155 lines
6 KiB
C++
155 lines
6 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 opencv_test { namespace {
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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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typedef testing::TestWithParam<Target> Reproducibility_GoogLeNet;
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TEST_P(Reproducibility_GoogLeNet, Batching)
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{
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const int targetId = GetParam();
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if (targetId == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
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Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt"),
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findDataFile("dnn/bvlc_googlenet.caffemodel", false));
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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net.setPreferableTarget(targetId);
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if (targetId == DNN_TARGET_OPENCL)
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{
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// Initialize network for a single image in the batch but test with batch size=2.
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Mat inp = Mat(224, 224, CV_8UC3);
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randu(inp, -1, 1);
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net.setInput(blobFromImage(inp));
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net.forward();
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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, 1.0f, Size(), Scalar(), false), "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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}
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TEST_P(Reproducibility_GoogLeNet, IntermediateBlobs)
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{
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const int targetId = GetParam();
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if (targetId == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
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Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt"),
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findDataFile("dnn/bvlc_googlenet.caffemodel", false));
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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net.setPreferableTarget(targetId);
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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(imread(_tf("googlenet_0.png")), 1.0f, Size(), Scalar(), false);
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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 (size_t 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_P(Reproducibility_GoogLeNet, SeveralCalls)
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{
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const int targetId = GetParam();
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if (targetId == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
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Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt"),
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findDataFile("dnn/bvlc_googlenet.caffemodel", false));
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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net.setPreferableTarget(targetId);
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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, 1.0f, Size(), Scalar(), false), "data");
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Mat out = net.forward();
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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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std::vector<Mat> outs;
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Mat in = blobFromImage(inpMats[0], 1.0f, Size(), Scalar(), false);
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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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ref = blobFromNPY(_tf("googlenet_conv1#7x7_s2.npy"));
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normAssert(outs[0], ref, "", 1E-4, 1E-2);
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}
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INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_GoogLeNet,
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testing::ValuesIn(getAvailableTargets(DNN_BACKEND_OPENCV)));
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}} // namespace
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