def run_model(onnx_model, data_inputs): # type: (onnx.ModelProto, List[np.ndarray]) -> List[np.ndarray] """ Convert ONNX model to an ngraph model and perform computation on input data. :param onnx_model: ONNX ModelProto describing an ONNX model :param data_inputs: list of numpy ndarrays with input data :return: list of numpy ndarrays with computed output """ ng_model_function = import_onnx_model(onnx_model) runtime = get_runtime() computation = runtime.computation(ng_model_function) return computation(*data_inputs)
def test_simple_graph(): node1 = make_node("Add", ["A", "B"], ["X"], name="add_node1") node2 = make_node("Add", ["X", "C"], ["Y"], name="add_node2") graph = make_graph( [node1, node2], "test_graph", [ make_tensor_value_info("A", onnx.TensorProto.FLOAT, [1]), make_tensor_value_info("B", onnx.TensorProto.FLOAT, [1]), make_tensor_value_info("C", onnx.TensorProto.FLOAT, [1]), ], [make_tensor_value_info("Y", onnx.TensorProto.FLOAT, [1])], ) model = make_model(graph, producer_name="ngraph ONNX Importer") ng_model_function = import_onnx_model(model) runtime = get_runtime() computation = runtime.computation(ng_model_function) assert np.array_equal(computation(1, 2, 3)[0], np.array([6.0], dtype=np.float32)) assert np.array_equal(computation(4, 5, 6)[0], np.array([15.0], dtype=np.float32))
def run_import(test_self: Any, device: Text) -> None: model = ModelImportRunner._load_onnx_model(model_test.model_dir, model_test.model) model_marker[0] = model assert import_onnx_model(model)