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)
