def test_cg_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
adj = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
conv = CGConv(8)
assert conv.__repr__() == 'CGConv(8, dim=0)'
out = conv(x1, edge_index)
assert out.size() == (4, 8)
assert conv(x1, edge_index, size=(4, 4)).tolist() == out.tolist()
assert conv(x1, adj.t()).tolist() == out.tolist()
t = '(Tensor, Tensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x1, edge_index).tolist() == out.tolist()
assert jit(x1, edge_index, size=(4, 4)).tolist() == out.tolist()
t = '(Tensor, SparseTensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x1, adj.t()).tolist() == out.tolist()
adj = adj.sparse_resize((4, 2))
conv = CGConv((8, 16))
assert conv.__repr__() == 'CGConv((8, 16), dim=0)'
out = conv((x1, x2), edge_index)
assert out.size() == (2, 16)
assert conv((x1, x2), edge_index, size=(4, 2)).tolist() == out.tolist()
assert conv((x1, x2), adj.t()).tolist() == out.tolist()
t = '(PairTensor, Tensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit((x1, x2), edge_index).tolist() == out.tolist()
assert jit((x1, x2), edge_index, size=(4, 2)).tolist() == out.tolist()
t = '(PairTensor, SparseTensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit((x1, x2), adj.t()).tolist() == out.tolist()
# Test batch_norm true:
adj = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
conv = CGConv(8, batch_norm=True)
assert conv.__repr__() == 'CGConv(8, dim=0)'
out = conv(x1, edge_index)
assert out.size() == (4, 8)
assert conv(x1, edge_index, size=(4, 4)).tolist() == out.tolist()
assert conv(x1, adj.t()).tolist() == out.tolist()
t = '(Tensor, Tensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x1, edge_index).tolist() == out.tolist()
assert jit(x1, edge_index, size=(4, 4)).tolist() == out.tolist()
def test_cg_conv():
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, 16))
pseudo = torch.rand((edge_index.size(1), 3))
conv = CGConv(16, 3)
assert conv.__repr__() == 'CGConv(16, 16, dim=3)'
assert conv(x, edge_index, pseudo).size() == (num_nodes, 16)
def test_cg_conv_with_edge_features():
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.rand(row.size(0), 3)
adj = SparseTensor(row=row, col=col, value=value, sparse_sizes=(4, 4))
conv = CGConv(8, dim=3)
assert conv.__repr__() == 'CGConv(8, dim=3)'
out = conv(x1, edge_index, value)
assert out.size() == (4, 8)
assert conv(x1, edge_index, value, size=(4, 4)).tolist() == out.tolist()
assert conv(x1, adj.t()).tolist() == out.tolist()
t = '(Tensor, Tensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x1, edge_index, value).tolist() == out.tolist()
assert jit(x1, edge_index, value, size=(4, 4)).tolist() == out.tolist()
t = '(Tensor, SparseTensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x1, adj.t()).tolist() == out.tolist()
adj = adj.sparse_resize((4, 2))
conv = CGConv((8, 16), dim=3)
assert conv.__repr__() == 'CGConv((8, 16), dim=3)'
out = conv((x1, x2), edge_index, value)
assert out.size() == (2, 16)
assert conv((x1, x2), edge_index, value, (4, 2)).tolist() == out.tolist()
assert conv((x1, x2), adj.t()).tolist() == out.tolist()
t = '(PairTensor, Tensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit((x1, x2), edge_index, value).tolist() == out.tolist()
assert jit((x1, x2), edge_index, value, (4, 2)).tolist() == out.tolist()
t = '(PairTensor, SparseTensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit((x1, x2), adj.t()).tolist() == out.tolist()
def test_cg_conv():
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, 16))
pseudo = torch.rand((edge_index.size(1), 3))
conv = CGConv(16, 3)
assert conv.__repr__() == 'CGConv(16, 16, dim=3)'
out = conv(x, edge_index, pseudo)
assert out.size() == (num_nodes, 16)
jit = torch.jit.script(conv.jittable())
assert jit(x, edge_index, pseudo).tolist() == out.tolist()