import glob
import os
import os.path as osp
from typing import Callable, List, Optional
import torch
from torch_geometric.data import (
Data,
InMemoryDataset,
download_url,
extract_zip,
)
from torch_geometric.io import read_off
[docs]class GeometricShapes(InMemoryDataset):
r"""Synthetic dataset of various geometric shapes like cubes, spheres or
pyramids.
.. note::
Data objects hold mesh faces instead of edge indices.
To convert the mesh to a graph, use the
:obj:`torch_geometric.transforms.FaceToEdge` as :obj:`pre_transform`.
To convert the mesh to a point cloud, use the
:obj:`torch_geometric.transforms.SamplePoints` as :obj:`transform` to
sample a fixed number of points on the mesh faces according to their
face area.
Args:
root (str): Root directory where the dataset should be saved.
train (bool, optional): If :obj:`True`, loads the training dataset,
otherwise the test dataset. (default: :obj:`True`)
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.Data` object and returns a transformed
version. The data object will be transformed before every access.
(default: :obj:`None`)
pre_transform (callable, optional): A function/transform that takes in
an :obj:`torch_geometric.data.Data` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
pre_filter (callable, optional): A function that takes in an
:obj:`torch_geometric.data.Data` object and returns a boolean
value, indicating whether the data object should be included in the
final dataset. (default: :obj:`None`)
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
**STATS:**
.. list-table::
:widths: 10 10 10 10 10
:header-rows: 1
* - #graphs
- #nodes
- #edges
- #features
- #classes
* - 80
- ~148.8
- ~859.5
- 3
- 40
"""
url = 'https://github.com/Yannick-S/geometric_shapes/raw/master/raw.zip'
def __init__(
self,
root: str,
train: bool = True,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
super().__init__(root, transform, pre_transform, pre_filter,
force_reload=force_reload)
path = self.processed_paths[0] if train else self.processed_paths[1]
self.load(path)
@property
def raw_file_names(self) -> str:
return '2d_circle'
@property
def processed_file_names(self) -> List[str]:
return ['training.pt', 'test.pt']
def download(self) -> None:
path = download_url(self.url, self.root)
extract_zip(path, self.root)
os.unlink(path)
def process(self) -> None:
self.save(self.process_set('train'), self.processed_paths[0])
self.save(self.process_set('test'), self.processed_paths[1])
def process_set(self, dataset: str) -> List[Data]:
categories = glob.glob(osp.join(self.raw_dir, '*', ''))
categories = sorted([x.split(os.sep)[-2] for x in categories])
data_list = []
for target, category in enumerate(categories):
folder = osp.join(self.raw_dir, category, dataset)
paths = glob.glob(f'{folder}/*.off')
for path in paths:
data = read_off(path)
assert data.pos is not None
data.pos = data.pos - data.pos.mean(dim=0, keepdim=True)
data.y = torch.tensor([target])
data_list.append(data)
if self.pre_filter is not None:
data_list = [d for d in data_list if self.pre_filter(d)]
if self.pre_transform is not None:
data_list = [self.pre_transform(d) for d in data_list]
return data_list