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8b3d6603d5
* another round of dnn optimization: * increased malloc alignment across OpenCV from 16 to 64 bytes to make it AVX2 and even AVX-512 friendly * improved SIMD optimization of pooling layer, optimized average pooling * cleaned up convolution layer implementation * made activation layer "attacheable" to all other layers, including fully connected and addition layer. * fixed bug in the fusion algorithm: "LayerData::consumers" should not be cleared, because it desctibes the topology. * greatly optimized permutation layer, which improved SSD performance * parallelized element-wise binary/ternary/... ops (sum, prod, max) * also, added missing copyrights to many of the layer implementation files * temporarily disabled (again) the check for intermediate blobs consistency; fixed warnings from various builders
108 lines
3.8 KiB
C++
108 lines
3.8 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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#include "npy_blob.hpp"
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#include <opencv2/core/ocl.hpp>
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#include <opencv2/ts/ocl_test.hpp>
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namespace cvtest
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{
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using namespace cv;
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using namespace cv::dnn;
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template<typename TString>
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static std::string _tf(TString filename)
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{
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return (getOpenCVExtraDir() + "/dnn/") + filename;
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}
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static void launchGoogleNetTest()
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{
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Net net;
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{
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const string proto = findDataFile("dnn/bvlc_googlenet.prototxt", false);
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const string model = findDataFile("dnn/bvlc_googlenet.caffemodel", false);
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Ptr<Importer> importer = createCaffeImporter(proto, model);
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ASSERT_TRUE(importer != NULL);
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importer->populateNet(net);
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}
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std::vector<Mat> inpMats;
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inpMats.push_back( imread(_tf("googlenet_0.png")) );
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inpMats.push_back( imread(_tf("googlenet_1.png")) );
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ASSERT_TRUE(!inpMats[0].empty() && !inpMats[1].empty());
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net.setInput(blobFromImages(inpMats), "data");
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Mat out = net.forward("prob");
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Mat ref = blobFromNPY(_tf("googlenet_prob.npy"));
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normAssert(out, ref);
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std::vector<String> blobsNames;
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blobsNames.push_back("conv1/7x7_s2");
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blobsNames.push_back("conv1/relu_7x7");
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blobsNames.push_back("inception_4c/1x1");
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blobsNames.push_back("inception_4c/relu_1x1");
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std::vector<Mat> outs;
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Mat in = blobFromImage(inpMats[0]);
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net.setInput(in, "data");
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net.forward(outs, blobsNames);
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CV_Assert(outs.size() == blobsNames.size());
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for (int i = 0; i < blobsNames.size(); i++)
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{
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std::string filename = blobsNames[i];
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std::replace( filename.begin(), filename.end(), '/', '#');
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Mat ref = blobFromNPY(_tf("googlenet_" + filename + ".npy"));
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//normAssert(outs[i], ref, "", 1E-4, 1E-2);
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}
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}
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TEST(Reproducibility_GoogLeNet, Accuracy)
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{
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launchGoogleNetTest();
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}
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}
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