Source code for torch_geometric.datasets.s3dis

import os
import os.path as osp
from typing import Callable, List, Optional

import torch

from import (
from import fs

[docs]class S3DIS(InMemoryDataset): r"""The (pre-processed) Stanford Large-Scale 3D Indoor Spaces dataset from the `"3D Semantic Parsing of Large-Scale Indoor Spaces" <>`_ 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:`` 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:`` 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:`` 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 = ('' '') # In case `` is offline, try to download the data # from here: # 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], 'r') as f: filenames = [x.split('/')[-1] for x in'\n')[:-1]] with open(self.raw_paths[1], 'r') as f: rooms ='\n')[:-1] xs, ys = [], [] 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.processed_paths[0]), self.processed_paths[1])