Source code for cogdl.data.dataset

import collections
import os.path as osp

import torch.utils.data

from .makedirs import makedirs


[docs]def to_list(x): if not isinstance(x, collections.Iterable) or isinstance(x, str): x = [x] return x
[docs]def files_exist(files): return all([osp.exists(f) for f in files])
[docs]class Dataset(torch.utils.data.Dataset): r"""Dataset base class for creating graph datasets. See `here <https://rusty1s.github.io/pycogdl/build/html/notes/ create_dataset.html>`__ for the accompanying tutorial. Args: root (string): Root directory where the dataset should be saved. transform (callable, optional): A function/transform that takes in an :obj:`cogdl.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:`cogdl.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:`cogdl.data.Data` object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default: :obj:`None`) """ @property
[docs] def raw_file_names(self): r"""The name of the files to find in the :obj:`self.raw_dir` folder in order to skip the download.""" raise NotImplementedError
@property
[docs] def processed_file_names(self): r"""The name of the files to find in the :obj:`self.processed_dir` folder in order to skip the processing.""" raise NotImplementedError
[docs] def download(self): r"""Downloads the dataset to the :obj:`self.raw_dir` folder.""" raise NotImplementedError
[docs] def process(self): r"""Processes the dataset to the :obj:`self.processed_dir` folder.""" raise NotImplementedError
[docs] def __len__(self): r"""The number of examples in the dataset.""" raise NotImplementedError
[docs] def get(self, idx): r"""Gets the data object at index :obj:`idx`.""" raise NotImplementedError
def __init__(self, root, transform=None, pre_transform=None, pre_filter=None): super(Dataset, self).__init__() self.root = osp.expanduser(osp.normpath(root)) self.raw_dir = osp.join(self.root, "raw") self.processed_dir = osp.join(self.root, "processed") self.transform = transform self.pre_transform = pre_transform self.pre_filter = pre_filter self._download() self._process() @property
[docs] def num_features(self): r"""Returns the number of features per node in the graph.""" return self[0].num_features
@property
[docs] def raw_paths(self): r"""The filepaths to find in order to skip the download.""" files = to_list(self.raw_file_names) return [osp.join(self.raw_dir, f) for f in files]
@property
[docs] def processed_paths(self): r"""The filepaths to find in the :obj:`self.processed_dir` folder in order to skip the processing.""" files = to_list(self.processed_file_names) return [osp.join(self.processed_dir, f) for f in files]
[docs] def _download(self): if files_exist(self.raw_paths): # pragma: no cover return makedirs(self.raw_dir) self.download()
[docs] def _process(self): if files_exist(self.processed_paths): # pragma: no cover return print("Processing...") makedirs(self.processed_dir) self.process() print("Done!")
[docs] def __getitem__(self, idx): # pragma: no cover r"""Gets the data object at index :obj:`idx` and transforms it (in case a :obj:`self.transform` is given).""" data = self.get(idx) data = data if self.transform is None else self.transform(data) return data
[docs] def __repr__(self): # pragma: no cover return "{}({})".format(self.__class__.__name__, len(self))