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Merge pull request #1929 from alalek:ocl_haar_amd_beta

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Andrey Pavlenko 2013-12-06 14:56:47 +04:00 committed by OpenCV Buildbot
commit b0b199ee8a
2 changed files with 100 additions and 87 deletions
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84
modules/ocl/src/opencl/haarobjectdetect.cl
View File

@ -62,13 +62,13 @@ typedef struct __attribute__((aligned (128) )) GpuHidHaarTreeNode
GpuHidHaarTreeNode;
typedef struct __attribute__((aligned (32))) GpuHidHaarClassifier
{
int count __attribute__((aligned (4)));
GpuHidHaarTreeNode* node __attribute__((aligned (8)));
float* alpha __attribute__((aligned (8)));
}
GpuHidHaarClassifier;
//typedef struct __attribute__((aligned (32))) GpuHidHaarClassifier
//{
// int count __attribute__((aligned (4)));
// GpuHidHaarTreeNode* node __attribute__((aligned (8)));
// float* alpha __attribute__((aligned (8)));
//}
//GpuHidHaarClassifier;
typedef struct __attribute__((aligned (64))) GpuHidHaarStageClassifier
@ -84,22 +84,22 @@ typedef struct __attribute__((aligned (64))) GpuHidHaarStageClassifier
GpuHidHaarStageClassifier;
typedef struct __attribute__((aligned (64))) GpuHidHaarClassifierCascade
{
int count __attribute__((aligned (4)));
int is_stump_based __attribute__((aligned (4)));
int has_tilted_features __attribute__((aligned (4)));
int is_tree __attribute__((aligned (4)));
int pq0 __attribute__((aligned (4)));
int pq1 __attribute__((aligned (4)));
int pq2 __attribute__((aligned (4)));
int pq3 __attribute__((aligned (4)));
int p0 __attribute__((aligned (4)));
int p1 __attribute__((aligned (4)));
int p2 __attribute__((aligned (4)));
int p3 __attribute__((aligned (4)));
float inv_window_area __attribute__((aligned (4)));
} GpuHidHaarClassifierCascade;
//typedef struct __attribute__((aligned (64))) GpuHidHaarClassifierCascade
//{
// int count __attribute__((aligned (4)));
// int is_stump_based __attribute__((aligned (4)));
// int has_tilted_features __attribute__((aligned (4)));
// int is_tree __attribute__((aligned (4)));
// int pq0 __attribute__((aligned (4)));
// int pq1 __attribute__((aligned (4)));
// int pq2 __attribute__((aligned (4)));
// int pq3 __attribute__((aligned (4)));
// int p0 __attribute__((aligned (4)));
// int p1 __attribute__((aligned (4)));
// int p2 __attribute__((aligned (4)));
// int p3 __attribute__((aligned (4)));
// float inv_window_area __attribute__((aligned (4)));
//} GpuHidHaarClassifierCascade;
#ifdef PACKED_CLASSIFIER
@ -196,10 +196,12 @@ __kernel void gpuRunHaarClassifierCascadePacked(
for(int stageloop = start_stage; (stageloop < end_stage) && result; stageloop++ )
{// iterate until candidate is exist
float stage_sum = 0.0f;
int2 stageinfo = *(global int2*)(stagecascadeptr+stageloop);
float stagethreshold = as_float(stageinfo.y);
__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*)
((__global uchar*)stagecascadeptr+stageloop*sizeof(GpuHidHaarStageClassifier));
int stagecount = stageinfo->count;
float stagethreshold = stageinfo->threshold;
int lcl_off = (lid_y*DATA_SIZE_X)+(lid_x);
for(int nodeloop = 0; nodeloop < stageinfo.x; nodecounter++,nodeloop++ )
for(int nodeloop = 0; nodeloop < stagecount; nodecounter++,nodeloop++ )
{
// simple macro to extract shorts from int
#define M0(_t) ((_t)&0xFFFF)
@ -358,11 +360,14 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
for(int stageloop = start_stage; (stageloop < split_stage) && result; stageloop++ )
{
float stage_sum = 0.f;
int2 stageinfo = *(global int2*)(stagecascadeptr+stageloop);
float stagethreshold = as_float(stageinfo.y);
for(int nodeloop = 0; nodeloop < stageinfo.x; )
__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*)
((__global uchar*)stagecascadeptr+stageloop*sizeof(GpuHidHaarStageClassifier));
int stagecount = stageinfo->count;
float stagethreshold = stageinfo->threshold;
for(int nodeloop = 0; nodeloop < stagecount; )
{
__global GpuHidHaarTreeNode* currentnodeptr = (nodeptr + nodecounter);
__global GpuHidHaarTreeNode* currentnodeptr = (__global GpuHidHaarTreeNode*)
(((__global uchar*)nodeptr) + nodecounter * sizeof(GpuHidHaarTreeNode));
int4 info1 = *(__global int4*)(&(currentnodeptr->p[0][0]));
int4 info2 = *(__global int4*)(&(currentnodeptr->p[1][0]));
@ -418,7 +423,7 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
#endif
}
result = (stage_sum >= stagethreshold);
result = (stage_sum >= stagethreshold) ? 1 : 0;
}
if(factor < 2)
{
@ -447,14 +452,17 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
lclcount[0]=0;
barrier(CLK_LOCAL_MEM_FENCE);
int2 stageinfo = *(global int2*)(stagecascadeptr+stageloop);
float stagethreshold = as_float(stageinfo.y);
//int2 stageinfo = *(global int2*)(stagecascadeptr+stageloop);
__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*)
((__global uchar*)stagecascadeptr+stageloop*sizeof(GpuHidHaarStageClassifier));
int stagecount = stageinfo->count;
float stagethreshold = stageinfo->threshold;
int perfscale = queuecount > 4 ? 3 : 2;
int queuecount_loop = (queuecount + (1<<perfscale)-1) >> perfscale;
int lcl_compute_win = lcl_sz >> perfscale;
int lcl_compute_win_id = (lcl_id >>(6-perfscale));
int lcl_loops = (stageinfo.x + lcl_compute_win -1) >> (6-perfscale);
int lcl_loops = (stagecount + lcl_compute_win -1) >> (6-perfscale);
int lcl_compute_id = lcl_id - (lcl_compute_win_id << (6-perfscale));
for(int queueloop=0; queueloop<queuecount_loop; queueloop++)
{
@ -469,10 +477,10 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
float part_sum = 0.f;
const int stump_factor = STUMP_BASED ? 1 : 2;
int root_offset = 0;
for(int lcl_loop=0; lcl_loop<lcl_loops && tempnodecounter<stageinfo.x;)
for(int lcl_loop=0; lcl_loop<lcl_loops && tempnodecounter<stagecount;)
{
__global GpuHidHaarTreeNode* currentnodeptr =
nodeptr + (nodecounter + tempnodecounter) * stump_factor + root_offset;
__global GpuHidHaarTreeNode* currentnodeptr = (__global GpuHidHaarTreeNode*)
(((__global uchar*)nodeptr) + sizeof(GpuHidHaarTreeNode) * ((nodecounter + tempnodecounter) * stump_factor + root_offset));
int4 info1 = *(__global int4*)(&(currentnodeptr->p[0][0]));
int4 info2 = *(__global int4*)(&(currentnodeptr->p[1][0]));
@ -549,7 +557,7 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
queuecount = lclcount[0];
barrier(CLK_LOCAL_MEM_FENCE);
nodecounter += stageinfo.x;
nodecounter += stagecount;
}//end for(int stageloop = splitstage; stageloop< endstage && queuecount>0;stageloop++)
if(lcl_id<queuecount)

97
modules/ocl/src/opencl/haarobjectdetect_scaled2.cl
View File

@ -59,13 +59,13 @@ typedef struct __attribute__((aligned(128))) GpuHidHaarTreeNode
int right __attribute__((aligned(4)));
}
GpuHidHaarTreeNode;
typedef struct __attribute__((aligned(32))) GpuHidHaarClassifier
{
int count __attribute__((aligned(4)));
GpuHidHaarTreeNode *node __attribute__((aligned(8)));
float *alpha __attribute__((aligned(8)));
}
GpuHidHaarClassifier;
//typedef struct __attribute__((aligned(32))) GpuHidHaarClassifier
//{
// int count __attribute__((aligned(4)));
// GpuHidHaarTreeNode *node __attribute__((aligned(8)));
// float *alpha __attribute__((aligned(8)));
//}
//GpuHidHaarClassifier;
typedef struct __attribute__((aligned(64))) GpuHidHaarStageClassifier
{
int count __attribute__((aligned(4)));
@ -77,27 +77,27 @@ typedef struct __attribute__((aligned(64))) GpuHidHaarStageClassifier
int reserved3 __attribute__((aligned(8)));
}
GpuHidHaarStageClassifier;
typedef struct __attribute__((aligned(64))) GpuHidHaarClassifierCascade
{
int count __attribute__((aligned(4)));
int is_stump_based __attribute__((aligned(4)));
int has_tilted_features __attribute__((aligned(4)));
int is_tree __attribute__((aligned(4)));
int pq0 __attribute__((aligned(4)));
int pq1 __attribute__((aligned(4)));
int pq2 __attribute__((aligned(4)));
int pq3 __attribute__((aligned(4)));
int p0 __attribute__((aligned(4)));
int p1 __attribute__((aligned(4)));
int p2 __attribute__((aligned(4)));
int p3 __attribute__((aligned(4)));
float inv_window_area __attribute__((aligned(4)));
} GpuHidHaarClassifierCascade;
//typedef struct __attribute__((aligned(64))) GpuHidHaarClassifierCascade
//{
// int count __attribute__((aligned(4)));
// int is_stump_based __attribute__((aligned(4)));
// int has_tilted_features __attribute__((aligned(4)));
// int is_tree __attribute__((aligned(4)));
// int pq0 __attribute__((aligned(4)));
// int pq1 __attribute__((aligned(4)));
// int pq2 __attribute__((aligned(4)));
// int pq3 __attribute__((aligned(4)));
// int p0 __attribute__((aligned(4)));
// int p1 __attribute__((aligned(4)));
// int p2 __attribute__((aligned(4)));
// int p3 __attribute__((aligned(4)));
// float inv_window_area __attribute__((aligned(4)));
//} GpuHidHaarClassifierCascade;
__kernel void gpuRunHaarClassifierCascade_scaled2(
global GpuHidHaarStageClassifier *stagecascadeptr,
global GpuHidHaarStageClassifier *stagecascadeptr_,
global int4 *info,
global GpuHidHaarTreeNode *nodeptr,
global GpuHidHaarTreeNode *nodeptr_,
global const int *restrict sum,
global const float *restrict sqsum,
global int4 *candidate,
@ -132,8 +132,7 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
int max_idx = rows * cols - 1;
for (int scalei = 0; scalei < loopcount; scalei++)
{
int4 scaleinfo1;
scaleinfo1 = info[scalei];
int4 scaleinfo1 = info[scalei];
int grpnumperline = (scaleinfo1.y & 0xffff0000) >> 16;
int totalgrp = scaleinfo1.y & 0xffff;
float factor = as_float(scaleinfo1.w);
@ -174,10 +173,13 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
for (int stageloop = start_stage; (stageloop < end_stage) && result; stageloop++)
{
float stage_sum = 0.f;
int stagecount = stagecascadeptr[stageloop].count;
__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*)
(((__global uchar*)stagecascadeptr_)+stageloop*sizeof(GpuHidHaarStageClassifier));
int stagecount = stageinfo->count;
for (int nodeloop = 0; nodeloop < stagecount;)
{
__global GpuHidHaarTreeNode *currentnodeptr = (nodeptr + nodecounter);
__global GpuHidHaarTreeNode* currentnodeptr = (__global GpuHidHaarTreeNode*)
(((__global uchar*)nodeptr_) + nodecounter * sizeof(GpuHidHaarTreeNode));
int4 info1 = *(__global int4 *)(&(currentnodeptr->p[0][0]));
int4 info2 = *(__global int4 *)(&(currentnodeptr->p[1][0]));
int4 info3 = *(__global int4 *)(&(currentnodeptr->p[2][0]));
@ -204,7 +206,7 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
sum[clamp(mad24(info3.w, step, info3.x), 0, max_idx)]
+ sum[clamp(mad24(info3.w, step, info3.z), 0, max_idx)]) * w.z;
bool passThres = classsum >= nodethreshold;
bool passThres = (classsum >= nodethreshold) ? 1 : 0;
#if STUMP_BASED
stage_sum += passThres ? alpha3.y : alpha3.x;
@ -234,7 +236,8 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
}
#endif
}
result = (int)(stage_sum >= stagecascadeptr[stageloop].threshold);
result = (stage_sum >= stageinfo->threshold) ? 1 : 0;
}
barrier(CLK_LOCAL_MEM_FENCE);
@ -281,11 +284,14 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
}
}
}
__kernel void gpuscaleclassifier(global GpuHidHaarTreeNode *orinode, global GpuHidHaarTreeNode *newnode, float scale, float weight_scale, int nodenum)
__kernel void gpuscaleclassifier(global GpuHidHaarTreeNode *orinode, global GpuHidHaarTreeNode *newnode, float scale, float weight_scale, const int nodenum)
{
int counter = get_global_id(0);
const int counter = get_global_id(0);
int tr_x[3], tr_y[3], tr_h[3], tr_w[3], i = 0;
GpuHidHaarTreeNode t1 = *(orinode + counter);
GpuHidHaarTreeNode t1 = *(__global GpuHidHaarTreeNode*)
(((__global uchar*)orinode) + counter * sizeof(GpuHidHaarTreeNode));
__global GpuHidHaarTreeNode* pNew = (__global GpuHidHaarTreeNode*)
(((__global uchar*)newnode) + (counter + nodenum) * sizeof(GpuHidHaarTreeNode));
#pragma unroll
for (i = 0; i < 3; i++)
@ -297,22 +303,21 @@ __kernel void gpuscaleclassifier(global GpuHidHaarTreeNode *orinode, global GpuH
}
t1.weight[0] = -(t1.weight[1] * tr_h[1] * tr_w[1] + t1.weight[2] * tr_h[2] * tr_w[2]) / (tr_h[0] * tr_w[0]);
counter += nodenum;
#pragma unroll
for (i = 0; i < 3; i++)
{
newnode[counter].p[i][0] = tr_x[i];
newnode[counter].p[i][1] = tr_y[i];
newnode[counter].p[i][2] = tr_x[i] + tr_w[i];
newnode[counter].p[i][3] = tr_y[i] + tr_h[i];
newnode[counter].weight[i] = t1.weight[i] * weight_scale;
pNew->p[i][0] = tr_x[i];
pNew->p[i][1] = tr_y[i];
pNew->p[i][2] = tr_x[i] + tr_w[i];
pNew->p[i][3] = tr_y[i] + tr_h[i];
pNew->weight[i] = t1.weight[i] * weight_scale;
}
newnode[counter].left = t1.left;
newnode[counter].right = t1.right;
newnode[counter].threshold = t1.threshold;
newnode[counter].alpha[0] = t1.alpha[0];
newnode[counter].alpha[1] = t1.alpha[1];
newnode[counter].alpha[2] = t1.alpha[2];
pNew->left = t1.left;
pNew->right = t1.right;
pNew->threshold = t1.threshold;
pNew->alpha[0] = t1.alpha[0];
pNew->alpha[1] = t1.alpha[1];
pNew->alpha[2] = t1.alpha[2];
}