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107 lines
3.2 KiB
C++
107 lines
3.2 KiB
C++
#include "perf_precomp.hpp"
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#include <opencv2/dnn/shape_utils.hpp>
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namespace cvtest
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{
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using std::tr1::tuple;
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using std::tr1::get;
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using std::tr1::make_tuple;
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using std::make_pair;
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using namespace perf;
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using namespace testing;
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using namespace cv;
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using namespace cv::dnn;
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enum {STRIDE_OFF = 1, STRIDE_ON = 2};
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CV_ENUM(StrideSize, STRIDE_OFF, STRIDE_ON);
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enum {GROUP_OFF = 1, GROUP_2 = 2};
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CV_ENUM(GroupSize, GROUP_OFF, GROUP_2);
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//Squared Size
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#define SSZ(n) cv::Size(n, n)
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typedef std::pair<MatShape, int> InpShapeNumOut;
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typedef tuple<Size, InpShapeNumOut, GroupSize, StrideSize> ConvParam; //kernel_size, inp shape, groups, stride
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typedef TestBaseWithParam<ConvParam> ConvolutionPerfTest;
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static inline MatShape blobShape(int count, int nplanes, int height, int width)
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{
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int data[] = {count, nplanes, height, width};
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return MatShape(data, data+4);
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}
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PERF_TEST_P( ConvolutionPerfTest, perf, Combine(
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Values(Size(1, 1), Size(3, 3), Size(5, 5), Size(11, 11)),
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Values(make_pair(blobShape(1, 4, 224, 224), 64),
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make_pair(blobShape(1, 64, 112, 122), 128),
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make_pair(blobShape(1, 256, 28, 28), 512)),
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GroupSize::all(),
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StrideSize::all())
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)
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{
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RNG rng(0);
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ConvParam params = GetParam();
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int ksz = get<0>(params).width;
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MatShape inpShape = get<1>(params).first;
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int outCn = get<1>(params).second;
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int groups = get<2>(params);
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int stride = (ksz >= 11) ? 4 : (int)get<3>(params);
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int inpCn = inpShape[1];
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int wgtSize[] = { outCn, inpCn/groups, ksz, ksz };
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int biasSize[] = { outCn, 1, 1, 1 };
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const int wtype = CV_32F;
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Mat wgtBlob(4, wgtSize, wtype), biasBlob(4, biasSize, wtype);
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Mat inpBlob(4, &inpShape[0], wtype);
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rng.fill(biasBlob, RNG::UNIFORM, -1, +1);
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rng.fill(wgtBlob, RNG::UNIFORM, -1, +1);
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rng.fill(inpBlob, RNG::UNIFORM, -1, +1);
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LayerParams lp;
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lp.set("num_output", outCn);
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lp.set("group", groups);
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lp.set("stride", stride);
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lp.set("kernel_size", ksz);
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lp.blobs.reserve(2);
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lp.blobs.push_back(wgtBlob);
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lp.blobs.push_back(biasBlob);
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std::vector<Mat*> inpBlobs(1, &inpBlob);
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std::vector<Mat> outBlobs, internalBlobs;
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cv::setNumThreads(cv::getNumberOfCPUs());
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Ptr<Layer> layer = cv::dnn::LayerFactory::createLayerInstance("Convolution", lp);
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std::vector<MatShape> inputShapes(1, shape(inpBlob)), outShapes, internals;
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layer->getMemoryShapes(inputShapes, 0, outShapes, internals);
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for (int i = 0; i < outShapes.size(); i++)
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{
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outBlobs.push_back(Mat(outShapes[i], CV_32F));
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}
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for (int i = 0; i < internals.size(); i++)
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{
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internalBlobs.push_back(Mat());
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if (total(internals[i]))
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internalBlobs.back().create(internals[i], CV_32F);
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}
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layer->finalize(inpBlobs, outBlobs);
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Mat inpBlob2D = inpBlob.reshape(1, outCn);
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Mat wgtBlob2D = wgtBlob.reshape(1, outCn*(inpCn/groups));
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Mat outBlob2D = outBlobs[0].reshape(1, outBlobs[0].size[0]);
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declare.in(inpBlob2D, wgtBlob2D, WARMUP_RNG).out(outBlob2D).tbb_threads(cv::getNumThreads());
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TEST_CYCLE_N(10)
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{
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layer->forward(inpBlobs, outBlobs, internalBlobs);
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}
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SANITY_CHECK_NOTHING();
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}
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}
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