OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
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Gladilov, Gleb 8ee263e7fb
[IE][VPU][GT]: Introduce Split by dynamic dimension check (#2802)
* [IE][VPU][GT]: Introduce Split by dynamic dimension check

At the moment, myriad plugin does not support split operation
by dynamic axis. To be sure there is no issue with optimized-out
split operation which should have been replaced with copy
stage - assertion before DTS transformation is introduced.

Check should be performed before loop with DTS transformations
because it requires dynamic context (dynamic dimension should be
visible as dynamic), otherwise dynamic dimension would be
replaced with upper-bound estimation and check will always pass.

Signed-off-by: Gladilov, Gleb <gleb.gladilov@intel.com>

* [IE][nGraph]: Fixes normalize_axis symbol exporting

Signed-off-by: Gladilov, Gleb <gleb.gladilov@intel.com>
2020-10-30 09:12:11 +03:00
.ci Add watchdog of OpenVino ONNX CI (#2550) 2020-10-23 14:16:43 +02:00
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docs Fix visual appearance (#2148) 2020-10-29 15:27:33 +03:00
inference-engine [IE][VPU][GT]: Introduce Split by dynamic dimension check (#2802) 2020-10-30 09:12:11 +03:00
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model-optimizer Improve support ONNX Resize-10 created by PyTorch (#1350) 2020-10-29 15:26:23 +03:00
ngraph [IE][VPU][GT]: Introduce Split by dynamic dimension check (#2802) 2020-10-30 09:12:11 +03:00
openvino Fix itt build (#2662) 2020-10-14 18:35:21 +03:00
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OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository

Stable release Apache License Version 2.0 Azure DevOps builds (branch)

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

How to Contribute

See CONTRIBUTING for contribution to the code. See CONTRIBUTING_DOCS for contribution to the documentation. Thank you!

Support

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