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)