def test_gmm_conv(separate_gaussians):
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 = GMMConv(8,
32,
dim=3,
kernel_size=25,
separate_gaussians=separate_gaussians)
assert conv.__repr__() == 'GMMConv(8, 32, dim=3)'
out = conv(x1, edge_index, value)
assert out.size() == (4, 32)
assert torch.allclose(conv(x1, edge_index, value, size=(4, 4)), out)
assert torch.allclose(conv(x1, adj.t()), out)
if is_full_test():
t = '(Tensor, Tensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert torch.allclose(jit(x1, edge_index, value), out)
assert torch.allclose(jit(x1, edge_index, value, size=(4, 4)), out)
t = '(Tensor, SparseTensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert torch.allclose(jit(x1, adj.t()), out)
adj = adj.sparse_resize((4, 2))
conv = GMMConv((8, 16),
32,
dim=3,
kernel_size=5,
separate_gaussians=separate_gaussians)
assert conv.__repr__() == 'GMMConv((8, 16), 32, dim=3)'
out1 = conv((x1, x2), edge_index, value)
out2 = conv((x1, None), edge_index, value, (4, 2))
assert out1.size() == (2, 32)
assert out2.size() == (2, 32)
assert torch.allclose(conv((x1, x2), edge_index, value, (4, 2)), out1)
assert torch.allclose(conv((x1, x2), adj.t()), out1)
assert torch.allclose(conv((x1, None), adj.t()), out2)
if is_full_test():
t = '(OptPairTensor, Tensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert torch.allclose(jit((x1, x2), edge_index, value), out1)
assert torch.allclose(jit((x1, x2), edge_index, value, size=(4, 2)),
out1)
assert torch.allclose(jit((x1, None), edge_index, value, size=(4, 2)),
out2)
t = '(OptPairTensor, SparseTensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert torch.allclose(jit((x1, x2), adj.t()), out1)
assert torch.allclose(jit((x1, None), adj.t()), out2)
def test_gmm_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))
pseudo = torch.rand((edge_index.size(1), 3))
conv = GMMConv(in_channels, out_channels, dim=3, kernel_size=25)
assert conv.__repr__() == 'GMMConv(16, 32)'
out = conv(x, edge_index, pseudo)
assert out.size() == (num_nodes, out_channels)
jit_conv = conv.jittable()
jit_conv = torch.jit.script(jit_conv)
assert jit_conv(x, edge_index, pseudo).tolist() == out.tolist()
conv = GMMConv(in_channels,
out_channels,
dim=3,
kernel_size=25,
separate_gaussians=True)
out = conv(x, edge_index, pseudo)
assert out.size() == (num_nodes, out_channels)
jit_conv = conv.jittable()
jit_conv = torch.jit.script(jit_conv)
assert jit_conv(x, edge_index, pseudo).tolist() == out.tolist()
def test_gmm_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))
pseudo = torch.rand((edge_index.size(1), 3))
conv = GMMConv(in_channels, out_channels, dim=3, kernel_size=25)
assert conv.__repr__() == 'GMMConv(16, 32)'
assert conv(x, edge_index, pseudo).size() == (num_nodes, out_channels)
def test_lazy_gmm_conv(separate_gaussians):
x1 = torch.randn(4, 8)
x2 = torch.randn(2, 16)
edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
value = torch.rand(edge_index.size(1), 3)
conv = GMMConv(-1,
32,
dim=3,
kernel_size=25,
separate_gaussians=separate_gaussians)
assert conv.__repr__() == 'GMMConv(-1, 32, dim=3)'
out = conv(x1, edge_index, value)
assert out.size() == (4, 32)
conv = GMMConv((-1, -1),
32,
dim=3,
kernel_size=25,
separate_gaussians=separate_gaussians)
assert conv.__repr__() == 'GMMConv((-1, -1), 32, dim=3)'
out = conv((x1, x2), edge_index, value)
assert out.size() == (2, 32)