dnn: refactor reduce (#23613)
* initial impl * remove reduce in8; fix reduce importer * fix bugs and add log sum exp * remove unnecessary header and fix indentationpull/23637/head
Yuantao Feng
2 years ago
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GitHub
parent
5229312ad2
commit
eefee8574a
5 changed files with 449 additions and 694 deletions
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "../precomp.hpp" |
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#include "layers_common.hpp" |
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#include <algorithm> |
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#include <stdlib.h> |
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#include <numeric> |
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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 ReduceLayerInt8Impl CV_FINAL : public ReduceLayerInt8 |
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{ |
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public: |
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ReduceLayerInt8Impl(const LayerParams& params) |
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{ |
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// Set reduce type
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CV_Assert(params.has("reduce")); |
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String typeString = toLowerCase(params.get<String>("reduce")); |
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if (typeString == "max") |
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reduceType = MAX; |
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else if (typeString == "min") |
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reduceType = MIN; |
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else |
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CV_Error(Error::StsBadArg, "Unknown reduce type \"" + typeString + "\""); |
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// Set deleted dims
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CV_Assert(params.has("deleted_dims")); |
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DictValue tempDims = params.get("deleted_dims"); |
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int i, n = tempDims.size(); |
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reduceDims.resize(n); |
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for (i = 0; i < n; i++) |
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{ |
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reduceDims[i] = tempDims.get<int>(i); |
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} |
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CV_Assert(params.has("target_dims")); |
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tempDims = params.get("target_dims"); |
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n = tempDims.size(); |
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targetDims.resize(n); |
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for (i = 0; i < n; i++) |
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{ |
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targetDims[i] = tempDims.get<int>(i); |
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} |
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} |
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virtual bool supportBackend(int backendId) CV_OVERRIDE |
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{ |
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if (backendId == DNN_BACKEND_OPENCV) |
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{ |
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return true; |
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} |
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return false; |
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} |
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// reduceType == MIN
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struct ReduceOpMIN |
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{ |
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int8_t apply(const int8_t* first, const int8_t* last) |
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{ |
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return std::accumulate(first, last, *first, |
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[](int8_t a, int8_t b) |
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{ |
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return std::min(a, b); |
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}); |
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} |
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}; |
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// reduceType == MAX
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struct ReduceOpMAX |
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{ |
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int8_t apply(const int8_t* first, const int8_t* last) |
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{ |
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return std::accumulate(first, last, *first, |
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[](int8_t a, int8_t b) |
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{ |
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return std::max(a, b); |
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}); |
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} |
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}; |
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template<typename Func> |
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class ReduceInvoker : public ParallelLoopBody |
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{ |
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public: |
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const Mat* src; |
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Mat *dst; |
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std::vector<size_t> reduceDims; |
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int nstripes; |
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int reduceType; |
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Ptr<Func> func; |
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ReduceInvoker() : src(0), dst(0), nstripes(0), reduceType(MAX), func(makePtr<Func>()) {} |
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static void run(const Mat& src, Mat& dst, std::vector<size_t> reduceDims, int reduceType, int nstripes) |
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{ |
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CV_Assert_N(src.isContinuous(), dst.isContinuous(), src.type() == CV_8S, src.type() == dst.type()); |
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ReduceInvoker<Func> p; |
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p.src = &src; |
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p.dst = &dst; |
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p.reduceDims = reduceDims; |
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p.nstripes = nstripes; |
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p.reduceType = reduceType; |
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parallel_for_(Range(0, nstripes), p, nstripes); |
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} |
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void operator()(const Range& r) const CV_OVERRIDE |
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{ |
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size_t total = dst->total(); |
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size_t stripeSize = (total + nstripes - 1)/nstripes; |
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size_t stripeStart = r.start*stripeSize; |
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size_t stripeEnd = std::min(r.end*stripeSize, total); |
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size_t totalDeleted = std::accumulate(reduceDims.begin(), reduceDims.end(), 1, std::multiplies<size_t>()); |
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int8_t *dstData = (int8_t *)dst->data; |
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int8_t *srcData = (int8_t *)src->data; |
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for (size_t ofs = stripeStart; ofs < stripeEnd;) |
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{ |
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const int8_t* first = srcData + ofs * totalDeleted; |
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const int8_t* last = srcData + (ofs + 1) * totalDeleted; |
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dstData[ofs] = func->apply(first, last); |
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ofs += 1; |
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} |
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} |
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}; |
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void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, 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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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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CV_Assert(inputs.size() == 1); |
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const int nstripes = getNumThreads(); |
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switch (reduceType) |
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{ |
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case MIN: |
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{ |
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ReduceInvoker<ReduceOpMIN>::run(inputs[0], outputs[0], reduceDims, reduceType, nstripes); |
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break; |
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} |
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case MAX: |
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{ |
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ReduceInvoker<ReduceOpMAX>::run(inputs[0], outputs[0], reduceDims, reduceType, nstripes); |
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break; |
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} |
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default: |
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CV_Error(Error::StsNotImplemented, "Not implemented"); |
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break; |
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} |
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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_Assert(inputs.size() > 0); |
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CV_Assert( reduceDims.size() !=0 && targetDims.size() != 0 && inputs[0].size() >= reduceDims.size()); |
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// outShapeTmp can save the right number of `total(outShapeTmp)`. And the outShape is used as the final output shape.
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std::vector<int> outShapeTmp, outShape; |
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outShape.assign(targetDims.begin(), targetDims.end()); |
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if (inputs[0].size() == reduceDims.size()) |
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outShapeTmp.push_back(1); |
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else |
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{ |
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for (int i = 0; i < inputs[0].size() - reduceDims.size(); i++) |
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{ |
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outShapeTmp.push_back(inputs[0][i]); |
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} |
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} |
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// Support dynamic shape of Batch size.
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// Note that: when there are multiple dynamic inputs, we will give an error.
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if (total(outShape) != total(outShapeTmp)) |
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{ |
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if (outShape[0] != outShapeTmp[0]) |
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outShape[0] = outShapeTmp[0]; |
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} |
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CV_Assert(total(outShape) == total(outShapeTmp)); |
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outputs.assign(1, outShape); |
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return false; |
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} |
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virtual bool tryQuantize(const std::vector<std::vector<float> > &scales, |
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const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE |
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{ |
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return false; |
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} |
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virtual int64 getFLOPS(const std::vector<MatShape> &inputs, |
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const std::vector<MatShape> &outputs) const CV_OVERRIDE |
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{ |
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CV_UNUSED(inputs); // suppress unused variable warning
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long flops = 0; |
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size_t totalDeleted = std::accumulate(reduceDims.begin(), reduceDims.end(), 1, std::multiplies<size_t>()); |
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for (int i = 0; i < outputs.size(); i++) |
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{ |
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flops += total(outputs[i])*(totalDeleted); |
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} |
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return flops; |
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} |
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private: |
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enum Type |
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{ |
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MAX, |
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MIN |
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}; |
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}; |
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Ptr<ReduceLayerInt8> ReduceLayerInt8::create(const LayerParams& params) |
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{ |
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return Ptr<ReduceLayerInt8>(new ReduceLayerInt8Impl(params)); |
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} |
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} |
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} |
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