10b18a00c6
* Fix ElementwiseInputReshape transformation Reshape node always needs to be inserted in order to preserve ShapeOf nodes (reshapability of a model) that can potentially be above elementwise node. Refactor EltwiseInputReshape_test and EltwiseInputNormalization_test since the logic of maintaining reshape for eltwise has been changed. Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Merge EltwiseInputNormalization and EltwiseInputReshape transformations Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Remove Unsqueeze from Fused_op Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix code after code review #1 Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix code after review #2 Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix code review #4 Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Perform full normalization based on shapes of all inputs to eltwise Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Refactor much to avoid old API and edges with unsqueeze_dims attribute Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix code after review Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> |
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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.