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142 lines
4.3 KiB
142 lines
4.3 KiB
/*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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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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TEST(Test_Caffe, read_gtsrb) |
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{ |
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Net net; |
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{ |
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Ptr<Importer> importer = createCaffeImporter(_tf("gtsrb.prototxt"), ""); |
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ASSERT_TRUE(importer != NULL); |
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importer->populateNet(net); |
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} |
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} |
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TEST(Test_Caffe, read_googlenet) |
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{ |
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Net net; |
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{ |
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Ptr<Importer> importer = createCaffeImporter(_tf("bvlc_googlenet.prototxt"), ""); |
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ASSERT_TRUE(importer != NULL); |
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importer->populateNet(net); |
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} |
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} |
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#if defined(ENABLE_CAFFE_MODEL_TESTS) |
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#if defined(ENABLE_CAFFE_ALEXNET_TEST) //AlexNet is disabled now |
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TEST(Reproducibility_AlexNet, Accuracy) |
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{ |
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Net net; |
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{ |
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Ptr<Importer> importer = createCaffeImporter(_tf("bvlc_alexnet.prototxt"), _tf("bvlc_alexnet.caffemodel")); |
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ASSERT_TRUE(importer != NULL); |
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importer->populateNet(net); |
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} |
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Mat sample = imread(_tf("grace_hopper_227.png")); |
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ASSERT_TRUE(!sample.empty()); |
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cv::cvtColor(sample, sample, cv::COLOR_BGR2RGB); |
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Size inputSize(227, 227); |
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if (sample.size() != inputSize) |
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resize(sample, sample, inputSize); |
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net.setBlob(".data", dnn::Blob::fromImages(sample)); |
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net.forward(); |
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Blob out = net.getBlob("prob"); |
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Blob ref = blobFromNPY(_tf("caffe_alexnet_prob.npy")); |
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normAssert(ref, out); |
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} |
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#endif |
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#if defined(ENABLE_CAFFE_FCN_TEST) |
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TEST(Reproducibility_FCN, Accuracy) |
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{ |
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Net net; |
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{ |
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Ptr<Importer> importer = createCaffeImporter(_tf("fcn8s-heavy-pascal.prototxt"), _tf("fcn8s-heavy-pascal.caffemodel")); |
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ASSERT_TRUE(importer != NULL); |
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importer->populateNet(net); |
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} |
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Mat sample = imread(_tf("street.png")); |
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ASSERT_TRUE(!sample.empty()); |
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Size inputSize(500, 500); |
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if (sample.size() != inputSize) |
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resize(sample, sample, inputSize); |
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cv::cvtColor(sample, sample, cv::COLOR_BGR2RGB); |
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net.setBlob(".data", dnn::Blob::fromImages(sample)); |
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net.forward(); |
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Blob out = net.getBlob("score"); |
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Blob ref = blobFromNPY(_tf("caffe_fcn8s_prob.npy")); |
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normAssert(ref, out); |
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} |
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#endif |
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#endif |
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}
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