Maxim Vafin 3f35e2a321 Enable new FP16 and support mixed precision by MO (#8514)
* Enable new FP16 format and support mixed precision

* Apply review comments

* Fix issue with fp64 in FakeQuantWithMinMaxVars.py

* Enabme decompression converts fusing for CPU plugin

* Apply review feedback

* Fix code style

* Fix issue with np.full and apply review feedback

* Apply review feedback

* Fix HardSigmoid onnx extractor

* Replace np.arrays that were skipped with mo_array

* Fix compress_quantized_weights_test.py

* Fix import issues

* Apply review feedback and fix type of fusing linops in MO

* Apply review feedback

* Fix types for Mean/Scales and MXNET zeros

* Add RandomUniform_8 to ConvertPrecision

* Fix merge issue

* Fix consts names collision in GPU plugin
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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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Languages
C++ 80.5%
Python 15.5%
C 2.8%
CMake 0.9%
Cython 0.1%