Gabriele Galiero Casay
91a8de127d
* Convolution: Enhance dynamic shape inference of validate and infer types method * Convolution: Change onnx test with dynamic shapes to float element type * Convolution: Remove test instances with integer precision * Convolution: Add backticks to types in spec * Convolution: Change element type variable for output element type * GroupConvolution: Add backticks to types in spec * GroupConvolution: Enhance dynamic shape inference of validate and infer types method * GroupConvolution: Remove serialization test instances with integer precision * GroupConvolutionBackpropData: Remove serialization test instances with integer precision * GroupConvolutionBackpropData: Enhance dynamic shape inference of validate and infer types method * Convolution: Add helper function to validate convolution parameters in ref impl * Convolution: Rewrite lambda to capture spatial dims of filters in validate and infer types * GroupConvolution: Refactor reference implementation * Remove call to old implementation of convolution using dilations * Added validation method to validate shapes * GroupConvolutionBackpropData: Add more type_prop unit test and refactor test names * Convolution: Extended validation of convolution parameters in reference implementation * GroupConvolution: Extended validation of group convolution parameters in reference implementation * GroupConvolutionBackpropData: Add helper function to validate convolution backprop parameters in ref impl * Clean up unnecessary lines * BinaryConvolution: Use validate helper function from convolution ref impl * Convolution: Refactor validate and infer types to improve readability * BinaryConvolution: Refactor validate and infer types to improve readability * Convolution: Add explicit tensor shape dims for inputs and outputs in spec * BinaryConvolution: Add explicit tensor shape dims for inputs and outputs in spec * GroupConvolution: Add explicit tensor shape dims for inputs and outputs in spec * Add helper function to infer convolution forward output shape * Convolution: Refactor validate and infer types to use helpers to infer output shape * BinaryConvolution: Refactor validate and infer types to use helpers to infer output shape * GroupConvolutionBackpropData: Fix formula to calculate output shape in validation functions * Remove symbol to export convolution output shape inference function * GroupConvolution: Add validation checks for input channels dim of data batch and filter shape * GroupConvolutionBackpropData: clean up type prop tests * Convolution: Change element type in onnx unit tests with dyn shapes and convolution nodes * GroupConvolutionBackpropData: Correct layout of filters input * GroupConvolution: Deduce groups from inputs shape during output shape inference * Change spec supported types of convolution operations to any numeric type * Revert "GroupConvolution: Remove serialization test instances with integer precision" This reverts commit781c2570d6. * Revert "GroupConvolutionBackpropData: Remove serialization test instances with integer precision" This reverts commit9a6ac23968. * Revert "Convolution: Remove test instances with integer precision" This reverts commit0b07052a62. * Revert "Convolution: Change element type in onnx unit tests with dyn shapes and convolution nodes" This reverts commitc9f5944b6b. * Revert "Convolution: Change onnx test with dynamic shapes to float element type" This reverts commit1f4202b010. * Allow integral types in validate and infer types method for convolution group of operations * Add i32 precision in single layer tests for convolution group of operations * BinaryConvolution: Fix shape of input and output tensors in spec * Address nitpick comments
OpenVINO™ Toolkit
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
- Storage: https://storage.openvinotoolkit.org/
- Additional OpenVINO™ 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
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