* initial version of transformation workable for loop * moved transformation to back + minor changes in result names * fixed mistake: to concatenate results of all iterations Unsqueeze should be added * added shape inference for new nodes and extend test * added support of TensorIterator * If support + test; fix to save model with TI after transformation * fix code and tests according to run with ir_reader * added finding max internal_layer_id in sub-graph + added comments to code * turn off transformation because it should not be used in MO scenarios * refactor code to find out iterations count for TensorIterator * chenged name of final result to srtucture loop1.loop2.node for path [loop1, loop2, node] * change port number to index of added output * return list of new nodes * change naming of output to standard way; return result node as output of transformation * refactor transformation, add more comments; fine up tests * review fixes: add more comments, refactoring of infer function, fix in iterations count calculation * added processing of dynamic iterations count + tests * moved iterations count calculation to TI * fixed bug in iterations count calculation * fix bug with adding iterations count to wrong dimension + test * review fixes: minor renaming + fixed bug with unset stride for TI * move logic with output record outside function calculation iteration count; fix case with negative start/end; fix case when division result is not integer; added tests for such cases * review fixes: refactoring of toerations count calculation |
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cmake | ||
docs | ||
inference-engine | ||
licensing | ||
model-optimizer | ||
ngraph | ||
openvino | ||
runtime | ||
samples | ||
scripts | ||
tests | ||
thirdparty | ||
tools | ||
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CMakeLists.txt | ||
CODEOWNERS | ||
install_build_dependencies.sh | ||
Jenkinsfile | ||
LICENSE | ||
README.md | ||
SECURITY.md |
OpenVINO™ Toolkit
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
- Storage: https://storage.openvinotoolkit.org/
- Additional OpenVINO™ modules: https://github.com/openvinotoolkit/openvino_contrib
- Intel® Distribution of OpenVINO™ toolkit Product Page
- Intel® Distribution of OpenVINO™ toolkit Release Notes
Support
Please report questions, issues and suggestions using:
- The
openvino
tag on StackOverflow* - GitHub* Issues
- Forum
* Other names and brands may be claimed as the property of others.