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Tetragramm
GitHub Repository: Tetragramm/opencv
 Path: blob/master/modules/dnn/perf/perf_caffe.cpp
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// This file is part of OpenCV project.

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// It is subject to the license terms in the LICENSE file found in the top-level directory

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// of this distribution and at http://opencv.org/license.html.

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//

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// Copyright (C) 2017, Intel Corporation, all rights reserved.

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// Third party copyrights are property of their respective owners.

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// Recommends run this performance test via

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// ./bin/opencv_perf_dnn 2> /dev/null | grep "PERFSTAT" -A 3

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// because whole output includes Caffe's logs.

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//

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// Note: Be sure that interesting version of Caffe was linked.

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// Note: There is an impact on Halide performance. Comment this tests if you

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//       want to run the last one.

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//

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// How to build Intel-Caffe with MKLDNN backend

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// ============================================

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// mkdir build && cd build

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// cmake -DCMAKE_BUILD_TYPE=Release \

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//       -DUSE_MKLDNN_AS_DEFAULT_ENGINE=ON \

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//       -DUSE_MKL2017_AS_DEFAULT_ENGINE=OFF \

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//       -DCPU_ONLY=ON \

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//       -DCMAKE_INSTALL_PREFIX=/usr/local .. && make -j8

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// sudo make install

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//

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// In case of problems with cublas_v2.h at include/caffe/util/device_alternate.hpp: add line

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// #define CPU_ONLY

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// before the first line

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// #ifdef CPU_ONLY  // CPU-only Caffe.

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#if defined(HAVE_CAFFE) || defined(HAVE_CLCAFFE)

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#include "perf_precomp.hpp"

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#include <iostream>

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#include <caffe/caffe.hpp>

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namespace opencv_test {

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static caffe::Net<float>* initNet(std::string proto, std::string weights)

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{

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    proto = findDataFile(proto, false);

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    weights = findDataFile(weights, false);

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#ifdef HAVE_CLCAFFE

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    caffe::Caffe::set_mode(caffe::Caffe::GPU);

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    caffe::Caffe::SetDevice(0);

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    caffe::Net<float>* net =

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        new caffe::Net<float>(proto, caffe::TEST, caffe::Caffe::GetDefaultDevice());

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#else

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    caffe::Caffe::set_mode(caffe::Caffe::CPU);

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    caffe::Net<float>* net = new caffe::Net<float>(proto, caffe::TEST);

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#endif

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    net->CopyTrainedLayersFrom(weights);

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    caffe::Blob<float>* input = net->input_blobs()[0];

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    CV_Assert(input->num() == 1);

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    CV_Assert(input->channels() == 3);

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    Mat inputMat(input->height(), input->width(), CV_32FC3, (char*)input->cpu_data());

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    randu(inputMat, 0.0f, 1.0f);

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    net->Forward();

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    return net;

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}

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PERF_TEST(AlexNet_caffe, CaffePerfTest)

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{

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    caffe::Net<float>* net = initNet("dnn/bvlc_alexnet.prototxt",

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                                     "dnn/bvlc_alexnet.caffemodel");

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    TEST_CYCLE() net->Forward();

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    SANITY_CHECK_NOTHING();

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}

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PERF_TEST(GoogLeNet_caffe, CaffePerfTest)

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{

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    caffe::Net<float>* net = initNet("dnn/bvlc_googlenet.prototxt",

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                                     "dnn/bvlc_googlenet.caffemodel");

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    TEST_CYCLE() net->Forward();

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    SANITY_CHECK_NOTHING();

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}

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PERF_TEST(ResNet50_caffe, CaffePerfTest)

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{

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    caffe::Net<float>* net = initNet("dnn/ResNet-50-deploy.prototxt",

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                                     "dnn/ResNet-50-model.caffemodel");

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    TEST_CYCLE() net->Forward();

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    SANITY_CHECK_NOTHING();

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}

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PERF_TEST(SqueezeNet_v1_1_caffe, CaffePerfTest)

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{

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    caffe::Net<float>* net = initNet("dnn/squeezenet_v1.1.prototxt",

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                                     "dnn/squeezenet_v1.1.caffemodel");

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    TEST_CYCLE() net->Forward();

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    SANITY_CHECK_NOTHING();

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}

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PERF_TEST(MobileNet_SSD, CaffePerfTest)

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{

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    caffe::Net<float>* net = initNet("dnn/MobileNetSSD_deploy.prototxt",

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                                     "dnn/MobileNetSSD_deploy.caffemodel");

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    TEST_CYCLE() net->Forward();

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    SANITY_CHECK_NOTHING();

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}

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} // namespace

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#endif  // HAVE_CAFFE

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