OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
aicomputer-visiondeep-learningdeploy-aidiffusion-modelsgenerative-aigood-first-issueinferencellm-inferencenatural-language-processingnlpopenvinooptimize-aiperformance-boostrecommendation-systemspeech-recognitionstable-diffusiontransformersyolo
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* [IE][nGraph]: Introduces PartialShape ctor from values vector Signed-off-by: Gladilov, Gleb <gleb.gladilov@intel.com> * [IE][VPU][nGraph]: Moves evaluateTargetShape to common utilities The same functionality - get upper-bound shape estimation for dynamic input - is needed in dynamic Reshape along with dynamic Broadcast. Return value type has been changed from PartialShape to vector<int64_t>. The reason is Reshape encodes special values (0, -1) into input values that define output shape. Representing those values (which upper-bound provides evaluateTargetShape) as PartialShape leads to incorrect representation vector with -1 as dynamic shape - which is not expected. Signed-off-by: Gladilov, Gleb <gleb.gladilov@intel.com> * [IE][VPU][nGraph]: Introduces StaticShapeReshape In comparison with original Reshape StaticShapeReshape propagates upper-bound shape through a function in case of dynamic input. To do so, shape inference method gets upper-bound shape from evaluateTargetShape, decodes special values (0, -1) in it and then propagate the result. Output shape processing happens only once, because if shape inference were called after ShapeOf operations have been optimized out on dynamic path, then evaluateTargetShape will require evaluate method for all operations that appear in function before current Reshape. Since evaluate method is implemented not for all operations it lead to Faster-RCNN compilation error. Signed-off-by: Gladilov, Gleb <gleb.gladilov@intel.com> * [IE][VPU][nGraph]: Updates Reshape DTS on StaticShapeReshape In case of non-const Reshape input that defines output shape DTS uses StaticShapeReshape which propagates upper-bound shape evaluated from this input through a function. Signed-off-by: Gladilov, Gleb <gleb.gladilov@intel.com> * [IE][VPU][nGraph][Tests]: Refactoring DTS Reshape tests The only changes are: * header files include reordering * indentation/wrapping fixing Signed-off-by: Gladilov, Gleb <gleb.gladilov@intel.com> * [IE][VPU][nGraph]: Moves ShapeOf transformation out of DTS scope In comparison with DTS ShapeOf transformation needs to work on whole function. Separating these 2 transformations makes testing easier since now it's possible to call specific DTS without ShapeOf transformation and vice versa. Also DynamicToStaticShapeOf has been renamed into EliminateShapeOfAfterDSR since transformation doesn't introduce new DSR operations. Signed-off-by: Gladilov, Gleb <gleb.gladilov@intel.com> * [VPU][Tests]: Introduces DTS Reshape tests with non-const pattern New StaticShapeReshape constructor has been added as well, since test fixture should create it from reshape parameters, not reshape itself. Signed-off-by: Gladilov, Gleb <gleb.gladilov@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
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.