25ac33440334fd2fdfe3fe4e4dd3dd1428601a47,examples/pytorch/rgcn/experimental/entity_classify_dist.py,,main,#Any#,438

Before Change

    print("//classes:", n_classes)

    // these two infor should have a better place to store and retrive
    num_of_ntype = len(th.unique(g.ndata[dgl.NTYPE][np.arange(g.number_of_nodes())]))
    num_rels = len(th.unique(g.edata[dgl.ETYPE][np.arange(g.number_of_edges())]))

    // no initial node features

After Change

          len(val_nid), len(np.intersect1d(val_nid.numpy(), local_nid)),
          len(test_nid), len(np.intersect1d(test_nid.numpy(), local_nid))))
    device = th.device("cpu")
    labels = g.nodes["paper"].data["labels"][np.arange(g.number_of_nodes("paper"))]
    all_val_nid = th.LongTensor(np.nonzero(g.nodes["paper"].data["val_mask"][np.arange(g.number_of_nodes("paper"))])).squeeze()
    all_test_nid = th.LongTensor(np.nonzero(g.nodes["paper"].data["test_mask"][np.arange(g.number_of_nodes("paper"))])).squeeze()
    n_classes = len(th.unique(labels[labels >= 0]))
    print("//classes:", n_classes)

Instances