构建数据集¶

尽管 PyTorch Geometric 已包含许多有用的数据集，但您可能希望创建自己的数据集 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: torch_geometric.data.Dataset and torch_geometric.data.InMemoryDataset. torch_geometric.data.InMemoryDataset inherits from torch_geometric.data.Dataset and should be used if the whole dataset fits into memory.

Following the 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 raw_dir, where the dataset gets downloaded to, and the processed_dir, where the processed dataset is being saved.

In addition, each dataset can be passed a transform, a pre_transform and a pre_filter function, which are None by default. The transform function dynamically transforms the data object before accessing (so it is best used for data augmentation). The 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 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 torch_geometric.data.InMemoryDataset, 您需要实现四种基本方法：

torch_geometric.data.InMemoryDataset.raw_file_names():: A list of files in the raw_dir which needs to be found in order to skip the download.
torch_geometric.data.InMemoryDataset.processed_file_names():: A list of files in the processed_dir which needs to be found in order to skip the processing.
torch_geometric.data.InMemoryDataset.download():: Downloads raw data into raw_dir.
torch_geometric.data.InMemoryDataset.process():: Processes raw data and saves it into the processed_dir.

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

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

Let’s see this process in a simplified example:

import torch
from torch_geometric.data import InMemoryDataset


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

    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]

        data, slices = self.collate(data_list)
        torch.save((data, slices), self.processed_paths[0])

Creating “Larger” Datasets¶

For creating datasets which do not fit into memory, the torch_geometric.data.Dataset must be used, where we closely follow the concepts of the torchvision datasets. 因此，需要进一步实现以下方法：

torch_geometric.data.Dataset.len():: Returns the number of examples in your dataset.
torch_geometric.data.Dataset.get():: Implements the logic to load a single graph.

Internally, torch_geometric.data.Dataset.__getitem__() gets data objects from torch_geometric.data.Dataset.get() and optionally transforms them according to transform.

Let’s see this process in a simplified example:

import os.path as osp

import torch
from torch_geometric.data import Dataset


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

    @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`.

    def process(self):
        i = 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, 'data_{}.pt'.format(i)))
            i += 1

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

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

Here, each graph data object gets saved individually in torch_geometric.data.Dataset.process(), and is manually loaded in torch_geometric.data.Dataset.get().

常见问题集¶

How can I skip the execution of download() and/or process() ?

You can skip downloading and/or processing by just not overriding the download() and process() methods:
class MyOwnDataset(Dataset):
    def __init__(self, transform=None, pre_transform=None):
        super(MyOwnDataset, self).__init__(None, transform, pre_transform)

Do I really need to use these dataset interfaces?
No! Just as in regular 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 torch_geometric.data.Data objects and pass them to torch_geometric.data.DataLoader:
from torch_geometric.data import Data, DataLoader data_list = [Data(...), ..., Data(...)] loader = DataLoader(data_list, batch_size=32)