设置目标延迟特性

class dgl.dataloading.base.set_dst_lazy_features(g, feature_names)[source]

基础类：

将懒加载特征分配给输入图的dstdata以进行预取优化。

When used in a Sampler, lazy features mark which data should be fetched before computation in model. See guide-minibatch-prefetching for a detailed explanation.

如果图是同质的，这相当于：

g.dstdata.update({k: LazyFeature(k, g.dstdata[dgl.NID]) for k in feature_names})

如果图是异构的，这相当于：

for type_, names in feature_names.items():
    g.dstnodes[type_].data.update(
        {k: LazyFeature(k, g.dstnodes[type_].data[dgl.NID]) for k in names})

Parameters:

g (DGLGraph) – The graph.
feature_names (list[str] or dict[str, list[str]]) – The feature names to prefetch.

另请参阅

dgl.LazyFeature