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
x = torch.randn((num_nodes, in_channels))
conv = GraphConv(in_channels, out_channels)
assert conv.__repr__() == 'GraphConv(16, 32)'
assert conv(x, edge_index).size() == (num_nodes, out_channels)
def test_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 = GraphConv(in_channels, out_channels)
assert conv.__repr__() == 'GraphConv(16, 32)'
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()
def test_graph_conv():
x1 = torch.randn(4, 8)
x2 = torch.randn(2, 16)
edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
row, col = edge_index
value = torch.randn(edge_index.size(1))
adj1 = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
adj2 = SparseTensor(row=row, col=col, value=value, sparse_sizes=(4, 4))
conv = GraphConv(8, 32)
assert conv.__repr__() == 'GraphConv(8, 32)'
out11 = conv(x1, edge_index)
assert out11.size() == (4, 32)
assert conv(x1, edge_index, size=(4, 4)).tolist() == out11.tolist()
assert conv(x1, adj1.t()).tolist() == out11.tolist()
out12 = conv(x1, edge_index, value)
assert out12.size() == (4, 32)
assert conv(x1, edge_index, value, size=(4, 4)).tolist() == out12.tolist()
assert conv(x1, adj2.t()).tolist() == out12.tolist()
t = '(Tensor, Tensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x1, edge_index).tolist() == out11.tolist()
assert jit(x1, edge_index, size=(4, 4)).tolist() == out11.tolist()
assert jit(x1, edge_index, value).tolist() == out12.tolist()
assert jit(x1, edge_index, value, size=(4, 4)).tolist() == out12.tolist()
t = '(Tensor, SparseTensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x1, adj1.t()).tolist() == out11.tolist()
assert jit(x1, adj2.t()).tolist() == out12.tolist()
adj1 = adj1.sparse_resize((4, 2))
adj2 = adj2.sparse_resize((4, 2))
conv = GraphConv((8, 16), 32)
assert conv.__repr__() == 'GraphConv((8, 16), 32)'
out21 = conv((x1, x2), edge_index)
out22 = conv((x1, x2), edge_index, value)
out23 = conv((x1, None), edge_index, size=(4, 2))
out24 = conv((x1, None), edge_index, value, size=(4, 2))
assert out21.size() == (2, 32)
assert out22.size() == (2, 32)
assert out23.size() == (2, 32)
assert out24.size() == (2, 32)
assert conv((x1, x2), edge_index, size=(4, 2)).tolist() == out21.tolist()
assert conv((x1, x2), edge_index, value, (4, 2)).tolist() == out22.tolist()
assert conv((x1, x2), adj1.t()).tolist() == out21.tolist()
assert conv((x1, x2), adj2.t()).tolist() == out22.tolist()
t = '(OptPairTensor, Tensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit((x1, x2), edge_index).tolist() == out21.tolist()
assert jit((x1, x2), edge_index, size=(4, 2)).tolist() == out21.tolist()
assert jit((x1, x2), edge_index, value).tolist() == out22.tolist()
assert jit((x1, x2), edge_index, value, (4, 2)).tolist() == out22.tolist()
assert jit((x1, None), edge_index, size=(4, 2)).tolist() == out23.tolist()
assert jit((x1, None), edge_index, value,
(4, 2)).tolist() == out24.tolist()
t = '(OptPairTensor, SparseTensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit((x1, x2), adj1.t()).tolist() == out21.tolist()
assert jit((x1, x2), adj2.t()).tolist() == out22.tolist()
assert jit((x1, None), adj1.t()).tolist() == out23.tolist()
assert jit((x1, None), adj2.t()).tolist() == out24.tolist()
