c6fc247f99
* Add nGraph function serialization. * Turn of execption throwing on failed visitation. * CNNNetworkNgrapImpl serialize also support fallback to v7 serialization. * Add error message for legacy IR not implemented case. * Store tests models in files. * Add tests with multiple layers. * Style aligned to IE rules. * Add visit_attributes to ExecutionNode. * Layer version XML atribute implementation. * Fix opset atribute creation for ExecutionGraph. Refactoring. * Add missing header. * Move opset collecting to private scope. * Add missing header. * Add test wit multiple oututs. Fix found issues: constant name, result outputs. * Move serialization to transformation library. * Add versioning to serialization transformation. * Add functional tests with ONNX importer path. * Add nodes unique name checking and correction. * Add checks for unsuported cases: dynamic shapes & GenericIE node * General refactoring. * Add comment describing type name translations. * Add serialization deterministicity tests. It's needed to ensure that subsequent calls to serialize() on the same function are giving the same results. * Serialization in CNNNetworkNGraphImpl::serialize executed via pass::Manager. Co-authored-by: Gleb Kazantaev <gleb.nnstu@gmail.com> * NGRAPH_CHECK messages refactored. * Performance and const correctness refactoring. * Style formatting applied. * Code simplifaction. * Serialize transformation documentation refactoring. * Changed compare_function() to throw on functions with multiple outputs. Before this check was implemented with assert which means it was working only in debug builds. Now it is working also in release build. * Adjust tests to new compare_functions() behaviour. * Replace cmakes add_definitions with more modern target_compile_definitions Co-authored-by: Gleb Kazantaev <gleb.nnstu@gmail.com> |
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| licensing | ||
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| openvino | ||
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| tools | ||
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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.