Inference Engine Extensibility API allows to add support of custom operations to the Inference Engine. Extension should contain operation sets with custom operations and execution kernels for custom operations. Physically, an extension library can be represented as a dynamic library exporting the single
CreateExtension function that allows to create a new extension instance.
Inference Engine Extension dynamic library contains several components:
InferenceEngine::Core::ReadNetworkto read Intermediate Representation (IR) with unsupported operations
ngraph::Functionwith unsupported operations
NOTE: This documentation is written based on the
Template extension, which demonstrates extension
development details. Find the complete code of the
Template extension, which is fully compilable and up-to-date, at
<dldt source tree>/docs/template_extension.
The Inference Engine workflow involves the creation of custom kernels and either custom or existing operations.
An Operation is a network building block implemented in the training framework, for example,
Convolution in Caffe*. A Kernel is defined as the corresponding implementation in the Inference Engine.
Refer to the Model Optimizer Extensibility for details on how a mapping between framework operations and Inference Engine kernels is registered.
In short, you can plug your own kernel implementations into the Inference Engine and map them to the operations in the original framework.
The following pages describe how to integrate custom kernels into the Inference Engine: