Pavel Esir
41d4fcdf25
* grouped convolution resolver fix * add reverse_infer for Transpose, Elementwise, Identity and some fixes to successfully OOB convert MXNet models * add reverse_infer for BatchNorms, dynamic groups fix, multi_box_prior.py fix * add reverse infer for trivial cases * input rank setting for image detection ops * reverse_infer for Gather, AttributedGather add todo comment for Parameter * reverse infer for LSTM, swapaxis; fixes and unit-tests for Elementwise, Gather, Transpose; also generalized Parameter reverse_infer; fixed DetectionOutput and partial reverse_infer * clarified comment for LSTM reverse_infer * detalized Elementwise reverse_infer, added several more unit-tests, Split reverse_infer fix, IteratorGetNext_ext.py fix * generalized group convolution resolver * specified in_ports explicitly * clarified Split, Concat, Parameter, other minor typos * relaxed compatibility check for elementwise; simplified scalar extracting for convolution.py and gather.py, fixed replacer order for ConvolutionNormalizer.py * added routine set_input_shapes for image detection ops * fixed typos to pass locally E2E, slightly changed LSTM reverse_infer * VariadicSplit reverse_infer bugfix * IteratorGetNextCut.py added check if port is connected * explicitly specified in_port, added asserts, improved Split reverse infer * sorted imports * reverse_infer for ONNX and MXNet LSTM, GRU, RNN operations * replaced Error raising with red warning * added missing return for defined input shapes, fixed getting num groups for tf conv * added mandatory attributes; fixed failing unit-tests, fixed typo with scalar group in ConvolutionNormalizer * removed priorbox reverse_infer; Parameter reverse_infer corrected to use the only one existing out_port * fixed clarify_partial_shape_test
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
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