wehub-resource-sync
77 lines
2.6 KiB
ReStructuredText
77 lines
2.6 KiB
ReStructuredText
.. _guide_cn-data-pipeline-loadogb:
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4.5 使用ogb包导入OGB数据集
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----------------------------------------------
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:ref:`(English Version) <guide-data-pipeline-loadogb>`
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`Open Graph Benchmark (OGB) <https://ogb.stanford.edu/docs/home/>`__ 是一个图深度学习的基准数据集。
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官方的 `ogb <https://github.com/snap-stanford/ogb>`__ 包提供了用于下载和处理OGB数据集到
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:class:`dgl.data.DGLGraph` 对象的API。本节会介绍它们的基本用法。
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首先使用pip安装ogb包：
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.. code::
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pip install ogb
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以下代码显示了如何为 *Graph Property Prediction* 任务加载数据集。
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.. code::
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# 载入OGB的Graph Property Prediction数据集
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import dgl
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import torch
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from ogb.graphproppred import DglGraphPropPredDataset
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from dgl.dataloading import GraphDataLoader
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def _collate_fn(batch):
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# 小批次是一个元组(graph, label)列表
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graphs = [e[0] for e in batch]
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g = dgl.batch(graphs)
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labels = [e[1] for e in batch]
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labels = torch.stack(labels, 0)
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return g, labels
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# 载入数据集
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dataset = DglGraphPropPredDataset(name='ogbg-molhiv')
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split_idx = dataset.get_idx_split()
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# dataloader
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train_loader = GraphDataLoader(dataset[split_idx["train"]], batch_size=32, shuffle=True, collate_fn=_collate_fn)
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valid_loader = GraphDataLoader(dataset[split_idx["valid"]], batch_size=32, shuffle=False, collate_fn=_collate_fn)
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test_loader = GraphDataLoader(dataset[split_idx["test"]], batch_size=32, shuffle=False, collate_fn=_collate_fn)
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加载 *Node Property Prediction* 数据集类似，但要注意的是这种数据集只有一个图对象。
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.. code::
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# 载入OGB的Node Property Prediction数据集
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from ogb.nodeproppred import DglNodePropPredDataset
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dataset = DglNodePropPredDataset(name='ogbn-proteins')
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split_idx = dataset.get_idx_split()
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# there is only one graph in Node Property Prediction datasets
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# 在Node Property Prediction数据集里只有一个图
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g, labels = dataset[0]
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# 获取划分的标签
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train_label = dataset.labels[split_idx['train']]
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valid_label = dataset.labels[split_idx['valid']]
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test_label = dataset.labels[split_idx['test']]
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每个 *Link Property Prediction* 数据集也只包括一个图。
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.. code::
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# 载入OGB的Link Property Prediction数据集
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from ogb.linkproppred import DglLinkPropPredDataset
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dataset = DglLinkPropPredDataset(name='ogbl-ppa')
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split_edge = dataset.get_edge_split()
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graph = dataset[0]
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print(split_edge['train'].keys())
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print(split_edge['valid'].keys())
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print(split_edge['test'].keys())
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