461d6e8a1d
* fix expand_onnx_functions * refactor + unit test * fixed function in function case * fixed expand_onnx_functions * fixed default value of shape in ValueInfo * enable xpass model * changed MergeFrom to Swap * added xfail with missing test data * added more unit tests * styles applied * used std::rotate, review remarks * removed debug code * after offline discussion remarks * fix checking input/output names on Windows * names comparator refactor * replace regex with custom comparison * review remarks * added RemoveConcatZeroDimInput transformation * added RemoveLoopDanglingParameters transformation * chage place of passes during replace * missing comment * code refactor + unit tests * remove unused headers * used std::any_of in RemoveConcatZeroDimInput * changed headers and namespaces to new ov convention * used std::any_of in RemoveConcatZeroDimInput * RemoveLoopDanglingParameters refactored * changed names to RemoveMultiSubGraphOpDanglingParams * handling multi-body cases * Handling If case during RemoveMultiSubGraphOpDanglingParams * comments and names refactor * More tests for If and TensorIterator * handle removing dagling param from one body and update all descriptors * fixed test * revert if change * moved RemoveConcatZeroDimInput and RemoveMultiSubGraphOpDanglingParams to NopElimantion * return false if node is not replaced * added validate_nodes_and_infer_types * Revert "moved RemoveConcatZeroDimInput and RemoveMultiSubGraphOpDanglingParams to NopElimantion" + remarks * review remarks * review remarks * fixed subgraph rtti * adjust passes to new structure |
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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.