mirror of https://github.com/opencv/opencv.git
Merge pull request #26079 from Abdurrahheem:ash/hardmax-support
Add Support for Hardmax Layer #26079 This PR add support for `Hardmax` layer, which as previously listed in conformance deny list. ### 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 CMakepull/26100/head
Abduragim Shtanchaev
3 months ago
committed by
GitHub
parent
8def9f75c8
commit
0f8bbf4677
5 changed files with 155 additions and 7 deletions
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#include <inttypes.h> |
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#include <opencv2/dnn/shape_utils.hpp> |
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#include "../precomp.hpp" |
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#include "layers_common.hpp" |
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#include "../ie_ngraph.hpp" |
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namespace cv |
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{ |
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namespace dnn |
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{ |
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class LayerHardmaxImpl CV_FINAL : public HardmaxLayer |
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{ |
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public: |
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int axis; |
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LayerHardmaxImpl(const LayerParams& params) |
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{ |
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axis = params.get<int>("axis", -1); |
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} |
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virtual bool supportBackend(int backendId) CV_OVERRIDE |
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{ |
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return backendId == DNN_BACKEND_OPENCV; |
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} |
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void getTypes(const std::vector<MatType>& inputs, |
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const int requiredOutputs, |
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const int requiredInternals, |
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std::vector<MatType>& outputs, |
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std::vector<MatType>& internals) const CV_OVERRIDE |
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{ |
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CV_Assert(inputs.size()); |
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for (auto input : inputs) |
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{ |
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CV_CheckType(input, input == CV_32F || input == CV_8S || input == CV_8U || input == CV_32S || input == CV_64S || input == CV_Bool, ""); |
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} |
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outputs.assign(requiredOutputs, inputs[0]); |
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} |
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bool getMemoryShapes(const std::vector<MatShape> &inputs, |
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const int requiredOutputs, |
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std::vector<MatShape> &outputs, |
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std::vector<MatShape> &internals) const CV_OVERRIDE |
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{ |
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CV_CheckEQ(inputs.size(), 1ull, "Hardmax: one input is expected"); |
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outputs.resize(1); |
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outputs[0] = inputs[0]; |
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return false; |
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} |
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void forward(InputArrayOfArrays inputs_arr, |
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OutputArrayOfArrays outputs_arr, |
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OutputArrayOfArrays internals_arr) CV_OVERRIDE |
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{ |
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CV_TRACE_FUNCTION(); |
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CV_TRACE_ARG_VALUE(name, "name", name.c_str()); |
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if (inputs_arr.depth() == CV_16F) |
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{ |
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forward_fallback(inputs_arr, outputs_arr, internals_arr); |
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return; |
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} |
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std::vector<Mat> inputs, outputs; |
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inputs_arr.getMatVector(inputs); |
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outputs_arr.getMatVector(outputs); |
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Mat src = inputs[0]; |
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Mat dst = outputs[0]; |
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axis = normalize_axis(axis, src.dims); |
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MatShape shape(src.size.p, src.size.p + src.dims); |
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// Prepare output
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memset(dst.ptr(), 0, dst.total() * dst.elemSize()); |
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switch (src.depth()) |
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{ |
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case CV_8U: hardmaxApply<uchar>(src, dst, axis); break; |
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case CV_8S: hardmaxApply<schar>(src, dst, axis); break; |
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case CV_16U: hardmaxApply<ushort>(src, dst, axis); break; |
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case CV_16S: hardmaxApply<short>(src, dst, axis); break; |
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case CV_32S: hardmaxApply<int>(src, dst, axis); break; |
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case CV_32F: hardmaxApply<float>(src, dst, axis); break; |
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case CV_64F: hardmaxApply<double>(src, dst, axis); break; |
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default: |
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CV_Error(Error::StsUnsupportedFormat, "Unsupported input data type"); |
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} |
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} |
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template <typename T> |
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void hardmaxApply(const cv::Mat& src, cv::Mat& dst, const int axis) |
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{ |
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const auto *src_ptr = src.ptr<const T>(); |
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auto *dst_ptr = dst.ptr<T>(); |
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const size_t outer_size = src.total(0, axis); |
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const auto mid_size = static_cast<size_t>(src.size[axis]); |
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const size_t inner_size = src.total(axis + 1); |
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const size_t outer_step = src.total(axis); |
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double nstripes = (double) outer_size * inner_size / 1024.0; |
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parallel_for_(Range(0, outer_size), [&](const Range& range) { |
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for (size_t outer = range.start; outer < range.end; ++outer) |
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{ |
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const size_t outer_offset = outer * outer_step; |
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for (size_t inner = 0; inner < inner_size; ++inner) |
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{ |
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T max_val = std::numeric_limits<T>::lowest(); |
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size_t max_idx = 0; |
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// Find max along the reduction axis
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for (size_t mid = 0; mid < mid_size; ++mid) |
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{ |
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const size_t src_idx = outer_offset + mid * inner_size + inner; |
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if (src_ptr[src_idx] > max_val) |
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{ |
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max_val = src_ptr[src_idx]; |
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max_idx = src_idx; |
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} |
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} |
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// Set 1 for max, 0 for others
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dst_ptr[max_idx] = 1; |
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} |
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} |
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}, nstripes); |
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} |
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}; |
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Ptr<HardmaxLayer> HardmaxLayer::create(const LayerParams& params) |
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{ |
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return Ptr<HardmaxLayer>(new LayerHardmaxImpl(params)); |
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} |
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}} |
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