GraphDataLoader

class dgl.dataloading.GraphDataLoader(dataset, collate_fn=None, use_ddp=False, ddp_seed=0, **kwargs)

Bases: DataLoader

批量图数据加载器。

PyTorch dataloader 用于对一组图进行批量迭代，生成所述小批量的批量图和相应的标签张量（如果提供）。

Parameters:

• 数据集 (torch.utils.data.Dataset) – 从中加载图形的数据集。

• collate_fn (Function, 默认值为 None) – 自定义的整理函数。如果未提供，将使用默认的整理函数。

• use_ddp (boolean, optional) –

  If True, tells the DataLoader to split the training set for each participating process appropriately using torch.utils.data.distributed.DistributedSampler.

  Overrides the sampler argument of torch.utils.data.DataLoader.

• ddp_seed (int, optional) –

  The seed for shuffling the dataset in torch.utils.data.distributed.DistributedSampler.

  Only effective when use_ddp is True.

• kwargs (dict) –

  Key-word arguments to be passed to the parent PyTorch torch.utils.data.DataLoader class. Common arguments are:

  • batch_size (int): The number of indices in each batch.

  • drop_last (bool): Whether to drop the last incomplete batch.

  • shuffle (bool): Whether to randomly shuffle the indices at each epoch.

示例

为了在dataset中的一组图上训练一个用于图分类的GNN：

>>> dataloader = dgl.dataloading.GraphDataLoader(
...     dataset, batch_size=1024, shuffle=True, drop_last=False, num_workers=4)
>>> for batched_graph, labels in dataloader:
...     train_on(batched_graph, labels)

使用分布式数据并行

如果你正在使用PyTorch的分布式训练（例如，当使用 torch.nn.parallel.DistributedDataParallel时），你可以通过开启 use_ddp选项来训练模型：

>>> dataloader = dgl.dataloading.GraphDataLoader(
...     dataset, use_ddp=True, batch_size=1024, shuffle=True, drop_last=False, num_workers=4)
>>> for epoch in range(start_epoch, n_epochs):
...     dataloader.set_epoch(epoch)
...     for batched_graph, labels in dataloader:
...         train_on(batched_graph, labels)