75d2d88b61
* Added Caffe Slice_ext * Added TFSlice, AttributedSlice (both with extractors and replacers), corrected SliceConverter and added unittests for all cases * added comments to each type of Slice operation; optimized shape inference; moved mxlice inside of slice.py; renamed slice_replacers * removed type annotation for get_shape_after_slice routine * replaced zeros_like with zeros * Corrected preserving node names, renamed attributes names, added tests fro slice_replacer onnx phase * Renamed slice_replacers.py * added more unittest cases * added type annotations, moved to more relevant place routines for shape calculation, and some other minor corrections * corrected a typo `normalize_slice_indices` comment * corrected shape calculation for Nonconstant inputs * corrected a few typos * corrected type declarations * corrected shape inference with rounding * refactored unit-tests for front transforms of Slice * added error raising for negative and zero shapes * removed magic_num * corrected AttributedSlice, clarified comments * fixed unit-test for AttributedSliceToSlice * typo in type hints corrected * removed supported_attrs * returned back default None for attrs of Slice
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
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.