def create_net(self, shape, scale, ir_version):
    '\n            ONNX net                           IR net\n\n            Input->ImageScaler->Output   =>    Input->ScaleShift(Power)\n\n        '
    import onnx
    from onnx import helper
    from onnx import TensorProto
    input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
    output = helper.make_tensor_value_info('output', TensorProto.FLOAT, shape)
    bias = np.random.randint((- 10), 10, shape[1]).astype(np.float)
    node_def = onnx.helper.make_node('ImageScaler', inputs=['input'], outputs=['output'], bias=bias, scale=scale)
    graph_def = helper.make_graph([node_def], 'test_model', [input], [output])
    onnx_net = helper.make_model(graph_def, producer_name='test_model')
    ref_net = None
    return (onnx_net, ref_net)
ONNX net                           IR net

Input->ImageScaler->Output   =>    Input->ScaleShift(Power)
def create_net(self, shape, scale, ir_version):
    '\n            ONNX net                           IR net\n\n            Input->ImageScaler->Output   =>    Input->ScaleShift(Power)\n\n        '
    import onnx
    from onnx import helper
    from onnx import TensorProto
    input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
    output = helper.make_tensor_value_info('output', TensorProto.FLOAT, shape)
    bias = np.random.randint((- 10), 10, shape[1]).astype(np.float)
    node_def = onnx.helper.make_node('ImageScaler', inputs=['input'], outputs=['output'], bias=bias, scale=scale)
    graph_def = helper.make_graph([node_def], 'test_model', [input], [output])
    onnx_net = helper.make_model(graph_def, producer_name='test_model')
    ref_net = None
    return (onnx_net, ref_net)