147 lines
6.2 KiB
C++
147 lines
6.2 KiB
C++
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/*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) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Copyright (C) 2013, OpenCV Foundation, 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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#pragma once
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#ifndef OPENCV_CUDEV_GRID_REDUCE_TO_COLUMN_DETAIL_HPP
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#define OPENCV_CUDEV_GRID_REDUCE_TO_COLUMN_DETAIL_HPP
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#include "../../common.hpp"
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#include "../../util/saturate_cast.hpp"
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#include "../../block/reduce.hpp"
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namespace cv { namespace cudev {
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namespace grid_reduce_to_vec_detail
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{
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template <int BLOCK_SIZE, typename work_type, typename work_elem_type, class Reductor, int cn> struct Reduce;
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template <int BLOCK_SIZE, typename work_type, typename work_elem_type, class Reductor> struct Reduce<BLOCK_SIZE, work_type, work_elem_type, Reductor, 1>
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{
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__device__ __forceinline__ static void call(work_elem_type smem[1][BLOCK_SIZE], work_type& myVal)
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{
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typename Reductor::template rebind<work_elem_type>::other op;
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blockReduce<BLOCK_SIZE>(smem[0], myVal, threadIdx.x, op);
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}
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};
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template <int BLOCK_SIZE, typename work_type, typename work_elem_type, class Reductor> struct Reduce<BLOCK_SIZE, work_type, work_elem_type, Reductor, 2>
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{
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__device__ __forceinline__ static void call(work_elem_type smem[2][BLOCK_SIZE], work_type& myVal)
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{
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typename Reductor::template rebind<work_elem_type>::other op;
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blockReduce<BLOCK_SIZE>(smem_tuple(smem[0], smem[1]), tie(myVal.x, myVal.y), threadIdx.x, make_tuple(op, op));
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}
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};
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template <int BLOCK_SIZE, typename work_type, typename work_elem_type, class Reductor> struct Reduce<BLOCK_SIZE, work_type, work_elem_type, Reductor, 3>
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{
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__device__ __forceinline__ static void call(work_elem_type smem[3][BLOCK_SIZE], work_type& myVal)
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{
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typename Reductor::template rebind<work_elem_type>::other op;
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blockReduce<BLOCK_SIZE>(smem_tuple(smem[0], smem[1], smem[2]), tie(myVal.x, myVal.y, myVal.z), threadIdx.x, make_tuple(op, op, op));
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}
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};
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template <int BLOCK_SIZE, typename work_type, typename work_elem_type, class Reductor> struct Reduce<BLOCK_SIZE, work_type, work_elem_type, Reductor, 4>
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{
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__device__ __forceinline__ static void call(work_elem_type smem[4][BLOCK_SIZE], work_type& myVal)
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{
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typename Reductor::template rebind<work_elem_type>::other op;
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blockReduce<BLOCK_SIZE>(smem_tuple(smem[0], smem[1], smem[2], smem[3]), tie(myVal.x, myVal.y, myVal.z, myVal.w), threadIdx.x, make_tuple(op, op, op, op));
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}
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};
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template <class Reductor, int BLOCK_SIZE, class SrcPtr, typename ResType, class MaskPtr>
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__global__ void reduceToColumn(const SrcPtr src, ResType* dst, const MaskPtr mask, const int cols)
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{
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typedef typename Reductor::work_type work_type;
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typedef typename VecTraits<work_type>::elem_type work_elem_type;
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const int cn = VecTraits<work_type>::cn;
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__shared__ work_elem_type smem[cn][BLOCK_SIZE];
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const int y = blockIdx.x;
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work_type myVal = Reductor::initialValue();
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Reductor op;
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for (int x = threadIdx.x; x < cols; x += BLOCK_SIZE)
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{
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if (mask(y, x))
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{
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myVal = op(myVal, saturate_cast<work_type>(src(y, x)));
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}
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}
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Reduce<BLOCK_SIZE, work_type, work_elem_type, Reductor, cn>::call(smem, myVal);
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if (threadIdx.x == 0)
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dst[y] = saturate_cast<ResType>(Reductor::result(myVal, cols));
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}
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template <class Reductor, class Policy, class SrcPtr, typename ResType, class MaskPtr>
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__host__ void reduceToColumn(const SrcPtr& src, ResType* dst, const MaskPtr& mask, int rows, int cols, cudaStream_t stream)
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{
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const int BLOCK_SIZE_X = Policy::block_size_x;
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const int BLOCK_SIZE_Y = Policy::block_size_y;
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const int BLOCK_SIZE = BLOCK_SIZE_X * BLOCK_SIZE_Y;
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const dim3 block(BLOCK_SIZE);
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const dim3 grid(rows);
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reduceToColumn<Reductor, BLOCK_SIZE><<<grid, block, 0, stream>>>(src, dst, mask, cols);
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CV_CUDEV_SAFE_CALL( cudaGetLastError() );
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if (stream == 0)
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CV_CUDEV_SAFE_CALL( cudaDeviceSynchronize() );
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}
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}
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}}
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#endif
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