openvino/docs/IE_DG/Migration_CoreAPI.md

Migration from Inference Engine Plugin API to Core API

For 2019 R2 Release, the new Inference Engine Core API is introduced. This guide is updated to reflect the new API approach. The Inference Engine Plugin API is still supported, but is going to be deprecated in future releases.

This section provides common steps to migrate your application written using the Inference Engine Plugin API (InferenceEngine::InferencePlugin) to the Inference Engine Core API (InferenceEngine::Core).

To learn how to write a new application using the Inference Engine, refer to Integrate the Inference Engine Request API with Your Application and Inference Engine Samples Overview.

Inference Engine Core Class

The Inference Engine Core class is implemented on top existing Inference Engine Plugin API and handles plugins internally. The main responsibility of the InferenceEngine::Core class is to hide plugin specifics inside and provide a new layer of abstraction that works with devices (InferenceEngine::Core::GetAvailableDevices). Almost all methods of this class accept deviceName as an additional parameter that denotes an actual device you are working with. Plugins are listed in the plugins.xml file, which is loaded during constructing InferenceEngine::Core objects:

<ie>
    <plugins>
        <plugin name="CPU" location="libMKLDNNPlugin.so">
        </plugin>
        ...
</ie>

Migration Steps

Common migration process includes the following steps:

1. Migrate from the InferenceEngine::InferencePlugin initialization:

InferenceEngine::InferencePlugin plugin = InferenceEngine::PluginDispatcher({ FLAGS_pp }).getPluginByDevice(FLAGS_d);

to the InferenceEngine::Core class initialization:

InferenceEngine::Core core;

2. Instead of using InferenceEngine::CNNNetReader to read IR:

CNNNetReader network_reader;
network_reader.ReadNetwork(fileNameToString(input_model));
network_reader.ReadWeights(fileNameToString(input_model).substr(0, input_model.size() - 4) + ".bin");
CNNNetwork network = network_reader.getNetwork();

read networks using the Core class:

CNNNetwork network = core.ReadNetwork(input_model);

The Core class also allows reading models from ONNX format:

CNNNetwork network = core.ReadNetwork("model.onnx");

3. Instead of adding CPU device extensions to the plugin:

plugin.AddExtension(std::make_shared<Extensions::Cpu::CpuExtensions>());

add extensions to CPU device using the Core class:

core.AddExtension(std::make_shared<Extensions::Cpu::CpuExtensions>(), "CPU");

4. Instead of setting configuration keys to a particular plugin, set (key, value) pairs via InferenceEngine::Core::SetConfig

core.SetConfig({{PluginConfigParams::KEY_CONFIG_FILE, FLAGS_c}}, "GPU");

Note

: If deviceName is omitted as the last argument, configuration is set for all Inference Engine devices.

5. Migrate from loading the network to a particular plugin:

auto execNetwork = plugin.LoadNetwork(network, { });

to InferenceEngine::Core::LoadNetwork to a particular device:

auto execNetwork = core.LoadNetwork(network, deviceName, { });

After you have an instance of InferenceEngine::ExecutableNetwork, all other steps are as usual.