OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
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Svetlana Dolinina ae6b16c084
Add outputs for nodes from loops (#7844)
* 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
2021-11-12 17:02:15 +03:00
.ci Added support of external modules in static build (#8518) 2021-11-12 08:56:57 +03:00
.github [Python API] Move samples and docs to the new directory (#7851) 2021-10-14 14:49:35 +03:00
cmake Added support of external modules in static build (#8518) 2021-11-12 08:56:57 +03:00
docs Migrate Movement 1 Operations (StridedSlice-1) (#8221) 2021-11-12 12:19:19 +03:00
inference-engine [GPU] Resolve performance regression by selecting ref kernel (#8550) 2021-11-12 16:39:47 +03:00
licensing Update third party files (#8382) 2021-11-03 15:29:42 +03:00
model-optimizer Add outputs for nodes from loops (#7844) 2021-11-12 17:02:15 +03:00
ngraph Fixed clone_with_new_inputs for type_relaxed ops (#8541) 2021-11-12 13:28:01 +03:00
openvino Added hash to type_info (#8175) 2021-11-11 12:59:37 +03:00
runtime Added support of external modules in static build (#8518) 2021-11-12 08:56:57 +03:00
samples samples: Print verbose error messages to stderr (#7795) 2021-11-10 11:42:52 +03:00
scripts [IE Sample Scripts] Use cmake to build samples (#8442) 2021-11-10 17:31:28 +03:00
tests [TF FE] Implement and refactor tensorflow layer tests (#8051) 2021-11-12 11:03:45 +03:00
thirdparty Install rules for static libraries case (#8384) 2021-11-10 18:25:51 +03:00
tools [POT] Update add node name (#8487) 2021-11-11 10:35:00 +03:00
.gitattributes [POT] Update tests with new data (#8209) 2021-10-27 12:40:19 +03:00
.gitignore [POT] Added missed file to POT (#8118) 2021-10-21 11:28:26 +03:00
.gitmodules Moved Post-training Optimization Tool to open-source (#7940) 2021-10-15 16:35:35 +03:00
CMakeLists.txt Added support of external modules in static build (#8518) 2021-11-12 08:56:57 +03:00
CODEOWNERS CODEOWNERS: Add /tools/pot/ @openvinotoolkit/openvino-pot-maintainers 2021-11-02 13:55:02 +03:00
install_build_dependencies.sh Added reporting of unresolved symbols for plugins (#7810) 2021-10-05 04:26:01 +03:00
Jenkinsfile Beautify Jenkinsfile a little bit 2021-05-31 15:24:56 +03:00
LICENSE Publishing R3 2018-10-16 13:45:03 +03:00
README.md [Docs] Add PyPI downloads badge (#7610) 2021-09-23 09:30:57 +00:00
SECURITY.md Added SECURITY.md back (#3177) 2020-11-17 16:44:44 +03:00

OpenVINO™ Toolkit

Stable release Apache License Version 2.0 GitHub branch checks state Azure DevOps builds (branch) PyPI Downloads

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:

Support

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* Other names and brands may be claimed as the property of others.