d225ba6e53
* [IE] Add batched blob support New `class BatchedBlob : public CompoundBlob` defined to allow to pass multiple blobs as 1 InferRequest input. Motivation: There is the special user case when a number of plain images (e.g. `NV12Blob`) should be passed as one input for network which batch size > 1. `class CompoundBlob` is not applicable for such cases due to: 1. `NV12Blob` is `CompoundBlob` which prevents to combine multiple NV12 images to a CompoundBlob 2. The default behavior in most of plugins - do not accept generic CompoundBlob as `SetBlob()` argument Adding `SetBlob(name, vector<Blob::Ptr>...)` to `class IInferRequest`, `class InferRequest`, `class IInferRequestInternal`, ... - is not effective solution due to limited and specific use cases for `batched inputs`. + Apply rule-of-zero to CompoundBlob and inherited classes. * Add "BATCHED_BLOB" optimization capability metric * Add BatchedBlob usage to hello_nv12_input_classification * Apply offline code review outcome: 1. Revert CompoundBlob public .ctors signatures 2. Remove 'workaround' .ctor for `BatchedBlob` 3. Revert tensor descriptors of `I420Blob` `NV12Blob` back to the 'fake' value. * Code review fix * Add functional tests for CPU, GPU, MULTI, HETERO * update doc comment * Apply code review change requests. |
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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 two components: namely Model Optimizer and Inference Engine, as well as CPU, GPU 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.
Documentation
- OpenVINO™ Release Notes
- OpenVINO™ Inference Engine Build Instructions
- Get Started with Deep Learning Deployment Toolkit on Linux*
- Introduction to Deep Learning Deployment Toolkit
- Inference Engine Developer Guide
- Model Optimizer Developer Guide
- Get Started with DockerHub CI for OpenVINO™ toolkit
How to Contribute
See CONTRIBUTING for contribution to the code. See CONTRIBUTING_DOCS for contribution to the documentation. Thank you!
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.