4edf85c928
* DynamicShapeResolver is able to save information about dynamic output in order to pass it in INFER_DYNAMIC_SHAPE mode. Previously, it propagated fully dynamic output shape (however ranks were equal) and dynamic Convolutions and Poolings were performed incorrectly. Now in the case of dynamic batch, DSR propagates only dynamic batch and Convolutions and Poolings are performed properly as a Loop of single-batch operations. * Fixed dynamicToStaticShapeTranspose transformation. There was a bug: transposition indices could not be applied with Scatter because the formula is not applicable for this. Replaced with Gather. i.e. Shape of output tensor of Transpose with transition [0,3,1,2] indices (NHWC [1, 224, 224, 3]->NCHW [1, 3, 224, 224]) was calculated by ScatterElementsUpdate. So output_shape[transposition[i]] = input_shape[i] and the result was output_shape=[1, 224, 3, 224] which was wrong. Vise-versa Gather does output_shape[i] = input_shape[transposition[i]] and the result is [1, 3, 224, 224] which is right. * MaxPool and AvgPool can be sliced for loop in case of dynamic batch * Convert stage for inputs is not inserted in the VPU model in the case of OV API 2.0. It did not cause a problem with non-dynamic functions because Graph Transformer has a pass to eliminate redundant converts (u8->f16, ~f16->f16~). In the case of dynamic inputs, yet another inserted Convert breaks data<->shape relations. |
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OpenVINO™ Toolkit
This toolkit allows developers to deploy pre-trained deep learning models through a high-level OpenVINO™ Runtime C++ and Python APIs integrated with application logic.
This open source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, 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 TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi.
Repository components
License
OpenVINO™ 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.openvino.ai/
- Wiki: https://github.com/openvinotoolkit/openvino/wiki
- Issue tracking: https://github.com/openvinotoolkit/openvino/issues
- Storage: https://storage.openvinotoolkit.org/
- Additional OpenVINO™ toolkit modules: https://github.com/openvinotoolkit/openvino_contrib
- Intel® Distribution of OpenVINO™ toolkit Product Page
- Intel® Distribution of OpenVINO™ toolkit 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.