openvino/docs/IE_DG/Extensibility_DG/Intro.md

Inference Engine Extensibility Mechanism

Inference Engine Extensibility API enables you 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 creates a new extension instance.

To load the Extensibility library to the InferenceEngine::Core object, use the InferenceEngine::Core::AddExtension method.

Inference Engine Extension Library

Inference Engine Extension dynamic library contains the following components:

• Extension Library:
  • Contains custom operation sets.
  • Provides CPU implementations for custom operations.
• Custom nGraph Operation:
  • Enables the use of InferenceEngine::Core::ReadNetwork to read Intermediate Representation (IR) with unsupported operations.
  • Enables the creation of ngraph::Function with unsupported operations.
  • Provides a shape inference mechanism for custom 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.

Execution Kernels

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:

• Introduction to development of custom CPU kernels
• Introduction to development of custom GPU kernels
• Introduction to development of custom VPU kernels

See Also

• Build an extension library using CMake*
• Using Inference Engine Samples
• Hello Shape Infer SSD sample