chore: import upstream snapshot with attribution

2026-07-13 13:35:51 +08:00
commit c36a561cd8
2172 changed files with 455595 additions and 0 deletions
@@ -0,0 +1,76 @@
+.. _guide_cn-data-pipeline-loadogb:
+
+4.5 使用ogb包导入OGB数据集
+----------------------------------------------
+
+:ref:`(English Version) <guide-data-pipeline-loadogb>`
+
+`Open Graph Benchmark (OGB) <https://ogb.stanford.edu/docs/home/>`__ 是一个图深度学习的基准数据集。
+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
+:class:`dgl.data.DGLGraph` 对象的API。本节会介绍它们的基本用法。
+
+首先使用pip安装ogb包：
+
+.. code:: 
+
+    pip install ogb
+
+
+以下代码显示了如何为 *Graph Property Prediction* 任务加载数据集。
+
+.. code:: 
+
+    # 载入OGB的Graph Property Prediction数据集
+    import dgl
+    import torch
+    from ogb.graphproppred import DglGraphPropPredDataset
+    from dgl.dataloading import GraphDataLoader
+    
+    def _collate_fn(batch):
+        # 小批次是一个元组(graph, label)列表
+        graphs = [e[0] for e in batch]
+        g = dgl.batch(graphs)
+        labels = [e[1] for e in batch]
+        labels = torch.stack(labels, 0)
+        return g, labels
+    
+    # 载入数据集
+    dataset = DglGraphPropPredDataset(name='ogbg-molhiv')
+    split_idx = dataset.get_idx_split()
+    # dataloader
+    train_loader = GraphDataLoader(dataset[split_idx["train"]], batch_size=32, shuffle=True, collate_fn=_collate_fn)
+    valid_loader = GraphDataLoader(dataset[split_idx["valid"]], batch_size=32, shuffle=False, collate_fn=_collate_fn)
+    test_loader = GraphDataLoader(dataset[split_idx["test"]], batch_size=32, shuffle=False, collate_fn=_collate_fn)
+
+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。
+
+.. code:: 
+
+    # 载入OGB的Node Property Prediction数据集
+    from ogb.nodeproppred import DglNodePropPredDataset
+    
+    dataset = DglNodePropPredDataset(name='ogbn-proteins')
+    split_idx = dataset.get_idx_split()
+    
+    # there is only one graph in Node Property Prediction datasets
+    # 在Node Property Prediction数据集里只有一个图
+    g, labels = dataset[0]
+    # 获取划分的标签
+    train_label = dataset.labels[split_idx['train']]
+    valid_label = dataset.labels[split_idx['valid']]
+    test_label = dataset.labels[split_idx['test']]
+
+每个 *Link Property Prediction* 数据集也只包括一个图。
+
+.. code::
+
+    # 载入OGB的Link Property Prediction数据集
+    from ogb.linkproppred import DglLinkPropPredDataset
+    
+    dataset = DglLinkPropPredDataset(name='ogbl-ppa')
+    split_edge = dataset.get_edge_split()
+    
+    graph = dataset[0]
+    print(split_edge['train'].keys())
+    print(split_edge['valid'].keys())
+    print(split_edge['test'].keys())