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Creating Graph Datasets

Although :pyg:`PyG` already contains a lot of useful datasets, you may wish to create your own dataset with self-recorded or non-publicly available data.

Implementing datasets by yourself is straightforward and you may want to take a look at the source code to find out how the various datasets are implemented. However, we give a brief introduction on what is needed to setup your own dataset.

We provide two abstract classes for datasets: :class:`torch_geometric.data.Dataset` and :class:`torch_geometric.data.InMemoryDataset`. :class:`torch_geometric.data.InMemoryDataset` inherits from :class:`torch_geometric.data.Dataset` and should be used if the whole dataset fits into CPU memory.

Following the :obj:`torchvision` convention, each dataset gets passed a root folder which indicates where the dataset should be stored. We split up the root folder into two folders: the :obj:`raw_dir`, where the dataset gets downloaded to, and the :obj:`processed_dir`, where the processed dataset is being saved.

In addition, each dataset can be passed a :obj:`transform`, a :obj:`pre_transform` and a :obj:`pre_filter` function, which are :obj:`None` by default. The :obj:`transform` function dynamically transforms the data object before accessing (so it is best used for data augmentation). The :obj:`pre_transform` function applies the transformation before saving the data objects to disk (so it is best used for heavy precomputation which needs to be only done once). The :obj:`pre_filter` function can manually filter out data objects before saving. Use cases may involve the restriction of data objects being of a specific class.

Creating "In Memory Datasets"

In order to create a :class:`torch_geometric.data.InMemoryDataset`, you need to implement four fundamental methods:

You can find helpful methods to download and extract data in :mod:`torch_geometric.data`.

The real magic happens in the body of :meth:`~torch_geometric.data.InMemoryDataset.process`. Here, we need to read and create a list of :class:`~torch_geometric.data.Data` objects and save it into the :obj:`processed_dir`. Because saving a huge python list is quite slow, we collate the list into one huge :class:`~torch_geometric.data.Data` object via :meth:`torch_geometric.data.InMemoryDataset.collate` before saving. The collated data object concatenates all examples into one big data object and, in addition, returns a :obj:`slices` dictionary to reconstruct single examples from this object. Finally, we need to load these two objects in the constructor into the properties :obj:`self.data` and :obj:`self.slices`.

Let's see this process in a simplified example:

import torch
from torch_geometric.data import InMemoryDataset, download_url


class MyOwnDataset(InMemoryDataset):
    def __init__(self, root, transform=None, pre_transform=None, pre_filter=None):
        super().__init__(root, transform, pre_transform, pre_filter)
        self.load(self.processed_paths[0])
        # For PyG<2.4:
        # self.data, self.slices = torch.load(self.processed_paths[0])

    @property
    def raw_file_names(self):
        return ['some_file_1', 'some_file_2', ...]

    @property
    def processed_file_names(self):
        return ['data.pt']

    def download(self):
        # Download to `self.raw_dir`.
        download_url(url, self.raw_dir)
        ...

    def process(self):
        # Read data into huge `Data` list.
        data_list = [...]

        if self.pre_filter is not None:
            data_list = [data for data in data_list if self.pre_filter(data)]

        if self.pre_transform is not None:
            data_list = [self.pre_transform(data) for data in data_list]

        self.save(data_list, self.processed_paths[0])
        # For PyG<2.4:
        # torch.save(self.collate(data_list), self.processed_paths[0])

Creating "Larger" Datasets

For creating datasets which do not fit into memory, the :class:`torch_geometric.data.Dataset` can be used, which closely follows the concepts of the :obj:`torchvision` datasets. It expects the following methods to be implemented in addition:

Internally, :meth:`torch_geometric.data.Dataset.__getitem__` gets data objects from :meth:`torch_geometric.data.Dataset.get` and optionally transforms them according to :obj:`transform`.

Let's see this process in a simplified example:

import os.path as osp

import torch
from torch_geometric.data import Dataset, download_url


class MyOwnDataset(Dataset):
    def __init__(self, root, transform=None, pre_transform=None, pre_filter=None):
        super().__init__(root, transform, pre_transform, pre_filter)

    @property
    def raw_file_names(self):
        return ['some_file_1', 'some_file_2', ...]

    @property
    def processed_file_names(self):
        return ['data_1.pt', 'data_2.pt', ...]

    def download(self):
        # Download to `self.raw_dir`.
        path = download_url(url, self.raw_dir)
        ...

    def process(self):
        idx = 0
        for raw_path in self.raw_paths:
            # Read data from `raw_path`.
            data = Data(...)

            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)

            torch.save(data, osp.join(self.processed_dir, f'data_{idx}.pt'))
            idx += 1

    def len(self):
        return len(self.processed_file_names)

    def get(self, idx):
        data = torch.load(osp.join(self.processed_dir, f'data_{idx}.pt'))
        return data

Here, each graph data object gets saved individually in :meth:`~torch_geometric.data.Dataset.process`, and is manually loaded in :meth:`~torch_geometric.data.Dataset.get`.

Frequently Asked Questions

  1. How can I skip the execution of :meth:`download` and/or :meth:`process` ?

    You can skip downloading and/or processing by just not overriding the :meth:`download()` and :meth:`process()` methods:

    class MyOwnDataset(Dataset):
    def __init__(self, transform=None, pre_transform=None):
        super().__init__(None, transform, pre_transform)

  2. Do I really need to use these dataset interfaces?

    No! Just as in regular :pytorch:`PyTorch`, you do not have to use datasets, e.g., when you want to create synthetic data on the fly without saving them explicitly to disk. In this case, simply pass a regular python list holding :class:`torch_geometric.data.Data` objects and pass them to :class:`torch_geometric.loader.DataLoader`:

    from torch_geometric.data import Data
from torch_geometric.loader import DataLoader

data_list = [Data(...), ..., Data(...)]
loader = DataLoader(data_list, batch_size=32)

Exercises

Consider the following :class:`~torch_geometric.data.InMemoryDataset` constructed from a list of :obj:`~torch_geometric.data.Data` objects:

class MyDataset(InMemoryDataset):
    def __init__(self, root, data_list, transform=None):
        self.data_list = data_list
        super().__init__(root, transform)
        self.load(self.processed_paths[0])

    @property
    def processed_file_names(self):
        return 'data.pt'

    def process(self):
        self.save(self.data_list, self.processed_paths[0])
  1. What is the output of :obj:`self.processed_paths[0]`?
  2. What does :meth:`~torch_geometric.data.InMemoryDataset.save` do?