Pavel Esir 4738bbd757 [MO][nG] add Gather-8 to MO, layer tests, nG python api unit-tests and up/down-grading transformations (#6560)
* gather-8 upgrade/downgrade transforms

* bump to opset8

* add Gather-8 to MO

* fix permutation for Gather

* added TF layer tests, ONNX layer tests for negative indices, and nG python api unit-tests for negative indices

* typo fix, disable downgrading transformation

* disable downgrade in clDNN, line width style fix

* all Gathers are converted to 7th version, transformations will be enabled/disabled while op will be added into plugins

* disabled Gather8LayerTest on GPU

* added common function for Op replacement

* concretized meaning of negative indices, fixed some typos

* applied review comments: left only meaningful layer tests

* removed op replacing functions from common utils

* returned back transformations without subroutines

* corrected style, added comments to common_optimizations.cpp
2021-08-13 11:56:49 +03:00
2021-08-11 16:15:53 +03:00
2021-08-13 09:16:47 +03:00
2021-08-12 11:38:03 +03:00
2021-05-31 15:24:56 +03:00
2018-10-16 13:45:03 +03:00
2020-11-17 16:44:44 +03:00

OpenVINO™ Toolkit

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

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*.

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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%