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Open Source Computer Vision Library
6af0394cd2
Added int support for OpenVINO dnn backend #25458 Modified dnn OpenVINO integration to support type inference and int operations. Added OpenVINO support to Cast, CumSum, Expand, Gather, GatherElements, Scatter, ScatterND, Tile layers. I tried to add Reduce layer, but looks like OpenVINO uses float values inside Reduce operation so it can't pass our int tests. OpenVINO uses int32 precision for int64 operations, so I've modified input values for int64 tests when backend is OpenVINO. OpenVINO has a strange behavior with custom layers and int64 values. After model compilation OpenVINO may change types, so the model can have different output type. That's why these tests were disabled: - Test_ArgMax_Int.random/0, where GetParam() = (4, NGRAPH/CPU) - Test_ArgMax_Int.random/6, where GetParam() = (11, NGRAPH/CPU) - Test_Reduce_Int.random/6, where GetParam() = (11, NGRAPH/CPU) - Test_Reduce_Int.two_axes/6, where GetParam() = (11, NGRAPH/CPU) Also these tests were temporary disabled, they didn't work on both 4.x and 5.x branches: - Test_Caffe_layers.layer_prelu_fc/0, where GetParam() = NGRAPH/CPU - Test_ONNX_layers.LSTM_Activations/0, where GetParam() = NGRAPH/CPU - Test_ONNX_layers.Quantized_Convolution/0, where GetParam() = NGRAPH/CPU - Test_ONNX_layers.Quantized_Eltwise_Scalar/0, where GetParam() = NGRAPH/CPU - Test_TFLite.EfficientDet_int8/0, where GetParam() = NGRAPH/CPU ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake |
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