def test_gin_conv():
x1 = torch.randn(4, 16)
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))
nn = Seq(Lin(16, 32), ReLU(), Lin(32, 32))
conv = GINConv(nn, train_eps=True)
assert conv.__repr__() == (
'GINConv(nn=Sequential(\n'
' (0): Linear(in_features=16, out_features=32, bias=True)\n'
' (1): ReLU()\n'
' (2): Linear(in_features=32, out_features=32, bias=True)\n'
'))')
out = conv(x1, edge_index)
assert out.size() == (4, 32)
assert conv(x1, edge_index, size=(4, 4)).tolist() == out.tolist()
assert conv(x1, adj.t()).tolist() == out.tolist()
if is_full_test():
t = '(Tensor, Tensor, 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, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x1, adj.t()).tolist() == out.tolist()
adj = adj.sparse_resize((4, 2))
out1 = conv((x1, x2), edge_index)
out2 = conv((x1, None), edge_index, (4, 2))
assert out1.size() == (2, 32)
assert out2.size() == (2, 32)
assert conv((x1, x2), edge_index, (4, 2)).tolist() == out1.tolist()
assert conv((x1, x2), adj.t()).tolist() == out1.tolist()
assert conv((x1, None), adj.t()).tolist() == out2.tolist()
if is_full_test():
t = '(OptPairTensor, Tensor, Size) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit((x1, x2), edge_index).tolist() == out1.tolist()
assert jit((x1, x2), edge_index, size=(4, 2)).tolist() == out1.tolist()
assert jit((x1, None), edge_index,
size=(4, 2)).tolist() == out2.tolist()
t = '(OptPairTensor, SparseTensor, 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_gin_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))
nn = Seq(Lin(in_channels, 32), ReLU(), Lin(32, out_channels))
conv = GINConv(nn, train_eps=True)
assert conv.__repr__() == (
'GINConv(nn=Sequential(\n'
' (0): Linear(in_features=16, out_features=32, bias=True)\n'
' (1): ReLU()\n'
' (2): Linear(in_features=32, out_features=32, bias=True)\n'
'))')
assert conv(x, edge_index).size() == (num_nodes, out_channels)
conv = GINConv(nn, train_eps=False)
assert conv(x, edge_index).size() == (num_nodes, out_channels)
