22169a05b9
* fix ss * successfully converted * successfully run moved infer and normalizer unit-tests * successfully rewritten StridedSlice infer unittests * int64 array * Successfully converter crash-when-loading, xj_feauture and toy nets (cherry-picked maxpoolV4 and tf_broadcast_ext) * successfully moved PermuteAttrs to general mechanism * successfully converted xj_feauture and crash when loading with the new rewritten SS infer * fixed get_shape_from_slice and moved to common utils * fixed extending masks and some other * some refactoring * fixed extending masks in extractor, fixed licence year and some other code clearing * corrected a couple of unittests * fox permute for 5 rank slice and 4 rank inputs/ * WIP * Added comments * fixed StridedSlice in ProposalMutation.py * rechecked shape_infer unittests added some new cases * added shape_infer unit-tests after StridedSliceNormalizer pass and Permute unit-tests * corrected unittests * Applied review comments * general permutations for inputs implemented, corrected ellipsis unrolling when shrink_axis is at the beginning, some other corrections * removed code duplication in infer and normalizer, moved 'slices' attr normalizing to StridedSliceNormalizer.py * removed some code duplication and other minor improvements * Added tests * minor corrections * wider range of unittests added (froze the number) * review comments applied * enabled skipped unit-test * comment corrections * applied review comments: changed op -> type, added some asserts, corrected comments and other minor corrections * sorted inputs, updated Supported_Frameworks_Layers.md, some minor |
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| openvino | ||
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| SECURITY.md | ||
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 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
- Additional OpenVINO modules: https://github.com/openvinotoolkit/openvino_contrib
- HomePage
- OpenVINO™ 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.