dmlc--dgl
Quan (Andy) Gan
74f0140533
* rename * Update node_classification.py * more fixes... Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
59 行
2.1 KiB
Python
59 行
2.1 KiB
Python
import dgl
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import torch as th
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from dgl.data.rdf import AIFBDataset, MUTAGDataset, BGSDataset, AMDataset
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def load_data(data_name, get_norm=False, inv_target=False):
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if data_name == 'aifb':
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dataset = AIFBDataset()
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elif data_name == 'mutag':
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dataset = MUTAGDataset()
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elif data_name == 'bgs':
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dataset = BGSDataset()
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else:
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dataset = AMDataset()
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# Load hetero-graph
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hg = dataset[0]
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num_rels = len(hg.canonical_etypes)
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category = dataset.predict_category
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num_classes = dataset.num_classes
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labels = hg.nodes[category].data.pop('labels')
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train_mask = hg.nodes[category].data.pop('train_mask')
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test_mask = hg.nodes[category].data.pop('test_mask')
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train_idx = th.nonzero(train_mask, as_tuple=False).squeeze()
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test_idx = th.nonzero(test_mask, as_tuple=False).squeeze()
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if get_norm:
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# Calculate normalization weight for each edge,
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# 1. / d, d is the degree of the destination node
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for cetype in hg.canonical_etypes:
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hg.edges[cetype].data['norm'] = dgl.norm_by_dst(hg, cetype).unsqueeze(1)
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edata = ['norm']
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else:
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edata = None
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# get target category id
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category_id = hg.ntypes.index(category)
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g = dgl.to_homogeneous(hg, edata=edata)
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# Rename the fields as they can be changed by for example DataLoader
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g.ndata['ntype'] = g.ndata.pop(dgl.NTYPE)
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g.ndata['type_id'] = g.ndata.pop(dgl.NID)
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node_ids = th.arange(g.num_nodes())
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# find out the target node ids in g
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loc = (g.ndata['ntype'] == category_id)
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target_idx = node_ids[loc]
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if inv_target:
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# Map global node IDs to type-specific node IDs. This is required for
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# looking up type-specific labels in a minibatch
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inv_target = th.empty((g.num_nodes(),), dtype=th.int64)
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inv_target[target_idx] = th.arange(0, target_idx.shape[0],
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dtype=inv_target.dtype)
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return g, num_rels, num_classes, labels, train_idx, test_idx, target_idx, inv_target
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else:
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return g, num_rels, num_classes, labels, train_idx, test_idx, target_idx
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