Datasets:
File size: 3,149 Bytes
395e1e6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | import datasets
import torch
from torch_geometric.data import Data
_CITATION = """\
@article{metamatbench,
title={MetamatBench: Integrating Heterogeneous Data, Computational Tools, and Visual Interface for Metamaterial Discovery},
author={Chen, Jianpeng and Zhan, Wangzhi and Wang, Haohui and Jia, Zian and Gan, Jingru and Zhang, Junkai and Qi, Jingyuan and Chen, Tingwei and Huang, Lifu and Chen, Muhao and others},
journal={arXiv preprint arXiv:2505.20299},
year={2025}
}
"""
_DESCRIPTION = """
This dataset contains lattice structure data for predicting modulus properties, preprocessed into PyTorch Geometric (PyG) compatible format.
"""
class LatticeModulusDataset(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
citation=_CITATION,
features=datasets.Features({
'frac_coords': datasets.Array2D(shape=(None, 3), dtype='float32'),
'cart_coords': datasets.Array2D(shape=(None, 3), dtype='float32'),
'node_feat': datasets.Array2D(shape=(None, 4), dtype='float32'),
'node_type': datasets.Sequence(datasets.Value('int64')),
'edge_feat': datasets.Array2D(shape=(None, 1), dtype='float32'),
'edge_index': datasets.Array2D(shape=(2, None), dtype='int64'),
'lengths': datasets.Array2D(shape=(1, 3), dtype='float32'),
'num_nodes': datasets.Value('int64'),
'num_atoms': datasets.Value('int64'),
'angles': datasets.Array2D(shape=(1, 3), dtype='float32'),
'vector': datasets.Array2D(shape=(1, 9), dtype='float32'),
'y': datasets.Array2D(shape=(1, 12), dtype='float32'),
'young': datasets.Array2D(shape=(1, 3), dtype='float32'),
'shear': datasets.Array2D(shape=(1, 3), dtype='float32'),
'poisson': datasets.Array2D(shape=(1, 6), dtype='float32'),
}),
)
def _split_generators(self, dl_manager):
file_path = dl_manager.download_and_extract('data.pkl')
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'filepath': file_path})]
def _generate_examples(self, filepath):
dataset = torch.load(filepath)
for idx, data in enumerate(dataset):
yield idx, {
'frac_coords': data.frac_coords.numpy(),
'cart_coords': data.cart_coords.numpy(),
'node_feat': data.node_feat.numpy(),
'node_type': data.node_type.numpy().tolist(),
'edge_feat': data.edge_feat.numpy(),
'edge_index': data.edge_index.numpy(),
'lengths': data.lengths.numpy(),
'num_nodes': data.num_nodes,
'num_atoms': data.num_atoms,
'angles': data.angles.numpy(),
'vector': data.vector.numpy(),
'y': data.y.numpy(),
'young': data.young.numpy(),
'shear': data.shear.numpy(),
'poisson': data.poisson.numpy(),
} |