Evgeny Lazarev
99f94ca09c
* Added support for Gelu-6 to the MO * Adding Gelu-6 to ngraph and python API + some tests * Fixed typo in the Gelu approximation mode * Fixed Gelu-6 reference implementation for Tanh mode * Added transformation to downgrade v6::Gelu to v2::Gelu * Added specification for the Gelu-6 * Code style fixes * The Gelu-6 operation specification update * Fixed compilation issue in reference implementation for Gelu * Fix compilation issues for some OSs * Code style fix * One more cpplint issue fix * Fixed Gelu6 reference implementation compilation on Windows. * Code style fix * Fixed various ngraph unit tests * Code style check * Reverted Gelu-2 to be fused op * Fixed Gelu6 downgrade transformation * Added unit test for Gelu6Downgrade transformation * Update copyright year * Updated copyright year * Replaced tab characters with 4 spaces in IR reader tests * Code style fixes * Added default value for GeluApproximation mode for Gelu-6 op * Fixed code style for Gelu-6 * Changed order of parameters for the Gelu evaluate to potentially avoid backward compatibility issues with ARM plugin * Fixed code style * Introduced opset7. Moved Gelu6 to opset7 * Fixed non-updated transformation * Fixed opset version in ngraph Python API for Gelu operation * Fixed typo in the opset number in the documentation * Reverted some changes related to Gelu6 * Updated MO to produce Gelu7 * Updated unit tests for Gelu * Updated Gelu7 specification * Changed gelu reference implementation. Added opset7 to Python packages * Updated Python API tests for Gelu operation * Code style fix * Marked get_approximation_mode function as const * Added missing "const" qualifier * Fixed code style issues in tests * Added extractor for MxNet operation Gelu * Spelling issues fix * Updated MxNet supported symbols * Added NGRAPH_OP_SCOPE for Gelu7 validate_and_infer_types * Fixed a typo in the comment
OpenVINO™ Toolkit
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.
Resources:
- Docs: https://docs.openvinotoolkit.org/
- Wiki: https://github.com/openvinotoolkit/openvino/wiki
- Issue tracking: https://github.com/openvinotoolkit/openvino/issues
- Storage: https://storage.openvinotoolkit.org/
- Additional OpenVINO™ modules: https://github.com/openvinotoolkit/openvino_contrib
- Intel® Distribution of OpenVINO™ toolkit Product Page
- Intel® Distribution of OpenVINO™ toolkit Release Notes
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