def test_jumping_knowledge(): num_nodes, channels, num_layers = 100, 17, 5 xs = list([torch.randn(num_nodes, channels) for _ in range(num_layers)]) model = JumpingKnowledge('cat') assert model.__repr__() == 'JumpingKnowledge(cat)' out = model(xs) assert out.size() == (num_nodes, channels * num_layers) if is_full_test(): jit = torch.jit.script(model) assert torch.allclose(jit(xs), out) model = JumpingKnowledge('max') assert model.__repr__() == 'JumpingKnowledge(max)' out = model(xs) assert out.size() == (num_nodes, channels) if is_full_test(): jit = torch.jit.script(model) assert torch.allclose(jit(xs), out) model = JumpingKnowledge('lstm', channels, num_layers) assert model.__repr__() == 'JumpingKnowledge(lstm)' out = model(xs) assert out.size() == (num_nodes, channels) if is_full_test(): jit = torch.jit.script(model) assert torch.allclose(jit(xs), out)
def test_jumping_knowledge(): num_nodes, channels, num_layers = 100, 16, 4 xs = list([torch.randn(num_nodes, channels) for _ in range(num_layers)]) model = JumpingKnowledge('cat') assert model.__repr__() == 'JumpingKnowledge(cat)' assert model(xs).size() == (num_nodes, channels * num_layers) model = JumpingKnowledge('max') assert model.__repr__() == 'JumpingKnowledge(max)' assert model(xs).size() == (num_nodes, channels) model = JumpingKnowledge('lstm', channels, num_layers) assert model.__repr__() == 'JumpingKnowledge(lstm)' assert model(xs).size() == (num_nodes, channels)