potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-30 07:43:03 +04:00
Code Packages Releases Wiki Activity

mvn layer ocl update

Browse Source 
it fuse ocl kernels to reduce kernel enqueue

Signed-off-by: Li Peng <peng.li@intel.com>
This commit is contained in:
Li Peng
2018-01-31 19:28:58 +08:00
parent 83b16ab7b7
commit 6aec71d7ee
2 changed files with 251 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/src/layers/mvn_layer.cpp
+71 -5
View File
@@ -93,6 +93,67 @@ public:
}
#ifdef HAVE_OPENCL
bool fast_forward_ocl(std::vector<UMat> &inputs, std::vector<UMat> &outputs)
{
if( fuse_batch_norm && scale.empty())
{
bnorm->getScaleShift(scale, shift);
bnorm_weight = scale.getUMat(ACCESS_READ);
bnorm_bias = shift.getUMat(ACCESS_READ);
}
int splitDim = (acrossChannels) ? 1 : 2;
for (size_t inpIdx = 0; inpIdx < inputs.size(); inpIdx++)
{
UMat &inpMat = inputs[inpIdx];
UMat &outMat = outputs[inpIdx];
int newRows = total(shape(inpMat), 0, splitDim);
MatShape s = shape(newRows, inpMat.total() / newRows);
UMat oneMat = UMat::ones(s[1], 1, CV_32F);
UMat meanMat = UMat(s[0], 1, CV_32F);
UMat tmpMat = UMat(s[0], s[1], CV_32F);
float alpha = 1.0f / s[1];
String buildopt = "-DNUM=4";
ocl::Kernel k("mean_fuse4", ocl::dnn::mvn_oclsrc, buildopt);
size_t localsize[] = { 128 };
size_t globalsize[] = { (size_t)s[0] / 4 * localsize[0] };
int argId = 0;
k.set(argId++, ocl::KernelArg::PtrReadOnly(inpMat));
k.set(argId++, (int)s[1]);
k.set(argId++, alpha);
k.set(argId++, ocl::KernelArg::PtrWriteOnly(meanMat));
k.set(argId++, ocl::KernelArg::PtrWriteOnly(tmpMat));
k.set(argId++, NULL, localsize[0] * sizeof(cl_float4));
bool ret = k.run(1, globalsize, localsize, false);
if (!ret)
return false;
buildopt += format(" %s %s", (fuse_batch_norm) ? "-DFUSE_BATCH_NORM" : "",
(fuse_relu) ? "-DFUSE_RELU" : "");
ocl::Kernel k1("mvn_fuse4", ocl::dnn::mvn_oclsrc, buildopt);
argId = 0;
k1.set(argId++, ocl::KernelArg::PtrReadOnly(tmpMat));
k1.set(argId++, ocl::KernelArg::PtrReadOnly(inpMat));
k1.set(argId++, ocl::KernelArg::PtrReadOnly(meanMat));
k1.set(argId++, (int)s[1]);
k1.set(argId++, (float)alpha);
k1.set(argId++, (float)eps);
k1.set(argId++, (float)relu_slope);
k1.set(argId++, ocl::KernelArg::PtrReadOnly(bnorm_weight));
k1.set(argId++, ocl::KernelArg::PtrReadOnly(bnorm_bias));
k1.set(argId++, ocl::KernelArg::PtrWriteOnly(outMat));
k1.set(argId++, NULL, localsize[0] * sizeof(cl_float4));
ret = k1.run(1, globalsize, localsize, false);
if (!ret)
return false;
}
return true;
}
bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
{
std::vector<UMat> inputs;
@@ -101,6 +162,15 @@ public:
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
int splitDim = (acrossChannels) ? 1 : 2;
int row_size = total(shape(inputs[0]), 0, splitDim);
int plane_size = total(shape(inputs[0]), splitDim);
if (normVariance && (row_size % 4 == 0) && (plane_size % 4 == 0))
{
bool ret = fast_forward_ocl(inputs, outputs);
return ret;
}
if( fuse_batch_norm && scale.empty())
{
bnorm->getScaleShift(scale, shift);
@@ -112,11 +182,7 @@ public:
{
UMat &inpMat = inputs[inpIdx];
UMat &outMat = outputs[inpIdx];
int splitDim = (acrossChannels) ? 1 : 2;
int i, newRows = 1;
for( i = 0; i < splitDim; i++ )
newRows *= inpMat.size[i];
int newRows = total(shape(inpMat), 0, splitDim);
MatShape s = shape(newRows, inpMat.total() / newRows);
UMat oneMat = UMat::ones(s[1], 1, CV_32F);