This section illustrates how to construct an nGraph function composed of operations from the
opset3 namespace. Once created, it can wrap into a
CNNNetwork, creating utility for data scientists or app developers to define a deep-learning model in a neutral way that does not depend on existing Deep Learning (DL) frameworks.
opsetX integrates a list of nGraph pre-compiled operations that work for this purpose. In other words,
opsetX defines a set of operations for building a graph.
For a complete list of operation sets supported by Inference Engine, see Available Operations Sets.
To add custom nGraph operations to an existing
CNNNetwork, see the Add Custom nGraph Operations document.
Now that you can build graphs with anything from the
opset3 definition, some parameters for shape-relevant (or shape-specific) inputs can be added. The following code prepares a graph for shape-relevant parameters.
validate_nodes_and_infer_types(ops)must be included for partial shape inference.
To wrap it into a CNNNetwork, use: