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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_txt_array
class TOSCA(InMemoryDataset):
r"""The TOSCA dataset from the `"Numerical Geometry of Non-Ridig Shapes"
<https://www.amazon.com/Numerical-Geometry-Non-Rigid-Monographs-Computer/
dp/0387733000>`_ book, containing 80 meshes.
Meshes within the same category have the same triangulation and an equal
number of vertices numbered in a compatible way.
.. 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.
categories (list, optional): List of categories to include in the
dataset. Can include the categories :obj:`"Cat"`, :obj:`"Centaur"`,
:obj:`"David"`, :obj:`"Dog"`, :obj:`"Gorilla"`, :obj:`"Horse"`,
:obj:`"Michael"`, :obj:`"Victoria"`, :obj:`"Wolf"`. If set to
:obj:`None`, the dataset will contain all categories. (default:
:obj:`None`)
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`)
"""
url = 'http://tosca.cs.technion.ac.il/data/toscahires-asci.zip'
categories = [
'cat', 'centaur', 'david', 'dog', 'gorilla', 'horse', 'michael',
'victoria', 'wolf'
]
def __init__(
self,
root: str,
categories: Optional[List[str]] = None,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
categories = self.categories if categories is None else categories
categories = [cat.lower() for cat in categories]
for cat in categories:
assert cat in self.categories
self.categories = categories
super().__init__(root, transform, pre_transform, pre_filter,
force_reload=force_reload)
self.load(self.processed_paths[0])
@property
def raw_file_names(self) -> List[str]:
return ['cat0.vert', 'cat0.tri']
@property
def processed_file_names(self) -> str:
name = '_'.join([cat[:2] for cat in self.categories])
return f'{name}.pt'
def download(self) -> None:
path = download_url(self.url, self.raw_dir)
extract_zip(path, self.raw_dir)
os.unlink(path)
def process(self) -> None:
data_list = []
for cat in self.categories:
paths = glob.glob(osp.join(self.raw_dir, f'{cat}*.tri'))
paths = [path[:-4] for path in paths]
paths = sorted(paths, key=lambda e: (len(e), e))
for path in paths:
pos = read_txt_array(f'{path}.vert')
face = read_txt_array(f'{path}.tri', dtype=torch.long)
face = face - face.min() # Ensure zero-based index.
data = Data(pos=pos, face=face.t().contiguous())
if self.pre_filter is not None and not self.pre_filter(data):
continue
if self.pre_transform is not None:
data = self.pre_transform(data)
data_list.append(data)
self.save(data_list, self.processed_paths[0])
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