OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
aicomputer-visiondeep-learningdeploy-aidiffusion-modelsgenerative-aigood-first-issueinferencellm-inferencenatural-language-processingnlpopenvinooptimize-aiperformance-boostrecommendation-systemspeech-recognitionstable-diffusiontransformersyolo
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* Written the header file for the nGraph operation DFT. * Small change. * Started to write the implementation of the nGraph shell of the operation DFT. * Started to write void op::v7::DFT::validate_and_infer_types(). * Small fixes. * Code style fixes. * Written the draft of the shape infer for the nGraph operation DFT. * Small fixes. * Code style fixes. * Added DFT into opset7 table. * Some additions. * Small fixes. * Code style fix. * Some fixes. * Some fix. * Small fixes. * Started to write shape infer tests for the nGraph operation DFT. * Written shape infer tests for the nGraph operation DFT. * Some code style fixes. * Small fix. * Code style fixes. * Code style fixes. * Deleted unused variables. * Added support for negative axes. * Started to write IE IR Reader tests for the nGraph operation DFT. * Small fix. * Added the second IE IR Reader test for the nGraph operation DFT. * Small fix. * Added the third IE IR Reader test for the nGraph operation DFT. * Corrected Doxygen comment. * Started to rewrite DFT type_prop tests as parametrized tests. * Small fixes. * Some additions. * Small fix. * Small fix. * Some tests were written as parametrized tests. Some code style fixes. * Code style fixes. * Continued to rewrite tests for DFT as parametrized ones. * Some deletions. * Some additions. * Deleted redundant tests. * Started to rewrite some tests. * Some changes. * Deleted commented code. * Started to convert tests for the case non-constant axes into parametrized tests. * Rewritten tests for the case non-constant axes as parametrized tests. * Started to convert tests for the case non-constant signal_size into parametrized tests. * Rewritten tests for the case non-constant signal size as parametrized tests. * Added checks for number of inputs. * Small fixes. * Small fixes. * Refactored shape infer and corrected tests. * Some refactoring. * Now the function validate() is protected. * Small refactoring. * Fixed typo. * Added some comments. * Fixes in infer function. * Added test. * Fixed test. |
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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
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.