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import os
import os.path as osp
from typing import Callable, List, Optional
import torch
from torch import Tensor
from torch_geometric.data import (
Data,
InMemoryDataset,
download_url,
extract_zip,
)
from torch_geometric.io import fs
class S3DIS(InMemoryDataset):
r"""The (pre-processed) Stanford Large-Scale 3D Indoor Spaces dataset from
the `"3D Semantic Parsing of Large-Scale Indoor Spaces"
<https://openaccess.thecvf.com/content_cvpr_2016/papers/Armeni_3D_Semantic_Parsing_CVPR_2016_paper.pdf>`_
paper, containing point clouds of six large-scale indoor parts in three
buildings with 12 semantic elements (and one clutter class).
Args:
root (str): Root directory where the dataset should be saved.
test_area (int, optional): Which area to use for testing (1-6).
(default: :obj:`6`)
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`)
"""
url = ('https://shapenet.cs.stanford.edu/media/'
'indoor3d_sem_seg_hdf5_data.zip')
# In case `shapenet.cs.stanford.edu` is offline, try to download the data
# from here:
# https://cvg-data.inf.ethz.ch/s3dis/
def __init__(
self,
root: str,
test_area: int = 6,
train: bool = True,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
assert test_area >= 1 and test_area <= 6
self.test_area = test_area
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) -> List[str]:
return ['all_files.txt', 'room_filelist.txt']
@property
def processed_file_names(self) -> List[str]:
return [f'{split}_{self.test_area}.pt' for split in ['train', 'test']]
def download(self) -> None:
path = download_url(self.url, self.root)
extract_zip(path, self.root)
os.unlink(path)
fs.rm(self.raw_dir)
name = self.url.split('/')[-1].split('.')[0]
os.rename(osp.join(self.root, name), self.raw_dir)
def process(self) -> None:
import h5py
with open(self.raw_paths[0]) as f:
filenames = [x.split('/')[-1] for x in f.read().split('\n')[:-1]]
with open(self.raw_paths[1]) as f:
rooms = f.read().split('\n')[:-1]
xs: List[Tensor] = []
ys: List[Tensor] = []
for filename in filenames:
h5 = h5py.File(osp.join(self.raw_dir, filename))
xs += torch.from_numpy(h5['data'][:]).unbind(0)
ys += torch.from_numpy(h5['label'][:]).to(torch.long).unbind(0)
test_area = f'Area_{self.test_area}'
train_data_list, test_data_list = [], []
for i, (x, y) in enumerate(zip(xs, ys)):
data = Data(pos=x[:, :3], x=x[:, 3:], y=y)
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)
if test_area not in rooms[i]:
train_data_list.append(data)
else:
test_data_list.append(data)
self.save(train_data_list, self.processed_paths[0])
self.save(test_data_list, self.processed_paths[1])
|
