Vladimir Gavrilov
0f4f912eab
* Created opset7.md to add the specification of the operation FFT. * Started to write the specification of the operation FFT. * Added link to the specification of the operation FFT. * Continued to write the specification of the FFT. * Written about inputs and outputs of FFT. * Started to write examples. * Added example when there is no input 'signal_size'. * Added more example. * Small fixes. * Small fix. * Renamed FFT to DFFT. * Small fix. * Small fix. * Started to write the algorithm of FFT. * Added asserts. * Started to write the method __call__ of the class of DFFT calculation. * Fixed category. * Continued to write the algorithm of the FFT calculation. * Continued to write the algorithm of the FFT calculation. * Continued to write the algorithm of the FFT calculation. * Continued to write the algorithm of the calculation of DFFT. * Written the algorithm of the calculation of DFFT. * Small fix. * Renamed operation. * Added examples of 3D input tensors. * Covered complex number representation. * Written formulas for FFT. * Written about start point of trimming. * Small fixes. * Small fixes. * Some fixes. * Added some note. * Added a description of the calculation of the output shape. * Added examples with unsorted axes and with (-1) in signal_size. * Fixed range of axes indices. * Small fix. * Small change. * Added T_SIZE type. * Added negative axes support. * Some fixes. * Some fixes.
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:
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openvinotag on StackOverflow* - GitHub* Issues
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