18f7a20f9098da9bd848e31b501b6b1c571accc1,test/nn/conv/test_appnp.py,,test_appnp,#,5

Before Change

def test_appnp():
    in_channels, out_channels = (16, 32)
    edge_index = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
    edge_weight = torch.rand(edge_index.size(1))
    num_nodes = edge_index.max().item() + 1
    x = torch.randn((num_nodes, in_channels))

    lin = torch.nn.Linear(in_channels, out_channels)
    conv = APPNP(K=10, alpha=0.1)
    assert conv.__repr__() == "APPNP(K=10, alpha=0.1)"
    out1 = conv(lin(x), edge_index)
    assert out1.size() == (num_nodes, out_channels)
    out2 = conv(lin(x), edge_index, edge_weight)
    assert out2.size() == (num_nodes, out_channels)

    jit = torch.jit.script(conv.jittable())
    assert jit(lin(x), edge_index).tolist() == out1.tolist()

After Change

    x = torch.randn(4, 16)
    edge_index = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
    row, col = edge_index
    adj = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))

    conv = APPNP(K=10, alpha=0.1)
    assert conv.__repr__() == "APPNP(K=10, alpha=0.1)"
    out = conv(x, edge_index)

Instances