def test_agnn_conv():
in_channels = 16
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))
conv = AGNNConv(requires_grad=True)
assert conv.__repr__() == 'AGNNConv()'
assert conv(x, edge_index).size() == (num_nodes, in_channels)
conv = AGNNConv(requires_grad=False)
assert conv(x, edge_index).size() == (num_nodes, in_channels)
def test_agnn_conv(requires_grad):
x = torch.randn(4, 16)
edge_index = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
row, col = edge_index
adj = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
conv = AGNNConv(requires_grad=requires_grad)
assert conv.__repr__() == 'AGNNConv()'
out = conv(x, edge_index)
assert out.size() == (4, 16)
assert torch.allclose(conv(x, adj.t()), out, atol=1e-6)
t = '(Tensor, Tensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x, edge_index).tolist() == out.tolist()
t = '(Tensor, SparseTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert torch.allclose(jit(x, adj.t()), out, atol=1e-6)
def test_agnn_conv():
in_channels = 16
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))
conv = AGNNConv(requires_grad=True)
assert conv.__repr__() == 'AGNNConv()'
out = conv(x, edge_index)
assert out.size() == (num_nodes, in_channels)
jit = torch.jit.script(conv.jittable())
assert jit(x, edge_index).tolist() == out.tolist()
conv = AGNNConv(requires_grad=False)
out = conv(x, edge_index)
assert out.size() == (num_nodes, in_channels)
jit = torch.jit.script(conv.jittable())
assert jit(x, edge_index).tolist() == out.tolist()