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dnn(ocl4dnn): Fix wrong measurement for tuning time

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convolution kernel use default queue to run, so that ocl::Timer
, to measure the kernel run time, should use the default queue too.
Also remove useless parameter for convolve()

Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>
pull/10097/head
Wu Zhiwen 7 years ago
parent 1fbdca83f5
commit 88e6daa315
2 changed files with 11 additions and 13 deletions
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  1. 3
      modules/dnn/src/ocl4dnn/include/ocl4dnn.hpp
  2. 21
      modules/dnn/src/ocl4dnn/src/ocl4dnn_conv_spatial.cpp

3
modules/dnn/src/ocl4dnn/include/ocl4dnn.hpp
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@ -217,8 +217,7 @@ class OCL4DNNConvSpatial
bool convolve(const UMat &bottom, UMat &top,
const UMat &weight, const UMat &bias,
int32_t numImages,
kernelConfig* config,
const cv::ocl::Queue& queue);
kernelConfig* config);
float timedConvolve(const UMat &bottom, UMat &top,
const UMat &weight, const UMat &bias,
int32_t numImages, kernelConfig* config);

21
modules/dnn/src/ocl4dnn/src/ocl4dnn_conv_spatial.cpp
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@ -381,7 +381,7 @@ bool OCL4DNNConvSpatial<Dtype>::Forward(const UMat& bottom,
prepareKernel(bottom, top, weight, bias, numImages);
if (bestKernelConfig.empty())
return false;
return convolve(bottom, top, weight, bias, numImages, bestKernelConfig, cv::ocl::Queue::getDefault());
return convolve(bottom, top, weight, bias, numImages, bestKernelConfig);
}
template<typename Dtype>
@ -392,7 +392,7 @@ void OCL4DNNConvSpatial<Dtype>::calculateBenchmark(const UMat &bottom, UMat &ver
options_.str(""); options_.clear(); // clear contents and state flags
createBasicKernel(1, 1, 1);
kernel_index_ = kernelQueue.size() - 1;
convolve(bottom, verifyTop, weight, bias, numImages, kernelQueue[kernel_index_], cv::ocl::Queue::getDefault());
convolve(bottom, verifyTop, weight, bias, numImages, kernelQueue[kernel_index_]);
CV_Assert(phash.find(kernelQueue[kernel_index_]->kernelName) != phash.end());
//unloadProgram(kernelQueue[kernel_index_]->kernelName);
kernelQueue.pop_back();
@ -649,8 +649,7 @@ void OCL4DNNConvSpatial<float>::CreateSubBuffer(const UMat& buffer, UMat& sub_bu
template<>
bool OCL4DNNConvSpatial<float>::convolve(const UMat &bottom, UMat &top,
const UMat &weight, const UMat &bias,
int32_t numImages, kernelConfig* config,
const cv::ocl::Queue& queue)
int32_t numImages, kernelConfig* config)
{
ocl::Program program;
phash_t::iterator it = phash.find(config->kernelName);
@ -926,17 +925,17 @@ float OCL4DNNConvSpatial<float>::timedConvolve(const UMat &bottom, UMat &top,
const UMat &weight, const UMat &bias,
int32_t numImages, kernelConfig* config)
{
cv::ocl::Queue profilingQueue;
cv::ocl::Queue queue;
try
{
profilingQueue = cv::ocl::Queue::getDefault().getProfilingQueue();
queue = cv::ocl::Queue::getDefault();
}
catch (const cv::Exception&)
{
static int warn_ = 0;
if (!warn_)
{
std::cout << "OpenCV(ocl4dnn): Can't create OpenCL profiling queue for auto-tuning." << std::endl;
std::cout << "OpenCV(ocl4dnn): Can't get OpenCL default queue for auto-tuning." << std::endl;
warn_ = true;
}
return 1e6;
@ -945,16 +944,16 @@ float OCL4DNNConvSpatial<float>::timedConvolve(const UMat &bottom, UMat &top,
// warm up.
bool saved_tuned = tuned_;
tuned_ = false;
convolve(bottom, top, weight, bias, numImages, config, profilingQueue);
convolve(bottom, top, weight, bias, numImages, config);
cv::ocl::Timer timer(profilingQueue);
cv::ocl::Timer timer(queue);
timer.start();
bool res = true;;
dbgPrint(std::cout << "Benchmarking kernel: " << config->kernelName << std::endl);
tuned_ = true;
int loop_cnt = 4;
for (int i = 0; i < loop_cnt; i++) {
res = convolve(bottom, top, weight, bias, numImages, config, profilingQueue);
res = convolve(bottom, top, weight, bias, numImages, config);
if (!res)
break;
}
@ -1009,7 +1008,7 @@ bool OCL4DNNConvSpatial<float>::verifyResult(const UMat &bottom,
top.zeros(4, sz, CV_32FC1);
bool saved_tuned = tuned_;
tuned_ = false;
convolve(bottom, top, weight, bias, numImages, config, cv::ocl::Queue::getDefault());
convolve(bottom, top, weight, bias, numImages, config);
tuned_ = saved_tuned;
float *data = (float *)top.getMat(ACCESS_READ).ptr<float>();

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