Luwei Zhou ce753f41dc [shape_infer]shape inference implement of Select Detectionoutput and Shufflechannels OPs (#8348)
* Implement detection_output shape infer

* revise and update the code flow

* update based on review.

* Update based on review

* Implement the shuffle_channels Op shape inference.

* Fix CI coding style issue.

* Implement the select OP shape inference.

* Update based on the review  comments

* Update based on the review comments.

* Add pragma once for the shape inference head.

* Add new shape_infer test file for detection_output OP.

* Ensure the header would only be included once.

* Add shuffle_channels OP shape infer test.

* Add shape_infer() invocations into shape_inference() API

shape_inference() API support Select, ShuffleChannels, DetectionOutput OPs
Fix extra pragma, unnecessary friend function declaration.

* Update based on the review comments.

* Move the shape infer API helpers into new folder.

* Applied review comments.

* Applied 2nd review comments

* Applied review comments

* Fix coding style.

* Update

* Applied review comments.

* Fix comipling issue of unused variable.

* Fix the CI issue.

* Update the coding style

* Move test cases into new folder

* Applied  review comments.
2021-12-29 05:39:50 +03:00
2021-12-24 12:55:22 +03:00
2021-12-13 00:04:56 +03:00
2021-05-31 15:24:56 +03:00
2018-10-16 13:45:03 +03:00
2020-11-17 16:44:44 +03:00

OpenVINO™ Toolkit

Stable release Apache License Version 2.0 GitHub branch checks state Azure DevOps builds (branch) PyPI Downloads

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*.

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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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