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134 lines
3.1 KiB
C++
134 lines
3.1 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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// Copyright (C) 2016, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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/*
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Test for Tensorflow models loading
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*/
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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_TensorFlow, read_inception)
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{
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Net net;
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{
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const string model = findDataFile("dnn/tensorflow_inception_graph.pb", false);
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Ptr<Importer> importer = createTensorflowImporter(model);
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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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Mat input;
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resize(sample, input, Size(224, 224));
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input -= 128; // mean sub
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Mat inputBlob = blobFromImage(input);
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net.setInput(inputBlob, "input");
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Mat out = net.forward("softmax2");
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std::cout << out.dims << std::endl;
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}
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TEST(Test_TensorFlow, inception_accuracy)
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{
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Net net;
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{
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const string model = findDataFile("dnn/tensorflow_inception_graph.pb", false);
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Ptr<Importer> importer = createTensorflowImporter(model);
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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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resize(sample, sample, Size(224, 224));
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Mat inputBlob = blobFromImage(sample);
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net.setInput(inputBlob, "input");
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Mat out = net.forward("softmax2");
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Mat ref = blobFromNPY(_tf("tf_inception_prob.npy"));
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normAssert(ref, out);
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}
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static std::string path(const std::string& file)
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{
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return findDataFile("dnn/tensorflow/" + file, false);
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}
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static void runTensorFlowNet(const std::string& prefix)
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{
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std::string netPath = path(prefix + "_net.pb");
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std::string inpPath = path(prefix + "_in.npy");
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std::string outPath = path(prefix + "_out.npy");
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Net net = readNetFromTensorflow(netPath);
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cv::Mat input = blobFromNPY(inpPath);
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cv::Mat target = blobFromNPY(outPath);
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net.setInput(input);
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cv::Mat output = net.forward();
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normAssert(target, output);
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}
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TEST(Test_TensorFlow, single_conv)
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{
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runTensorFlowNet("single_conv");
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}
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TEST(Test_TensorFlow, padding)
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{
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runTensorFlowNet("padding_same");
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runTensorFlowNet("padding_valid");
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}
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TEST(Test_TensorFlow, eltwise_add_mul)
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{
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runTensorFlowNet("eltwise_add_mul");
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}
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TEST(Test_TensorFlow, pad_and_concat)
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{
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runTensorFlowNet("pad_and_concat");
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}
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TEST(Test_TensorFlow, fused_batch_norm)
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{
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runTensorFlowNet("fused_batch_norm");
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}
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TEST(Test_TensorFlow, pooling)
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{
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runTensorFlowNet("max_pool_even");
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runTensorFlowNet("max_pool_odd_valid");
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runTensorFlowNet("max_pool_odd_same");
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}
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TEST(Test_TensorFlow, deconvolution)
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{
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runTensorFlowNet("deconvolution");
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}
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}
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