def test_global_functional_wrong_num_spatial_shift(self):
with self.assertRaisesRegex(
ValueError,
'num_spatial_shift can not be less than 1 but got 0'):
neural_xc.global_functional(
neural_xc.build_unet(num_filters_list=[4, 2],
core_num_filters=4,
activation='swish'),
grids=self.grids,
# Wrong num_spatial_shift
num_spatial_shift=0)
def test_global_functional_wrong_num_grids(self):
with self.assertRaisesRegex(
ValueError,
'The num_grids must be power of two plus one for global functional '
'but got 6'):
neural_xc.global_functional(
neural_xc.build_unet(num_filters_list=[4, 2],
core_num_filters=4,
activation='softplus'),
# grids with wrong num_grids.
grids=jnp.linspace(-1, 1, 6))
def test_global_functional_with_sliding_net(self, activation):
init_fn, xc_energy_density_fn = (neural_xc.global_functional(
neural_xc.build_sliding_net(window_size=3,
num_filters_list=[4, 2, 2],
activation=activation),
grids=self.grids))
init_params = init_fn(rng=random.PRNGKey(0))
xc_energy_density = xc_energy_density_fn(self.density, init_params)
self.assertEqual(xc_energy_density.shape, (17, ))
def test_global_functional_with_sliding_net_wrong_output_shape(self):
init_fn, xc_energy_density_fn = (
neural_xc.global_functional(
stax.serial(
neural_xc.build_sliding_net(window_size=3,
num_filters_list=[4, 2, 2],
activation='softplus'),
# Additional conv layer to make the output shape wrong.
neural_xc.Conv1D(1,
filter_shape=(1, ),
strides=(2, ),
padding='VALID')),
grids=self.grids))
init_params = init_fn(rng=random.PRNGKey(0))
with self.assertRaisesRegex(
ValueError, r'The output shape of the network '
r'should be \(-1, 17\) but got \(1, 9\)'):
xc_energy_density_fn(self.density, init_params)
