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dnn(perf): update perf tests

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pull/9692/head
Alexander Alekhin 7 years ago
parent b8af7c5f86
commit 78788e1efb
4 changed files with 162 additions and 201 deletions
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  1. 27
      modules/dnn/perf/perf_convolution.cpp
  2. 174
      modules/dnn/perf/perf_halide_net.cpp
  3. 149
      modules/dnn/perf/perf_net.cpp
  4. 13
      modules/dnn/perf/perf_precomp.hpp

27
modules/dnn/perf/perf_convolution.cpp
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@ -1,27 +1,15 @@
#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;
@ -77,11 +65,11 @@ PERF_TEST_P( ConvolutionPerfTest, perf, Combine(
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]))
@ -95,12 +83,13 @@ PERF_TEST_P( ConvolutionPerfTest, perf, Combine(
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

174
modules/dnn/perf/perf_halide_net.cpp
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@ -1,174 +0,0 @@
// 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

149
modules/dnn/perf/perf_net.cpp
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@ -0,0 +1,149 @@
// 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

13
modules/dnn/perf/perf_precomp.hpp
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@ -1,11 +1,3 @@
#ifdef __GNUC__
# pragma GCC diagnostic ignored "-Wmissing-declarations"
# if defined __clang__ || defined __APPLE__
# pragma GCC diagnostic ignored "-Wmissing-prototypes"
# pragma GCC diagnostic ignored "-Wextra"
# endif
#endif
#ifndef __OPENCV_PERF_PRECOMP_HPP__
#define __OPENCV_PERF_PRECOMP_HPP__
@ -14,4 +6,9 @@
#include <opencv2/highgui.hpp>
#include <opencv2/dnn.hpp>
using namespace cvtest;
using namespace perf;
using namespace cv;
using namespace dnn;
#endif

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