72e8ef33d6a6239b71dbd1b24edccf1e5d03d9a1,test/nn/conv/test_gcn_conv.py,,test_gcn_conv,#,5

Before Change

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

    conv = GCNConv(in_channels, out_channels)
    assert conv.__repr__() == "GCNConv(16, 32)"
    out = conv(x, edge_index)
    assert out.size() == (num_nodes, out_channels)
    assert conv(x, edge_index, edge_weight).size() == (num_nodes, out_channels)

    jit_conv = conv.jittable()
    jit_conv = torch.jit.script(jit_conv)
    assert jit_conv(x, edge_index).tolist() == out.tolist()

    conv = GCNConv(in_channels, out_channels, cached=True)
    out = conv(x, edge_index)
    out = conv(x, edge_index)
    assert out.size() == (num_nodes, out_channels)
    assert conv(x, edge_index, edge_weight).size() == (num_nodes, out_channels)

    jit_conv = conv.jittable()

After Change

    assert conv.__repr__() == "GCNConv(16, 32)"
    out1 = conv(x, edge_index)
    assert out1.size() == (4, 32)
    assert torch.allclose(conv(x, adj1.t()), out1, atol=1e-6)
    out2 = conv(x, edge_index, value)
    assert out2.size() == (4, 32)
    assert torch.allclose(conv(x, adj2.t()), out2, atol=1e-6)

Instances