4e4b04bbd3
* Fix LSTMSequence/GPUSequence validation behavior consistent with RNNSequence Fixed issue with no exception if num_directions=2, but 'm_direction' is not set to BIDIRECTIONAL. Previously there was no error with this (and luckily it failed later in some CPU transformations during compile_network) Corrected several tests which use copy-pasted num_directions=2 without m_direction set Also for dynamic 'num_directions' - output shape still has 1 or 2 directions, because m_direction is known. Tests for GRU/LSTM are updated for this Also several tests worked incorrectly for LSTMv0 - expectation was specific error to be thrown, but no expection was also allowed * Fixed clang-format
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.openvino.ai/
- 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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Please report questions, issues and suggestions using:
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