def test_point_conv():
x1 = torch.randn(4, 16)
pos1 = torch.randn(4, 3)
pos2 = torch.randn(2, 3)
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))
local_nn = Seq(Lin(16 + 3, 32), ReLU(), Lin(32, 32))
global_nn = Seq(Lin(32, 32))
conv = PointConv(local_nn, global_nn)
assert conv.__repr__() == (
'PointConv(local_nn=Sequential(\n'
' (0): Linear(in_features=19, out_features=32, bias=True)\n'
' (1): ReLU()\n'
' (2): Linear(in_features=32, out_features=32, bias=True)\n'
'), global_nn=Sequential(\n'
' (0): Linear(in_features=32, out_features=32, bias=True)\n'
'))')
out = conv(x1, pos1, edge_index)
assert out.size() == (4, 32)
assert torch.allclose(conv(x1, pos1, adj.t()), out, atol=1e-6)
t = '(OptTensor, Tensor, Tensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x1, pos1, edge_index).tolist() == out.tolist()
t = '(OptTensor, Tensor, SparseTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert torch.allclose(jit(x1, pos1, adj.t()), out, atol=1e-6)
adj = adj.sparse_resize((4, 2))
out = conv(x1, (pos1, pos2), edge_index)
assert out.size() == (2, 32)
assert conv((x1, None), (pos1, pos2), edge_index).tolist() == out.tolist()
assert torch.allclose(conv(x1, (pos1, pos2), adj.t()), out, atol=1e-6)
assert torch.allclose(conv((x1, None), (pos1, pos2), adj.t()),
out,
atol=1e-6)
t = '(PairOptTensor, PairTensor, Tensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit((x1, None), (pos1, pos2), edge_index).tolist() == out.tolist()
t = '(PairOptTensor, PairTensor, SparseTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert torch.allclose(jit((x1, None), (pos1, pos2), adj.t()),
out,
atol=1e-6)
def test_point_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))
pos = torch.rand((num_nodes, 3))
local_nn = Seq(Lin(in_channels + 3, 32), ReLU(), Lin(32, out_channels))
global_nn = Seq(Lin(out_channels, out_channels))
conv = PointConv(local_nn, global_nn)
assert conv.__repr__() == (
'PointConv(local_nn=Sequential(\n'
' (0): Linear(in_features=19, out_features=32, bias=True)\n'
' (1): ReLU()\n'
' (2): Linear(in_features=32, out_features=32, bias=True)\n'
'), global_nn=Sequential(\n'
' (0): Linear(in_features=32, out_features=32, bias=True)\n'
'))')
out = conv(x, pos, edge_index)
assert out.size() == (num_nodes, out_channels)
jit_conv = conv.jittable(x=x, pos=pos, edge_index=edge_index)
jit_conv = torch.jit.script(jit_conv)
assert jit_conv(x, pos, edge_index).tolist() == out.tolist()