def test_serialize():
"""Tests that the Concat layer correctly serializes itself.
"""
n_inputs = 125
fullyconnected_outputs = 246
weights = np.random.uniform(size=(n_inputs, fullyconnected_outputs))
biases = np.random.uniform(size=(fullyconnected_outputs))
fullyconnected_layer = FullyConnectedLayer(weights, biases)
means = np.random.uniform(size=(n_inputs)).astype(np.float32)
stds = np.random.uniform(size=(n_inputs)).astype(np.float32)
normalize_layer = NormalizeLayer(means, stds)
concat_layer = ConcatLayer([fullyconnected_layer, normalize_layer],
ConcatAlong.FLAT)
serialized = concat_layer.serialize()
assert serialized.WhichOneof("layer_data") == "concat_data"
assert (serialized.concat_data.concat_along ==
transformer_pb.ConcatLayerData.ConcatAlong.Value(
"CONCAT_ALONG_FLAT"))
assert len(serialized.concat_data.layers) == 2
assert serialized.concat_data.layers[0] == fullyconnected_layer.serialize()
assert serialized.concat_data.layers[1] == normalize_layer.serialize()
# TODO: This does not check that deserialized.input_layers was done
# correctly, but that should be the case as long as their deserialize
# methods work (tested in their respective files).
deserialized = ConcatLayer.deserialize(serialized)
assert deserialized.concat_along == ConcatAlong.FLAT
serialized.relu_data.SetInParent()
deserialized = ConcatLayer.deserialize(serialized)
assert deserialized is None
concat_layer.concat_along = ConcatAlong.CHANNELS
deserialized = ConcatLayer.deserialize(concat_layer.serialize())
assert deserialized.concat_along == ConcatAlong.CHANNELS
try:
ConcatAlong.deserialize(5)
assert False, "Should have errored on unrecognized serialization."
except NotImplementedError:
pass
def test_serialize():
"""Tests the Network's serialize and deserialize methods.
"""
input_dims = np.random.randint(1, 32)
output_dims = np.random.randint(1, 64)
weights = np.random.uniform(size=(input_dims, output_dims))
biases = np.random.uniform(size=(output_dims))
fullyconnected_layer = FullyConnectedLayer(weights, biases)
relu_layer = ReluLayer()
network = Network([fullyconnected_layer, relu_layer])
serialized = network.serialize()
assert len(serialized.layers) == 2
assert serialized.layers[0] == fullyconnected_layer.serialize()
assert serialized.layers[1] == relu_layer.serialize()
deserialized = Network.deserialize(serialized)
assert deserialized.serialize() == serialized
def test_serialize():
"""Tests that the Concat layer correctly serializes itself.
"""
n_inputs = 1025
fullyconnected_outputs = 2046
weights = np.random.uniform(size=(n_inputs, fullyconnected_outputs))
biases = np.random.uniform(size=(fullyconnected_outputs))
fullyconnected_layer = FullyConnectedLayer(weights, biases)
means = np.random.uniform(size=(n_inputs)).astype(np.float32)
stds = np.random.uniform(size=(n_inputs)).astype(np.float32)
normalize_layer = NormalizeLayer(means, stds)
concat_layer = ConcatLayer([fullyconnected_layer, normalize_layer],
ConcatAlong.FLAT)
serialized = concat_layer.serialize()
assert serialized.WhichOneof("layer_data") == "concat_data"
assert (serialized.concat_data.concat_along == transformer_pb.
ConcatLayerData.ConcatAlong.Value("CONCAT_ALONG_FLAT"))
assert len(serialized.concat_data.layers) == 2
assert serialized.concat_data.layers[0] == fullyconnected_layer.serialize()
assert serialized.concat_data.layers[1] == normalize_layer.serialize()
def test_transform_planes():
open_stub_ = transformer_client.open_stub
input_dims = np.random.randint(1, 32)
output_dims = np.random.randint(1, 64)
weights = np.random.uniform(size=(input_dims, output_dims))
biases = np.random.uniform(size=(output_dims))
fullyconnected_layer = FullyConnectedLayer(weights, biases)
relu_layer = ReluLayer()
network = Network([fullyconnected_layer, relu_layer])
planes = list(np.random.uniform(size=(100, 3, input_dims)))
response_messages = []
for i, plane in enumerate(planes):
transformed_polytope = transformer_pb.UPolytope()
transformed_polytope.space_dimensions = output_dims
transformed_polytope.subspace_dimensions = 2
for j in range(i + 2):
polytope = transformer_pb.VPolytope()
polytope.vertices.extend(np.matmul(plane, weights).flatten())
polytope.combinations.extend(np.eye(3).flatten())
polytope.num_vertices = 3
transformed_polytope.polytopes.append(polytope)
response = transformer_pb.TransformResponse()
response.transformed_upolytope.CopyFrom(transformed_polytope)
response_messages.append(response)
# With include_post = True.
stub = ServerStubMock(response_messages)
transformer_client.open_stub = lambda: stub
transformed = transformer_client.transform_planes(network, planes)
assert len(transformed) == len(planes)
for i, plane in enumerate(planes):
upolytope = transformed[i]
assert len(upolytope) == (i + 2)
for vpolytope in upolytope:
transformed_pre, transformed_post = vpolytope
assert len(transformed_pre) == len(transformed_post) == 3
assert np.allclose(transformed_pre, np.eye(3))
assert np.allclose(transformed_post, np.matmul(plane, weights))
assert len(stub.received_messages) == 1
received = stub.received_messages[0]
assert len(received) == 102
assert received[0].WhichOneof("request_data") == "layer"
assert received[0].layer == fullyconnected_layer.serialize()
assert received[1].layer == relu_layer.serialize()
for i, request in enumerate(received[2:]):
assert request.WhichOneof("request_data") == "upolytope"
assert request.upolytope.space_dimensions == input_dims
assert request.upolytope.subspace_dimensions == 2
assert len(request.upolytope.polytopes) == 1
assert request.upolytope.polytopes[0].num_vertices == 3
assert np.allclose(request.upolytope.polytopes[0].vertices,
planes[i].flatten())
assert np.allclose(request.upolytope.polytopes[0].combinations,
np.eye(3).flatten())