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63 lines
1.5 KiB
63 lines
1.5 KiB
10 years ago
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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 std;
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using namespace testing;
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using namespace cv;
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using namespace cv::dnn;
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static std::string getOpenCVExtraDir()
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{
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return cvtest::TS::ptr()->get_data_path();
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}
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template<typename TStr>
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static std::string getTestFile(TStr filename)
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{
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return (getOpenCVExtraDir() + "/dnn/") + filename;
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}
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inline void normAssert(InputArray ref, InputArray get, const char *comment = "")
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{
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double normL1 = cvtest::norm(ref, get, NORM_L1)/ ref.getMat().total();
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EXPECT_LE(normL1, 0.0001) << comment;
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double normInf = cvtest::norm(ref, get, NORM_INF);
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EXPECT_LE(normInf, 0.001) << comment;
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}
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inline void normAssert(Blob ref, Blob test, const char *comment = "")
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{
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normAssert(ref.getMatRef(), test.getMatRef(), comment);
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}
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TEST(Reproducibility_GoogLeNet, Accuracy)
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{
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Net net;
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{
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Ptr<Importer> importer = createCaffeImporter(getTestFile("bvlc_googlenet.prototxt"), getTestFile("bvlc_googlenet.caffemodel"));
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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(2);
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inpMats[0] = imread(getTestFile("googlenet_0.png"));
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inpMats[1] = imread(getTestFile("googlenet_1.png"));
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ASSERT_TRUE(!inpMats[0].empty() && !inpMats[1].empty());
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inpMats[0].convertTo(inpMats[0], CV_32F);
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Blob inp(inpMats[0]);
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net.setBlob("data", inp);
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net.forward();
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Blob out = net.getBlob("prob");
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Blob ref = blobFromNPY(getTestFile("googlenet.npy"));
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normAssert(out, ref);
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}
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}
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