def test_dead_layer_remove(self):
input_features = [("data", datatypes.Array(*(3, 4)))]
output_features = [("out", None)]
builder = neural_network.NeuralNetworkBuilder(
input_features, output_features, disable_rank5_shape_mapping=True
)
builder.add_activation("relu1", "RELU", "data", "relu1")
builder.add_load_constant_nd(
"const1", "c1", constant_value=np.ones((5,)), shape=(5,)
)
builder.add_load_constant_nd(
"const2", "c2", constant_value=np.ones((5,)), shape=(5,)
)
builder.add_split_nd(
"splitnd1", "const2", ["s1", "s2", "s3"], axis=0, num_splits=3
)
builder.add_squeeze("squeeze", "s1", "squeeze_out")
builder.add_activation("relu4", "RELU", "s2", "relu4")
builder.add_activation("relu5", "RELU", "relu4", "relu5")
builder.add_load_constant_nd(
"const3", "c3", constant_value=np.ones((5,)), shape=(5,)
)
builder.add_activation("relu2", "RELU", "relu1", "out")
spec = builder.spec
np.testing.assert_equal(9, len(spec.neuralNetwork.layers))
remove_disconnected_layers(spec)
np.testing.assert_equal(2, len(spec.neuralNetwork.layers))
def test_dead_layer_partial_branch(self):
convergence_tolerance = 1e-8
input_features = [("input", datatypes.Array(*(2,)))]
output_features = [("out", None)]
builder = neural_network.NeuralNetworkBuilder(
input_features, output_features, disable_rank5_shape_mapping=True
)
# add condition to break from the loop, if convergence criterion is met
builder.add_less_than("cond", ["input"], "cond", alpha=convergence_tolerance)
branch_layer = builder.add_branch("branch_layer", "cond")
builder_ifbranch = neural_network.NeuralNetworkBuilder(
nn_spec=branch_layer.branch.ifBranch
)
builder_ifbranch.add_activation("relu1", "RELU", "input", "relu1_out")
builder_ifbranch.add_activation("relu2_out", "RELU", "relu1_out", "relu2_out")
builder_elsebranch = neural_network.NeuralNetworkBuilder(
nn_spec=branch_layer.branch.elseBranch
)
builder_elsebranch.add_activation("linear1", "LINEAR", "input", "linear1_out")
builder_elsebranch.add_activation(
"linear_red_1", "LINEAR", "input", "linear_red1_out"
)
builder_elsebranch.add_activation(
"linear_red_2", "LINEAR", "linear_red1_out", "linear_red2_out"
)
builder_elsebranch.add_activation(
"linear2", "LINEAR", "linear1_out", "relu2_out"
)
builder.add_squeeze("out", "relu2_out", "out", squeeze_all=True)
mlmodel = MLModel(builder.spec, compute_units=ComputeUnit.CPU_ONLY)
if not _IS_MACOS:
# Can not get predictions unless on macOS.
return
data = np.random.rand(2,)
data_dict = {"input": data}
before_pass_out = mlmodel.predict(data_dict)["out"]
if DEBUG:
print("\n mlmodel description before remove disconnected layers pass: \n")
print_network_spec(builder.spec, style="coding")
old_spec = copy.copy(builder.spec)
remove_disconnected_layers(builder.spec)
if DEBUG:
print("\n mlmodel description after remove disconnected layers pass: \n")
print_network_spec(builder.spec, style="coding")
mlmodel = MLModel(builder.spec, compute_units=ComputeUnit.CPU_ONLY)
after_pass_out = mlmodel.predict(data_dict)["out"]
np.testing.assert_almost_equal(before_pass_out, after_pass_out, decimal=2)
np.testing.assert_equal(
len(old_spec.neuralNetwork.layers[1].branch.ifBranch.layers),
len(builder.spec.neuralNetwork.layers[1].branch.ifBranch.layers),
)
np.testing.assert_equal(
len(builder.spec.neuralNetwork.layers[1].branch.elseBranch.layers), 2
)
def test_load_constant_remove():
input_features = [("data", datatypes.Array(*(3, 4)))]
output_features = [("out", None)]
builder = neural_network.NeuralNetworkBuilder(
input_features, output_features, disable_rank5_shape_mapping=True
)
builder.add_activation("relu1", "RELU", "data", "relu1")
builder.add_load_constant_nd(
"const1", "c1", constant_value=np.ones((5,)), shape=(5,)
)
builder.add_activation("relu2", "RELU", "relu1", "out")
builder.add_load_constant_nd(
"const2", "c2", constant_value=np.ones((5,)), shape=(5,)
)
builder.add_load_constant_nd(
"const3", "c3", constant_value=np.ones((5,)), shape=(5,)
)
spec = builder.spec
np.testing.assert_equal(5, len(spec.neuralNetwork.layers))
remove_disconnected_layers(spec)
np.testing.assert_equal(2, len(spec.neuralNetwork.layers))