def test_get_config(self, random_onehot_rbf_vectors, random_features_and_targets):
_, *point_cloud = random_onehot_rbf_vectors
features, targets = random_features_and_targets
conv = layers.Convolution()
_ = conv(list(point_cloud) + list(features))
config = conv.get_config()
assert config["trainable"] is True and config["si_units"] == 16
def test_custom_radial(
self, random_onehot_rbf_vectors, random_features_and_targets
):
class MyRadial(layers.DenseRadialFactory):
def __init__(self, num_units, **kwargs):
super().__init__()
self.num_units = num_units
def get_radial(self, feature_dim, input_order=None, filter_order=None):
return Dense(feature_dim)
@classmethod
def from_json(cls, json_str: str):
return cls(**json.loads(json_str))
get_custom_objects().update({MyRadial.__name__: MyRadial})
_, *point_cloud = random_onehot_rbf_vectors
features, targets = random_features_and_targets
conv = layers.Convolution(
radial_factory="MyRadial", factory_kwargs={"num_units": 6}
)
_ = conv(list(point_cloud) + list(features))
config = json.loads(conv.radial_factory.to_json())
assert config["type"] == "MyRadial"
assert config["num_units"] == 6
def test_provided_radial_string(
self, random_onehot_rbf_vectors, random_features_and_targets
):
_, *point_cloud = random_onehot_rbf_vectors
features, targets = random_features_and_targets
conv = layers.Convolution(radial_factory="DenseRadialFactory")
_ = conv(list(point_cloud) + list(features))
config = json.loads(conv.radial_factory.to_json())
assert config["type"] == "DenseRadialFactory"
assert config["units"] == 32
def test_provided_radial_string_and_kwargs(
self, random_onehot_rbf_vectors, random_features_and_targets
):
_, *point_cloud = random_onehot_rbf_vectors
features, targets = random_features_and_targets
conv = layers.Convolution(
radial_factory="DenseRadialFactory",
factory_kwargs={"num_layers": 3, "units": 4, "kernel_lambda": 0.01},
)
_ = conv(list(point_cloud) + list(features))
config = json.loads(conv.radial_factory.to_json())
assert config["units"] == 4
def test_provided_radial_json(
self, random_onehot_rbf_vectors, random_features_and_targets
):
_, *point_cloud = random_onehot_rbf_vectors
features, targets = random_features_and_targets
d = {
"type": "DenseRadialFactory",
"num_layers": 3,
"units": 4,
"kernel_lambda": 0.01,
}
_ = layers.Convolution(radial_factory=json.dumps(d))(
list(point_cloud) + list(features)
)
def test_defaults(self, random_onehot_rbf_vectors, random_features_and_targets):
_, *point_cloud = random_onehot_rbf_vectors
features, targets = random_features_and_targets
output = layers.Convolution()(list(point_cloud) + features)
assert len(output) == 2