- python3.8
- This package automatically install
test-evocraft-py(https://github.com/shyamsn97/test-evocraft-py), but for further functionality follow installation steps here: https://github.com/real-itu/Evocraft-py
python setup.py install
python -m pip install -r ray-requirements.txt
python setup.py install
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.
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
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
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
See generation notebook for more details
- 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
}
)
- Create
MinecraftClientto 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))
- Spawn 100 iterations and display progress every 5 time steps
m.spawn(100)
Output should look like this:
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:
Shyam Sudhakaran shyamsnair@protonmail.com, https://github.com/shyamsn97
Djordje Grbic djgr@itu.dk, https://github.com/djole
Siyan Li lisiyansylvia@gmail.com, https://github.com/sli613
Adam Katona ak1774@york.ac.uk, https://github.com/adam-katona
Elias Najarro https://github.com/enajx
Claire Glanois https://github.com/claireaoi
Sebastian Risi sebr@itu.dk, https://github.com/sebastianrisi
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}
}