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#include "../perf_precomp.hpp"
#include "opencv2/ts/ocl_perf.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#ifdef HAVE_OPENCL
namespace opencv_test { namespace ocl {
using namespace ::perf;
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);
} // namespace
//Squared Size
#define SSZ(n) cv::Size(n, n)
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);
}
OCL_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;
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);
layer->preferableTarget = DNN_TARGET_OPENCL;
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);
// warmup
layer->forward(inpBlobs, outBlobs, internalBlobs);
TEST_CYCLE()
{
layer->forward(inpBlobs, outBlobs, internalBlobs);
}
SANITY_CHECK_NOTHING();
}
}
}
#endif