c309bb77d2
* Loop op ngraph implementation, update IE IR Reader and ngraph to cnn converter * refactoring SubGraphOp class * type prop unit tests * ngraph code style * update comment * single layer tests for Loop operation * fix file name * Add SpecialBodyPorts attribute in Loop op, update single layer tests * first debug version * more tests * missing test file * removed not needed shapes from test data * move test data to new folder * shape infer tests * Added execution tests * add several new tests cases, strict checks in Loop impl, temporary disable single layer tests * ngraph codestyle, refactoring, clone_new_args test * resolve review remarks * fix build * fix tests * more execution tests * add a new constructor of Loop op, resolve review remarks * execution tests * synchro with current version * handle scalars and more tests * scalar test enabled * loop reference impl * bug fixes in tests, onnx importer part and in the ref implementation of the Loop op * applied remarks * handle unsupported cases * rewrite unit tests * update INTERPRETER manifest * is_termination_condition_always_true simplification * [TEST] update python models tests * review remarks * added xfail to tiny_yolov3 * missing model test * revert test data * fixed numbers of failing tests * fixed failed test description * fix test message * fix xfail test * reference implementation for ngraph::function * update loop reference implementation * Refactor loop reference implementation * ngraph codestyle * Refactoring * Submodule update * Skip check for Reduce ops in mkl for scalar cases, support for yolov3 * fix ngraph reader tests * revert ceiling op, renaming * Add allias(Ceiling) for Ceil op in mkl * delete xfails * fix build * single layer tests for tensor iterarator * Refactor TensorIterator and Loop ref impls * revert dynamic tensor creation, disable some dynamic test cases * fix warning * Resolve review remarks * revert Predefined values in Loop tests Co-authored-by: Mateusz Bencer <mateusz.bencer@intel.com> |
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| openvino | ||
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| tools | ||
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| CODEOWNERS | ||
| CONTRIBUTING_DOCS.md | ||
| CONTRIBUTING.md | ||
| get-started-linux.md | ||
| install_build_dependencies.sh | ||
| Jenkinsfile | ||
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OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository
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 two components: namely Model Optimizer and Inference Engine, as well as CPU, GPU 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.
Documentation
- OpenVINO™ Release Notes
- OpenVINO™ Inference Engine Build Instructions
- Get Started with Deep Learning Deployment Toolkit on Linux*
- Introduction to Deep Learning Deployment Toolkit
- Inference Engine Developer Guide
- Model Optimizer Developer Guide
- Get Started with DockerHub CI for OpenVINO™ toolkit
How to Contribute
See CONTRIBUTING for contribution to the code. See CONTRIBUTING_DOCS for contribution to the documentation. Thank you!
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.