Mikhail Nosov be8600af38 [OV20] ov::Layout improvements (#7551)
* Fix leftovers for ov::Layout
Added runtime info to Tensor
Add get_layout/set_layout/has_layout methods for parameter
Moved preprocess steps code to separate CPP file for readability purposes
Test code coverage = 100%

* Fixed review comments

* Try fix windows build

* Fix 1:
Correction: Dynamic channels dimension is ok on preprocessing
[?,?,?,?], Layout "NCHW" => scale({2.0, 3.0, 4.0}) is ok.
If HostTensor's channels dimension mismatches - it will fail on 'evaluate' stage (as usual)

Fix 2:
Verify that TensorInfo().set_layout(...) reuses element type from original parameter

* Removed 'using RTMap'
2021-09-21 16:02:55 +03:00
2021-09-20 18:37:32 +03:00
2021-09-13 13:39:42 +03:00
2021-09-20 18:37:32 +03:00
2021-09-21 11:10:00 +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)

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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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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