Szymon Durawa 772465da1e Add output shape and output padding for Convolution Backprop SLTs. (#5576)
* Create output shape for Convoution Backprop SLTs.

* Add output_padding attribute to SLT scope.

* Introduce SLT for Serializaton.

* Introduce new test layer class ConvolutionBackpropLayerTest which contains output_padding attribute and output_shape input. Old one is deprecated, but cannot be removed due to kmb plugin dependency.

* Add ConvolutionBackpropDataLayerTest into TEST_P.

* ConvolutionBackpropDataLayerTest left as legacy class used by kmb_plugin.

* Remove redundant variables.

* Switch to new API for gpu SLTs.

* Remove legacy API.

* Introduce legacy API to match dependency for KMB and ARM plugins.

* Create test cases for output_padding attribute.

* Fixing smoke_Deconv tests.
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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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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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