Mikhail Nosov 6e05cead11 [OV20] Reference implementation for NV12toRGB and NV12toBGR operations (#7601)
* Reference implementation for NV12toRGB and NV12toBGR operations
Tests:
- ngraph: visitor + type_prop
- template plugin: reference implementation
- inference-engine: shared tests for plugins
- cpu plugin: compare with ref implementation tests

* Fix clang

* Serialization tests

* Fix clang-format

* Changed 'f32' to 'any supported floating-point type'
Added appropriate shape inference tests
Added error test for >2 inputs
Fixed failed CI tests

* Updates after rebase
+ Try to fix Ninja build

* Fix CI

* Support endianness + potential fix of win32 test fails

* Fix review comment

* Fix review comments

* Fix unit test build

* Fix unit test build #2

* Possible build fix 3

* Simplified reference tests
Observed issue with shuffling Y pixels on little-endian systems, added tests
2021-09-30 16:34:46 +03:00
2021-09-24 17:24:44 +03:00
2021-09-13 13:39:42 +03:00
2021-09-22 18:53:22 +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) 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*.

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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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