wehub-resource-sync
80 lines
2.6 KiB
ReStructuredText
80 lines
2.6 KiB
ReStructuredText
.. _guide-data-pipeline-loadogb:
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4.5 Loading OGB datasets using ``ogb`` package
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----------------------------------------------
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:ref:`(中文版) <guide_cn-data-pipeline-loadogb>`
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`Open Graph Benchmark (OGB) <https://ogb.stanford.edu/docs/home/>`__ is
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a collection of benchmark datasets. The official OGB package
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`ogb <https://github.com/snap-stanford/ogb>`__ provides APIs for
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downloading and processing OGB datasets into :class:`dgl.data.DGLGraph` objects. The section
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introduce their basic usage here.
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First install ogb package using pip:
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.. code::
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pip install ogb
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The following code shows how to load datasets for *Graph Property
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Prediction* tasks.
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.. code::
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# Load Graph Property Prediction datasets in OGB
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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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# batch is a list of tuple (graph, label)
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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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# load dataset
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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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Loading *Node Property Prediction* datasets is similar, but note that
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there is only one graph object in this kind of dataset.
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.. code::
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# Load Node Property Prediction datasets in OGB
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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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g, labels = dataset[0]
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# get split labels
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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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*Link Property Prediction* datasets also contain one graph per dataset.
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.. code::
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# Load Link Property Prediction datasets in OGB
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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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