def test_gated_graph_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 = GatedGraphConv(32, num_layers=3)
assert conv.__repr__() == 'GatedGraphConv(32, num_layers=3)'
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)
t = '(Tensor, Tensor, OptTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x, edge_index).tolist() == out1.tolist()
assert jit(x, edge_index, value).tolist() == out2.tolist()
t = '(Tensor, SparseTensor, OptTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert torch.allclose(jit(x, adj1.t()), out1, atol=1e-6)
assert torch.allclose(jit(x, adj2.t()), out2, atol=1e-6)
def test_gated_graph_conv():
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.randn(edge_index.size(1))
num_nodes = edge_index.max().item() + 1
x = torch.randn((num_nodes, in_channels))
conv = GatedGraphConv(out_channels, num_layers=3)
assert conv.__repr__() == 'GatedGraphConv(32, num_layers=3)'
out1 = conv(x, edge_index)
assert out1.size() == (num_nodes, out_channels)
out2 = conv(x, edge_index, edge_weight)
assert out2.size() == (num_nodes, out_channels)
jit_conv = conv.jittable(x=x, edge_index=edge_index)
jit_conv = torch.jit.script(jit_conv)
assert jit_conv(x, edge_index).tolist() == out1.tolist()
assert jit_conv(x, edge_index, edge_weight).tolist() == out2.tolist()