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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* Commit. * Written the header file of the nGraph operation ExperimentalDetectronROIFeatureExtractor. * Started to write cpp-file for the nGraph operation ExperimentalDetectronROIFeatureExtractor. * Deleted in_ports_count attribute for the MO operation ExperimentalDetectronROIFeatureExtractor. * Written validate_and_infer_type() method of ngraph::op::v6::ExperimentalDetectronROIFeatureExtractor. * Code style fixes. * Code style fixes. * Small fixes. * Code style fixes. * Now operation ExperimentalDetectronROIFeatureExtractor is read as nGraph operation ExperimentalDetectronROIFeatureExtractor. * Implemented op::v6::ExperimentalDetectronROIFeatureExtractor::clone_with_new_inputs(). * Added macro NGRAPH_OP_SCOPE to the cpp-file of the nGraph operation ExperimentalDetectronROIFeatureExtractor. * Fixes in some tests. * Code style fix. * Fixed yet another test for ExperimentalDetectronROIFeatureExtractor. * Added tests for reading ExperimentalDetectronROIFeatureExtractor as operation from opset6. * Added more test for reading ExperimentalDetectronROIFeatureExtractor as operation from opset6. * Started to write shape infer tests for the nGraph operation ExperimentalDetectronROIFeatureExtractor. * Corrected ngraph/test/CMakeLists.txt. * Small changes. * Code style fix. * Small fixes. * Added ctor of ExperimentalDetectronROIFeatureExtractor with NodeVector as argument. * Added setting the attribute in_ports_count when the MO operation ExperimentalDetectronROIFeatureExtractor is creating in the MO transformation ONNXMaskRCNNTransformation. * Added shape infer for the second output of the nGraph operation ExperimentalDetectronROIFeatureExtractor. * Written shape infer tests for nGraph operation ExperimentalDetectronROIFeatureExtractor (case when input shapes are partially dynamic). * Small fixes. * Code style fix. * Deleted redundant &expected_channels. * Code style fix. * Small refactoring. * Small fixes. * Small changes. * Small fixes. * Deleted attribute distribute_rois_between_levels of ExperimentalDetectronROIFeatureExtractoe. * Deleted attribute preserve_rois_order of ExperimentalDetectronROIFeatureExtractoe. * Deleted attribute image_id of ExperimentalDetectronROIFeatureExtractoe. * Now MO generates attribute 'aligned' of ExperimentalDetectronROIFeatureExtractor only with values 'true' or 'false'. * Small fix. * Fix in the conversion of the attribute 'aligned' of MO operation ExperimentalDetectronROIFeatureExtractor to string. * Tabs were replaced by spaces in some XMLs. * Tabs were replaced by spaces in ngraph_reshape_tests.cpp. * Fixed copyrights. * Applied small patch to IREngine from PR https://github.com/openvinotoolkit/openvino/pull/3814. * Tabs were replaced by spaces in cnn_ngraph_impl_tests.cpp. * op::v6::ExperimentalDetectronROIFeatureExtractor::validate_and_infer_types() was rewritten using operator & for channels. * Added tests for input shapes of op::v6::ExperimentalDetectronROIFeatureExtractor in the case when inputs shapes consist of intervals. * Fixes in test type_prop.detectron_roi_feature_extractor_intervals. |
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| cmake | ||
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| inference-engine | ||
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| model-optimizer | ||
| ngraph | ||
| openvino | ||
| scripts | ||
| tests | ||
| tools | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| CMakeLists.txt | ||
| CODEOWNERS | ||
| install_build_dependencies.sh | ||
| Jenkinsfile | ||
| LICENSE | ||
| README.md | ||
| SECURITY.md | ||
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 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
- Additional OpenVINO modules: https://github.com/openvinotoolkit/openvino_contrib
- HomePage
- OpenVINO™ Release Notes
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.