210ad8194a
* Reference implementation for Proposal, enable CPU SLT * code style fix * add type prop test for invalid anchor count * add unit test * fix shapes in attribute test * temp workaround- disable maring end of boxes list * Disable CPU smoke test- spec misalignment * code style fixes * add some details to the specification * disable myriadx proposal slt * review changes, using usigned int and size_t * improve proposal op shape inference to cover dynamic too, add unit test coverage * remove unused variable in test body * remove batch size in tests where its not used * add post nms topn initialization in tests where it was missing * review comments * style fix * style fix 2 * add tests, remove unused variables, change shape inference checks * style fix * add input tensors type checks and test coverage * align input type in attribute and ngraphreader tests to match specification * fix wrong dimension in error message * proposalv4 ref impl * enable single layer and unit tests for proposalv4 ref impl * align output termination with cpu, enable cpu slt * custom slt compares to detect less-than-predicted number of boxes * custom slt compares to detect less-than-predicted number of boxes * Clarify output termination in spec * review comments * smaller input data for unit tests * add check for batch_dim being static * disable gpu slt for proposal * test data style fix * test data style fix 2 * add type section to specification * shape inference improvement * multiply expected 1st dim in tests by post_nms_topn * add checks and testcases for dynamic ranks * indentation, review comments * reduce code redundancy in ref implementation * remove comment * Fix typo in proposal1 spec * Fix typo in proposal4 spec
OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository
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*.
Repository components:
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.
Resources:
- Docs: https://docs.openvinotoolkit.org/
- Wiki: https://github.com/openvinotoolkit/openvino/wiki
- Issue tracking: https://github.com/openvinotoolkit/openvino/issues
- Additional OpenVINO modules: https://github.com/openvinotoolkit/openvino_contrib
- HomePage
- OpenVINO™ Release Notes
Support
Please report questions, issues and suggestions using:
- The
openvinotag on StackOverflow* - GitHub* Issues
- Forum
* Other names and brands may be claimed as the property of others.