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221 lines
7.9 KiB
C++
221 lines
7.9 KiB
C++
/*M ///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Copyright (C) 2017, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "../precomp.hpp"
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#include "../op_inf_engine.hpp"
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#include <opencv2/dnn/shape_utils.hpp>
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#include <opencv2/dnn/all_layers.hpp>
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#ifdef HAVE_OPENCL
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#include "opencl_kernels_dnn.hpp"
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#endif
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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 ReorgLayerImpl CV_FINAL : public ReorgLayer
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{
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int reorgStride;
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public:
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ReorgLayerImpl(const LayerParams& params)
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{
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setParamsFrom(params);
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reorgStride = params.get<int>("reorg_stride", 2);
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CV_Assert(reorgStride > 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_Assert(inputs.size() > 0);
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outputs = std::vector<MatShape>(inputs.size(), shape(
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inputs[0][0],
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inputs[0][1] * reorgStride * reorgStride,
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inputs[0][2] / reorgStride,
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inputs[0][3] / reorgStride));
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CV_Assert(outputs[0][0] > 0 && outputs[0][1] > 0 && outputs[0][2] > 0 && outputs[0][3] > 0);
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CV_Assert(total(outputs[0]) == total(inputs[0]));
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return false;
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}
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virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
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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 inp = inputs[0];
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Mat out = outputs[0];
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int batchSize = inp.size[0];
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LayerParams permParams;
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if (batchSize == 1)
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{
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int order[] = {1, 3, 0, 2};
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permParams.set("order", DictValue::arrayInt(&order[0], 4));
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permuteInpShape.resize(4);
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permuteInpShape[0] = inp.size[1] * inp.size[2] / (reorgStride * reorgStride); // (channels*height)/(r*r)
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permuteInpShape[1] = reorgStride;
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permuteInpShape[2] = inp.size[3]; // width
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permuteInpShape[3] = reorgStride;
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permuteOutShape.resize(4);
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for (int i = 0; i < 4; ++i)
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permuteOutShape[i] = permuteInpShape[order[i]];
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}
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else
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{
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int order[] = {0, 2, 4, 1, 3};
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permParams.set("order", DictValue::arrayInt(&order[0], 5));
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permuteInpShape.resize(5);
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permuteInpShape[0] = batchSize;
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permuteInpShape[1] = inp.size[1] * inp.size[2] / (reorgStride * reorgStride); // (channels*height)/(r*r)
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permuteInpShape[2] = reorgStride;
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permuteInpShape[3] = inp.size[3]; // width
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permuteInpShape[4] = reorgStride;
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permuteOutShape.resize(5);
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for (int i = 0; i < 5; ++i)
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permuteOutShape[i] = permuteInpShape[order[i]];
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}
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permute = PermuteLayer::create(permParams);
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std::vector<Mat> permuteInputs(1, inp.reshape(1, permuteInpShape));
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std::vector<Mat> permuteOutputs(1, out.reshape(1, permuteOutShape));
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permute->finalize(permuteInputs, permuteOutputs);
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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 || backendId == DNN_BACKEND_INFERENCE_ENGINE;
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}
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#ifdef HAVE_OPENCL
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bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
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{
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std::vector<UMat> inputs;
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std::vector<UMat> outputs;
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inps.getUMatVector(inputs);
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outs.getUMatVector(outputs);
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inputs[0] = inputs[0].reshape(1, permuteInpShape.size(), &permuteInpShape[0]);
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outputs[0] = outputs[0].reshape(1, permuteOutShape.size(), &permuteOutShape[0]);
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permute->preferableTarget = preferableTarget;
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permute->forward(inputs, outputs, internals);
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return true;
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}
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#endif
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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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CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
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OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
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forward_ocl(inputs_arr, outputs_arr, internals_arr))
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if (inputs_arr.depth() == CV_16S)
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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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inputs[0] = inputs[0].reshape(1, permuteInpShape);
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outputs[0] = outputs[0].reshape(1, permuteOutShape);
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permute->forward(inputs, outputs, internals_arr);
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}
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virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
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{
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#ifdef HAVE_INF_ENGINE
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InferenceEngine::LayerParams lp;
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lp.name = name;
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lp.type = "ReorgYolo";
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lp.precision = InferenceEngine::Precision::FP32;
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std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
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ieLayer->params["stride"] = format("%d", reorgStride);
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return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
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#endif // HAVE_INF_ENGINE
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return Ptr<BackendNode>();
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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(outputs); // suppress unused variable warning
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int64 flops = 0;
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for(int i = 0; i < inputs.size(); i++)
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{
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flops += 21*total(inputs[i]);
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}
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return flops;
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}
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private:
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Ptr<PermuteLayer> permute;
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std::vector<int> permuteInpShape, permuteOutShape;
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};
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Ptr<ReorgLayer> ReorgLayer::create(const LayerParams& params)
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{
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return Ptr<ReorgLayer>(new ReorgLayerImpl(params));
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}
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} // namespace dnn
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} // namespace cv
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