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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* Generate TensorIterator without back edges from TensorFlow models * Added a check in the MarkSubgraphsWithCorrectLayout to not fail when port is not connected * Updated the 'protobuf2nx' to consume the graph protobuf message * Cleanup TI from the IRv7 specific code * Do not run some front transformations recursively * Draft support for the ONNX Loop operation when 'cond' = True * LoopToTI transformation changes * Added draft of Loop operation and parser for ONNX Loop operation body * Updated Loop body parser + added shape and type infer for the Loop operation * Fixes for ONNX Loop operation parser * Moved Loop parsing to Loop op extractor. Added generation of external edges for the Loop body ops * Added support for ThresholdedRelu using decomposition * Added support for Min ONNX operation * Draft fixes for port_map generation for the Loop * Rename transformation file and fix BOM * Fixed shape inference for Loop scan outputs (axis is not None) * Fixed shape inference for ONNX Loop operation * Refactor checks in the TensorIteratorMerge transformation * Code refactoring. Enabled commented transformations * Documentation update for ONNX Loop, ThresholdedRelu and Min * Fixed typo in the Loop front transformation where execution condition input is connected. Other refactorings * Fixed in the Loop extractor * Added printing 'internal_layer_id' attribute in the graph dumper * Updated calculation of iterations number for the Loop * Added missing code * Fixed output port shapes and types generation for Loop operation * Update function names and variable names in the Loop operation * Fixed type inference for iteration count input * Added removal of input/output ports of the Loop if they are not used * Fixed renumbering Loop operations input/output ports to keep mandatory * Fixed ThresholdedReluDecomposition transformation * Updated MO IR Reader to know about Loop operation. But it is still not supported by the MO IR Reader * Added unit test for Slice op shape infer (reverse the sequence of elements) * Reverted changes in the ONNX loader function call to protobuf2nx * Enable Reshape0DToSqueeze transformation recursively * Refactored Loop operation support implementation * Changed ThresholdedReluDecomposition to generate Const with shape [1] instead of scalar * Code style and wording fixes * Restored accidentally removed 'return' statement in the TI shape infer function * Fixed comments * Fixed comment Co-authored-by: Evgeny Lazarev <elazarev.nnov@gmail.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.