def test_spline_conv():
warnings.filterwarnings('ignore', '.*non-optimized CPU version.*')
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 = SplineConv(8, 32, dim=3, kernel_size=5)
assert conv.__repr__() == 'SplineConv(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 = SplineConv((8, 16), 32, dim=3, kernel_size=5)
assert conv.__repr__() == 'SplineConv((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_lazy_spline_conv():
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 = SplineConv(-1, 32, dim=3, kernel_size=5)
assert conv.__repr__() == 'SplineConv(-1, 32, dim=3)'
out = conv(x1, edge_index, value)
assert out.size() == (4, 32)
conv = SplineConv((-1, -1), 32, dim=3, kernel_size=5)
assert conv.__repr__() == 'SplineConv((-1, -1), 32, dim=3)'
out = conv((x1, x2), edge_index, value)
assert out.size() == (2, 32)
def test_spline_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.rand(row.size(0), 3)
adj = SparseTensor(row=row, col=col, value=value, sparse_sizes=(4, 4))
conv = SplineConv(8, 32, dim=3, kernel_size=5)
assert conv.__repr__() == 'SplineConv(8, 32, dim=3)'
out = conv(x1, edge_index, value)
assert out.size() == (4, 32)
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 = SplineConv((8, 16), 32, dim=3, kernel_size=5)
assert conv.__repr__() == 'SplineConv((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 conv((x1, x2), edge_index, value, (4, 2)).tolist() == out1.tolist()
assert conv((x1, x2), adj.t()).tolist() == out1.tolist()
assert conv((x1, None), adj.t()).tolist() == out2.tolist()
t = '(OptPairTensor, Tensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit((x1, x2), edge_index, value).tolist() == out1.tolist()
assert jit((x1, x2), edge_index, value,
size=(4, 2)).tolist() == out1.tolist()
assert jit((x1, None), edge_index, value,
size=(4, 2)).tolist() == out2.tolist()
t = '(OptPairTensor, SparseTensor, OptTensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit((x1, x2), adj.t()).tolist() == out1.tolist()
assert jit((x1, None), adj.t()).tolist() == out2.tolist()
def test_lazy_spline_conv():
warnings.filterwarnings('ignore', '.*non-optimized CPU version.*')
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 = SplineConv(-1, 32, dim=3, kernel_size=5)
assert conv.__repr__() == 'SplineConv(-1, 32, dim=3)'
out = conv(x1, edge_index, value)
assert out.size() == (4, 32)
conv = SplineConv((-1, -1), 32, dim=3, kernel_size=5)
assert conv.__repr__() == 'SplineConv((-1, -1), 32, dim=3)'
out = conv((x1, x2), edge_index, value)
assert out.size() == (2, 32)
def test_spline_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 = SplineConv(in_channels, out_channels, dim=3, kernel_size=5)
assert conv.__repr__() == 'SplineConv(16, 32)'
assert conv(x, edge_index, pseudo).size() == (num_nodes, out_channels)
def test_spline_conv(self):
in_channels, out_channels = (16, 32)
edge_index = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0,
0]]).to(device)
num_nodes = edge_index.max().item() + 1
x = torch.randn((num_nodes, in_channels)).to(device)
pseudo = torch.rand((edge_index.size(1), 3)).to(device)
conv = SplineConv(in_channels, out_channels, dim=3,
kernel_size=5).to(device)
self.assertEqual('SplineConv(16, 32)', conv.__repr__())
with torch_geometric.debug():
self.assertEqual((num_nodes, out_channels),
conv(x, edge_index, pseudo).size())
def test_spline_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 = SplineConv(in_channels, out_channels, dim=3, kernel_size=5)
assert conv.__repr__() == 'SplineConv(16, 32, dim=3)'
out = conv(x, edge_index, pseudo)
assert out.size() == (num_nodes, out_channels)
jit_conv = conv.jittable(x=x, edge_index=edge_index, pseudo=pseudo)
jit_conv = torch.jit.script(jit_conv)
assert jit_conv(x, edge_index, pseudo).tolist() == out.tolist()
