#include "perf_precomp.hpp"
#include <opencv2/dnn/shape_utils.hpp>
-namespace cvtest
+namespace
{
-using std::tr1::tuple;
-using std::tr1::get;
-using std::tr1::make_tuple;
-using std::make_pair;
-using namespace perf;
-using namespace testing;
-using namespace cv;
-using namespace cv::dnn;
-
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);
-//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;
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 (int i = 0; i < outShapes.size(); i++)
+ for (size_t i = 0; i < outShapes.size(); i++)
{
outBlobs.push_back(Mat(outShapes[i], CV_32F));
}
- for (int i = 0; i < internals.size(); i++)
+ for (size_t i = 0; i < internals.size(); i++)
{
internalBlobs.push_back(Mat());
if (total(internals[i]))
Mat outBlob2D = outBlobs[0].reshape(1, outBlobs[0].size[0]);
declare.in(inpBlob2D, wgtBlob2D, WARMUP_RNG).out(outBlob2D).tbb_threads(cv::getNumThreads());
- TEST_CYCLE_N(10)
- {
+ layer->forward(inpBlobs, outBlobs, internalBlobs); /// warmup
+
+ PERF_SAMPLE_BEGIN()
layer->forward(inpBlobs, outBlobs, internalBlobs);
- }
+ PERF_SAMPLE_END()
SANITY_CHECK_NOTHING();
}
-}
+} // namespace
+++ /dev/null
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-//
-// Copyright (C) 2017, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-
-#include "perf_precomp.hpp"
-
-namespace cvtest
-{
-
-#ifdef HAVE_HALIDE
-using namespace cv;
-using namespace dnn;
-
-static void loadNet(std::string weights, std::string proto, std::string scheduler,
- int inWidth, int inHeight, const std::string& outputLayer,
- const std::string& framework, int targetId, Net* net)
-{
- Mat input(inHeight, inWidth, CV_32FC3);
- randu(input, 0.0f, 1.0f);
-
- weights = findDataFile(weights, false);
- if (!proto.empty())
- proto = findDataFile(proto, false);
- if (!scheduler.empty())
- scheduler = findDataFile(scheduler, false);
- if (framework == "caffe")
- {
- *net = cv::dnn::readNetFromCaffe(proto, weights);
- }
- else if (framework == "torch")
- {
- *net = cv::dnn::readNetFromTorch(weights);
- }
- else if (framework == "tensorflow")
- {
- *net = cv::dnn::readNetFromTensorflow(weights);
- }
- else
- CV_Error(Error::StsNotImplemented, "Unknown framework " + framework);
-
- net->setInput(blobFromImage(input, 1.0, Size(), Scalar(), false));
- net->setPreferableBackend(DNN_BACKEND_HALIDE);
- net->setPreferableTarget(targetId);
- net->setHalideScheduler(scheduler);
- net->forward(outputLayer);
-}
-
-////////////////////////////////////////////////////////////////////////////////
-// CPU target
-////////////////////////////////////////////////////////////////////////////////
-PERF_TEST(GoogLeNet, HalidePerfTest)
-{
- Net net;
- loadNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
- "", 224, 224, "prob", "caffe", DNN_TARGET_CPU, &net);
- TEST_CYCLE() net.forward();
- SANITY_CHECK_NOTHING();
-}
-
-PERF_TEST(AlexNet, HalidePerfTest)
-{
- Net net;
- loadNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
- "dnn/halide_scheduler_alexnet.yml", 227, 227, "prob", "caffe",
- DNN_TARGET_CPU, &net);
- TEST_CYCLE() net.forward();
- SANITY_CHECK_NOTHING();
-}
-
-PERF_TEST(ResNet50, HalidePerfTest)
-{
- Net net;
- loadNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
- "dnn/halide_scheduler_resnet_50.yml", 224, 224, "prob", "caffe",
- DNN_TARGET_CPU, &net);
- TEST_CYCLE() net.forward();
- SANITY_CHECK_NOTHING();
-}
-
-PERF_TEST(SqueezeNet_v1_1, HalidePerfTest)
-{
- Net net;
- loadNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
- "dnn/halide_scheduler_squeezenet_v1_1.yml", 227, 227, "prob",
- "caffe", DNN_TARGET_CPU, &net);
- TEST_CYCLE() net.forward();
- SANITY_CHECK_NOTHING();
-}
-
-PERF_TEST(Inception_5h, HalidePerfTest)
-{
- Net net;
- loadNet("dnn/tensorflow_inception_graph.pb", "",
- "dnn/halide_scheduler_inception_5h.yml",
- 224, 224, "softmax2", "tensorflow", DNN_TARGET_CPU, &net);
- TEST_CYCLE() net.forward("softmax2");
- SANITY_CHECK_NOTHING();
-}
-
-PERF_TEST(ENet, HalidePerfTest)
-{
- Net net;
- loadNet("dnn/Enet-model-best.net", "", "dnn/halide_scheduler_enet.yml",
- 512, 256, "l367_Deconvolution", "torch", DNN_TARGET_CPU, &net);
- TEST_CYCLE() net.forward();
- SANITY_CHECK_NOTHING();
-}
-////////////////////////////////////////////////////////////////////////////////
-// OpenCL target
-////////////////////////////////////////////////////////////////////////////////
-PERF_TEST(GoogLeNet_opencl, HalidePerfTest)
-{
- Net net;
- loadNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
- "", 227, 227, "prob", "caffe", DNN_TARGET_OPENCL, &net);
- TEST_CYCLE() net.forward();
- SANITY_CHECK_NOTHING();
-}
-
-PERF_TEST(AlexNet_opencl, HalidePerfTest)
-{
- Net net;
- loadNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
- "dnn/halide_scheduler_opencl_alexnet.yml", 227, 227, "prob", "caffe",
- DNN_TARGET_OPENCL, &net);
- TEST_CYCLE() net.forward();
- SANITY_CHECK_NOTHING();
-}
-
-PERF_TEST(ResNet50_opencl, HalidePerfTest)
-{
- Net net;
- loadNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
- "dnn/halide_scheduler_opencl_resnet_50.yml", 224, 224, "prob", "caffe",
- DNN_TARGET_OPENCL, &net);
- TEST_CYCLE() net.forward();
- SANITY_CHECK_NOTHING();
-}
-
-
-PERF_TEST(SqueezeNet_v1_1_opencl, HalidePerfTest)
-{
- Net net;
- loadNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
- "dnn/halide_scheduler_opencl_squeezenet_v1_1.yml", 227, 227, "prob",
- "caffe", DNN_TARGET_OPENCL, &net);
- TEST_CYCLE() net.forward();
- SANITY_CHECK_NOTHING();
-}
-
-PERF_TEST(Inception_5h_opencl, HalidePerfTest)
-{
- Net net;
- loadNet("dnn/tensorflow_inception_graph.pb", "",
- "dnn/halide_scheduler_opencl_inception_5h.yml",
- 224, 224, "softmax2", "tensorflow", DNN_TARGET_OPENCL, &net);
- TEST_CYCLE() net.forward("softmax2");
- SANITY_CHECK_NOTHING();
-}
-
-PERF_TEST(ENet_opencl, HalidePerfTest)
-{
- Net net;
- loadNet("dnn/Enet-model-best.net", "", "dnn/halide_scheduler_opencl_enet.yml",
- 512, 256, "l367_Deconvolution", "torch", DNN_TARGET_OPENCL, &net);
- TEST_CYCLE() net.forward();
- SANITY_CHECK_NOTHING();
-}
-#endif // HAVE_HALIDE
-
-} // namespace cvtest
--- /dev/null
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2017, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+
+#include "perf_precomp.hpp"
+#include "opencv2/core/ocl.hpp"
+
+#include "opencv2/dnn/shape_utils.hpp"
+
+namespace
+{
+
+#ifdef HAVE_HALIDE
+#define TEST_DNN_BACKEND DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE
+#else
+#define TEST_DNN_BACKEND DNN_BACKEND_DEFAULT
+#endif
+#define TEST_DNN_TARGET DNN_TARGET_CPU, DNN_TARGET_OPENCL
+
+CV_ENUM(DNNBackend, DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE)
+CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL)
+
+class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<DNNBackend, DNNTarget> >
+{
+public:
+ dnn::Backend backend;
+ dnn::Target target;
+
+ dnn::Net net;
+
+ void processNet(std::string weights, std::string proto, std::string halide_scheduler,
+ int inWidth, int inHeight, const std::string& outputLayer,
+ const std::string& framework)
+ {
+ backend = (dnn::Backend)(int)get<0>(GetParam());
+ target = (dnn::Target)(int)get<1>(GetParam());
+
+ if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL)
+ {
+#if 0 //defined(HAVE_OPENCL)
+ if (!cv::ocl::useOpenCL())
+#endif
+ {
+ throw ::SkipTestException("OpenCL is not available/disabled in OpenCV");
+ }
+ }
+
+ Mat input(inHeight, inWidth, CV_32FC3);
+ randu(input, 0.0f, 1.0f);
+
+
+ weights = findDataFile(weights, false);
+ if (!proto.empty())
+ proto = findDataFile(proto, false);
+ if (!halide_scheduler.empty() && backend == DNN_BACKEND_HALIDE)
+ halide_scheduler = findDataFile(std::string("dnn/halide_scheduler_") + (target == DNN_TARGET_OPENCL ? "opencl_" : "") + halide_scheduler, true);
+ if (framework == "caffe")
+ {
+ net = cv::dnn::readNetFromCaffe(proto, weights);
+ }
+ else if (framework == "torch")
+ {
+ net = cv::dnn::readNetFromTorch(weights);
+ }
+ else if (framework == "tensorflow")
+ {
+ net = cv::dnn::readNetFromTensorflow(weights);
+ }
+ else
+ CV_Error(Error::StsNotImplemented, "Unknown framework " + framework);
+
+ net.setInput(blobFromImage(input, 1.0, Size(), Scalar(), false));
+ net.setPreferableBackend(backend);
+ net.setPreferableTarget(target);
+ if (backend == DNN_BACKEND_HALIDE)
+ {
+ net.setHalideScheduler(halide_scheduler);
+ }
+
+ MatShape netInputShape = shape(1, 3, inHeight, inWidth);
+ size_t weightsMemory = 0, blobsMemory = 0;
+ net.getMemoryConsumption(netInputShape, weightsMemory, blobsMemory);
+ int64 flops = net.getFLOPS(netInputShape);
+
+ net.forward(outputLayer); // warmup
+
+ std::cout << "Memory consumption:" << std::endl;
+ std::cout << " Weights(parameters): " << divUp(weightsMemory, 1u<<20) << " Mb" << std::endl;
+ std::cout << " Blobs: " << divUp(blobsMemory, 1u<<20) << " Mb" << std::endl;
+ std::cout << "Calculation complexity: " << flops * 1e-9 << " GFlops" << std::endl;
+
+ PERF_SAMPLE_BEGIN()
+ net.forward();
+ PERF_SAMPLE_END()
+
+ SANITY_CHECK_NOTHING();
+ }
+};
+
+
+PERF_TEST_P_(DNNTestNetwork, AlexNet)
+{
+ processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
+ "alexnet.yml", 227, 227, "prob", "caffe");
+}
+
+PERF_TEST_P_(DNNTestNetwork, GoogLeNet)
+{
+ processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
+ "", 224, 224, "prob", "caffe");
+}
+
+PERF_TEST_P_(DNNTestNetwork, ResNet50)
+{
+ processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
+ "resnet_50.yml", 224, 224, "prob", "caffe");
+}
+
+PERF_TEST_P_(DNNTestNetwork, SqueezeNet_v1_1)
+{
+ processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
+ "squeezenet_v1_1.yml", 227, 227, "prob", "caffe");
+}
+
+PERF_TEST_P_(DNNTestNetwork, Inception_5h)
+{
+ processNet("dnn/tensorflow_inception_graph.pb", "",
+ "inception_5h.yml",
+ 224, 224, "softmax2", "tensorflow");
+}
+
+PERF_TEST_P_(DNNTestNetwork, ENet)
+{
+ processNet("dnn/Enet-model-best.net", "", "enet.yml",
+ 512, 256, "l367_Deconvolution", "torch");
+}
+
+
+INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork,
+ testing::Combine(
+ ::testing::Values(TEST_DNN_BACKEND),
+ DNNTarget::all()
+ )
+);
+
+} // namespace
projects / platform / upstream / opencv.git / commitdiff