2110a29b7c
* [MO] [Kaldi] Added TDNN Component * TdnnComponent replacer graphical comment updated * Added SpecAugmentTimeMaskComponent * some refactor of memoryoffset shape_infer * moved memoryoffset splitting to the middle stage * some corrections - set `need_shape_inferenc`=False in split_memoryoffset - use cycle instead of pattern in tdnn_replacer * separated splitting of MemoryOffsets in LSTM and TDNN blocks * set transpose_weights=True in TdnnComponent * Corrected Supported_Frameworks_Layers * corrected comments * separate naming for tdnn and lstm memoryoffset splits * corrected BOM file * corrected generaldropout_ext.py and removed 'has_default' for tdnn_component * corrections after PR review * renamed LSTM -> recurrent; added setting element_size for paired nodes of tdnn_memoffset and othe minor changes * Update split_tdnn_memoryoffset.py * corrected partial infer with new API in elemental.py and split_tdnn_memoryoffset.py
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.