It is closely integrated with PyTorch and is desi

1.5k Jan 02, 2023
[CVPR21] LightTrack: Finding Lightweight Neural Network for Object Tracking via One-Shot Architecture Search

LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search The official implementation of the paper LightTra

Multimedia Research 290 Dec 24, 2022
Details about the wide minima density hypothesis and metrics to compute width of a minima

wide-minima-density-hypothesis Details about the wide minima density hypothesis and metrics to compute width of a minima This repo presents the wide m

Nikhil Iyer 9 Dec 27, 2022
4th place solution for the SIGIR 2021 challenge.

SIGIR-2021 (Tinkoff.AI) How to start Download train and test data: https://sigir-ecom.github.io/data-task.html Place it under sigir-2021/data/. Run py

Tinkoff.AI 4 Jul 01, 2022
Bio-OFC gym implementation and Gym-Fly environment

Bio-OFC gym implementation and Gym-Fly environment This repository includes the gym compatible implementation of the Bio-OFC algorithm from the paper

Siavash Golkar 1 Nov 16, 2021
A library for uncertainty representation and training in neural networks.

Epistemic Neural Networks A library for uncertainty representation and training in neural networks. Introduction Many applications in deep learning re

DeepMind 211 Dec 12, 2022
Differentiable rasterization applied to 3D model simplification tasks

nvdiffmodeling Differentiable rasterization applied to 3D model simplification tasks, as described in the paper: Appearance-Driven Automatic 3D Model

NVIDIA Research Projects 336 Dec 30, 2022
Implement slightly different caffe-segnet in tensorflow

Tensorflow-SegNet Implement slightly different (see below for detail) SegNet in tensorflow, successfully trained segnet-basic in CamVid dataset. Due t

Tseng Kuan Lun 364 Oct 27, 2022
New approach to benchmark VQA models

VQA Benchmarking This repository contains the web application & the python interface to evaluate VQA models. Documentation Please see the documentatio

4 Jul 25, 2022
Code for STFT Transformer used in BirdCLEF 2021 competition.

STFT_Transformer Code for STFT Transformer used in BirdCLEF 2021 competition. The STFT Transformer is a new way to use Transformers similar to Vision

Jean-François Puget 69 Sep 29, 2022
Code for the Interspeech 2021 paper "AST: Audio Spectrogram Transformer".

AST: Audio Spectrogram Transformer Introduction Citing Getting Started ESC-50 Recipe Speechcommands Recipe AudioSet Recipe Pretrained Models Contact I

Yuan Gong 603 Jan 07, 2023
U^2-Net - Portrait matting This repository explores possibilities of using the original u^2-net model for portrait matting.

U^2-Net - Portrait matting This repository explores possibilities of using the original u^2-net model for portrait matting.

Dennis Bappert 104 Nov 25, 2022
Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE

SMU A Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE arXiv https://arxiv.org/abs/211

Fuhang 5 Jan 18, 2022
Pytorch implementation of the paper DocEnTr: An End-to-End Document Image Enhancement Transformer.

DocEnTR Description Pytorch implementation of the paper DocEnTr: An End-to-End Document Image Enhancement Transformer. This model is implemented on to

Mohamed Ali Souibgui 74 Jan 07, 2023
YOLOv7 - Framework Beyond Detection

🔥🔥🔥🔥 YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥

JinTian 3k Jan 01, 2023
The code for SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network.

SAG-DTA The code is the implementation for the paper 'SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network'. Requirements py

Shugang Zhang 7 Aug 02, 2022
Hierarchical Few-Shot Generative Models

Hierarchical Few-Shot Generative Models Giorgio Giannone, Ole Winther This repo contains code and experiments for the paper Hierarchical Few-Shot Gene

Giorgio Giannone 6 Dec 12, 2022