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397 lines
13 KiB
C++
397 lines
13 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 <float.h>
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#include <algorithm>
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#include "opencl_kernels_dnn.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 PermuteLayerImpl : public PermuteLayer
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{
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public:
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void checkCurrentOrder(int currentOrder)
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{
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if(currentOrder < 0 || currentOrder > 3)
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{
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CV_Error(
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Error::StsBadArg,
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"Orders of dimensions in Permute layer parameter"
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"must be in [0...3] interval");
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}
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if(std::find(_order.begin(), _order.end(), currentOrder) != _order.end())
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{
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CV_Error(Error::StsBadArg,
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"Permute layer parameter contains duplicated orders.");
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}
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}
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void checkNeedForPermutation()
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{
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_needsPermute = false;
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for (size_t i = 0; i < _numAxes; ++i)
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{
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if (_order[i] != i)
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{
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_needsPermute = true;
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break;
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}
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}
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}
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PermuteLayerImpl(const LayerParams ¶ms)
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: _count(0), _needsPermute(false), _numAxes(0)
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{
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if (!params.has("order"))
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{
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return;
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}
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DictValue paramOrder = params.get("order");
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if(paramOrder.size() > 4)
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{
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CV_Error(
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Error::StsBadArg,
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"Too many (> 4) orders of dimensions in Permute layer");
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}
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_numAxes = paramOrder.size();
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for (size_t i = 0; i < _numAxes; i++)
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{
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int currentOrder = paramOrder.get<int>(i);
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checkCurrentOrder(currentOrder);
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_order.push_back(currentOrder);
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}
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setParamsFrom(params);
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checkNeedForPermutation();
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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
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{
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if(!_needsPermute)
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{
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Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
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return true;
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}
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CV_Assert(inputs.size() > 0);
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CV_Assert((int)_numAxes == inputs[0].size());
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MatShape shapeBefore = inputs[0], shapeAfter;
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for (size_t i = 0; i < _numAxes; i++)
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{
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shapeAfter.push_back(shapeBefore[_order[i]]);
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}
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outputs.clear();
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for (size_t i = 0; i < inputs.size(); i++)
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{
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CV_Assert(inputs[i].size() == 4);
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CV_Assert(inputs[i][2] == shapeBefore[2] && inputs[i][3] == shapeBefore[3]);
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CV_Assert(total(inputs[i]) == total(shapeAfter));
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outputs.push_back(shapeAfter);
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}
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return false;
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}
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void computeStrides(const MatShape &shapeBefore, const MatShape &shapeAfter)
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{
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_oldStride.resize(_numAxes);
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_newStride.resize(_numAxes);
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_oldStride[_numAxes - 1] = 1;
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_newStride[_numAxes - 1] = 1;
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for(int i = _numAxes - 2; i >= 0; i--)
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{
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_oldStride[i] = _oldStride[i + 1] * shapeBefore[i + 1];
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_newStride[i] = _newStride[i + 1] * shapeAfter[i + 1];
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}
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_count = _oldStride[0] * shapeBefore[0];
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}
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void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
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{
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if(!_needsPermute)
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{
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return;
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}
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CV_Assert(inputs.size() > 0);
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const Mat& inp0 = *inputs[0];
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CV_Assert((int)_numAxes == inp0.dims);
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computeStrides(shape(*inputs[0]), shape(outputs[0]));
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#ifdef HAVE_OPENCL
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if (uorder.empty())
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{
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std::vector<int> orderVec(_order.begin(), _order.end());;
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Mat morder(1, orderVec.size(), CV_32SC1, &orderVec[0]);
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std::vector<int> oldStrideVec(_oldStride.begin(), _oldStride.end());
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Mat mold_stride(1, _oldStride.size(), CV_32SC1, &oldStrideVec[0]);
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std::vector<int> newStrideVec(_newStride.begin(), _newStride.end());
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Mat mnew_stride(1, newStrideVec.size(), CV_32SC1, &newStrideVec[0]);
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morder.copyTo(uorder);
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mold_stride.copyTo(uold_stride);
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mnew_stride.copyTo(unew_stride);
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}
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#endif
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}
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class PermuteInvoker : public ParallelLoopBody
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{
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public:
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const Mat* inp;
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Mat* out;
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const std::vector<size_t>* order;
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int nstripes;
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static void run(const Mat& inp, Mat& out, const std::vector<size_t>& order, int nstripes)
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{
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PermuteInvoker p;
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p.inp = &inp;
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p.out = &out;
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p.order = ℴ
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p.nstripes = nstripes;
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CV_Assert( out.size[0] == inp.size[order[0]] &&
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out.size[1] == inp.size[order[1]] &&
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out.size[2] == inp.size[order[2]] &&
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out.size[3] == inp.size[order[3]]);
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parallel_for_(Range(0, nstripes), p, nstripes);
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}
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PermuteInvoker() : inp(0), out(0), order(0), nstripes(0) {}
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void operator()(const Range& r) const
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{
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int n0 = out->size[0], n1 = out->size[1], n2 = out->size[2], n3 = out->size[3];
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size_t orows = (size_t)n0*n1*n2;
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size_t stripeSize = (orows + 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, orows);
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const size_t esz = sizeof(float);
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size_t ostep0 = out->step[0]/esz, ostep1 = out->step[1]/esz, ostep2 = out->step[2]/esz;
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const size_t* ord = &order->at(0);
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size_t istep0 = inp->step[ord[0]]/esz, istep1 = inp->step[ord[1]]/esz,
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istep2 = inp->step[ord[2]]/esz, istep3 = inp->step[ord[3]]/esz;
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size_t val = stripeStart;
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int i2 = (int)(val % n2);
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val /= n2;
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int i1 = (int)(val % n1);
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int i0 = (int)(val / n1);
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const float* inptr_orig = inp->ptr<float>();
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float* outptr_orig = out->ptr<float>();
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for( size_t ofs = stripeStart; ofs < stripeEnd; ofs++ )
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{
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const float* inptr = inptr_orig + i0*istep0 + i1*istep1 + i2*istep2;
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float* outptr = outptr_orig + i0*ostep0 + i1*ostep1 + i2*ostep2;
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for( int i3 = 0; i3 < n3; i3++ )
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outptr[i3] = inptr[i3*istep3];
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if( ++i2 >= n2 )
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{
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i2 = 0;
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if( ++i1 >= n1 )
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{
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i1 = 0;
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if( ++i0 >= n0 )
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break;
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}
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}
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}
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}
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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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if (!_needsPermute)
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return false;
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for (size_t i = 0; i < inputs.size(); i++)
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{
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ocl::Kernel kernel("permute", ocl::dnn::permute_oclsrc);
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kernel.set(0, (int)_count);
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kernel.set(1, ocl::KernelArg::PtrReadOnly(inputs[i]));
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kernel.set(2, ocl::KernelArg::PtrReadOnly(uorder));
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kernel.set(3, ocl::KernelArg::PtrReadOnly(uold_stride));
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kernel.set(4, ocl::KernelArg::PtrReadOnly(unew_stride));
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kernel.set(5, (int)_numAxes);
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kernel.set(6, ocl::KernelArg::PtrWriteOnly(outputs[i]));
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if (!kernel.run(1, &_count, NULL, false))
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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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size_t k, ninputs = inputs.size();
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if(!_needsPermute)
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{
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for (k = 0; k < ninputs; k++)
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{
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CV_Assert(outputs[k].total() == inputs[k]->total());
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if (outputs[k].data != inputs[k]->data)
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inputs[k]->copyTo(outputs[k]);
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}
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}
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else
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{
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size_t i, j, count = _count, numAxes = _numAxes;
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const size_t* newStride = &_newStride[0];
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const size_t* oldStride = &_oldStride[0];
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const size_t* order = &_order[0];
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for (k = 0; k < ninputs; k++)
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{
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const Mat& inp = *inputs[k];
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Mat& out = outputs[k];
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CV_Assert(inp.dims == numAxes && inp.size == inputs[0]->size);
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CV_Assert(out.dims == numAxes && out.size == outputs[0].size);
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CV_Assert(inp.isContinuous() && out.isContinuous());
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CV_Assert(inp.type() == CV_32F && out.type() == CV_32F);
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if( numAxes == 4 )
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{
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int nstripes = getNumThreads();
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PermuteInvoker::run(inp, out, _order, nstripes);
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}
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else
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{
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const float *srcData = inp.ptr<float>();
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float *dstData = out.ptr<float>();
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for (i = 0; i < count; ++i)
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{
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size_t oldPosition = 0;
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size_t newPosition = i;
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for (j = 0; j < numAxes; ++j)
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{
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oldPosition += (newPosition / newStride[j]) * oldStride[order[j]];
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newPosition %= newStride[j];
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}
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dstData[i] = srcData[oldPosition];
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}
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}
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}
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}
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}
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size_t _count;
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std::vector<size_t> _order;
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std::vector<int> _oldDimensionSize;
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std::vector<int> _newDimensionSize;
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std::vector<size_t> _oldStride;
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std::vector<size_t> _newStride;
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bool _needsPermute;
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#ifdef HAVE_OPENCL
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UMat uorder, uold_stride, unew_stride;
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#endif
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size_t _numAxes;
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};
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Ptr<PermuteLayer> PermuteLayer::create(const LayerParams ¶ms)
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{
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return Ptr<PermuteLayer>(new PermuteLayerImpl(params));
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}
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}
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}
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