dnn(perf): fix and merge Convolution tests

- OpenCL tests didn't run any OpenCL kernels
- use real configuration from existed models (the first 100 cases)
- batch size = 1

modules/dnn/perf/opencl/perf_convolution.cpp

-#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

modules/dnn/perf/perf_precomp.hpp

@@ -4,6 +4,8 @@
 #include <opencv2/ts.hpp>
 #include <opencv2/dnn.hpp>
 
+#include "../test/test_common.hpp"
+
 namespace opencv_test {
 using namespace perf;
 using namespace cv::dnn;

modules/dnn/test/test_common.hpp

@@ -42,6 +42,43 @@
 #ifndef __OPENCV_TEST_COMMON_HPP__
 #define __OPENCV_TEST_COMMON_HPP__
 
+namespace cv { namespace dnn {
+CV__DNN_EXPERIMENTAL_NS_BEGIN
+static inline void PrintTo(const cv::dnn::Backend& v, std::ostream* os)
+{
+    switch (v) {
+    case DNN_BACKEND_DEFAULT: *os << "DEFAULT"; return;
+    case DNN_BACKEND_HALIDE: *os << "HALIDE"; return;
+    case DNN_BACKEND_INFERENCE_ENGINE: *os << "DLIE"; return;
+    case DNN_BACKEND_OPENCV: *os << "OCV"; return;
+    } // don't use "default:" to emit compiler warnings
+    *os << "DNN_BACKEND_UNKNOWN(" << v << ")";
+}
+
+static inline void PrintTo(const cv::dnn::Target& v, std::ostream* os)
+{
+    switch (v) {
+    case DNN_TARGET_CPU: *os << "CPU"; return;
+    case DNN_TARGET_OPENCL: *os << "OCL"; return;
+    case DNN_TARGET_OPENCL_FP16: *os << "OCL_FP16"; return;
+    case DNN_TARGET_MYRIAD: *os << "MYRIAD"; return;
+    } // don't use "default:" to emit compiler warnings
+    *os << "DNN_TARGET_UNKNOWN(" << v << ")";
+}
+
+using opencv_test::tuple;
+using opencv_test::get;
+static inline void PrintTo(const tuple<cv::dnn::Backend, cv::dnn::Target> v, std::ostream* os)
+{
+    PrintTo(get<0>(v), os);
+    *os << "/";
+    PrintTo(get<1>(v), os);
+}
+
+CV__DNN_EXPERIMENTAL_NS_END
+}} // namespace
+
+
 static inline const std::string &getOpenCVExtraDir()
 {
     return cvtest::TS::ptr()->get_data_path();

modules/dnn/test/test_precomp.hpp

@@ -49,35 +49,6 @@
 #include "opencv2/dnn.hpp"
 #include "test_common.hpp"
 
-namespace cv {
-namespace dnn {
-CV__DNN_EXPERIMENTAL_NS_BEGIN
-
-static inline void PrintTo(const cv::dnn::Backend& v, std::ostream* os)
-{
-    switch (v) {
-    case DNN_BACKEND_DEFAULT: *os << "DNN_BACKEND_DEFAULT"; return;
-    case DNN_BACKEND_HALIDE: *os << "DNN_BACKEND_HALIDE"; return;
-    case DNN_BACKEND_INFERENCE_ENGINE: *os << "DNN_BACKEND_INFERENCE_ENGINE"; return;
-    case DNN_BACKEND_OPENCV: *os << "DNN_BACKEND_OPENCV"; return;
-    } // don't use "default:" to emit compiler warnings
-    *os << "DNN_BACKEND_UNKNOWN(" << v << ")";
-}
-
-static inline void PrintTo(const cv::dnn::Target& v, std::ostream* os)
-{
-    switch (v) {
-    case DNN_TARGET_CPU: *os << "DNN_TARGET_CPU"; return;
-    case DNN_TARGET_OPENCL: *os << "DNN_TARGET_OPENCL"; return;
-    case DNN_TARGET_OPENCL_FP16: *os << "DNN_TARGET_OPENCL_FP16"; return;
-    case DNN_TARGET_MYRIAD: *os << "DNN_TARGET_MYRIAD"; return;
-    } // don't use "default:" to emit compiler warnings
-    *os << "DNN_TARGET_UNKNOWN(" << v << ")";
-}
-
-CV__DNN_EXPERIMENTAL_NS_END
-}} // namespace
-
 namespace opencv_test {
 using namespace cv::dnn;