import glob
import os
import os.path as osp
from typing import Callable, List, Optional
import torch
from torch_geometric.data import InMemoryDataset, download_url, extract_zip
from torch_geometric.io import read_off, read_txt_array
[docs]class SHREC2016(InMemoryDataset):
r"""The SHREC 2016 partial matching dataset from the `"SHREC'16: Partial
Matching of Deformable Shapes"
<http://www.dais.unive.it/~shrec2016/shrec16-partial.pdf>`_ paper.
The reference shape can be referenced via :obj:`dataset.ref`.
.. 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.
partiality (str): The partiality of the dataset (one of :obj:`"Holes"`,
:obj:`"Cuts"`).
category (str): The category of the dataset (one of
:obj:`"Cat"`, :obj:`"Centaur"`, :obj:`"David"`, :obj:`"Dog"`,
:obj:`"Horse"`, :obj:`"Michael"`, :obj:`"Victoria"`,
:obj:`"Wolf"`).
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`)
"""
train_url = ('http://www.dais.unive.it/~shrec2016/data/'
'shrec2016_PartialDeformableShapes.zip')
test_url = ('http://www.dais.unive.it/~shrec2016/data/'
'shrec2016_PartialDeformableShapes_TestSet.zip')
categories = [
'cat', 'centaur', 'david', 'dog', 'horse', 'michael', 'victoria',
'wolf'
]
partialities = ['holes', 'cuts']
def __init__(
self,
root: str,
partiality: str,
category: str,
train: bool = True,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
assert partiality.lower() in self.partialities
self.part = partiality.lower()
assert category.lower() in self.categories
self.cat = category.lower()
super().__init__(root, transform, pre_transform, pre_filter,
force_reload=force_reload)
self.__ref__ = torch.load(self.processed_paths[0])
path = self.processed_paths[1] if train else self.processed_paths[2]
self.load(path)
@property
def ref(self) -> str:
ref = self.__ref__
if self.transform is not None:
ref = self.transform(ref)
return ref
@property
def raw_file_names(self) -> List[str]:
return ['training', 'test']
@property
def processed_file_names(self) -> List[str]:
name = f'{self.part}_{self.cat}.pt'
return [f'{i}_{name}' for i in ['ref', 'training', 'test']]
def download(self) -> None:
path = download_url(self.train_url, self.raw_dir)
extract_zip(path, self.raw_dir)
os.unlink(path)
path = osp.join(self.raw_dir, 'shrec2016_PartialDeformableShapes')
os.rename(path, osp.join(self.raw_dir, 'training'))
path = download_url(self.test_url, self.raw_dir)
extract_zip(path, self.raw_dir)
os.unlink(path)
path = osp.join(self.raw_dir,
'shrec2016_PartialDeformableShapes_TestSet')
os.rename(path, osp.join(self.raw_dir, 'test'))
def process(self) -> None:
ref_data = read_off(
osp.join(self.raw_paths[0], 'null', f'{self.cat}.off'))
train_list = []
name = f'{self.part}_{self.cat}_*.off'
paths = glob.glob(osp.join(self.raw_paths[0], self.part, name))
paths = [path[:-4] for path in paths]
paths = sorted(paths, key=lambda e: (len(e), e))
for path in paths:
data = read_off(f'{path}.off')
y = read_txt_array(f'{path}.baryc_gt')
data.y = y[:, 0].to(torch.long) - 1
data.y_baryc = y[:, 1:]
train_list.append(data)
test_list = []
name = f'{self.part}_{self.cat}_*.off'
paths = glob.glob(osp.join(self.raw_paths[1], self.part, name))
paths = [path[:-4] for path in paths]
paths = sorted(paths, key=lambda e: (len(e), e))
for path in paths:
test_list.append(read_off(f'{path}.off'))
if self.pre_filter is not None:
train_list = [d for d in train_list if self.pre_filter(d)]
test_list = [d for d in test_list if self.pre_filter(d)]
if self.pre_transform is not None:
ref_data = self.pre_transform(ref_data)
train_list = [self.pre_transform(d) for d in train_list]
test_list = [self.pre_transform(d) for d in test_list]
torch.save(ref_data, self.processed_paths[0])
self.save(train_list, self.processed_paths[1])
self.save(test_list, self.processed_paths[2])
def __repr__(self) -> str:
return (f'{self.__class__.__name__}({len(self)}, '
f'partiality={self.part}, category={self.cat})')