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8f99083726
Add layer forward interface with InputArrayOfArrays and OutputArrayOfArrays parameters, it allows UMat buffer to be processed and transferred in the layers. Signed-off-by: Li Peng <peng.li@intel.com>
702 lines
29 KiB
C++
702 lines
29 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 "layers_common.hpp"
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#include "opencv2/core/hal/intrin.hpp"
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#include "op_halide.hpp"
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#include "opencl_kernels_dnn.hpp"
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#include <float.h>
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#include <algorithm>
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using std::max;
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using std::min;
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#ifdef HAVE_OPENCL
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using namespace cv::dnn::ocl4dnn;
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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 PoolingLayerImpl : public PoolingLayer
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{
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public:
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PoolingLayerImpl(const LayerParams& params)
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{
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type = PoolingLayer::MAX;
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computeMaxIdx = true;
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if (params.has("pool"))
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{
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String pool = params.get<String>("pool").toLowerCase();
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if (pool == "max")
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type = PoolingLayer::MAX;
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else if (pool == "ave")
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type = PoolingLayer::AVE;
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else if (pool == "stochastic")
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type = PoolingLayer::STOCHASTIC;
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else
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CV_Error(Error::StsBadArg, "Unknown pooling type \"" + pool + "\"");
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}
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getPoolingKernelParams(params, kernel.height, kernel.width, globalPooling,
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pad.height, pad.width, stride.height, stride.width, padMode);
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setParamsFrom(params);
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ceilMode = params.get<bool>("ceil_mode", true);
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}
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#ifdef HAVE_OPENCL
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Ptr<OCL4DNNPool<float> > poolOp;
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#endif
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void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
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{
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CV_Assert(inputs.size() == 1);
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cv::Size inp(inputs[0]->size[3], inputs[0]->size[2]),
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out(outputs[0].size[3], outputs[0].size[2]);
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if(globalPooling)
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{
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kernel = inp;
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}
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getConvPoolPaddings(inp, out, kernel, stride, padMode, Size(1, 1), pad);
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}
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virtual bool supportBackend(int backendId)
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{
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return backendId == DNN_BACKEND_DEFAULT ||
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backendId == DNN_BACKEND_HALIDE && haveHalide() &&
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(type == PoolingLayer::MAX ||
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type == PoolingLayer::AVE && !pad.width && !pad.height);
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}
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#ifdef HAVE_OPENCL
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bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, InputArrayOfArrays 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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if (poolOp.empty())
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{
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OCL4DNNPoolConfig config;
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config.in_shape = shape(inputs[0]);
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config.out_shape = shape(outputs[0]);
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config.kernel = kernel;
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config.pad = pad;
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config.stride = stride;
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config.channels = inputs[0].size[1];
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config.pool_method = type == MAX ? LIBDNN_POOLING_METHOD_MAX :
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(type == AVE ? LIBDNN_POOLING_METHOD_AVE :
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LIBDNN_POOLING_METHOD_STO);
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poolOp = Ptr<OCL4DNNPool<float> >(new OCL4DNNPool<float>(config));
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}
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for (size_t ii = 0; ii < inputs.size(); ii++)
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{
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UMat& inpMat = inputs[ii];
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int out_index = (type == MAX) ? 2 : 1;
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UMat& outMat = outputs[out_index * ii];
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UMat maskMat = (type == MAX) ? outputs[2 * ii + 1] : UMat();
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CV_Assert(inpMat.offset == 0 && outMat.offset == 0);
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if (!poolOp->Forward(inpMat, outMat, maskMat))
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return false;
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}
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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)
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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((preferableTarget == DNN_TARGET_OPENCL) &&
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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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Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
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}
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void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
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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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for (size_t ii = 0; ii < inputs.size(); ii++)
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{
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switch (type)
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{
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case MAX:
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maxPooling(*inputs[ii], outputs[2 * ii], outputs[2 * ii + 1]);
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break;
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case AVE:
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avePooling(*inputs[ii], outputs[ii]);
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break;
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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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}
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virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
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{
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if (type == PoolingLayer::MAX)
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return initMaxPoolingHalide(inputs);
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else if (type == PoolingLayer::AVE)
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return initAvePoolingHalide(inputs);
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else
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return Ptr<BackendNode>();
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}
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class PoolingInvoker : public ParallelLoopBody
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{
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public:
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const Mat* src;
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Mat *dst, *mask;
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Size kernel, stride, pad;
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int nstripes;
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bool computeMaxIdx;
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std::vector<int> ofsbuf;
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int poolingType;
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PoolingInvoker() : src(0), dst(0), mask(0), nstripes(0), computeMaxIdx(0), poolingType(PoolingLayer::MAX) {}
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static void run(const Mat& src, Mat& dst, Mat& mask, Size kernel,
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Size stride, Size pad, int poolingType,
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bool computeMaxIdx, int nstripes)
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{
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CV_Assert(src.isContinuous() && dst.isContinuous() &&
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src.type() == CV_32F && src.type() == dst.type() &&
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src.dims == 4 && dst.dims == 4 &&
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src.size[0] == dst.size[0] && src.size[1] == dst.size[1] &&
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(mask.empty() || (mask.type() == src.type() && mask.size == dst.size)));
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PoolingInvoker p;
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p.src = &src;
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p.dst = &dst;
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p.mask = &mask;
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p.kernel = kernel;
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p.stride = stride;
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p.pad = pad;
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p.nstripes = nstripes;
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p.computeMaxIdx = computeMaxIdx;
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p.poolingType = poolingType;
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if( !computeMaxIdx )
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{
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p.ofsbuf.resize(kernel.width*kernel.height);
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for( int i = 0; i < kernel.height; i++ )
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for( int j = 0; j < kernel.width; j++ )
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p.ofsbuf[i*kernel.width + j] = src.size[3]*i + j;
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}
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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
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{
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int channels = dst->size[1], width = dst->size[3], height = dst->size[2];
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int inp_width = src->size[3], inp_height = src->size[2];
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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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int kernel_w = kernel.width, kernel_h = kernel.height;
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int pad_w = pad.width, pad_h = pad.height;
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int stride_w = stride.width, stride_h = stride.height;
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bool compMaxIdx = computeMaxIdx;
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#if CV_SIMD128
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const int* ofsptr = &ofsbuf[0];
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v_float32x4 idx00(0.f, (float)stride_w, (float)(stride_w*2), (float)(stride_w*3));
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v_float32x4 ones = v_setall_f32(1.f);
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v_float32x4 idx_delta = v_setall_f32((float)(inp_width - kernel_w));
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#endif
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for( size_t ofs0 = stripeStart; ofs0 < stripeEnd; )
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{
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size_t ofs = ofs0;
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int x0 = (int)(ofs % width);
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ofs /= width;
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int y0 = (int)(ofs % height);
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ofs /= height;
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int c = (int)(ofs % channels);
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int n = (int)(ofs / channels);
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int ystart = y0 * stride_h - pad_h;
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int yend = min(ystart + kernel_h, inp_height + pad_h);
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int ydelta = yend - ystart;
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ystart = max(ystart, 0);
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yend = min(yend, inp_height);
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const float *srcData = src->ptr<float>(n, c);
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float *dstData = dst->ptr<float>(n, c, y0);
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float *dstMaskData = mask->data ? mask->ptr<float>(n, c, y0) : 0;
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int delta = std::min((int)(stripeEnd - ofs0), width - x0);
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ofs0 += delta;
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int x1 = x0 + delta;
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if( poolingType == PoolingLayer::MAX )
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for( ; x0 < x1; x0++ )
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{
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int xstart = x0 * stride_w - pad_w;
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int xend = min(xstart + kernel_w, inp_width);
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xstart = max(xstart, 0);
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#if CV_SIMD128
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if( xstart > 0 && x0 + 7 < x1 && (x0 + 7) * stride_w - pad_w + kernel_w < inp_width )
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{
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if( compMaxIdx )
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{
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v_float32x4 max_val0 = v_setall_f32(-FLT_MAX);
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v_float32x4 max_val1 = max_val0;
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v_float32x4 max_idx0 = v_setall_f32(-1.f);
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v_float32x4 max_idx1 = max_idx0;
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int index0 = ystart * inp_width + xstart;
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v_float32x4 idx0 = idx00 + v_setall_f32((float)index0);
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v_float32x4 idx1 = idx0 + v_setall_f32((float)(stride_w*4));
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for (int y = ystart; y < yend; ++y)
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{
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for (int x = xstart; x < xend; ++x, idx0 += ones, idx1 += ones)
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{
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const int index = y * inp_width + x;
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v_float32x4 v0(srcData[index], srcData[index + stride_w],
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srcData[index + stride_w*2], srcData[index + stride_w*3]);
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v_float32x4 v1(srcData[index + stride_w*4], srcData[index + stride_w*5],
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srcData[index + stride_w*6], srcData[index + stride_w*7]);
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max_idx0 = v_select(v0 > max_val0, idx0, max_idx0);
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max_idx1 = v_select(v1 > max_val1, idx1, max_idx1);
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max_val0 = v_max(max_val0, v0);
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max_val1 = v_max(max_val1, v1);
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}
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idx0 += idx_delta;
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idx1 += idx_delta;
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}
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v_store(dstData + x0, max_val0);
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v_store(dstData + x0 + 4, max_val1);
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if (dstMaskData)
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{
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v_store(dstMaskData + x0, max_idx0);
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v_store(dstMaskData + x0 + 4, max_idx1);
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}
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x0 += 7;
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}
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else
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{
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v_float32x4 max_val0 = v_setall_f32(-FLT_MAX);
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v_float32x4 max_val1 = max_val0;
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if( yend - ystart == kernel_h )
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{
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const float* srcData1 = srcData + ystart*inp_width + xstart;
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if( stride_w == 1 )
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for (int k = 0; k < kernel_w*kernel_h; k++)
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{
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int index = ofsptr[k];
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v_float32x4 v0 = v_load(srcData1 + index);
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v_float32x4 v1 = v_load(srcData1 + index + 4);
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max_val0 = v_max(max_val0, v0);
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max_val1 = v_max(max_val1, v1);
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}
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#if CV_SSE2
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else if( stride_w == 2 )
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for (int k = 0; k < kernel_w*kernel_h; k++)
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{
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int index = ofsptr[k];
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v_float32x4 v00 = v_load(srcData1 + index), v01 = v_load(srcData1 + index + 4);
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v_float32x4 v0(_mm_shuffle_ps(v00.val, v01.val, _MM_SHUFFLE(2, 0, 2, 0)));
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v_float32x4 v10 = v_load(srcData1 + index + 8), v11 = v_load(srcData1 + index + 12);
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v_float32x4 v1(_mm_shuffle_ps(v10.val, v11.val, _MM_SHUFFLE(2, 0, 2, 0)));
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max_val0 = v_max(max_val0, v0);
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max_val1 = v_max(max_val1, v1);
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}
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#endif
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else
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for (int k = 0; k < kernel_w*kernel_h; k++)
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{
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int index = ofsptr[k];
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v_float32x4 v0(srcData1[index], srcData1[index + stride_w],
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srcData1[index + stride_w*2], srcData1[index + stride_w*3]);
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v_float32x4 v1(srcData1[index + stride_w*4], srcData1[index + stride_w*5],
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srcData1[index + stride_w*6], srcData1[index + stride_w*7]);
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max_val0 = v_max(max_val0, v0);
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max_val1 = v_max(max_val1, v1);
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}
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}
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else
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{
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for (int y = ystart; y < yend; ++y)
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{
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for (int x = xstart; x < xend; ++x)
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{
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const int index = y * inp_width + x;
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v_float32x4 v0(srcData[index], srcData[index + stride_w],
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srcData[index + stride_w*2], srcData[index + stride_w*3]);
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v_float32x4 v1(srcData[index + stride_w*4], srcData[index + stride_w*5],
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srcData[index + stride_w*6], srcData[index + stride_w*7]);
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max_val0 = v_max(max_val0, v0);
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max_val1 = v_max(max_val1, v1);
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}
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}
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}
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v_store(dstData + x0, max_val0);
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v_store(dstData + x0 + 4, max_val1);
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x0 += 7;
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}
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}
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else
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#endif
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{
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float max_val = -FLT_MAX;
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if( compMaxIdx )
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{
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int max_index = -1;
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for (int y = ystart; y < yend; ++y)
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for (int x = xstart; x < xend; ++x)
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{
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const int index = y * inp_width + x;
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float val = srcData[index];
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if (val > max_val)
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{
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max_val = val;
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max_index = index;
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}
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}
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dstData[x0] = max_val;
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if (dstMaskData)
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dstMaskData[x0] = max_index;
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}
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else
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{
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for (int y = ystart; y < yend; ++y)
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for (int x = xstart; x < xend; ++x)
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{
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const int index = y * inp_width + x;
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float val = srcData[index];
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max_val = std::max(max_val, val);
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}
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dstData[x0] = max_val;
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}
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}
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}
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else
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{
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for( ; x0 < x1; x0++ )
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{
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int xstart = x0 * stride_w - pad_w;
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int xend = min(xstart + kernel_w, inp_width + pad_w);
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int xdelta = xend - xstart;
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xstart = max(xstart, 0);
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xend = min(xend, inp_width);
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float inv_kernel_area = 1.f/(ydelta*xdelta);
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#if CV_SIMD128
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if( xstart > 0 && x0 + 7 < x1 && (x0 + 7) * stride_w - pad_w + kernel_w < inp_width )
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{
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v_float32x4 sum_val0 = v_setzero_f32(), sum_val1 = v_setzero_f32();
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v_float32x4 ikarea = v_setall_f32(inv_kernel_area);
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for (int y = ystart; y < yend; ++y)
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{
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for (int x = xstart; x < xend; ++x)
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{
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const int index = y * inp_width + x;
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v_float32x4 v0(srcData[index], srcData[index + stride_w],
|
|
srcData[index + stride_w*2], srcData[index + stride_w*3]);
|
|
v_float32x4 v1(srcData[index + stride_w*4], srcData[index + stride_w*5],
|
|
srcData[index + stride_w*6], srcData[index + stride_w*7]);
|
|
sum_val0 += v0;
|
|
sum_val1 += v1;
|
|
}
|
|
}
|
|
v_store(dstData + x0, sum_val0*ikarea);
|
|
v_store(dstData + x0 + 4, sum_val1*ikarea);
|
|
x0 += 7;
|
|
}
|
|
else
|
|
#endif
|
|
{
|
|
float sum_val = 0.f;
|
|
for (int y = ystart; y < yend; ++y)
|
|
for (int x = xstart; x < xend; ++x)
|
|
{
|
|
const int index = y * inp_width + x;
|
|
float val = srcData[index];
|
|
sum_val += val;
|
|
}
|
|
|
|
dstData[x0] = sum_val*inv_kernel_area;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
void maxPooling(Mat &src, Mat &dst, Mat &mask)
|
|
{
|
|
const int nstripes = getNumThreads();
|
|
PoolingInvoker::run(src, dst, mask, kernel, stride, pad, type, computeMaxIdx, nstripes);
|
|
}
|
|
|
|
void avePooling(Mat &src, Mat &dst)
|
|
{
|
|
const int nstripes = getNumThreads();
|
|
Mat mask;
|
|
PoolingInvoker::run(src, dst, mask, kernel, stride, pad, type, computeMaxIdx, nstripes);
|
|
}
|
|
|
|
virtual Ptr<BackendNode> initMaxPoolingHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
|
|
{
|
|
#ifdef HAVE_HALIDE
|
|
Halide::Buffer<float> inputBuffer = halideBuffer(inputs[0]);
|
|
const int inWidth = inputBuffer.width();
|
|
const int inHeight = inputBuffer.height();
|
|
|
|
Halide::Var x("x"), y("y"), c("c"), n("n");
|
|
Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
|
|
Halide::RDom r(0, kernel.width, 0, kernel.height);
|
|
Halide::Expr kx, ky;
|
|
if (pad.width || pad.height)
|
|
{
|
|
kx = clamp(x * stride.width + r.x - pad.width, 0, inWidth - 1);
|
|
ky = clamp(y * stride.height + r.y - pad.height, 0, inHeight - 1);
|
|
}
|
|
else
|
|
{
|
|
kx = min(x * stride.width + r.x, inWidth - 1);
|
|
ky = min(y * stride.height + r.y, inHeight - 1);
|
|
}
|
|
|
|
// Halide::argmax returns tuple (r.x, r.y, max).
|
|
Halide::Tuple res = argmax(inputBuffer(kx, ky, c, n));
|
|
|
|
// Compute offset from argmax in range [0, kernel_size).
|
|
Halide::Expr max_index;
|
|
if (pad.width || pad.height)
|
|
{
|
|
max_index = clamp(y * stride.height + res[1] - pad.height,
|
|
0, inHeight - 1) * inWidth +
|
|
clamp(x * stride.width + res[0] - pad.width,
|
|
0, inWidth - 1);
|
|
}
|
|
else
|
|
{
|
|
max_index = min(y * stride.height + res[1], inHeight - 1) * inWidth +
|
|
min(x * stride.width + res[0], inWidth - 1);
|
|
}
|
|
top(x, y, c, n) = { res[2], Halide::cast<float>(max_index) };
|
|
return Ptr<BackendNode>(new HalideBackendNode(top));
|
|
#endif // HAVE_HALIDE
|
|
return Ptr<BackendNode>();
|
|
}
|
|
|
|
virtual Ptr<BackendNode> initAvePoolingHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
|
|
{
|
|
#ifdef HAVE_HALIDE
|
|
Halide::Buffer<float> inputBuffer = halideBuffer(inputs[0]);
|
|
|
|
const int inW = inputBuffer.width(), inH = inputBuffer.height();
|
|
if ((inW - kernel.width) % stride.width || (inH - kernel.height) % stride.height)
|
|
{
|
|
CV_Error(cv::Error::StsNotImplemented,
|
|
"Halide backend for average pooling with partial "
|
|
"kernels is not implemented");
|
|
}
|
|
|
|
const float norm = 1.0f / (kernel.width * kernel.height);
|
|
|
|
Halide::Var x("x"), y("y"), c("c"), n("n");
|
|
Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
|
|
Halide::RDom r(0, kernel.width, 0, kernel.height);
|
|
top(x, y, c, n) = sum(
|
|
inputBuffer(x * stride.width + r.x,
|
|
y * stride.height + r.y, c, n)) * norm;
|
|
return Ptr<BackendNode>(new HalideBackendNode(top));
|
|
#endif // HAVE_HALIDE
|
|
return Ptr<BackendNode>();
|
|
}
|
|
|
|
virtual void applyHalideScheduler(Ptr<BackendNode>& node,
|
|
const std::vector<Mat*> &inputs,
|
|
const std::vector<Mat> &outputs,
|
|
int targetId) const
|
|
{
|
|
#ifdef HAVE_HALIDE
|
|
if (targetId != DNN_TARGET_CPU)
|
|
{
|
|
Layer::applyHalideScheduler(node, inputs, outputs, targetId);
|
|
return;
|
|
}
|
|
Halide::Var x("x"), y("y"), c("c"), n("n"), tile("tile"),
|
|
xi("xi"), yi("yi"), ci("ci"), xo("xo"), yo("yo"), co("co");
|
|
Halide::Func& top = node.dynamicCast<HalideBackendNode>()->funcs.back();
|
|
|
|
int outW, outH, outC, outN;
|
|
getCanonicalSize(outputs[0].size, &outW, &outH, &outC, &outN);
|
|
|
|
if (outW < 8 || outH < 8)
|
|
{
|
|
if (outC > 8)
|
|
top.split(c, co, ci, 8)
|
|
.fuse(x, y, tile).fuse(co, tile, tile).fuse(n, tile, tile)
|
|
.parallel(tile)
|
|
.vectorize(ci);
|
|
else
|
|
{
|
|
top.fuse(y, c, tile).fuse(n, tile, tile)
|
|
.parallel(tile);
|
|
if (outW > 1)
|
|
top.vectorize(x);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if (outC > 8)
|
|
top.split(x, xo, xi, 8).split(y, yo, yi, 8).split(c, co, ci, 8)
|
|
.fuse(xo, yo, tile).fuse(co, tile, tile).fuse(n, tile, tile)
|
|
.parallel(tile)
|
|
.vectorize(xi);
|
|
else
|
|
top.split(x, xo, xi, 8).split(y, yo, yi, 8)
|
|
.fuse(xo, yo, tile).fuse(c, tile, tile).fuse(n, tile, tile)
|
|
.parallel(tile)
|
|
.vectorize(xi);
|
|
}
|
|
#endif // HAVE_HALIDE
|
|
}
|
|
|
|
bool getMemoryShapes(const std::vector<MatShape> &inputs,
|
|
const int requiredOutputs,
|
|
std::vector<MatShape> &outputs,
|
|
std::vector<MatShape> &internals) const
|
|
{
|
|
CV_Assert(inputs.size() != 0);
|
|
Size in(inputs[0][3], inputs[0][2]), out;
|
|
|
|
if (globalPooling)
|
|
{
|
|
out.height = 1;
|
|
out.width = 1;
|
|
}
|
|
else if (padMode.empty())
|
|
{
|
|
float height = (float)(in.height + 2 * pad.height - kernel.height) / stride.height;
|
|
float width = (float)(in.width + 2 * pad.width - kernel.width) / stride.width;
|
|
out.height = 1 + (ceilMode ? ceil(height) : floor(height));
|
|
out.width = 1 + (ceilMode ? ceil(width) : floor(width));
|
|
|
|
if (pad.height || pad.width)
|
|
{
|
|
// If we have padding, ensure that the last pooling starts strictly
|
|
// inside the image (instead of at the padding); otherwise clip the last.
|
|
if ((out.height - 1) * stride.height >= in.height + pad.height)
|
|
--out.height;
|
|
if ((out.width - 1) * stride.width >= in.width + pad.width)
|
|
--out.width;
|
|
CV_Assert((out.height - 1) * stride.height < in.height + pad.height);
|
|
CV_Assert((out.width - 1) * stride.width < in.width + pad.width);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
getConvPoolOutParams(in, kernel, stride, padMode, Size(1, 1), out);
|
|
}
|
|
|
|
outputs.resize(type == MAX ? 2 * inputs.size() : inputs.size());
|
|
for (size_t i = 0; i < inputs.size(); i++)
|
|
{
|
|
size_t index = type == MAX ? 2*i : i;
|
|
int dims[] = {inputs[i][0], inputs[i][1], out.height, out.width};
|
|
outputs[index] = shape(dims);
|
|
|
|
if (type == MAX)
|
|
outputs[index + 1] = shape(dims);
|
|
}
|
|
|
|
return false;
|
|
}
|
|
|
|
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
|
|
const std::vector<MatShape> &outputs) const
|
|
{
|
|
(void)inputs; // suppress unused variable warning
|
|
long flops = 0;
|
|
|
|
for(int i = 0; i < outputs.size(); i++)
|
|
{
|
|
if (type == MAX)
|
|
{
|
|
if (i%2 == 0)
|
|
flops += total(outputs[i])*kernel.area();
|
|
}
|
|
else
|
|
{
|
|
flops += total(outputs[i])*(kernel.area() + 1);
|
|
}
|
|
}
|
|
return flops;
|
|
}
|
|
};
|
|
|
|
Ptr<PoolingLayer> PoolingLayer::create(const LayerParams& params)
|
|
{
|
|
return Ptr<PoolingLayer>(new PoolingLayerImpl(params));
|
|
}
|
|
|
|
}
|
|
}
|