Mateusz Bencer fb11560b82 Implemented missing methods of ONNX Place API (#7518)
* Implemented get_consuming_ports, get_producing_operation and is_equal for PlaceOpONNX

* fixed unambiguous_node_check

* removed PlaceTensorONNX::get_input_port

* added PlaceOpONNX::is_input, PlaceOpONNX::is_output

* fixed python styles

* added get_consuming_operations implementation

* added missing get_consuming_operations for PlaceOpONNX

* added missing get_target_tensor for PlaceOpONNX

* changed place spec

* add support of get_source_tensor

* add support of get_producing_operation for PlaceOpONNX

* add support of get_producing_port for PlaceInputEdgeONNX

* fixed python styles

* missing ref in std::transform
2021-09-23 09:47:33 +03:00
2021-09-20 18:37:32 +03:00
2021-09-13 13:39:42 +03:00
2021-09-22 18:53:22 +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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Languages
C++ 80.5%
Python 15.5%
C 2.8%
CMake 0.9%
Cython 0.1%