f0b10bf071
* fake quantize single layer test for GNA plugin * implemented fakequantize for fp32 case as an activation function * added proper seed randomisation within single test run * [GNA] [FAKEQUANTIZE] fixed ref-fp32 implementation on GNA to use nearbyint instead of roundf * [GNA] [FAKEQUANTIZE] restored random seed * [GNA][FAKEQUANTIZE] disabled 4d and integer tests for FakeQuantize * [GNA][FAKEQUANTIZE]updated ngraph FakeQuantize builder to accept seed * [GNA][FAKEQUANTIZE]aligned FP calculations order on GNA with reference ngraph - this however gives more error * [CPU]build of FakeQuantise tests restored * [TESTS][FAKEQUANTIZE] ignore extra inferRequests for disabled tests * [GNA] Fixed legacy unit test failuers appeared due to extra check for possible segfault in import frames * [GNA] adopted fuse multiple identities for FakeQunatize layer * [GNA]fp32 runtime code review
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
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.