Ivan Tikhonov dbd2b5dc08 ONNX/IR/Common FrontEnds Refactoring (#9174)
* Refactor ONNX,IR,Common FrontEnds

* Rename frontends dir to frontend

* Rename frontend_manager, frontend_defs -> manager, defs; move Place, InputModel to src for Paddle FE

* remove exports for ir/input_model

* fix unit tests

* Fix unit tests

* revert ONNX_API/IR_API defines

* move manager.hpp to frontend dir

* delete common folder; rename defs to visibility; fix includes

* revert local changes

* codestyle

* fix build: set correct includes

* fix includes

* fix build; resolve review comments

* fix install paths

* correct installation folder for onnx_import

* use IE_THROW in ir frontend
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OpenVINO™ Toolkit

Stable release Apache License Version 2.0 GitHub branch checks state Azure DevOps builds (branch) PyPI Downloads

This toolkit allows developers to deploy pre-trained deep learning models through a high-level C++ Inference Engine API integrated with application logic.

This open source version includes several components: namely Model Optimizer, nGraph and Inference Engine, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as Caffe*, TensorFlow*, MXNet* and ONNX*.

Repository components:

License

Deep Learning Deployment Toolkit is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.

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C++ 80.5%
Python 15.5%
C 2.8%
CMake 0.9%
Cython 0.1%