896532ace2
* Calculate model layout based on 'tensor' layout and convert steps Previously, 'model layout' is set to '...' by default, thus no shape conversion happened when tensor layout is set to 'NHWC', then there was explicit convert_layout "NCHW" Now "model layout" is calculated based on tensor layout and conversion steps: Examples: 1) Tensor: NHWC, Convert: NCHW. Result: NCHW 2) Tensor: NHWC, Convert: 0312. Result: NCHW * Initial move of tensor data calculation * Moved 'impls' to new file * Postprocessing + unit tests * clang-format fix * Added more details to preprocessing nodes - Mean/Scale - will print mean/scale values - Convert type - will print type - Convert layout - will print destination layout - Convert color - will print destination color It is needed to troubleshoot the problems. If error occurs, message will not display last op's target shape/layout/type * Add python bindings * update tests * Added memory type to dump if set * Code style fix * unity build fix * Dump tensor if only memory type is set * Added debug print * Fix Param->Result case Previously, layout was set by preprocessing set to old parameter as well This is incorrect because in case of exception layout info will not be reverted In this case old Result pointed to old Parameter and was able to preserve runtime info After fixing of this, case Param->Result was broken if revalidation is not triggerred Fix is to detect 'Result' as a consumer of some parameter and force revalidation in this case * Revert occasionally committed line * And one more line |
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OpenVINO™ Toolkit
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
- Storage: https://storage.openvinotoolkit.org/
- Additional OpenVINO™ modules: https://github.com/openvinotoolkit/openvino_contrib
- Intel® Distribution of OpenVINO™ toolkit Product Page
- Intel® Distribution of OpenVINO™ toolkit 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.