def run_node(onnx_node, data_inputs, **kwargs):
# type: (onnx.NodeProto, List[np.ndarray], Dict[Text, Any]) -> List[np.ndarray]
"""
Convert ONNX node to ngraph node and perform computation on input data.
:param onnx_node: ONNX NodeProto describing a computation node
:param data_inputs: list of numpy ndarrays with input data
:return: list of numpy ndarrays with computed output
"""
NgraphBackend.backend_name = pytest.config.getoption('backend',
default='CPU')
if NgraphBackend.supports_ngraph_device(NgraphBackend.backend_name):
return NgraphBackend.run_node(onnx_node, data_inputs, **kwargs)
else:
raise RuntimeError('The requested nGraph backend <' +
NgraphBackend.backend_name + '> is not supported!')
def test_run_model(_get_data_shapes):
a_shape, b_shape, c_shape, d_shape, out_shape = _get_data_shapes
input_a, input_b, input_c, input_d = _get_input_data(a_shape, b_shape, c_shape, d_shape)
model = _get_simple_model(a_shape, b_shape, c_shape, d_shape, out_shape)
ng_results = NgraphBackend.run_model(model, [input_a, input_b, input_c, input_d])
expected = np.dot(np.abs(input_a + input_b), input_c) + input_d
assert np.allclose(ng_results, [expected])
def test_prepare(_get_data_shapes):
a_shape, b_shape, c_shape, d_shape, out_shape = _get_data_shapes
model = _get_simple_model(a_shape, b_shape, c_shape, d_shape, out_shape)
backend = NgraphBackend.prepare(model)
for idx in range(10):
input_a, input_b, input_c, input_d = _get_input_data(a_shape, b_shape, c_shape, d_shape)
ng_results = backend.run([input_a, input_b, input_c, input_d])
expected = np.dot(np.abs(input_a + input_b), input_c) + input_d
assert np.allclose(ng_results, [expected])
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
"""
NgraphBackend.backend_name = pytest.config.getoption('backend',
default='CPU')
if NgraphBackend.supports_ngraph_device(NgraphBackend.backend_name):
ng_model_function = import_onnx_model(onnx_model)
runtime = get_runtime()
computation = runtime.computation(ng_model_function)
return computation(*data_inputs)
else:
raise RuntimeError('The requested nGraph backend <' +
NgraphBackend.backend_name + '> is not supported!')
def test_supports_device_gpu():
assert NgraphBackend.supports_device('CUDA')
def test_supports_ngraph_device_gpu():
assert NgraphBackend.supports_ngraph_device('GPU')
def test_supports_ngraph_device_nnp():
assert NgraphBackend.supports_ngraph_device('NNP')
def test_supports_ngraph_device_interpreter():
assert NgraphBackend.supports_ngraph_device('INTERPRETER')
def test_run_node():
input_data = _get_input_data([2, 3, 4, 5])
node = onnx.helper.make_node('Abs', inputs=['x'], outputs=['y'])
ng_results = NgraphBackend.run_node(node, input_data)
expected = np.abs(input_data)
assert np.array_equal(ng_results, expected)