dab1a34aa2
* checking the network batchability (internal helper func on top of batch tracking) before doing hetero * more general logic with respect to batch-ability of the network * a dynamism check that I've owed from the PR-10560 * using the DO-detached mechanism for early hetero exit, also fixed this flag in the Batching plugin (although minor, as the DO is removed by HETERO) * adding the dimension tracking logic depending on whether implicitly/expicitly the auto-batching is enabled * changed the DetectionOutput affinity markup to go over results, also accomodate Convert, so only 2 subgraphs are made by the HETERO
OpenVINO™ Toolkit
This toolkit allows developers to deploy pre-trained deep learning models through a high-level OpenVINO™ Runtime C++ and Python APIs integrated with application logic.
This open source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, 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 TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi.
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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.
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- Additional OpenVINO™ toolkit 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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