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// 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) 2017-2019, 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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#include <opencv2/dnn/layer.details.hpp> // CV_DNN_REGISTER_LAYER_CLASS
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namespace opencv_test
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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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net = readNetFromTensorflow(model);
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ASSERT_FALSE(net.empty());
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}
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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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 -= Scalar::all(117); // 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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net = readNetFromTensorflow(model);
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ASSERT_FALSE(net.empty());
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}
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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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 inputBlob = blobFromImage(sample, 1.0, Size(224, 224), Scalar(), /*swapRB*/true);
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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);
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}
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class Test_TensorFlow_layers : public DNNTestLayer
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{
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public:
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void runTensorFlowNet(const std::string& prefix, bool hasText = false,
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double l1 = 0.0, double lInf = 0.0, bool memoryLoad = false)
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{
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std::string netPath = path(prefix + "_net.pb");
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std::string netConfig = (hasText ? path(prefix + "_net.pbtxt") : "");
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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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cv::Mat input = blobFromNPY(inpPath);
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cv::Mat ref = blobFromNPY(outPath);
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checkBackend(&input, &ref);
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Net net;
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if (memoryLoad)
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{
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// Load files into a memory buffers
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std::vector<char> dataModel;
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readFileContent(netPath, dataModel);
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std::vector<char> dataConfig;
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if (hasText)
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{
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readFileContent(netConfig, dataConfig);
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}
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net = readNetFromTensorflow(dataModel.data(), dataModel.size(),
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dataConfig.data(), dataConfig.size());
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}
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else
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net = readNetFromTensorflow(netPath, netConfig);
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ASSERT_FALSE(net.empty());
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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net.setInput(input);
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cv::Mat output = net.forward();
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normAssert(ref, output, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
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}
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};
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TEST_P(Test_TensorFlow_layers, conv)
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{
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runTensorFlowNet("single_conv");
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runTensorFlowNet("atrous_conv2d_valid");
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runTensorFlowNet("atrous_conv2d_same");
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runTensorFlowNet("depthwise_conv2d");
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runTensorFlowNet("keras_atrous_conv2d_same");
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runTensorFlowNet("conv_pool_nchw");
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}
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TEST_P(Test_TensorFlow_layers, Convolution3D)
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{
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
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throw SkipTestException("Test is enabled starts from 2019R1");
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#endif
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if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
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throw SkipTestException("Only DLIE backend on CPU is supported");
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runTensorFlowNet("conv3d");
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}
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TEST_P(Test_TensorFlow_layers, padding)
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{
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runTensorFlowNet("padding_valid");
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runTensorFlowNet("spatial_padding");
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runTensorFlowNet("keras_pad_concat");
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runTensorFlowNet("mirror_pad");
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}
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TEST_P(Test_TensorFlow_layers, padding_same)
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{
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// Reference output values are in range [0.0006, 2.798]
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runTensorFlowNet("padding_same");
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}
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TEST_P(Test_TensorFlow_layers, eltwise)
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{
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runTensorFlowNet("eltwise_add_mul");
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runTensorFlowNet("eltwise_sub");
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}
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TEST_P(Test_TensorFlow_layers, pad_and_concat)
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{
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runTensorFlowNet("pad_and_concat");
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}
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TEST_P(Test_TensorFlow_layers, concat_axis_1)
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{
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runTensorFlowNet("concat_axis_1");
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}
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TEST_P(Test_TensorFlow_layers, batch_norm)
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{
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runTensorFlowNet("batch_norm");
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runTensorFlowNet("batch_norm", false, 0.0, 0.0, true);
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runTensorFlowNet("fused_batch_norm");
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runTensorFlowNet("fused_batch_norm", false, 0.0, 0.0, true);
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runTensorFlowNet("batch_norm_text", true);
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runTensorFlowNet("batch_norm_text", true, 0.0, 0.0, true);
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runTensorFlowNet("unfused_batch_norm");
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runTensorFlowNet("fused_batch_norm_no_gamma");
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runTensorFlowNet("unfused_batch_norm_no_gamma");
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runTensorFlowNet("mvn_batch_norm");
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runTensorFlowNet("mvn_batch_norm_1x1");
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runTensorFlowNet("switch_identity");
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runTensorFlowNet("keras_batch_norm_training");
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}
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TEST_P(Test_TensorFlow_layers, batch_norm3D)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
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{
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if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
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if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL);
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if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
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throw SkipTestException("");
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}
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runTensorFlowNet("batch_norm3d");
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}
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TEST_P(Test_TensorFlow_layers, slim_batch_norm)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
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// Output values range: [-40.0597, 207.827]
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double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.041 : default_l1;
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double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.33 : default_lInf;
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runTensorFlowNet("slim_batch_norm", false, l1, lInf);
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}
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TEST_P(Test_TensorFlow_layers, 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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runTensorFlowNet("reduce_mean"); // an average pooling over all spatial dimensions.
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}
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// TODO: fix tests and replace to pooling
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TEST_P(Test_TensorFlow_layers, ave_pool_same)
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{
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// Reference output values are in range [-0.519531, 0.112976]
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
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&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
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)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
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#endif
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runTensorFlowNet("ave_pool_same");
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}
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TEST_P(Test_TensorFlow_layers, MaxPooling3D)
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{
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
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throw SkipTestException("Test is enabled starts from 2019R1");
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#endif
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if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
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throw SkipTestException("Only DLIE backend on CPU is supported");
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runTensorFlowNet("max_pool3d");
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}
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TEST_P(Test_TensorFlow_layers, AvePooling3D)
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{
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
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throw SkipTestException("Test is enabled starts from 2019R1");
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#endif
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if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
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throw SkipTestException("Only DLIE backend on CPU is supported");
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runTensorFlowNet("ave_pool3d");
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}
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TEST_P(Test_TensorFlow_layers, deconvolution)
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{
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runTensorFlowNet("deconvolution");
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runTensorFlowNet("deconvolution_same");
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runTensorFlowNet("deconvolution_stride_2_same");
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runTensorFlowNet("deconvolution_adj_pad_valid");
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runTensorFlowNet("deconvolution_adj_pad_same");
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runTensorFlowNet("keras_deconv_valid");
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runTensorFlowNet("keras_deconv_same");
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runTensorFlowNet("keras_deconv_same_v2");
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}
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TEST_P(Test_TensorFlow_layers, matmul)
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{
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
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runTensorFlowNet("matmul");
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runTensorFlowNet("nhwc_transpose_reshape_matmul");
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// Reference output values are in range [-5.688, 4.484]
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double l1 = target == DNN_TARGET_MYRIAD ? 6.1e-3 : default_l1;
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runTensorFlowNet("nhwc_reshape_matmul", false, l1);
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}
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TEST_P(Test_TensorFlow_layers, reshape)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
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runTensorFlowNet("shift_reshape_no_reorder");
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runTensorFlowNet("reshape_no_reorder");
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runTensorFlowNet("reshape_reduce");
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runTensorFlowNet("reshape_as_shape");
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}
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TEST_P(Test_TensorFlow_layers, flatten)
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{
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#if defined(INF_ENGINE_RELEASE)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
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&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2
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)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2);
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#endif
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runTensorFlowNet("flatten", true);
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}
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TEST_P(Test_TensorFlow_layers, unfused_flatten)
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{
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runTensorFlowNet("unfused_flatten");
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runTensorFlowNet("unfused_flatten_unknown_batch");
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}
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TEST_P(Test_TensorFlow_layers, leaky_relu)
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{
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
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#endif
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runTensorFlowNet("leaky_relu_order1");
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runTensorFlowNet("leaky_relu_order2");
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runTensorFlowNet("leaky_relu_order3");
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}
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TEST_P(Test_TensorFlow_layers, l2_normalize)
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{
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
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&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
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)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
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#endif
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runTensorFlowNet("l2_normalize");
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}
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// TODO: fix it and add to l2_normalize
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TEST_P(Test_TensorFlow_layers, l2_normalize_3d)
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{
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE
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&& (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)
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)
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applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
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#endif
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#if defined(INF_ENGINE_RELEASE)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
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#endif
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runTensorFlowNet("l2_normalize_3d");
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}
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class Test_TensorFlow_nets : public DNNTestLayer {};
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TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
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{
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#if defined(INF_ENGINE_RELEASE)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
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{
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#if INF_ENGINE_VER_MAJOR_GE(2019010000)
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if (getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
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#else
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
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#endif
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}
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#endif
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checkBackend();
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std::string imgPath = findDataFile("dnn/street.png");
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std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt");
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std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false);
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Mat inp;
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resize(imread(imgPath), inp, Size(300, 300));
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inp = blobFromImage(inp, 1.0f / 127.5, Size(), Scalar(127.5, 127.5, 127.5), true);
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Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco.detection_out.npy"));
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Net net = readNetFromTensorflow(netPath, netConfig);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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net.setInput(inp);
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Mat out = net.forward();
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double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0043 : default_l1;
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double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.037 : default_lInf;
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normAssertDetections(ref, out, "", 0.2, scoreDiff, iouDiff);
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2019010000
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expectNoFallbacksFromIE(net);
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#endif
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}
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TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
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{
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applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
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#if defined(INF_ENGINE_RELEASE)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
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&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
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)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
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#endif
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checkBackend();
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Mat img = imread(findDataFile("dnn/street.png"));
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std::string proto = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pbtxt");
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std::string model = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pb", false);
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Net net = readNetFromTensorflow(model, proto);
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Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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net.setInput(blob);
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// Output has shape 1x1xNx7 where N - number of detections.
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// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
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Mat out = net.forward();
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Mat ref = (Mat_<float>(5, 7) << 0, 1, 0.90176028, 0.19872092, 0.36311883, 0.26461923, 0.63498729,
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0, 3, 0.93569964, 0.64865261, 0.45906419, 0.80675775, 0.65708131,
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0, 3, 0.75838411, 0.44668293, 0.45907149, 0.49459291, 0.52197015,
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0, 10, 0.95932811, 0.38349164, 0.32528657, 0.40387636, 0.39165527,
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0, 10, 0.93973452, 0.66561931, 0.37841269, 0.68074018, 0.42907384);
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double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0097 : default_l1;
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double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09 : default_lInf;
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normAssertDetections(ref, out, "", 0.5, scoreDiff, iouDiff);
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expectNoFallbacksFromIE(net);
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}
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TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD)
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{
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checkBackend();
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std::string proto = findDataFile("dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt");
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std::string model = findDataFile("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", false);
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Net net = readNetFromTensorflow(model, proto);
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Mat img = imread(findDataFile("dnn/dog416.png"));
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Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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net.setInput(blob);
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Mat out = net.forward();
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Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco_2017_11_17.detection_out.npy"));
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float scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 7e-3 : 1.5e-5;
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float iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.012 : 1e-3;
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float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.35 : 0.3;
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#if defined(INF_ENGINE_RELEASE)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
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&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
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)
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scoreDiff = 0.061;
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iouDiff = 0.12;
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detectionConfThresh = 0.36;
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#endif
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normAssertDetections(ref, out, "", detectionConfThresh, scoreDiff, iouDiff);
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expectNoFallbacksFromIE(net);
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}
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TEST_P(Test_TensorFlow_nets, Faster_RCNN)
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{
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// FIXIT split test
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applyTestTag(
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(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
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CV_TEST_TAG_LONG,
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CV_TEST_TAG_DEBUG_VERYLONG
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);
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static std::string names[] = {"faster_rcnn_inception_v2_coco_2018_01_28",
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"faster_rcnn_resnet50_coco_2018_01_28"};
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checkBackend();
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if (backend == DNN_BACKEND_INFERENCE_ENGINE)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
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double scoresDiff = backend == DNN_BACKEND_INFERENCE_ENGINE ? 2.9e-5 : 1e-5;
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for (int i = 0; i < 2; ++i)
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{
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std::string proto = findDataFile("dnn/" + names[i] + ".pbtxt");
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std::string model = findDataFile("dnn/" + names[i] + ".pb", false);
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Net net = readNetFromTensorflow(model, proto);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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Mat img = imread(findDataFile("dnn/dog416.png"));
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Mat blob = blobFromImage(img, 1.0f, Size(800, 600), Scalar(), true, false);
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net.setInput(blob);
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Mat out = net.forward();
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Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/" + names[i] + ".detection_out.npy"));
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normAssertDetections(ref, out, names[i].c_str(), 0.3, scoresDiff);
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}
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}
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TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
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{
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
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applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
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#endif
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checkBackend();
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std::string proto = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pbtxt");
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std::string model = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pb", false);
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Net net = readNetFromTensorflow(model, proto);
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Mat img = imread(findDataFile("dnn/dog416.png"));
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Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_ppn_coco.detection_out.npy"));
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Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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net.setInput(blob);
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Mat out = net.forward();
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double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.048 : 1.1e-5;
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double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.058 : default_lInf;
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normAssertDetections(ref, out, "", 0.45, scoreDiff, iouDiff);
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expectNoFallbacksFromIE(net);
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}
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TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
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{
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checkBackend();
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std::string proto = findDataFile("dnn/opencv_face_detector.pbtxt");
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std::string model = findDataFile("dnn/opencv_face_detector_uint8.pb", false);
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Net net = readNetFromTensorflow(model, proto);
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Mat img = imread(findDataFile("gpu/lbpcascade/er.png"));
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Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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net.setInput(blob);
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// Output has shape 1x1xNx7 where N - number of detections.
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// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
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Mat out = net.forward();
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// References are from test for Caffe model.
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Mat ref = (Mat_<float>(6, 7) << 0, 1, 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631,
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0, 1, 0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168,
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0, 1, 0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290,
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0, 1, 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477,
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0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
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0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
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double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 4e-3 : 3.4e-3;
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double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.024 : 1e-2;
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normAssertDetections(ref, out, "", 0.9, scoreDiff, iouDiff);
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expectNoFallbacksFromIE(net);
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}
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// inp = cv.imread('opencv_extra/testdata/cv/ximgproc/sources/08.png')
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// inp = inp[:,:,[2, 1, 0]].astype(np.float32).reshape(1, 512, 512, 3)
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// outs = sess.run([sess.graph.get_tensor_by_name('feature_fusion/Conv_7/Sigmoid:0'),
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// sess.graph.get_tensor_by_name('feature_fusion/concat_3:0')],
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// feed_dict={'input_images:0': inp})
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// scores = np.ascontiguousarray(outs[0].transpose(0, 3, 1, 2))
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// geometry = np.ascontiguousarray(outs[1].transpose(0, 3, 1, 2))
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// np.save('east_text_detection.scores.npy', scores)
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// np.save('east_text_detection.geometry.npy', geometry)
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TEST_P(Test_TensorFlow_nets, EAST_text_detection)
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{
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applyTestTag(
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(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
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CV_TEST_TAG_DEBUG_LONG
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);
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#if defined(INF_ENGINE_RELEASE)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
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#endif
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checkBackend();
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std::string netPath = findDataFile("dnn/frozen_east_text_detection.pb", false);
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std::string imgPath = findDataFile("cv/ximgproc/sources/08.png");
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std::string refScoresPath = findDataFile("dnn/east_text_detection.scores.npy");
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std::string refGeometryPath = findDataFile("dnn/east_text_detection.geometry.npy");
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Net net = readNet(netPath);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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Mat img = imread(imgPath);
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Mat inp = blobFromImage(img, 1.0, Size(), Scalar(123.68, 116.78, 103.94), true, false);
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net.setInput(inp);
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std::vector<Mat> outs;
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std::vector<String> outNames(2);
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outNames[0] = "feature_fusion/Conv_7/Sigmoid";
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outNames[1] = "feature_fusion/concat_3";
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net.forward(outs, outNames);
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Mat scores = outs[0];
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Mat geometry = outs[1];
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// Scores are in range [0, 1]. Geometry values are in range [-0.23, 290]
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double l1_scores = default_l1, lInf_scores = default_lInf;
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double l1_geometry = default_l1, lInf_geometry = default_lInf;
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if (target == DNN_TARGET_OPENCL_FP16)
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{
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lInf_scores = backend == DNN_BACKEND_INFERENCE_ENGINE ? 0.16 : 0.11;
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l1_geometry = 0.28; lInf_geometry = 5.94;
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}
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else if (target == DNN_TARGET_MYRIAD)
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{
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lInf_scores = 0.41;
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l1_geometry = 0.28; lInf_geometry = 5.94;
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}
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else
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{
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l1_geometry = 1e-4, lInf_geometry = 3e-3;
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}
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normAssert(scores, blobFromNPY(refScoresPath), "scores", l1_scores, lInf_scores);
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normAssert(geometry, blobFromNPY(refGeometryPath), "geometry", l1_geometry, lInf_geometry);
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expectNoFallbacksFromIE(net);
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}
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INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_nets, dnnBackendsAndTargets());
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TEST_P(Test_TensorFlow_layers, fp16_weights)
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|
{
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float l1 = 0.00078;
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float lInf = 0.012;
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runTensorFlowNet("fp16_single_conv", false, l1, lInf);
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runTensorFlowNet("fp16_max_pool_odd_same", false, l1, lInf);
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runTensorFlowNet("fp16_eltwise_add_mul", false, l1, lInf);
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runTensorFlowNet("fp16_pad_and_concat", false, l1, lInf);
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runTensorFlowNet("fp16_padding_valid", false, l1, lInf);
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// Reference output values are in range [0.0889, 1.651]
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|
runTensorFlowNet("fp16_max_pool_even", false, (target == DNN_TARGET_MYRIAD) ? 0.003 : l1, lInf);
|
|
|
|
if (target == DNN_TARGET_MYRIAD) {
|
|
|
|
l1 = 0.0041;
|
|
|
|
lInf = 0.024;
|
|
|
|
}
|
|
|
|
// Reference output values are in range [0, 10.75]
|
|
|
|
runTensorFlowNet("fp16_deconvolution", false, l1, lInf);
|
|
|
|
// Reference output values are in range [0.418, 2.297]
|
|
|
|
runTensorFlowNet("fp16_max_pool_odd_valid", false, l1, lInf);
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST_P(Test_TensorFlow_layers, fp16_padding_same)
|
|
|
|
{
|
|
|
|
// Reference output values are in range [-3.504, -0.002]
|
|
|
|
runTensorFlowNet("fp16_padding_same", false, 7e-4, 4e-3);
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST_P(Test_TensorFlow_layers, defun)
|
|
|
|
{
|
|
|
|
runTensorFlowNet("defun_dropout");
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST_P(Test_TensorFlow_layers, quantized)
|
|
|
|
{
|
|
|
|
runTensorFlowNet("uint8_single_conv");
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST_P(Test_TensorFlow_layers, lstm)
|
|
|
|
{
|
|
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
|
|
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
|
|
|
|
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
|
|
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
|
|
|
|
runTensorFlowNet("lstm", true);
|
|
|
|
runTensorFlowNet("lstm", true, 0.0, 0.0, true);
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST_P(Test_TensorFlow_layers, split)
|
|
|
|
{
|
|
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
|
|
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
|
|
|
|
runTensorFlowNet("split_equals");
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST_P(Test_TensorFlow_layers, resize_nearest_neighbor)
|
|
|
|
{
|
|
|
|
runTensorFlowNet("resize_nearest_neighbor");
|
|
|
|
runTensorFlowNet("keras_upsampling2d");
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST_P(Test_TensorFlow_layers, slice)
|
|
|
|
{
|
|
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
|
|
|
|
(target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
|
|
|
|
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
|
|
|
|
runTensorFlowNet("slice_4d");
|
|
|
|
runTensorFlowNet("strided_slice");
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST_P(Test_TensorFlow_layers, softmax)
|
|
|
|
{
|
|
|
|
runTensorFlowNet("keras_softmax");
|
|
|
|
runTensorFlowNet("slim_softmax");
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST_P(Test_TensorFlow_layers, slim_softmax_v2)
|
|
|
|
{
|
|
|
|
#if defined(INF_ENGINE_RELEASE)
|
|
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD &&
|
|
|
|
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2
|
|
|
|
)
|
|
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2);
|
|
|
|
#endif
|
|
|
|
runTensorFlowNet("slim_softmax_v2");
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST_P(Test_TensorFlow_layers, relu6)
|
|
|
|
{
|
|
|
|
runTensorFlowNet("keras_relu6");
|
|
|
|
runTensorFlowNet("keras_relu6", /*hasText*/ true);
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST_P(Test_TensorFlow_layers, subpixel)
|
|
|
|
{
|
|
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
|
|
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
|
|
|
|
runTensorFlowNet("subpixel");
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST_P(Test_TensorFlow_layers, keras_mobilenet_head)
|
|
|
|
{
|
|
|
|
runTensorFlowNet("keras_mobilenet_head");
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST_P(Test_TensorFlow_layers, resize_bilinear)
|
|
|
|
{
|
|
|
|
runTensorFlowNet("resize_bilinear");
|
|
|
|
runTensorFlowNet("resize_bilinear_factor");
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST_P(Test_TensorFlow_layers, squeeze)
|
|
|
|
{
|
|
|
|
#if defined(INF_ENGINE_RELEASE)
|
|
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
|
|
|
|
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2
|
|
|
|
)
|
|
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2);
|
|
|
|
#endif
|
|
|
|
int inpShapes[][4] = {{1, 3, 4, 2}, {1, 3, 1, 2}, {1, 3, 4, 1}, {1, 3, 4, 1}}; // TensorFlow's shape (NHWC)
|
|
|
|
int outShapes[][3] = {{3, 4, 2}, {1, 3, 2}, {1, 3, 4}, {1, 3, 4}};
|
|
|
|
int squeeze_dims[] = {0, 2, 3, -1};
|
|
|
|
for (int i = 0; i < 4; ++i)
|
|
|
|
{
|
|
|
|
SCOPED_TRACE(format("i=%d", i));
|
|
|
|
std::string pbtxt =
|
|
|
|
"node { name: \"input\" op: \"Placeholder\""
|
|
|
|
"attr { key: \"data_format\" value { s: \"NHWC\" } } }"
|
|
|
|
"node { name: \"squeeze\" op: \"Squeeze\" input: \"input\""
|
|
|
|
"attr { key: \"squeeze_dims\" value { list { i:" + format("%d", squeeze_dims[i]) + "}}}}";
|
|
|
|
Net net = readNetFromTensorflow(0, 0, pbtxt.c_str(), pbtxt.size());
|
|
|
|
net.setPreferableBackend(backend);
|
|
|
|
net.setPreferableTarget(target);
|
|
|
|
Mat tfInp(4, &inpShapes[i][0], CV_32F);
|
|
|
|
randu(tfInp, -1, 1);
|
|
|
|
|
|
|
|
// NHWC to NCHW
|
|
|
|
CV_Assert(inpShapes[i][0] == 1);
|
|
|
|
std::swap(inpShapes[i][2], inpShapes[i][3]);
|
|
|
|
std::swap(inpShapes[i][1], inpShapes[i][2]);
|
|
|
|
Mat cvInp = tfInp.reshape(1, tfInp.total() / inpShapes[i][1]).t();
|
|
|
|
cvInp = cvInp.reshape(1, 4, &inpShapes[i][0]);
|
|
|
|
|
|
|
|
net.setInput(cvInp);
|
|
|
|
Mat out = net.forward();
|
|
|
|
normAssert(tfInp.reshape(1, 3, &outShapes[i][0]), out, "", default_l1, default_lInf);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_layers, dnnBackendsAndTargets());
|
|
|
|
|
|
|
|
TEST(Test_TensorFlow, two_inputs)
|
|
|
|
{
|
|
|
|
Net net = readNet(path("two_inputs_net.pbtxt"));
|
|
|
|
net.setPreferableBackend(DNN_BACKEND_OPENCV);
|
|
|
|
|
|
|
|
Mat firstInput(2, 3, CV_32FC1), secondInput(2, 3, CV_32FC1);
|
|
|
|
randu(firstInput, -1, 1);
|
|
|
|
randu(secondInput, -1, 1);
|
|
|
|
|
|
|
|
net.setInput(firstInput, "first_input");
|
|
|
|
net.setInput(secondInput, "second_input");
|
|
|
|
Mat out = net.forward();
|
|
|
|
|
|
|
|
normAssert(out, firstInput + secondInput);
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST(Test_TensorFlow, Mask_RCNN)
|
|
|
|
{
|
|
|
|
applyTestTag(CV_TEST_TAG_MEMORY_1GB, CV_TEST_TAG_DEBUG_VERYLONG);
|
|
|
|
Mat img = imread(findDataFile("dnn/street.png"));
|
|
|
|
std::string proto = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pbtxt");
|
|
|
|
std::string model = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pb", false);
|
|
|
|
|
|
|
|
Net net = readNetFromTensorflow(model, proto);
|
|
|
|
Mat refDetections = blobFromNPY(path("mask_rcnn_inception_v2_coco_2018_01_28.detection_out.npy"));
|
|
|
|
Mat refMasks = blobFromNPY(path("mask_rcnn_inception_v2_coco_2018_01_28.detection_masks.npy"));
|
|
|
|
Mat blob = blobFromImage(img, 1.0f, Size(800, 800), Scalar(), true, false);
|
|
|
|
|
|
|
|
net.setPreferableBackend(DNN_BACKEND_OPENCV);
|
|
|
|
|
|
|
|
net.setInput(blob);
|
|
|
|
|
|
|
|
// Mask-RCNN predicts bounding boxes and segmentation masks.
|
|
|
|
std::vector<String> outNames(2);
|
|
|
|
outNames[0] = "detection_out_final";
|
|
|
|
outNames[1] = "detection_masks";
|
|
|
|
|
|
|
|
std::vector<Mat> outs;
|
|
|
|
net.forward(outs, outNames);
|
|
|
|
|
|
|
|
Mat outDetections = outs[0];
|
|
|
|
Mat outMasks = outs[1];
|
|
|
|
normAssertDetections(refDetections, outDetections, "", /*threshold for zero confidence*/1e-5);
|
|
|
|
|
|
|
|
// Output size of masks is NxCxHxW where
|
|
|
|
// N - number of detected boxes
|
|
|
|
// C - number of classes (excluding background)
|
|
|
|
// HxW - segmentation shape
|
|
|
|
const int numDetections = outDetections.size[2];
|
|
|
|
|
|
|
|
int masksSize[] = {1, numDetections, outMasks.size[2], outMasks.size[3]};
|
|
|
|
Mat masks(4, &masksSize[0], CV_32F);
|
|
|
|
|
|
|
|
std::vector<cv::Range> srcRanges(4, cv::Range::all());
|
|
|
|
std::vector<cv::Range> dstRanges(4, cv::Range::all());
|
|
|
|
|
|
|
|
outDetections = outDetections.reshape(1, outDetections.total() / 7);
|
|
|
|
for (int i = 0; i < numDetections; ++i)
|
|
|
|
{
|
|
|
|
// Get a class id for this bounding box and copy mask only for that class.
|
|
|
|
int classId = static_cast<int>(outDetections.at<float>(i, 1));
|
|
|
|
srcRanges[0] = dstRanges[1] = cv::Range(i, i + 1);
|
|
|
|
srcRanges[1] = cv::Range(classId, classId + 1);
|
|
|
|
outMasks(srcRanges).copyTo(masks(dstRanges));
|
|
|
|
}
|
|
|
|
cv::Range topRefMasks[] = {Range::all(), Range(0, numDetections), Range::all(), Range::all()};
|
|
|
|
normAssert(masks, refMasks(&topRefMasks[0]));
|
|
|
|
}
|
|
|
|
|
|
|
|
}
|