def test_shape_elemental_gate(
elemental_gate_layer,
elements,
random_int_input,
random_shape,
):
out_shape = random_shape + [len(elements)]
assert_output_shape_valid(elemental_gate_layer, [random_int_input], out_shape)
def test_shape_cfconv(
cfconv_layer,
random_atomic_env,
r_ij,
neighbors,
neighbor_mask,
f_ij,
cfconv_output_shape,
):
inputs = [random_atomic_env, r_ij, neighbors, neighbor_mask, f_ij]
assert_output_shape_valid(cfconv_layer, inputs, cfconv_output_shape)
def test_shape_schnetinteraction(
schnet_interaction,
random_atomic_env,
r_ij,
neighbors,
neighbor_mask,
f_ij,
interaction_output_shape,
):
inputs = [random_atomic_env, r_ij, neighbors, neighbor_mask, f_ij]
assert_output_shape_valid(schnet_interaction, inputs, interaction_output_shape)
def test_shape_scale_shift(random_float_input, random_shape):
mean = torch.rand(1)
std = torch.rand(1)
model = spk.nn.ScaleShift(mean, std)
assert_output_shape_valid(model, [random_float_input], random_shape)
def test_shape_dense(dense_layer, random_float_input, random_shape, random_output_dim):
out_shape = random_shape[:-1] + [random_output_dim]
assert_output_shape_valid(dense_layer, [random_float_input], out_shape)
def test_gaussian_smearing(
gaussion_smearing_layer, random_interatomic_distances, gaussian_smearing_shape
):
assert_output_shape_valid(
gaussion_smearing_layer, [random_interatomic_distances], gaussian_smearing_shape
)
def test_shape_schnet(schnet, schnet_batch, schnet_output_shape):
assert_output_shape_valid(schnet, [schnet_batch], schnet_output_shape)
def x_test_shape_neighbor_elements(atomic_numbers, neighbors):
# ToDo: change Docstring or squeeze()
model = spk.nn.NeighborElements()
inputs = [atomic_numbers.unsqueeze(-1), neighbors]
out_shape = list(neighbors.shape)
assert_output_shape_valid(model, inputs, out_shape)
def test_shape_cutoff(cutoff_layer, random_interatomic_distances):
out_shape = list(random_interatomic_distances.shape)
assert_output_shape_valid(cutoff_layer, [random_interatomic_distances], out_shape)
def test_shape_tiled_multilayer_network(
tiled_mlp_layer, n_mlp_tiles, random_float_input, random_shape, random_output_dim
):
out_shape = random_shape[:-1] + [random_output_dim * n_mlp_tiles]
assert_output_shape_valid(tiled_mlp_layer, [random_float_input], out_shape)
def test_shape_aggregate():
model = spk.nn.Aggregate(axis=1)
input_data = torch.rand((3, 4, 5))
inputs = [input_data]
out_shape = [3, 5]
assert_output_shape_valid(model, inputs, out_shape)
def test_shape_standardize(random_float_input, random_shape):
mean = torch.rand(1)
std = torch.rand(1)
model = spk.nn.Standardize(mean, std)
assert_output_shape_valid(model, [random_float_input], random_shape)