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https://github.com/opencv/opencv.git
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8ad5eb521a
OCL_FP16 MatMul with large batch * Workaround FP16 MatMul with large batch * Fix OCL reinitialization * Higher thresholds for INT8 quantization * Try fix gemm_buffer_NT for half (columns) * Fix GEMM by rows * Add batch dimension to InnerProduct layer test * Fix Test_ONNX_conformance.Layer_Test/test_basic_conv_with_padding * Batch 16 * Replace all vload4 * Version suffix for MobileNetSSD_deploy Caffe model
112 lines
3.4 KiB
C++
112 lines
3.4 KiB
C++
// 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);
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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_19e3ec3.prototxt",
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"dnn/MobileNetSSD_deploy_19e3ec3.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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