2c4c3a777a
* Commit. * Written the structure InfoForLinearONNXMode5D that contains info to perform interpolation in 'linear_onnx' mode for 5D tensors. * Started to write the method get_info_for_linear_onnx_mode5D() that returns info for calculations of 'linear_onnx' mode in 5D case. * Written the method InterpolateEvalHelper::get_info_for_linear_onnx_mode5D(). * Code style fix. * Started to write calculation of 5D case of 'linear_onnx' mode. * Written the method void InterpolateEval<T>::linear_onnx5D_func(const T* input_data, T* out). * Added dispatching of 4D/5D cases of the mode 'linear_onnx'. * Fixed code style. * Some fixes. * Code style fixes. * Now linear_onnx_func throws an exception for incorrect input rank. * Code style fix. * Started to write tests for evaluation of 'linear_onnx' mode in the 5D case. * Added first test for linear_onnx 5D. * Small fixes. * Written tests for evaluation of Interpolate-4 in linear_onnx 5D case. * Some code style fixes. * Small fix. * Corrected documentation. * Started to write generic implementation of 'linear_onnx' mode, for any ranks. * Written the draft of a generic (for all ranks) implementation of 'linear_onnx' mode. * Small fixes. * Small fix. * Small fix. * Small fix. * Code style fix. * Small fix. * Code style fix. * Some fixes. * Some fix. * Small fix. * Small fix. * Code style fix. * Added check for axes correctness into a generic implementation of the 'linear_onnx' mode. * Now 5D case of the 'linear_onnx' mode is calculated using generic function. * Code style fix. * Deleted unused variable. * Added debug prints. * Small fix. * Some fixes. * Code style fix. * Now all ranks are processed by a generic implementation in the 'linear_onnx' mode. * Deleted name of missed test. * Deleted 4D case implementation of the 'linear_onnx' mode. * Reverted change in tests. * Added needed 'const' modifiers and added a comment about the variable 'axis_idx_offset'. * Small fixes. |
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| cmake | ||
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| inference-engine | ||
| licensing | ||
| model-optimizer | ||
| ngraph | ||
| openvino | ||
| scripts | ||
| tests | ||
| thirdparty | ||
| tools | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| CMakeLists.txt | ||
| CODEOWNERS | ||
| install_build_dependencies.sh | ||
| Jenkinsfile | ||
| LICENSE | ||
| README.md | ||
| 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.