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import os.path as osp
from typing import Callable, List, Optional
import torch
from torch_geometric.data import InMemoryDataset, extract_zip
from torch_geometric.io import fs, read_ply
class FAUST(InMemoryDataset):
r"""The FAUST humans dataset from the `"FAUST: Dataset and Evaluation for
3D Mesh Registration"
<http://files.is.tue.mpg.de/black/papers/FAUST2014.pdf>`_ paper,
containing 100 watertight meshes representing 10 different poses for 10
different subjects.
.. 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
* - 100
- 6,890
- 41,328
- 3
- 10
"""
url = 'http://faust.is.tue.mpg.de/'
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 'MPI-FAUST.zip'
@property
def processed_file_names(self) -> List[str]:
return ['training.pt', 'test.pt']
def download(self) -> None:
raise RuntimeError(
f"Dataset not found. Please download '{self.raw_file_names}' from "
f"'{self.url}' and move it to '{self.raw_dir}'")
def process(self) -> None:
extract_zip(self.raw_paths[0], self.raw_dir, log=False)
path = osp.join(self.raw_dir, 'MPI-FAUST', 'training', 'registrations')
path = osp.join(path, 'tr_reg_{0:03d}.ply')
data_list = []
for i in range(100):
data = read_ply(path.format(i))
data.y = torch.tensor([i % 10], dtype=torch.long)
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[:80], self.processed_paths[0])
self.save(data_list[80:], self.processed_paths[1])
fs.rm(osp.join(self.raw_dir, 'MPI-FAUST'))
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