* [shape_infer] add shape_infer for ExperimentalDetectronROIFeatureExtractor op Signed-off-by: Li, Tingqian <tingqian.li@intel.com> * add test * Use compatible & merge for intersection checks * Update Signed-off-by: Li, Tingqian <tingqian.li@intel.com> * Add perf_test Signed-off-by: Li, Tingqian <tingqian.li@intel.com> * Initial commit * fix compile issue * Add test * fix clang format issue * support for pads_begin/pads_end with different sizes * fix bug in EDGE mode checking * fix padding mode checks * fix according to jane's review comment * fix const reference Signed-off-by: Li, Tingqian <tingqian.li@intel.com> * Initial commit Signed-off-by: Li, Tingqian <tingqian.li@intel.com> * fix bugs Signed-off-by: Li, Tingqian <tingqian.li@intel.com> * Switch to use single generic code with small helper template Signed-off-by: Li, Tingqian <tingqian.li@intel.com> * Initial commit on Split Signed-off-by: Li, Tingqian <tingqian.li@intel.com> * Convolution update * Adds pragma once * Reductions shape infer * Shape nodes * style * Update * add exp detectron roi feature * Update Signed-off-by: Li, Tingqian <tingqian.li@intel.com> * Use get_data_as_int64 + constant_data * Add test * Add utils.hpp into cpuUnit shape inference test * avoid using friend template function * fix topk axis bug * Add bucketize * Add embeddingbag offsets sum * Add embedding segments sum * fix code style issue * Add Range_4 * Update tests * Add range * Add region Yolo * Add reorg * fix according to Globev's comment * call shape_infer in evaluate_variadic_split() * fix CI issue * fix CI issue * fix CI issue, topk change revert * fix flake8 E302 * fix myriad smoke test issue * fix according to Vladislav's second round review * fix format * Add StridedSlice & Einsum * fix pad_test.cpp build issue * fix according to review comment * insert directly into output shape * revert infer_slice_shape() change since vpux compiler uses this function * move tests Co-authored-by: Stepyreva, Evgenya <evgenya.stepyreva@intel.com> |
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SECURITY.md |
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
openvino
tag on StackOverflow* - GitHub* Issues
- Forum
* Other names and brands may be claimed as the property of others.