3b6cb0e0cd
Myriad plugin treats DSR operation in a way removing such operations and connecting inputs with each other (replacing output with one of them). Semantic of connection is one inputs contains shape of another. Since the same data object can have exactly one shape it's prohibited to have DSR inputs connected with another data objects (the only allowed exception is inputs that are already connected between each other). As a result of nGraph -> CNN conversion some operations could be optimized out which in turn could lead to subsequent DSR operations where each has its own shape sub-graph. Even if shape sub-graphs are identical it's not visible to plugin that sees incorrect inputs (inputs of DSR are already connected, but now with each other, when second DSR is parsed). To overcome such issue (the reason is when operations are optimized out, their shape sub-graphs are still there), additional ngraph transformation should be introduced to merge subsequent DSR into single DSR operation. Signed-off-by: Gladilov, Gleb <gleb.gladilov@intel.com> |
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| .ci/openvino-onnx | ||
| .github/workflows | ||
| cmake | ||
| docs | ||
| inference-engine | ||
| model-optimizer | ||
| ngraph | ||
| scripts | ||
| tests | ||
| tools | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| azure-pipelines.yml | ||
| build-instruction.md | ||
| CMakeLists.txt | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| get-started-linux.md | ||
| install_dependencies.sh | ||
| Jenkinsfile | ||
| LICENSE | ||
| 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 details. 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.