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

def test_gcn_conv():
    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
    value = torch.rand(row.size(0))
    adj2 = SparseTensor(row=row, col=col, value=value, sparse_sizes=(4, 4))
    adj1 = adj2.set_value(None)

    conv = GCNConv(16, 32)
    assert conv.__repr__() == "GCNConv(16, 32)"
    out1 = conv(x, edge_index)

Instances