def test_sage_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
adj = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
conv = SAGEConv(8, 32)
assert conv.__repr__() == 'SAGEConv(8, 32)'
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))
conv = SAGEConv((8, 16), 32)
assert conv.__repr__() == 'SAGEConv((8, 16), 32)'
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_sage_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))
conv = SAGEConv(in_channels, out_channels)
assert conv.__repr__() == 'SAGEConv(16, 32)'
assert conv(x, edge_index).size() == (num_nodes, out_channels)
def test_sage_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))
conv = SAGEConv(in_channels, out_channels)
assert conv.__repr__() == 'SAGEConv(16, 32)'
out = conv(x, edge_index)
assert out.size() == (num_nodes, out_channels)
assert conv((x, x), edge_index).tolist() == out.tolist()
jit_conv = conv.jittable(x=x, edge_index=edge_index)
jit_conv = torch.jit.script(jit_conv)
assert jit_conv(x, edge_index).tolist() == out.tolist()
def test_sage_conv():
row = np.array([0, 1, 1, 2])
col = np.array([1, 0, 2, 1])
data = np.array([1, 3, 5])
in_channels, out_channels = (1, 1)
edge_index = torch.tensor([row, col])
num_nodes = edge_index.max().item() + 1
x = torch.tensor([data], dtype=torch.float).permute(1, 0)
assert x.size() == (num_nodes, in_channels)
conv = SAGEConv(in_channels, out_channels, normalize=False, bias=False)
conv.weight[0][
0] = 1.0 # Forcing the weight to 1.0 for testing convenience!
assert conv.__repr__() == f'SAGEConv({in_channels}, {out_channels})'
output = conv(x, edge_index)
assert output[0][0] == 2 # (1+3)/2
assert output[1][0] == 3 # (1+3+5)/3
assert output[2][0] == 4 # (3+5)/2
assert conv(x, edge_index).size() == (num_nodes, out_channels)
