def test_cheb_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
edge_weight = torch.rand(edge_index.size(1))
x = torch.randn((num_nodes, in_channels))
conv = ChebConv(in_channels, out_channels, K=3)
assert conv.__repr__() == 'ChebConv(16, 32, K=3, normalization=sym)'
assert conv(x, edge_index).size() == (num_nodes, out_channels)
assert conv(x, edge_index, edge_weight).size() == (num_nodes, out_channels)
assert conv(x, edge_index, edge_weight,
lambda_max=2.0).size() == (num_nodes, out_channels)
batch = torch.tensor([0, 0, 1, 1])
edge_index = torch.tensor([[0, 1, 2, 3], [1, 0, 3, 2]])
num_nodes = edge_index.max().item() + 1
edge_weight = torch.rand(edge_index.size(1))
x = torch.randn((num_nodes, in_channels))
lambda_max = torch.tensor([2.0, 2.0])
assert conv(x, edge_index, edge_weight,
batch).size() == (num_nodes, out_channels)
assert conv(x, edge_index, edge_weight, batch,
lambda_max).size() == (num_nodes, out_channels)
def test_cheb_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
edge_attr = torch.rand(edge_index.size(1))
x = torch.randn((num_nodes, in_channels))
conv = ChebConv(in_channels, out_channels, K=3)
assert conv.__repr__() == 'ChebConv(16, 32, K=3)'
assert conv(x, edge_index).size() == (num_nodes, out_channels)
assert conv(x, edge_index, edge_attr).size() == (num_nodes, out_channels)
def test_cheb_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
edge_weight = torch.rand(edge_index.size(1))
x = torch.randn((num_nodes, in_channels))
conv = ChebConv(in_channels, out_channels, K=3)
assert conv.__repr__() == 'ChebConv(16, 32, K=3, normalization=sym)'
out1 = conv(x, edge_index)
assert out1.size() == (num_nodes, out_channels)
out2 = conv(x, edge_index, edge_weight)
assert out2.size() == (num_nodes, out_channels)
out3 = conv(x, edge_index, edge_weight, lambda_max=3.0)
assert out3.size() == (num_nodes, out_channels)
jit_conv = conv.jittable(x=x, edge_index=edge_index)
jit_conv = torch.jit.script(jit_conv)
assert jit_conv(x, edge_index).tolist() == out1.tolist()
assert jit_conv(x, edge_index, edge_weight).tolist() == out2.tolist()
assert jit_conv(x, edge_index, edge_weight,
lambda_max=torch.tensor(3.0)).tolist() == out3.tolist()
batch = torch.tensor([0, 0, 1, 1])
edge_index = torch.tensor([[0, 1, 2, 3], [1, 0, 3, 2]])
num_nodes = edge_index.max().item() + 1
edge_weight = torch.rand(edge_index.size(1))
x = torch.randn((num_nodes, in_channels))
lambda_max = torch.tensor([2.0, 3.0])
out4 = conv(x, edge_index, edge_weight, batch)
assert out4.size() == (num_nodes, out_channels)
out5 = conv(x, edge_index, edge_weight, batch, lambda_max)
assert out5.size() == (num_nodes, out_channels)
assert jit_conv(x, edge_index, edge_weight,
batch).tolist() == out4.tolist()
assert jit_conv(x, edge_index, edge_weight, batch,
lambda_max).tolist() == out5.tolist()
