bf467e097f
* fixed: gna split-concat-concat sequence issue fixed: gna permute-conv-maxpool-permute issue * fix issue with multiple connections between split|crop|memory & concat fix issue with serializing IR V7 model * fixed: gna split-concat-concat sequence issue fixed: gna permute-conv-maxpool-permute issue * fix issue with multiple connections between split|crop|memory & concat fix issue with serializing IR V7 model * fixed issues after rebase * Fix for the test TEST_F(FP32NonQuantizedTest, LSTMCellPropagateForward) Input x[96] = 0.1 Scaled input = 0.01 Affine output = 64 * 0.01 * 0.1 + 0.1 = 0.164 Sigmoid(Affine output) = 0.541 Tanh(Affine output) = 0.163 Sigmoid(Affine output)*Tanh(Affine output) = 0.088 Sigmoid(Affine output)*Scaled input = 0.005 H = Sigmoid(Affine output)*Tanh(Affine output) + Sigmoid(Affine output)*Scaled input = 0.093 tanh(H) = 0.093 Result = H + Sigmoid(Affine output)*Scaled input = 0.093 + 0.541*0.093 = 0.093 + 0.050 = 0.143 * Updated copyright date * [GNA] Added tests for cases connection split->concat * [GNA] Added fix memory -> concat case * fixed inf loop during quantization * fixed code formatting * fixed failing test smoke_concat_quant_memory_requant/ConcatQuantDuringMemoryRequantTest.CompareWithRefs/netPRC=FP16_IS=128_HS=128_targetDevice=GNA * fixed removed & mark from pass manager main for loop * added split=>concat case to InsertCopyLayerPass Co-authored-by: Andrey Dmitriev <andrey.dmitriev@intel.com> |
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| openvino | ||
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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 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.