在使用优化和内联汇编进行编译时，我在使代码工作时遇到了一些麻烦。
我认为实际发布大会不会对我有所帮助，因为这是一个更普遍的问题。
我的内联汇编使用与C版本相同的变量（作为输入参数传递）
asm ( "" : : "r" (arg1), "r" (arg2) :);
我的问题是，如何让编译器不优化这些变量？在使用优化进行编译时，该功能仅会破坏程序集。我尝试过挥发性的，但它仍然没有正确行事。
感谢。
更新
我正在尝试为OpenCV实现NEON优化，特别是lkpyramid.cpp文件。问题是，在发布模式（设置了优化）中，它无法正常工作。但是，在调试模式下，它工作正常。我跟踪了一个特定的变量（FLT_SCALE），该变量正在被优化并使其变得易变，之后该部分工作正常，但我仍然有来自另一个优化的错误行为。
gcc版本可能会有所不同，因为这是一个开源项目，但我目前使用的是4.8.1。目标架构是ARMv7 w / NEON。我正在测试的处理器是ARM Cortex-A15（big.LITTLE）。
以下是我当前状态下的代码。忽略所有评论和挥发物（用于测试此问题）到处都是。它是WIP。我删除了不相关的代码，所以我可以在这里使用它。我认为问题出在最底部的asm块中，因为如果我if(false)跳过它，我就不会遇到问题。感谢。
#include "precomp.hpp"
#include <float.h>
#include <stdio.h>
#include "lkpyramid.hpp"
namespace
{
static void calcSharrDeriv(const cv::Mat& src, cv::Mat& dst)
{
using namespace cv;
using cv::detail::deriv_type;
int rows = src.rows, cols = src.cols, cn = src.channels(), colsn = cols*cn, depth = src.depth();
CV_Assert(depth == CV_8U);
dst.create(rows, cols, CV_MAKETYPE(DataType<deriv_type>::depth, cn*2));
#ifdef HAVE_TEGRA_OPTIMIZATION
if (tegra::calcSharrDeriv(src, dst))
return;
#endif
int x, y, delta = (int)alignSize((cols + 2)*cn, 16);
AutoBuffer<deriv_type> _tempBuf(delta*2 + 64);
deriv_type *trow0 = alignPtr(_tempBuf + cn, 16), *trow1 = alignPtr(trow0 + delta, 16);
int three = 3, ten = 10;
for( y = 0; y < rows; y++ )
{
const uchar* srow0 = src.ptr<uchar>(y > 0 ? y-1 : rows > 1 ? 1 : 0);
const uchar* srow1 = src.ptr<uchar>(y);
const uchar* srow2 = src.ptr<uchar>(y < rows-1 ? y+1 : rows > 1 ? rows-2 : 0);
deriv_type* drow = dst.ptr<deriv_type>(y);
// do vertical convolution
x = 0;
#ifdef CV_NEON
//assumes deriv_type is 16 bits
if(sizeof(deriv_type) == 2 && colsn >= 16)
{
__asm__ volatile ( "vdup.16 q8, %0\n\t"
"vdup.8 d18, %1\n\t"
:
: "r" (three), "r" (ten)
: );
for( ; x <= colsn - 8; x += 8)
{
__asm__ volatile ( "vld1.8 {d0}, [%0]\n\t"
"vld1.8 {d1}, [%1]\n\t"
"vld1.8 {d2}, [%2]\n\t"
"vaddl.u8 q4, d0, d2\n\t"
"vsubl.u8 q11, d2, d0\n\t"
"vmul.u16 q5, q4, q8\n\t"
"vmull.u8 q6, d1, d18\n\t"
"vadd.u16 q10, q6, q5\n\t"
"vst1.16 {q10}, [%3]\n\t"
"vst1.16 {q11}, [%4]\n\t"
:
: "r" (srow0 + x),
"r" (srow1 + x),
"r" (srow2 + x),
"r" (trow0 + x),
"r" (trow1 + x)
:
);
}
}
#endif
for( ; x < colsn; x++ )
{
int t0 = (srow0[x] + srow2[x])*3 + srow1[x]*10;
int t1 = srow2[x] - srow0[x];
trow0[x] = (deriv_type)t0;
trow1[x] = (deriv_type)t1;
}
// make border
int x0 = (cols > 1 ? 1 : 0)*cn, x1 = (cols > 1 ? cols-2 : 0)*cn;
for( int k = 0; k < cn; k++ )
{
trow0[-cn + k] = trow0[x0 + k]; trow0[colsn + k] = trow0[x1 + k];
trow1[-cn + k] = trow1[x0 + k]; trow1[colsn + k] = trow1[x1 + k];
}
#ifdef CV_NEON
__asm__ volatile ( "vdup.16 q8, %0\n\t"
"vdup.16 q9, %1\n\t"
:
: "r" (three), "r" (ten)
: );
#endif
// do horizontal convolution, interleave the results and store them to dst
x = 0;
#ifdef CV_NEON
//assumes size of deriv_type is 16 bits
if(sizeof(deriv_type) == 2 && colsn >= 16)
{
for( ; x <= colsn - 8; x += 8 )
{
__asm__ volatile (
"vld1.16 {q0}, [%0]\n\t" //trow0[x + cn]
"vld1.16 {q1}, [%1]\n\t" //trow0[x - cn]
"vsub.i16 q5, q0, q1\n\t" //this is t0
"vld1.16 {q2}, [%2]\n\t" //trow1[x + cn]
"vld1.16 {q3}, [%3]\n\t" //trow1[x - cn]
"vadd.i16 q6, q2, q3\n\t" //this needs mult by 3
"vld1.16 {q4}, [%4]\n\t" //trow1[x]
"vmul.i16 q7, q6, q8\n\t" //this needs to add to trow1[x]*10
"vmul.i16 q10, q4, q9\n\t" //this is trow1[x]*10
"vadd.i16 q11, q7, q10\n\t" //this is t1
"vswp d22, d11\n\t"
"vst2.16 {q5}, [%5]\n\t" //interleave
"vst2.16 {q11}, [%6]\n\t" //interleave
:
: "r" (trow0 + x + cn), //0
"r" (trow0 + x - cn), //1
"r" (trow1 + x + cn), //2
"r" (trow1 + x - cn), //3
"r" (trow1 + x), //4
"r" (drow + (x*2)), //5
"r" (drow + (x*2)+8) //6
:
);
}
}
#endif
for( ; x < colsn; x++ )
{
deriv_type t0 = (deriv_type)(trow0[x+cn] - trow0[x-cn]);
deriv_type t1 = (deriv_type)((trow1[x+cn] + trow1[x-cn])*3 + trow1[x]*10);
drow[x*2] = t0; drow[x*2+1] = t1;
}
}
}
}//namespace
cv::detail::LKTrackerInvoker::LKTrackerInvoker(
const Mat& _prevImg, const Mat& _prevDeriv, const Mat& _nextImg,
const Point2f* _prevPts, Point2f* _nextPts,
uchar* _status, float* _err,
Size _winSize, TermCriteria _criteria,
int _level, int _maxLevel, int _flags, float _minEigThreshold )
{
prevImg = &_prevImg;
prevDeriv = &_prevDeriv;
nextImg = &_nextImg;
prevPts = _prevPts;
nextPts = _nextPts;
status = _status;
err = _err;
winSize = _winSize;
criteria = _criteria;
level = _level;
maxLevel = _maxLevel;
flags = _flags;
minEigThreshold = _minEigThreshold;
}
void cv::detail::LKTrackerInvoker::operator()(const Range& range) const
{
Point2f halfWin((winSize.width-1)*0.5f, (winSize.height-1)*0.5f);
const Mat& I = *prevImg;
const Mat& J = *nextImg;
const Mat& derivI = *prevDeriv;
int j, cn = I.channels(), cn2 = cn*2;
cv::AutoBuffer<deriv_type> _buf(winSize.area()*(cn + cn2));
int derivDepth = DataType<deriv_type>::depth;
Mat IWinBuf(winSize, CV_MAKETYPE(derivDepth, cn), (deriv_type*)_buf);
Mat derivIWinBuf(winSize, CV_MAKETYPE(derivDepth, cn2), (deriv_type*)_buf + winSize.area()*cn);
for( int ptidx = range.start; ptidx < range.end; ptidx++ )
{
Point2f prevPt = prevPts[ptidx]*(float)(1./(1 << level));
Point2f nextPt;
if( level == maxLevel )
{
if( flags & OPTFLOW_USE_INITIAL_FLOW )
nextPt = nextPts[ptidx]*(float)(1./(1 << level));
else
nextPt = prevPt;
}
else
nextPt = nextPts[ptidx]*2.f;
nextPts[ptidx] = nextPt;
Point2i iprevPt, inextPt;
prevPt -= halfWin;
iprevPt.x = cvFloor(prevPt.x);
iprevPt.y = cvFloor(prevPt.y);
if( iprevPt.x < -winSize.width || iprevPt.x >= derivI.cols ||
iprevPt.y < -winSize.height || iprevPt.y >= derivI.rows )
{
if( level == 0 )
{
if( status )
status[ptidx] = false;
if( err )
err[ptidx] = 0;
}
continue;
}
volatile float a = prevPt.x - iprevPt.x;
volatile float b = prevPt.y - iprevPt.y;
volatile const int W_BITS = 14, W_BITS1 = 14;
volatile const float FLT_SCALE = 1.f/(1 << 20); //volatile is needed because compiler will optimize this out for NEON
volatile int iw00 = cvRound((1.f - a)*(1.f - b)*(1 << W_BITS));
volatile int iw01 = cvRound(a*(1.f - b)*(1 << W_BITS));
volatile int iw10 = cvRound((1.f - a)*b*(1 << W_BITS));
volatile int iw11 = (1 << W_BITS) - iw00 - iw01 - iw10;
volatile int dstep = (int)(derivI.step/derivI.elemSize1());
volatile int stepI = (int)(I.step/I.elemSize1());
volatile int stepJ = (int)(J.step/J.elemSize1());
volatile float A11 = 0, A12 = 0, A22 = 0;
#ifdef CV_NEON
volatile int CV_DECL_ALIGNED(16) nA11[] = {0, 0, 0, 0}, nA12[] = {0, 0, 0, 0}, nA22[] = {0, 0, 0, 0};
volatile const int shifter1 = -(W_BITS - 5); //negative so it shifts right
volatile const int shifter2 = -(W_BITS);
if(sizeof(deriv_type) == 2)
{
__asm__ volatile ( "vdup.16 d26, %0\n\t"
"vdup.16 d27, %1\n\t"
"vdup.16 d28, %2\n\t"
"vdup.16 d29, %3\n\t"
"vdup.32 q11, %4\n\t"
"vdup.32 q12, %5\n\t"
:
: "r" ((short)iw00),
"r" ((short)iw01),
"r" ((short)iw10),
"r" ((short)iw11),
"r" (shifter1),
"r" (shifter2)
: );
}
#endif
// extract the patch from the first image, compute covariation matrix of derivatives
volatile int x, y;
for( y = 0; y < winSize.height; y++ )
{
volatile const uchar* src = (const uchar*)I.data + (y + iprevPt.y)*stepI + iprevPt.x*cn;
volatile const deriv_type* dsrc = (const deriv_type*)derivI.data + (y + iprevPt.y)*dstep + iprevPt.x*cn2;
volatile deriv_type* Iptr = (deriv_type*)(IWinBuf.data + y*IWinBuf.step);
volatile deriv_type* dIptr = (deriv_type*)(derivIWinBuf.data + y*derivIWinBuf.step);
x = 0;
#ifdef CV_NEON
if(sizeof(deriv_type) == 2 && winSize.width*cn >= 12)
{
for( ; x <= winSize.width*cn - 4; x += 4, dsrc += 4*2, dIptr += 4*2 )
{
__asm__ volatile (
"vld1.8 {d0}, [%0]\n\t" //ignores last 4 bytes
"vmovl.u8 q0, d0\n\t" //expand to 16-bit
"vld1.8 {d2}, [%1]\n\t"
"vmovl.u8 q1, d2\n\t"
"vmull.s16 q5, d0, d26\n\t"
"vmull.s16 q6, d2, d27\n\t"
"vld1.8 {d4}, [%2]\n\t"
"vmovl.u8 q2, d4\n\t" //expand
"vld1.8 {d6}, [%3]\n\t"
"vmovl.u8 q3, d6\n\t"
"vmull.s16 q7, d4, d28\n\t"
"vmull.s16 q8, d6, d29\n\t"
"vadd.i32 q5, q5, q6\n\t"
"vadd.i32 q7, q7, q8\n\t"
"vadd.i32 q5, q5, q7\n\t"
"vld2.16 {d0, d1}, [%4]\n\t" //evens in d0 and d2
"vld2.16 {d2, d3}, [%5]\n\t"
"vqrshl.s32 q5, q5, q11\n\t"
"vmull.s16 q4, d0, d26\n\t" //q4 is mult of even 1
"vmull.s16 q6, d1, d26\n\t" //q6 is mult of odd 1
"vmovn.s32 d0, q5\n\t"
"vmull.s16 q7, d2, d27\n\t" //q7 is mult of even 2
"vmull.s16 q8, d3, d27\n\t" //q8 is mult of odd 2
"vst1.16 {d0}, [%8]\n\t"
"vld2.16 {d4, d5}, [%6]\n\t" //evens in d4 and d6
"vld2.16 {d6, d7}, [%7]\n\t"
"vadd.i32 q4, q4, q7\n\t" //this frees up q7 and q8
"vadd.i32 q6, q6, q8\n\t" //q4 is added even 1 and 2
//q6 is added odd 1 and 2
"vmull.s16 q7, d4, d28\n\t" //q7 is mult of even 3
"vmull.s16 q0, d5, d28\n\t" //q0 is mult of odd 3
"vmull.s16 q8, d6, d29\n\t" //q8 is mult of even 4
"vmull.s16 q15, d7, d29\n\t" //q15 is mult of odd 4
"vadd.i32 q7, q7, q8\n\t" //q7 is added even 3 and 4
"vadd.i32 q0, q0, q15\n\t" //q0 is added odd 3 and 4
"vadd.i32 q4, q4, q7\n\t" //q4 is added even 1,2,3,4 -- will be ixval
"vadd.i32 q6, q6, q0\n\t" //q6 is added odd 1,2,3,4 -- will be iyval
"vld1.32 {q1}, [%11]\n\t"
"vld1.32 {q2}, [%12]\n\t"
"vld1.32 {q0}, [%10]\n\t" //get the loads prepared
"vqrshl.s32 q4, q4, q12\n\t" //q4 is descaled evens added
"vqrshl.s32 q6, q6, q12\n\t" //q6 is descaled odds added
//now ixval is stored in q4 and iyval is stored in q6 and ival is in q5
"vmul.s32 q7, q4, q4\n\t"
"vmul.s32 q8, q4, q6\n\t"
"vmul.s32 q15, q6, q6\n\t"
"vadd.i32 q0, q0, q7\n\t"
"vadd.i32 q1, q1, q8\n\t"
"vadd.i32 q2, q2, q15\n\t"
"vst1.32 {q0}, [%10]\n\t"
"vst1.32 {q1}, [%11]\n\t"
"vst1.32 {q2}, [%12]\n\t"
"vmovn.i32 d8, q4\n\t" //bring ixval to short
"vmovn.i32 d12, q6\n\t" //bring iyval to short
"vswp d9, d12\n\t" //now d8 is ixval and d9 is iyval
"vst2.16 {d8, d9}, [%9]\n\t"
:
: "r" (src + x), //0
"r" (src + x + cn), //1
"r" (src + x + stepI), //2
"r" (src + x + stepI + cn), //3
"r" (dsrc), //4
"r" (dsrc + cn2), //5
"r" (dsrc + dstep), //6
"r" (dsrc + dstep + cn2), //7
"r" (Iptr + x), //8
"r" (dIptr), //9
"r" (nA11), //10
"r" (nA12), //11
"r" (nA22) //12
: );
}
}
#endif
for( ; x < winSize.width*cn; x++, dsrc += 2, dIptr += 2 )
{
int ival = CV_DESCALE(src[x]*iw00 + src[x+cn]*iw01 +
src[x+stepI]*iw10 + src[x+stepI+cn]*iw11, W_BITS1-5);
int ixval = CV_DESCALE(dsrc[0]*iw00 + dsrc[cn2]*iw01 +
dsrc[dstep]*iw10 + dsrc[dstep+cn2]*iw11, W_BITS1);
int iyval = CV_DESCALE(dsrc[1]*iw00 + dsrc[cn2+1]*iw01 + dsrc[dstep+1]*iw10 +
dsrc[dstep+cn2+1]*iw11, W_BITS1);
Iptr[x] = (short)ival;
dIptr[0] = (short)ixval;
dIptr[1] = (short)iyval;
A11 += (float)(ixval*ixval);
A12 += (float)(ixval*iyval);
A22 += (float)(iyval*iyval);
}
}
#ifdef CV_NEON
A11 += (float)(nA11[0] + nA11[1] + nA11[2] + nA11[3]);
A12 += (float)(nA12[0] + nA12[1] + nA12[2] + nA12[3]);
A22 += (float)(nA22[0] + nA22[1] + nA22[2] + nA22[3]);
#endif
A11 *= FLT_SCALE;
A12 *= FLT_SCALE;
A22 *= FLT_SCALE;
volatile float D = A11*A22 - A12*A12;
float minEig = (A22 + A11 - std::sqrt((A11-A22)*(A11-A22) +
4.f*A12*A12))/(2*winSize.width*winSize.height);
if( err && (flags & CV_LKFLOW_GET_MIN_EIGENVALS) != 0 )
err[ptidx] = (float)minEig;
if( minEig < minEigThreshold || D < FLT_EPSILON )
{
if( level == 0 && status )
status[ptidx] = false;
continue;
}
D = 1.f/D;
nextPt -= halfWin;
Point2f prevDelta;
for( j = 0; j < criteria.maxCount; j++ )
{
inextPt.x = cvFloor(nextPt.x);
inextPt.y = cvFloor(nextPt.y);
if( inextPt.x < -winSize.width || inextPt.x >= J.cols ||
inextPt.y < -winSize.height || inextPt.y >= J.rows )
{
if( level == 0 && status )
status[ptidx] = false;
break;
}
a = nextPt.x - inextPt.x;
b = nextPt.y - inextPt.y;
iw00 = cvRound((1.f - a)*(1.f - b)*(1 << W_BITS));
iw01 = cvRound(a*(1.f - b)*(1 << W_BITS));
iw10 = cvRound((1.f - a)*b*(1 << W_BITS));
iw11 = (1 << W_BITS) - iw00 - iw01 - iw10;
float b1 = 0, b2 = 0;
for( y = 0; y < winSize.height; y++ )
{
const uchar* Jptr = (const uchar*)J.data + (y + inextPt.y)*stepJ + inextPt.x*cn;
const deriv_type* Iptr = (const deriv_type*)(IWinBuf.data + y*IWinBuf.step);
const deriv_type* dIptr = (const deriv_type*)(derivIWinBuf.data + y*derivIWinBuf.step);
x = 0;
for( ; x < winSize.width*cn; x++, dIptr += 2 )
{
int diff = CV_DESCALE(Jptr[x]*iw00 + Jptr[x+cn]*iw01 +
Jptr[x+stepJ]*iw10 + Jptr[x+stepJ+cn]*iw11,
W_BITS1-5) - Iptr[x];
b1 += (float)(diff*dIptr[0]);
b2 += (float)(diff*dIptr[1]);
}
}
b1 *= FLT_SCALE;
b2 *= FLT_SCALE;
Point2f delta( (float)((A12*b2 - A22*b1) * D),
(float)((A12*b1 - A11*b2) * D));
//delta = -delta;
nextPt += delta;
nextPts[ptidx] = nextPt + halfWin;
if( delta.ddot(delta) <= criteria.epsilon )
break;
if( j > 0 && std::abs(delta.x + prevDelta.x) < 0.01 &&
std::abs(delta.y + prevDelta.y) < 0.01 )
{
nextPts[ptidx] -= delta*0.5f;
break;
}
prevDelta = delta;
}
if( status[ptidx] && err && level == 0 && (flags & CV_LKFLOW_GET_MIN_EIGENVALS) == 0 )
{
Point2f nextPoint = nextPts[ptidx] - halfWin;
Point inextPoint;
inextPoint.x = cvFloor(nextPoint.x);
inextPoint.y = cvFloor(nextPoint.y);
if( inextPoint.x < -winSize.width || inextPoint.x >= J.cols ||
inextPoint.y < -winSize.height || inextPoint.y >= J.rows )
{
if( status )
status[ptidx] = false;
continue;
}
float aa = nextPoint.x - inextPoint.x;
float bb = nextPoint.y - inextPoint.y;
iw00 = cvRound((1.f - aa)*(1.f - bb)*(1 << W_BITS));
iw01 = cvRound(aa*(1.f - bb)*(1 << W_BITS));
iw10 = cvRound((1.f - aa)*bb*(1 << W_BITS));
iw11 = (1 << W_BITS) - iw00 - iw01 - iw10;
float errval = 0.f;
for( y = 0; y < winSize.height; y++ )
{
const uchar* Jptr = (const uchar*)J.data + (y + inextPoint.y)*stepJ + inextPoint.x*cn;
const deriv_type* Iptr = (const deriv_type*)(IWinBuf.data + y*IWinBuf.step);
for( x = 0; x < winSize.width*cn; x++ )
{
int diff = CV_DESCALE(Jptr[x]*iw00 + Jptr[x+cn]*iw01 +
Jptr[x+stepJ]*iw10 + Jptr[x+stepJ+cn]*iw11,
W_BITS1-5) - Iptr[x];
errval += std::abs((float)diff);
}
}
err[ptidx] = errval * 1.f/(32*winSize.width*cn*winSize.height);
}
}
}
答案 0 :(得分：2)
仔细阅读GCC文档的extended asm部分。
请告诉我们您正在使用的GCC版本，目标处理器以及真实的asm指令....
您可能需要asm volatile或asm goto
您可以将这些变量声明为 volatile ，也许
{ volatile auto varg1 = arg1;
volatile auto varg2 = arg2;
volatile asm ("" : : "r" (varg1), "r" (varg2) :);
}
你应该使用最新版本的GCC，例如GCC 4.8（今天，2014年2月）以及即将到来的GCC 4.9
几周内或许gcc-help@gcc.gnu.org是一个更好的地方。
答案 1 :(得分：0)
<强>校正的
在GCC邮件列表的帮助下。我发现，对于一个，我需要添加用于clobber列表的寄存器。另外，我必须做两件事之一：
感谢Basile的建议以及评论的所有人。