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Merge pull request opencv#9313 from dkurt:dnn_perf_test
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‎doc/tutorials/dnn/dnn_halide/dnn_halide.markdown

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@@ -8,20 +8,7 @@ according to specific device and evaluate it with a quite good efficiency.
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An official website of the Halide project: http://halide-lang.org/.
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## Efficiency comparison
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Measured on Intel® Core™ i7-6700K CPU @ 4.00GHz x 8.
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Single image forward pass (in milliseconds):
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| Architecture | MKL backend | Halide backend | Speed Up ratio |
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|-----------------:|------------:|---------------:|---------------:|
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| AlexNet | 16.55 | 22.38 | x0.73 |
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| ResNet-50 | 63.69 | 73.91 | x0.86 |
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| SqueezeNet v1.1 | 10.11 | 8.21 | x1.23 |
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| Inception-5h | 35.38 | 37.06 | x0.95 |
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| ENet @ 3x512x256 | 82.26 | 41.21 | x1.99 |
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Scheduling directives might be found @ [opencv_extra/testdata/dnn](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn).
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An up to date efficiency comparison: https://github.com/opencv/opencv/wiki/DNN-Efficiency
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## Requirements
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### LLVM compiler
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## Build OpenCV with Halide backend
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When you build OpenCV add the following configuration flags:
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- `ENABLE_CXX11` - enable C++11 standard
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- `WITH_HALIDE` - enable Halide linkage
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- `HALIDE_ROOT_DIR` - path to Halide build directory

‎modules/dnn/CMakeLists.txt

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@@ -77,6 +77,24 @@ ocv_add_samples()
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ocv_add_accuracy_tests()
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ocv_add_perf_tests()
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ocv_option(${the_module}_PERF_CAFFE "Add performance tests of Caffe framework" OFF)
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ocv_option(${the_module}_PERF_CLCAFFE "Add performance tests of clCaffe framework" OFF)
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if(BUILD_PERF_TESTS)
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if (${the_module}_PERF_CAFFE)
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find_package(Caffe QUIET)
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if (Caffe_FOUND)
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add_definitions(-DHAVE_CAFFE=1)
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ocv_target_link_libraries(opencv_perf_dnn caffe)
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endif()
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elseif(${the_module}_PERF_CLCAFFE)
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find_package(Caffe QUIET)
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if (Caffe_FOUND)
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add_definitions(-DHAVE_CLCAFFE=1)
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ocv_target_link_libraries(opencv_perf_dnn caffe)
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endif()
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endif()
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endif()
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# ----------------------------------------------------------------------------
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# Torch7 importer of blobs and models, produced by Torch.nn module
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# ----------------------------------------------------------------------------

‎modules/dnn/include/opencv2/dnn/dnn.hpp

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@@ -433,21 +433,21 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
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* specific target. For layers that not represented in scheduling file
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* or if no manual scheduling used at all, automatic scheduling will be applied.
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*/
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void setHalideScheduler(const String& scheduler);
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CV_WRAP void setHalideScheduler(const String& scheduler);
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/**
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* @brief Ask network to use specific computation backend where it supported.
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* @param[in] backendId backend identifier.
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* @see Backend
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*/
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void setPreferableBackend(int backendId);
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CV_WRAP void setPreferableBackend(int backendId);
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/**
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* @brief Ask network to make computations on specific target device.
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* @param[in] targetId target identifier.
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* @see Target
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*/
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void setPreferableTarget(int targetId);
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CV_WRAP void setPreferableTarget(int targetId);
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/** @brief Sets the new value for the layer output blob
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* @param name descriptor of the updating layer output blob.

‎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 cvtest
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{
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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(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(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(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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} // namespace cvtest
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#endif // HAVE_CAFFE

‎modules/dnn/perf/perf_halide_net.cpp

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{
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Net net;
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loadNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
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"", 227, 227, "prob", "caffe", DNN_TARGET_CPU, &net);
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"", 224, 224, "prob", "caffe", DNN_TARGET_CPU, &net);
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TEST_CYCLE() net.forward();
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SANITY_CHECK_NOTHING();
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}

‎modules/dnn/test/test_halide_nets.cpp

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{
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test(findDataFile("dnn/bvlc_googlenet.caffemodel", false),
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findDataFile("dnn/bvlc_googlenet.prototxt", false),
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"", 227, 227, "prob", "caffe", DNN_TARGET_CPU);
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"", 224, 224, "prob", "caffe", DNN_TARGET_CPU);
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};
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TEST(Reproducibility_AlexNet_Halide, Accuracy)

‎samples/dnn/squeezenet_halide.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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// Sample of using Halide backend in OpenCV deep learning module.
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// Based on caffe_googlenet.cpp.
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