4ecdd19653
* Moves splitLargeKernelConv tests to unit tests Originally, file with tests has been placed in a wrong place so it was not integrated into any testing application. Now it is a part of unit tests on VPU. Test itself has been disabled due to issue with NCE unit usage described in #-33366 * Introduces pass I/O memory types annotation of stages It is useful to see where inputs and outputs are located in performance report for analysing possible issues. * Introduces endsWith and tuple2Vector utilities endsWith checks if source has suffix equals to second argument. tuple2Vector converts tuple of arbitrary size containing the same type to vector. It could be useful working with gtest parameter generators that have std::tuple as return type. * Introduces unit tests on annotating stages memory types * Introduces missing format placeholders * Makes memory types annotation optional Enables private option "enableMemoryTypesAnnotation" which disabled by default. Disabling annotation by default allows avoid issues with tests which rely on stages names. Signed-off-by: Gladilov, Gleb <gleb.gladilov@intel.com> |
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OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository
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 two components: namely Model Optimizer and Inference Engine, as well as CPU, GPU 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.
Documentation
- OpenVINO™ Release Notes
- OpenVINO™ Inference Engine Build Instructions
- Get Started with Deep Learning Deployment Toolkit on Linux*
- Introduction to Deep Learning Deployment Toolkit
- Inference Engine Developer Guide
- Model Optimizer Developer Guide
How to Contribute
See CONTRIBUTING for contribution to the code. See CONTRIBUTING_DOCS for contribution to the documentation. Thank you!
Support
Please report questions, issues and suggestions using:
- The
openvinotag on StackOverflow* - GitHub* Issues
- Forum
* Other names and brands may be claimed as the property of others.