mirror of
https://github.com/opencv/opencv.git
synced 2024-12-15 01:39:10 +08:00
96 lines
3.0 KiB
C++
96 lines
3.0 KiB
C++
#include "perf_precomp.hpp"
|
|
#include <opencv2/dnn/shape_utils.hpp>
|
|
|
|
namespace
|
|
{
|
|
|
|
enum {STRIDE_OFF = 1, STRIDE_ON = 2};
|
|
CV_ENUM(StrideSize, STRIDE_OFF, STRIDE_ON);
|
|
|
|
enum {GROUP_OFF = 1, GROUP_2 = 2};
|
|
CV_ENUM(GroupSize, GROUP_OFF, GROUP_2);
|
|
|
|
typedef std::pair<MatShape, int> InpShapeNumOut;
|
|
typedef tuple<Size, InpShapeNumOut, GroupSize, StrideSize> ConvParam; //kernel_size, inp shape, groups, stride
|
|
typedef TestBaseWithParam<ConvParam> ConvolutionPerfTest;
|
|
|
|
static inline MatShape blobShape(int count, int nplanes, int height, int width)
|
|
{
|
|
int data[] = {count, nplanes, height, width};
|
|
return MatShape(data, data+4);
|
|
}
|
|
|
|
PERF_TEST_P( ConvolutionPerfTest, perf, Combine(
|
|
Values(Size(1, 1), Size(3, 3), Size(5, 5), Size(11, 11)),
|
|
Values(make_pair(blobShape(1, 4, 224, 224), 64),
|
|
make_pair(blobShape(1, 64, 112, 122), 128),
|
|
make_pair(blobShape(1, 256, 28, 28), 512)),
|
|
GroupSize::all(),
|
|
StrideSize::all())
|
|
)
|
|
{
|
|
RNG rng(0);
|
|
|
|
ConvParam params = GetParam();
|
|
int ksz = get<0>(params).width;
|
|
MatShape inpShape = get<1>(params).first;
|
|
int outCn = get<1>(params).second;
|
|
int groups = get<2>(params);
|
|
int stride = (ksz >= 11) ? 4 : (int)get<3>(params);
|
|
|
|
int inpCn = inpShape[1];
|
|
int wgtSize[] = { outCn, inpCn/groups, ksz, ksz };
|
|
int biasSize[] = { outCn, 1, 1, 1 };
|
|
const int wtype = CV_32F;
|
|
Mat wgtBlob(4, wgtSize, wtype), biasBlob(4, biasSize, wtype);
|
|
Mat inpBlob(4, &inpShape[0], wtype);
|
|
rng.fill(biasBlob, RNG::UNIFORM, -1, +1);
|
|
rng.fill(wgtBlob, RNG::UNIFORM, -1, +1);
|
|
rng.fill(inpBlob, RNG::UNIFORM, -1, +1);
|
|
|
|
LayerParams lp;
|
|
lp.set("num_output", outCn);
|
|
lp.set("group", groups);
|
|
lp.set("stride", stride);
|
|
lp.set("kernel_size", ksz);
|
|
lp.blobs.reserve(2);
|
|
lp.blobs.push_back(wgtBlob);
|
|
lp.blobs.push_back(biasBlob);
|
|
|
|
std::vector<Mat*> inpBlobs(1, &inpBlob);
|
|
std::vector<Mat> outBlobs, internalBlobs;
|
|
|
|
cv::setNumThreads(cv::getNumberOfCPUs());
|
|
|
|
Ptr<Layer> layer = cv::dnn::LayerFactory::createLayerInstance("Convolution", lp);
|
|
std::vector<MatShape> inputShapes(1, shape(inpBlob)), outShapes, internals;
|
|
layer->getMemoryShapes(inputShapes, 0, outShapes, internals);
|
|
for (size_t i = 0; i < outShapes.size(); i++)
|
|
{
|
|
outBlobs.push_back(Mat(outShapes[i], CV_32F));
|
|
}
|
|
for (size_t i = 0; i < internals.size(); i++)
|
|
{
|
|
internalBlobs.push_back(Mat());
|
|
if (total(internals[i]))
|
|
internalBlobs.back().create(internals[i], CV_32F);
|
|
}
|
|
|
|
layer->finalize(inpBlobs, outBlobs);
|
|
|
|
Mat inpBlob2D = inpBlob.reshape(1, outCn);
|
|
Mat wgtBlob2D = wgtBlob.reshape(1, outCn*(inpCn/groups));
|
|
Mat outBlob2D = outBlobs[0].reshape(1, outBlobs[0].size[0]);
|
|
declare.in(inpBlob2D, wgtBlob2D, WARMUP_RNG).out(outBlob2D).tbb_threads(cv::getNumThreads());
|
|
|
|
layer->forward(inpBlobs, outBlobs, internalBlobs); /// warmup
|
|
|
|
PERF_SAMPLE_BEGIN()
|
|
layer->forward(inpBlobs, outBlobs, internalBlobs);
|
|
PERF_SAMPLE_END()
|
|
|
|
SANITY_CHECK_NOTHING();
|
|
}
|
|
|
|
} // namespace
|