eeafc8e7dc
* change tile reference implementation * remove tile tests from interpreter manifest * add repeats parameter to tile * improve tile reference implementation * add repeats parameter to tile reference call in tile evaluate method * style apply * include <numeric> * add unnamed namespace to helper functions. Change stdio.h to cstdio. Change input_rank to be constant int * add const reference to parameter repeats in tile reference function * change createPitches function to use partial_sum instead of accumulate * change a little bit createPitches function * style-apply * fix function naming * style-apply * fix calling functions name bug * Add description of create_pitches function * first version with debug logs * reduce footprint * single layer tests * added more tests * fixed handling bool type * styles applied * fix tile * [ONLY DEBUG] print error scenario message * fixed problem with e2e tests * fixed casting of start_axis for numpy mode Co-authored-by: pszmel <piotr.szmelczynski@intel.com> |
||
|---|---|---|
| .ci | ||
| .github | ||
| cmake | ||
| docs | ||
| inference-engine | ||
| licensing | ||
| model-optimizer | ||
| ngraph | ||
| openvino | ||
| scripts | ||
| tests | ||
| tools | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| build-instruction.md | ||
| CMakeLists.txt | ||
| CODEOWNERS | ||
| CONTRIBUTING_DOCS.md | ||
| CONTRIBUTING.md | ||
| get-started-linux.md | ||
| 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 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.