def test_compress_model_transformation():
node_constant = ov.opset8.constant(np.array([[0.0, 0.1, -0.1], [-2.5, 2.5, 3.0]], dtype=np.float32))
node_ceil = ov.opset8.ceiling(node_constant)
func = Function(node_ceil, [], "TestFunction")
assert func.get_ordered_ops()[0].get_element_type().get_type_name() == "f32"
compress_model_transformation(func)
assert func is not None
assert func.get_ordered_ops()[0].get_element_type().get_type_name() == "f16"
def test_core_class():
input_shape = [1, 3, 4, 4]
param = ov.parameter(input_shape, np.float32, name="parameter")
relu = ov.relu(param, name="relu")
func = Function([relu], [param], "test")
func.get_ordered_ops()[2].friendly_name = "friendly"
core = Core()
model = core.compile_model(func, "CPU", {})
request = model.create_infer_request()
input_data = np.random.rand(*input_shape).astype(np.float32) - 0.5
expected_output = np.maximum(0.0, input_data)
input_tensor = Tensor(input_data)
results = request.infer({"parameter": input_tensor})
assert np.allclose(results, expected_output)
def test_serialize_pass_v2():
core = Core()
xml_path = "./serialized_function.xml"
bin_path = "./serialized_function.bin"
shape = [100, 100, 2]
parameter_a = ov.opset8.parameter(shape, dtype=np.float32, name="A")
parameter_b = ov.opset8.parameter(shape, dtype=np.float32, name="B")
model = ov.opset8.floor(ov.opset8.minimum(ov.opset8.abs(parameter_a), parameter_b))
func = Function(model, [parameter_a, parameter_b], "Function")
serialize(func, xml_path, bin_path)
assert func is not None
res_func = core.read_model(model=xml_path, weights=bin_path)
assert func.get_parameters() == res_func.get_parameters()
assert func.get_ordered_ops() == res_func.get_ordered_ops()
os.remove(xml_path)
os.remove(bin_path)
