e94393df10
* FQ+Mul fusion transform skeleton * FQ+Mul fusion transform tests prep * Basic UT for the transform * Basic implementation of the transform * Parametrized UTs for FQMul transform * Parametrization of FQ+Mul UTs * Make sure that the shapes of constants match * Check if the mul constant matches FQ data * CentOs compilation error fix * PR feedback and adjusted tests * NHWC layout of the mul constant * UT: FQ output limits 4D * Redundant CF pass removed * Rewrite the graph in a different way * Shape checking infrastructure skeleton * Handle some negative cases * Check the rt info in the fusion test * Fuse all Mul nodes detected after FQ node * Dont cast the original FQ node * Dont throw if CF fails in new output range calculation * More UTs * Accept any type of input to FQ in the transformation * Test the fusion when all FQ inputs are non-const * Fusion test when only one output limit is const |
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| cmake | ||
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| inference-engine | ||
| model-optimizer | ||
| ngraph | ||
| openvino | ||
| scripts | ||
| tests | ||
| tools | ||
| .gitattributes | ||
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| azure-pipelines.yml | ||
| build-instruction.md | ||
| CMakeLists.txt | ||
| CODEOWNERS | ||
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| CONTRIBUTING.md | ||
| get-started-linux.md | ||
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| README.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
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.