mirror of https://github.com/opencv/opencv.git
Vladislav Vinogradov
12 years ago
parent
fe2e89df1b
commit
36e42084f0
5 changed files with 967 additions and 0 deletions
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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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// 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 bpied warranties, including, but not limited to, the bpied |
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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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|
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#if !defined CUDA_DISABLER |
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|
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#include "opencv2/gpu/device/common.hpp" |
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#include "opencv2/gpu/device/limits.hpp" |
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#include "opencv2/gpu/device/functional.hpp" |
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#include "opencv2/gpu/device/reduce.hpp" |
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using namespace cv::gpu; |
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using namespace cv::gpu::device; |
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namespace optflowbm |
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{ |
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texture<uchar, cudaTextureType2D, cudaReadModeElementType> tex_prev(false, cudaFilterModePoint, cudaAddressModeClamp); |
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texture<uchar, cudaTextureType2D, cudaReadModeElementType> tex_curr(false, cudaFilterModePoint, cudaAddressModeClamp); |
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__device__ int cmpBlocks(int X1, int Y1, int X2, int Y2, int2 blockSize) |
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{ |
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int s = 0; |
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for (int y = 0; y < blockSize.y; ++y) |
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{ |
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for (int x = 0; x < blockSize.x; ++x) |
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s += ::abs(tex2D(tex_prev, X1 + x, Y1 + y) - tex2D(tex_curr, X2 + x, Y2 + y)); |
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} |
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return s; |
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} |
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__global__ void calcOptFlowBM(PtrStepSzf velx, PtrStepf vely, const int2 blockSize, const int2 shiftSize, const bool usePrevious, |
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const int maxX, const int maxY, const int acceptLevel, const int escapeLevel, |
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const short2* ss, const int ssCount) |
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{ |
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const int j = blockIdx.x * blockDim.x + threadIdx.x; |
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const int i = blockIdx.y * blockDim.y + threadIdx.y; |
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if (i >= velx.rows || j >= velx.cols) |
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return; |
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const int X1 = j * shiftSize.x; |
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const int Y1 = i * shiftSize.y; |
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const int offX = usePrevious ? __float2int_rn(velx(i, j)) : 0; |
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const int offY = usePrevious ? __float2int_rn(vely(i, j)) : 0; |
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int X2 = X1 + offX; |
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int Y2 = Y1 + offY; |
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int dist = numeric_limits<int>::max(); |
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if (0 <= X2 && X2 <= maxX && 0 <= Y2 && Y2 <= maxY) |
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dist = cmpBlocks(X1, Y1, X2, Y2, blockSize); |
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int countMin = 1; |
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int sumx = offX; |
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int sumy = offY; |
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if (dist > acceptLevel) |
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{ |
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// do brute-force search |
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for (int k = 0; k < ssCount; ++k) |
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{ |
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const short2 ssVal = ss[k]; |
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const int dx = offX + ssVal.x; |
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const int dy = offY + ssVal.y; |
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X2 = X1 + dx; |
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Y2 = Y1 + dy; |
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if (0 <= X2 && X2 <= maxX && 0 <= Y2 && Y2 <= maxY) |
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{ |
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const int tmpDist = cmpBlocks(X1, Y1, X2, Y2, blockSize); |
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if (tmpDist < acceptLevel) |
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{ |
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sumx = dx; |
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sumy = dy; |
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countMin = 1; |
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break; |
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} |
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if (tmpDist < dist) |
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{ |
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dist = tmpDist; |
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sumx = dx; |
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sumy = dy; |
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countMin = 1; |
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} |
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else if (tmpDist == dist) |
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{ |
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sumx += dx; |
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sumy += dy; |
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countMin++; |
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} |
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} |
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} |
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if (dist > escapeLevel) |
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{ |
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sumx = offX; |
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sumy = offY; |
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countMin = 1; |
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} |
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} |
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velx(i, j) = static_cast<float>(sumx) / countMin; |
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vely(i, j) = static_cast<float>(sumy) / countMin; |
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} |
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void calc(PtrStepSzb prev, PtrStepSzb curr, PtrStepSzf velx, PtrStepSzf vely, int2 blockSize, int2 shiftSize, bool usePrevious, |
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int maxX, int maxY, int acceptLevel, int escapeLevel, const short2* ss, int ssCount, cudaStream_t stream) |
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{ |
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bindTexture(&tex_prev, prev); |
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bindTexture(&tex_curr, curr); |
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const dim3 block(32, 8); |
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const dim3 grid(divUp(velx.cols, block.x), divUp(vely.rows, block.y)); |
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calcOptFlowBM<<<grid, block, 0, stream>>>(velx, vely, blockSize, shiftSize, usePrevious, |
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maxX, maxY, acceptLevel, escapeLevel, ss, ssCount); |
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cudaSafeCall( cudaGetLastError() ); |
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if (stream == 0) |
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cudaSafeCall( cudaDeviceSynchronize() ); |
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} |
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} |
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///////////////////////////////////////////////////////// |
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// Fast approximate version |
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namespace optflowbm_fast |
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{ |
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enum |
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{ |
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CTA_SIZE = 128, |
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TILE_COLS = 128, |
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TILE_ROWS = 32, |
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STRIDE = CTA_SIZE |
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}; |
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template <typename T> __device__ __forceinline__ int calcDist(T a, T b) |
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{ |
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return ::abs(a - b); |
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} |
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template <class T> struct FastOptFlowBM |
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{ |
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int search_radius; |
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int block_radius; |
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int search_window; |
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int block_window; |
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PtrStepSz<T> I0; |
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PtrStep<T> I1; |
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mutable PtrStepi buffer; |
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FastOptFlowBM(int search_window_, int block_window_, |
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PtrStepSz<T> I0_, PtrStepSz<T> I1_, |
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PtrStepi buffer_) : |
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search_radius(search_window_ / 2), block_radius(block_window_ / 2), |
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search_window(search_window_), block_window(block_window_), |
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I0(I0_), I1(I1_), |
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buffer(buffer_) |
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{ |
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} |
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__device__ __forceinline__ void initSums_BruteForce(int i, int j, int* dist_sums, PtrStepi& col_sums, PtrStepi& up_col_sums) const |
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{ |
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for (int index = threadIdx.x; index < search_window * search_window; index += STRIDE) |
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{ |
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dist_sums[index] = 0; |
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for (int tx = 0; tx < block_window; ++tx) |
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col_sums(tx, index) = 0; |
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int y = index / search_window; |
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int x = index - y * search_window; |
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int ay = i; |
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int ax = j; |
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int by = i + y - search_radius; |
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int bx = j + x - search_radius; |
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for (int tx = -block_radius; tx <= block_radius; ++tx) |
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{ |
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int col_sum = 0; |
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for (int ty = -block_radius; ty <= block_radius; ++ty) |
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{ |
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int dist = calcDist(I0(ay + ty, ax + tx), I1(by + ty, bx + tx)); |
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dist_sums[index] += dist; |
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col_sum += dist; |
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} |
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col_sums(tx + block_radius, index) = col_sum; |
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} |
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up_col_sums(j, index) = col_sums(block_window - 1, index); |
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} |
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} |
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__device__ __forceinline__ void shiftRight_FirstRow(int i, int j, int first, int* dist_sums, PtrStepi& col_sums, PtrStepi& up_col_sums) const |
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{ |
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for (int index = threadIdx.x; index < search_window * search_window; index += STRIDE) |
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{ |
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int y = index / search_window; |
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int x = index - y * search_window; |
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int ay = i; |
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int ax = j + block_radius; |
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int by = i + y - search_radius; |
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int bx = j + x - search_radius + block_radius; |
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int col_sum = 0; |
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for (int ty = -block_radius; ty <= block_radius; ++ty) |
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col_sum += calcDist(I0(ay + ty, ax), I1(by + ty, bx)); |
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dist_sums[index] += col_sum - col_sums(first, index); |
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col_sums(first, index) = col_sum; |
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up_col_sums(j, index) = col_sum; |
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} |
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} |
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__device__ __forceinline__ void shiftRight_UpSums(int i, int j, int first, int* dist_sums, PtrStepi& col_sums, PtrStepi& up_col_sums) const |
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{ |
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int ay = i; |
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int ax = j + block_radius; |
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T a_up = I0(ay - block_radius - 1, ax); |
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T a_down = I0(ay + block_radius, ax); |
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for(int index = threadIdx.x; index < search_window * search_window; index += STRIDE) |
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{ |
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int y = index / search_window; |
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int x = index - y * search_window; |
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int by = i + y - search_radius; |
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int bx = j + x - search_radius + block_radius; |
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T b_up = I1(by - block_radius - 1, bx); |
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T b_down = I1(by + block_radius, bx); |
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int col_sum = up_col_sums(j, index) + calcDist(a_down, b_down) - calcDist(a_up, b_up); |
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dist_sums[index] += col_sum - col_sums(first, index); |
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col_sums(first, index) = col_sum; |
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up_col_sums(j, index) = col_sum; |
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} |
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} |
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__device__ __forceinline__ void convolve_window(int i, int j, const int* dist_sums, float& velx, float& vely) const |
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{ |
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int bestDist = numeric_limits<int>::max(); |
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int bestInd = -1; |
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for (int index = threadIdx.x; index < search_window * search_window; index += STRIDE) |
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{ |
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int curDist = dist_sums[index]; |
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if (curDist < bestDist) |
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{ |
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bestDist = curDist; |
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bestInd = index; |
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} |
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} |
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__shared__ int cta_dist_buffer[CTA_SIZE]; |
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__shared__ int cta_ind_buffer[CTA_SIZE]; |
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reduceKeyVal<CTA_SIZE>(cta_dist_buffer, bestDist, cta_ind_buffer, bestInd, threadIdx.x, less<int>()); |
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if (threadIdx.x == 0) |
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{ |
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int y = bestInd / search_window; |
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int x = bestInd - y * search_window; |
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velx = x - search_radius; |
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vely = y - search_radius; |
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} |
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} |
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__device__ __forceinline__ void operator()(PtrStepf velx, PtrStepf vely) const |
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{ |
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int tbx = blockIdx.x * TILE_COLS; |
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int tby = blockIdx.y * TILE_ROWS; |
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int tex = ::min(tbx + TILE_COLS, I0.cols); |
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int tey = ::min(tby + TILE_ROWS, I0.rows); |
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PtrStepi col_sums; |
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col_sums.data = buffer.ptr(I0.cols + blockIdx.x * block_window) + blockIdx.y * search_window * search_window; |
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col_sums.step = buffer.step; |
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PtrStepi up_col_sums; |
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up_col_sums.data = buffer.data + blockIdx.y * search_window * search_window; |
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up_col_sums.step = buffer.step; |
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extern __shared__ int dist_sums[]; //search_window * search_window |
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int first = 0; |
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for (int i = tby; i < tey; ++i) |
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{ |
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for (int j = tbx; j < tex; ++j) |
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{ |
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__syncthreads(); |
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if (j == tbx) |
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{ |
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initSums_BruteForce(i, j, dist_sums, col_sums, up_col_sums); |
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first = 0; |
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} |
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else |
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{ |
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if (i == tby) |
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shiftRight_FirstRow(i, j, first, dist_sums, col_sums, up_col_sums); |
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else |
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shiftRight_UpSums(i, j, first, dist_sums, col_sums, up_col_sums); |
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first = (first + 1) % block_window; |
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} |
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__syncthreads(); |
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convolve_window(i, j, dist_sums, velx(i, j), vely(i, j)); |
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} |
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} |
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} |
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}; |
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template<typename T> __global__ void optflowbm_fast_kernel(const FastOptFlowBM<T> fbm, PtrStepf velx, PtrStepf vely) |
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{ |
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fbm(velx, vely); |
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} |
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void get_buffer_size(int src_cols, int src_rows, int search_window, int block_window, int& buffer_cols, int& buffer_rows) |
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{ |
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dim3 grid(divUp(src_cols, TILE_COLS), divUp(src_rows, TILE_ROWS)); |
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buffer_cols = search_window * search_window * grid.y; |
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buffer_rows = src_cols + block_window * grid.x; |
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} |
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template <typename T> |
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void calc(PtrStepSzb I0, PtrStepSzb I1, PtrStepSzf velx, PtrStepSzf vely, PtrStepi buffer, int search_window, int block_window, cudaStream_t stream) |
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{ |
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FastOptFlowBM<T> fbm(search_window, block_window, I0, I1, buffer); |
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dim3 block(CTA_SIZE, 1); |
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dim3 grid(divUp(I0.cols, TILE_COLS), divUp(I0.rows, TILE_ROWS)); |
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size_t smem = search_window * search_window * sizeof(int); |
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optflowbm_fast_kernel<<<grid, block, smem, stream>>>(fbm, velx, vely); |
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cudaSafeCall ( cudaGetLastError () ); |
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if (stream == 0) |
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cudaSafeCall( cudaDeviceSynchronize() ); |
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} |
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template void calc<uchar>(PtrStepSzb I0, PtrStepSzb I1, PtrStepSzf velx, PtrStepSzf vely, PtrStepi buffer, int search_window, int block_window, cudaStream_t stream); |
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} |
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#endif // !defined CUDA_DISABLER |
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@ -0,0 +1,243 @@ |
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/*M///////////////////////////////////////////////////////////////////////////////////////
|
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//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// 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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using namespace std; |
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using namespace cv; |
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using namespace cv::gpu; |
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#if !defined HAVE_CUDA || defined(CUDA_DISABLER) |
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void cv::gpu::calcOpticalFlowBM(const GpuMat&, const GpuMat&, Size, Size, Size, bool, GpuMat&, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); } |
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void cv::gpu::FastOpticalFlowBM::operator ()(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, int, int, Stream&) { throw_nogpu(); } |
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#else // HAVE_CUDA
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namespace optflowbm |
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{ |
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void calc(PtrStepSzb prev, PtrStepSzb curr, PtrStepSzf velx, PtrStepSzf vely, int2 blockSize, int2 shiftSize, bool usePrevious, |
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int maxX, int maxY, int acceptLevel, int escapeLevel, const short2* ss, int ssCount, cudaStream_t stream); |
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} |
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void cv::gpu::calcOpticalFlowBM(const GpuMat& prev, const GpuMat& curr, Size blockSize, Size shiftSize, Size maxRange, bool usePrevious, GpuMat& velx, GpuMat& vely, GpuMat& buf, Stream& st) |
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{ |
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CV_Assert( prev.type() == CV_8UC1 ); |
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CV_Assert( curr.size() == prev.size() && curr.type() == prev.type() ); |
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const Size velSize((prev.cols - blockSize.width + shiftSize.width) / shiftSize.width, |
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(prev.rows - blockSize.height + shiftSize.height) / shiftSize.height); |
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velx.create(velSize, CV_32FC1); |
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vely.create(velSize, CV_32FC1); |
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// scanning scheme coordinates
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vector<short2> ss((2 * maxRange.width + 1) * (2 * maxRange.height + 1)); |
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int ssCount = 0; |
||||
|
||||
// Calculate scanning scheme
|
||||
const int minCount = std::min(maxRange.width, maxRange.height); |
||||
|
||||
// use spiral search pattern
|
||||
//
|
||||
// 9 10 11 12
|
||||
// 8 1 2 13
|
||||
// 7 * 3 14
|
||||
// 6 5 4 15
|
||||
//... 20 19 18 17
|
||||
//
|
||||
|
||||
for (int i = 0; i < minCount; ++i) |
||||
{ |
||||
// four cycles along sides
|
||||
int x = -i - 1, y = x; |
||||
|
||||
// upper side
|
||||
for (int j = -i; j <= i + 1; ++j, ++ssCount) |
||||
{ |
||||
ss[ssCount].x = ++x; |
||||
ss[ssCount].y = y; |
||||
} |
||||
|
||||
// right side
|
||||
for (int j = -i; j <= i + 1; ++j, ++ssCount) |
||||
{ |
||||
ss[ssCount].x = x; |
||||
ss[ssCount].y = ++y; |
||||
} |
||||
|
||||
// bottom side
|
||||
for (int j = -i; j <= i + 1; ++j, ++ssCount) |
||||
{ |
||||
ss[ssCount].x = --x; |
||||
ss[ssCount].y = y; |
||||
} |
||||
|
||||
// left side
|
||||
for (int j = -i; j <= i + 1; ++j, ++ssCount) |
||||
{ |
||||
ss[ssCount].x = x; |
||||
ss[ssCount].y = --y; |
||||
} |
||||
} |
||||
|
||||
// the rest part
|
||||
if (maxRange.width < maxRange.height) |
||||
{ |
||||
const int xleft = -minCount; |
||||
|
||||
// cycle by neighbor rings
|
||||
for (int i = minCount; i < maxRange.height; ++i) |
||||
{ |
||||
// two cycles by x
|
||||
int y = -(i + 1); |
||||
int x = xleft; |
||||
|
||||
// upper side
|
||||
for (int j = -maxRange.width; j <= maxRange.width; ++j, ++ssCount, ++x) |
||||
{ |
||||
ss[ssCount].x = x; |
||||
ss[ssCount].y = y; |
||||
} |
||||
|
||||
x = xleft; |
||||
y = -y; |
||||
|
||||
// bottom side
|
||||
for (int j = -maxRange.width; j <= maxRange.width; ++j, ++ssCount, ++x) |
||||
{ |
||||
ss[ssCount].x = x; |
||||
ss[ssCount].y = y; |
||||
} |
||||
} |
||||
} |
||||
else if (maxRange.width > maxRange.height) |
||||
{ |
||||
const int yupper = -minCount; |
||||
|
||||
// cycle by neighbor rings
|
||||
for (int i = minCount; i < maxRange.width; ++i) |
||||
{ |
||||
// two cycles by y
|
||||
int x = -(i + 1); |
||||
int y = yupper; |
||||
|
||||
// left side
|
||||
for (int j = -maxRange.height; j <= maxRange.height; ++j, ++ssCount, ++y) |
||||
{ |
||||
ss[ssCount].x = x; |
||||
ss[ssCount].y = y; |
||||
} |
||||
|
||||
y = yupper; |
||||
x = -x; |
||||
|
||||
// right side
|
||||
for (int j = -maxRange.height; j <= maxRange.height; ++j, ++ssCount, ++y) |
||||
{ |
||||
ss[ssCount].x = x; |
||||
ss[ssCount].y = y; |
||||
} |
||||
} |
||||
} |
||||
|
||||
const cudaStream_t stream = StreamAccessor::getStream(st); |
||||
|
||||
ensureSizeIsEnough(1, ssCount, CV_16SC2, buf); |
||||
if (stream == 0) |
||||
cudaSafeCall( cudaMemcpy(buf.data, &ss[0], ssCount * sizeof(short2), cudaMemcpyHostToDevice) ); |
||||
else |
||||
cudaSafeCall( cudaMemcpyAsync(buf.data, &ss[0], ssCount * sizeof(short2), cudaMemcpyHostToDevice, stream) ); |
||||
|
||||
const int maxX = prev.cols - blockSize.width; |
||||
const int maxY = prev.rows - blockSize.height; |
||||
|
||||
const int SMALL_DIFF = 2; |
||||
const int BIG_DIFF = 128; |
||||
|
||||
const int blSize = blockSize.area(); |
||||
const int acceptLevel = blSize * SMALL_DIFF; |
||||
const int escapeLevel = blSize * BIG_DIFF; |
||||
|
||||
optflowbm::calc(prev, curr, velx, vely, |
||||
make_int2(blockSize.width, blockSize.height), make_int2(shiftSize.width, shiftSize.height), usePrevious, |
||||
maxX, maxY, acceptLevel, escapeLevel, buf.ptr<short2>(), ssCount, stream); |
||||
} |
||||
|
||||
namespace optflowbm_fast |
||||
{ |
||||
void get_buffer_size(int src_cols, int src_rows, int search_window, int block_window, int& buffer_cols, int& buffer_rows); |
||||
|
||||
template <typename T> |
||||
void calc(PtrStepSzb I0, PtrStepSzb I1, PtrStepSzf velx, PtrStepSzf vely, PtrStepi buffer, int search_window, int block_window, cudaStream_t stream); |
||||
} |
||||
|
||||
void cv::gpu::FastOpticalFlowBM::operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy, int search_window, int block_window, Stream& stream) |
||||
{ |
||||
CV_Assert( I0.type() == CV_8UC1 ); |
||||
CV_Assert( I1.size() == I0.size() && I1.type() == I0.type() ); |
||||
|
||||
int border_size = search_window / 2 + block_window / 2; |
||||
Size esize = I0.size() + Size(border_size, border_size) * 2; |
||||
|
||||
ensureSizeIsEnough(esize, I0.type(), extended_I0); |
||||
ensureSizeIsEnough(esize, I0.type(), extended_I1); |
||||
|
||||
copyMakeBorder(I0, extended_I0, border_size, border_size, border_size, border_size, cv::BORDER_DEFAULT, Scalar(), stream); |
||||
copyMakeBorder(I1, extended_I1, border_size, border_size, border_size, border_size, cv::BORDER_DEFAULT, Scalar(), stream); |
||||
|
||||
GpuMat I0_hdr = extended_I0(Rect(Point2i(border_size, border_size), I0.size())); |
||||
GpuMat I1_hdr = extended_I1(Rect(Point2i(border_size, border_size), I0.size())); |
||||
|
||||
int bcols, brows; |
||||
optflowbm_fast::get_buffer_size(I0.cols, I0.rows, search_window, block_window, bcols, brows); |
||||
|
||||
ensureSizeIsEnough(brows, bcols, CV_32SC1, buffer); |
||||
|
||||
flowx.create(I0.size(), CV_32FC1); |
||||
flowy.create(I0.size(), CV_32FC1); |
||||
|
||||
optflowbm_fast::calc<uchar>(I0_hdr, I1_hdr, flowx, flowy, buffer, search_window, block_window, StreamAccessor::getStream(stream)); |
||||
} |
||||
|
||||
#endif // HAVE_CUDA
|
||||
Loading…
Reference in new issue