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1273 lines
47 KiB
C++
1273 lines
47 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) 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, reduce_mean)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
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runTensorFlowNet("global_pool_by_axis");
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}
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TEST_P(Test_TensorFlow_layers, reduce_sum)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
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runTensorFlowNet("sum_pool_by_axis");
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}
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TEST_P(Test_TensorFlow_layers, conv_single_conv)
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{
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runTensorFlowNet("single_conv");
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}
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TEST_P(Test_TensorFlow_layers, conv_atrous_conv2d_valid)
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{
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runTensorFlowNet("atrous_conv2d_valid");
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}
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TEST_P(Test_TensorFlow_layers, conv_atrous_conv2d_same)
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{
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runTensorFlowNet("atrous_conv2d_same");
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}
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TEST_P(Test_TensorFlow_layers, conv_depthwise_conv2d)
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{
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runTensorFlowNet("depthwise_conv2d");
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}
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TEST_P(Test_TensorFlow_layers, conv_keras_atrous_conv2d_same)
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{
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runTensorFlowNet("keras_atrous_conv2d_same");
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}
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TEST_P(Test_TensorFlow_layers, conv_pool_nchw)
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{
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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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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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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if (backend == DNN_BACKEND_CUDA)
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{
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// ok
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}
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else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
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else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
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else if (target != DNN_TARGET_CPU)
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throw SkipTestException("Only 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("mirror_pad");
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019020000)
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if (target == DNN_TARGET_MYRIAD)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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}
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#endif
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runTensorFlowNet("keras_pad_concat");
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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, channel_broadcast)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
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runTensorFlowNet("channel_broadcast");
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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, concat_3d)
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{
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if (backend == DNN_BACKEND_OPENCV && 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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}
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if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
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backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
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runTensorFlowNet("concat_3d");
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}
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TEST_P(Test_TensorFlow_layers, batch_norm_1)
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{
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runTensorFlowNet("batch_norm");
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}
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TEST_P(Test_TensorFlow_layers, batch_norm_2)
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{
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runTensorFlowNet("batch_norm", false, 0.0, 0.0, true);
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}
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TEST_P(Test_TensorFlow_layers, batch_norm_3)
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{
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runTensorFlowNet("fused_batch_norm");
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}
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TEST_P(Test_TensorFlow_layers, batch_norm_4)
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{
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runTensorFlowNet("fused_batch_norm", false, 0.0, 0.0, true);
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}
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TEST_P(Test_TensorFlow_layers, batch_norm_5)
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{
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runTensorFlowNet("batch_norm_text", true);
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}
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TEST_P(Test_TensorFlow_layers, batch_norm_6)
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{
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runTensorFlowNet("batch_norm_text", true, 0.0, 0.0, true);
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}
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TEST_P(Test_TensorFlow_layers, batch_norm_7)
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{
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runTensorFlowNet("unfused_batch_norm");
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}
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TEST_P(Test_TensorFlow_layers, batch_norm_8)
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{
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runTensorFlowNet("fused_batch_norm_no_gamma");
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}
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TEST_P(Test_TensorFlow_layers, batch_norm_9)
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{
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runTensorFlowNet("unfused_batch_norm_no_gamma");
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}
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TEST_P(Test_TensorFlow_layers, batch_norm_10)
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{
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runTensorFlowNet("mvn_batch_norm");
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}
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TEST_P(Test_TensorFlow_layers, batch_norm_11)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
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runTensorFlowNet("mvn_batch_norm_1x1");
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}
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TEST_P(Test_TensorFlow_layers, batch_norm_12)
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{
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runTensorFlowNet("switch_identity");
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}
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TEST_P(Test_TensorFlow_layers, batch_norm_13)
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{
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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_NN_BUILDER_2019 && 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, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
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if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
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if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
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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_NN_BUILDER_2019)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
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// Output values range: [-40.0597, 207.827]
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double l1 = default_l1, lInf = default_lInf;
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if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
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{
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l1 = 0.041;
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lInf = 0.33;
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}
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else if (target == DNN_TARGET_CUDA_FP16)
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{
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l1 = 0.005;
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lInf = 0.33;
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}
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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_max_pool_even)
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{
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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runTensorFlowNet("max_pool_even");
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}
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TEST_P(Test_TensorFlow_layers, pooling_max_pool_odd_valid)
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{
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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runTensorFlowNet("max_pool_odd_valid");
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}
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TEST_P(Test_TensorFlow_layers, pooling_max_pool_odd_same)
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{
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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runTensorFlowNet("max_pool_odd_same");
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}
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TEST_P(Test_TensorFlow_layers, pooling_reduce_mean)
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{
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runTensorFlowNet("reduce_mean"); // an average pooling over all spatial dimensions.
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}
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TEST_P(Test_TensorFlow_layers, pooling_reduce_sum)
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{
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runTensorFlowNet("reduce_sum"); // a SUM pooling over all spatial dimensions.
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}
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TEST_P(Test_TensorFlow_layers, max_pool_grad)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
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runTensorFlowNet("max_pool_grad");
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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 (target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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}
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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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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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if (backend == DNN_BACKEND_CUDA)
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{
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// ok
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}
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else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
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else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
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else if (target != DNN_TARGET_CPU)
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throw SkipTestException("Only 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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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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if (backend == DNN_BACKEND_CUDA)
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{
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// ok
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}
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else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
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else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
|
|
else if (target != DNN_TARGET_CPU)
|
|
throw SkipTestException("Only CPU is supported");
|
|
|
|
runTensorFlowNet("ave_pool3d");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, deconvolution)
|
|
{
|
|
if (backend == DNN_BACKEND_CUDA)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
|
|
runTensorFlowNet("deconvolution");
|
|
runTensorFlowNet("deconvolution_same");
|
|
runTensorFlowNet("deconvolution_stride_2_same");
|
|
runTensorFlowNet("deconvolution_adj_pad_valid");
|
|
runTensorFlowNet("deconvolution_adj_pad_same");
|
|
runTensorFlowNet("keras_deconv_valid");
|
|
runTensorFlowNet("keras_deconv_same");
|
|
runTensorFlowNet("keras_deconv_same_v2");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, matmul)
|
|
{
|
|
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
|
|
runTensorFlowNet("matmul");
|
|
runTensorFlowNet("nhwc_transpose_reshape_matmul");
|
|
// Reference output values are in range [-5.688, 4.484]
|
|
double l1 = target == DNN_TARGET_MYRIAD ? 6.1e-3 : default_l1;
|
|
runTensorFlowNet("nhwc_reshape_matmul", false, l1);
|
|
runTensorFlowNet("matmul_layout");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, reshape)
|
|
{
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
|
|
runTensorFlowNet("shift_reshape_no_reorder");
|
|
runTensorFlowNet("reshape_no_reorder");
|
|
runTensorFlowNet("reshape_reduce");
|
|
runTensorFlowNet("reshape_as_shape");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, flatten)
|
|
{
|
|
#if defined(INF_ENGINE_RELEASE)
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
|
|
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2
|
|
)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
#endif
|
|
|
|
runTensorFlowNet("flatten", true);
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, unfused_flatten)
|
|
{
|
|
runTensorFlowNet("unfused_flatten");
|
|
runTensorFlowNet("unfused_flatten_unknown_batch");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, leaky_relu)
|
|
{
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
|
#endif
|
|
runTensorFlowNet("leaky_relu_order1");
|
|
runTensorFlowNet("leaky_relu_order2");
|
|
runTensorFlowNet("leaky_relu_order3");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, l2_normalize)
|
|
{
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
|
|
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
|
|
)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
|
#endif
|
|
|
|
runTensorFlowNet("l2_normalize");
|
|
}
|
|
|
|
// TODO: fix it and add to l2_normalize
|
|
TEST_P(Test_TensorFlow_layers, l2_normalize_3d)
|
|
{
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
|
|
&& (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,
|
|
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
|
#endif
|
|
#if defined(INF_ENGINE_RELEASE)
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
|
|
#endif
|
|
|
|
runTensorFlowNet("l2_normalize_3d");
|
|
}
|
|
|
|
class Test_TensorFlow_nets : public DNNTestLayer {};
|
|
|
|
TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
|
|
{
|
|
#if defined(INF_ENGINE_RELEASE)
|
|
if (target == DNN_TARGET_MYRIAD)
|
|
{
|
|
#if INF_ENGINE_VER_MAJOR_GE(2019020000)
|
|
if (getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X,
|
|
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ?
|
|
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER :
|
|
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH,
|
|
CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
|
#endif
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
|
}
|
|
#endif
|
|
|
|
checkBackend();
|
|
std::string imgPath = findDataFile("dnn/street.png");
|
|
std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt");
|
|
std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false);
|
|
|
|
Mat inp;
|
|
resize(imread(imgPath), inp, Size(300, 300));
|
|
inp = blobFromImage(inp, 1.0f / 127.5, Size(), Scalar(127.5, 127.5, 127.5), true);
|
|
|
|
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco.detection_out.npy"));
|
|
|
|
Net net = readNetFromTensorflow(netPath, netConfig);
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
net.setInput(inp);
|
|
Mat out = net.forward();
|
|
|
|
double scoreDiff = default_l1, iouDiff = default_lInf;
|
|
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
|
|
{
|
|
scoreDiff = 0.0043;
|
|
iouDiff = 0.037;
|
|
}
|
|
else if (target == DNN_TARGET_CUDA_FP16)
|
|
{
|
|
iouDiff = 0.04;
|
|
}
|
|
normAssertDetections(ref, out, "", 0.2, scoreDiff, iouDiff);
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2019010000
|
|
expectNoFallbacksFromIE(net);
|
|
#endif
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
|
|
{
|
|
applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD &&
|
|
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
|
#endif
|
|
|
|
checkBackend();
|
|
Mat img = imread(findDataFile("dnn/street.png"));
|
|
std::string proto = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pbtxt");
|
|
std::string model = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pb", false);
|
|
|
|
Net net = readNetFromTensorflow(model, proto);
|
|
Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
|
|
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
net.setInput(blob);
|
|
// Output has shape 1x1xNx7 where N - number of detections.
|
|
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
|
|
Mat out = net.forward();
|
|
Mat ref = (Mat_<float>(5, 7) << 0, 1, 0.90176028, 0.19872092, 0.36311883, 0.26461923, 0.63498729,
|
|
0, 3, 0.93569964, 0.64865261, 0.45906419, 0.80675775, 0.65708131,
|
|
0, 3, 0.75838411, 0.44668293, 0.45907149, 0.49459291, 0.52197015,
|
|
0, 10, 0.95932811, 0.38349164, 0.32528657, 0.40387636, 0.39165527,
|
|
0, 10, 0.93973452, 0.66561931, 0.37841269, 0.68074018, 0.42907384);
|
|
|
|
double scoreDiff = default_l1, iouDiff = default_lInf;
|
|
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
|
|
{
|
|
scoreDiff = 0.0097;
|
|
iouDiff = 0.09;
|
|
}
|
|
else if (target == DNN_TARGET_CUDA_FP16)
|
|
{
|
|
scoreDiff = 6e-3;
|
|
iouDiff = 0.05;
|
|
}
|
|
normAssertDetections(ref, out, "", 0.5, scoreDiff, iouDiff);
|
|
expectNoFallbacksFromIE(net);
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD)
|
|
{
|
|
checkBackend();
|
|
std::string proto = findDataFile("dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt");
|
|
std::string model = findDataFile("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", false);
|
|
|
|
Net net = readNetFromTensorflow(model, proto);
|
|
Mat img = imread(findDataFile("dnn/dog416.png"));
|
|
Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
|
|
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
net.setInput(blob);
|
|
Mat out = net.forward();
|
|
|
|
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco_2017_11_17.detection_out.npy"));
|
|
float scoreDiff = 1.5e-5, iouDiff = 1e-3;
|
|
float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.35 : 0.3;
|
|
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
|
|
{
|
|
scoreDiff = 0.011;
|
|
iouDiff = 0.012;
|
|
}
|
|
else if (target == DNN_TARGET_CUDA_FP16)
|
|
{
|
|
scoreDiff = 0.006;
|
|
iouDiff = 0.01;
|
|
}
|
|
#if defined(INF_ENGINE_RELEASE)
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD &&
|
|
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
|
|
{
|
|
scoreDiff = 0.061;
|
|
iouDiff = 0.12;
|
|
detectionConfThresh = 0.36;
|
|
}
|
|
#endif
|
|
normAssertDetections(ref, out, "", detectionConfThresh, scoreDiff, iouDiff);
|
|
expectNoFallbacksFromIE(net);
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_nets, Faster_RCNN)
|
|
{
|
|
// FIXIT split test
|
|
applyTestTag(
|
|
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
|
|
CV_TEST_TAG_LONG,
|
|
CV_TEST_TAG_DEBUG_VERYLONG
|
|
);
|
|
static std::string names[] = {"faster_rcnn_inception_v2_coco_2018_01_28",
|
|
"faster_rcnn_resnet50_coco_2018_01_28"};
|
|
|
|
#ifdef INF_ENGINE_RELEASE
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
|
|
(INF_ENGINE_VER_MAJOR_LT(2019020000) || target != DNN_TARGET_CPU))
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
|
|
|
if (INF_ENGINE_VER_MAJOR_GT(2019030000) &&
|
|
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
|
|
#endif
|
|
// segfault: inference-engine/thirdparty/clDNN/src/gpu/detection_output_cpu.cpp:111:
|
|
// Assertion `prior_height > 0' failed.
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
|
|
|
|
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
|
|
|
|
if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
|
|
|
|
checkBackend();
|
|
|
|
double scoresDiff = backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ? 2.9e-5 : 1e-5;
|
|
double iouDiff = 1e-4;
|
|
if (target == DNN_TARGET_CUDA)
|
|
{
|
|
// for faster_rcnn_resnet50_coco_2018_01_28
|
|
scoresDiff = 0.06;
|
|
iouDiff = 0.08;
|
|
}
|
|
for (int i = 0; i < 2; ++i)
|
|
{
|
|
std::string proto = findDataFile("dnn/" + names[i] + ".pbtxt");
|
|
std::string model = findDataFile("dnn/" + names[i] + ".pb", false);
|
|
|
|
Net net = readNetFromTensorflow(model, proto);
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
Mat img = imread(findDataFile("dnn/dog416.png"));
|
|
Mat blob = blobFromImage(img, 1.0f, Size(800, 600), Scalar(), true, false);
|
|
|
|
net.setInput(blob);
|
|
Mat out = net.forward();
|
|
|
|
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/" + names[i] + ".detection_out.npy"));
|
|
normAssertDetections(ref, out, names[i].c_str(), 0.3, scoresDiff, iouDiff);
|
|
}
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
|
|
{
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (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,
|
|
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
|
#endif
|
|
checkBackend();
|
|
std::string proto = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pbtxt");
|
|
std::string model = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pb", false);
|
|
|
|
Net net = readNetFromTensorflow(model, proto);
|
|
Mat img = imread(findDataFile("dnn/dog416.png"));
|
|
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_ppn_coco.detection_out.npy"));
|
|
Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
|
|
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
net.setInput(blob);
|
|
Mat out = net.forward();
|
|
|
|
double scoreDiff = 1.1e-5, iouDiff = default_lInf;
|
|
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
|
|
{
|
|
scoreDiff = 0.048;
|
|
iouDiff = 0.058;
|
|
}
|
|
else if (target == DNN_TARGET_CUDA_FP16)
|
|
{
|
|
scoreDiff = 0.006;
|
|
iouDiff = 0.05;
|
|
}
|
|
normAssertDetections(ref, out, "", 0.45, scoreDiff, iouDiff);
|
|
expectNoFallbacksFromIE(net);
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
|
|
{
|
|
checkBackend();
|
|
std::string proto = findDataFile("dnn/opencv_face_detector.pbtxt");
|
|
std::string model = findDataFile("dnn/opencv_face_detector_uint8.pb", false);
|
|
|
|
Net net = readNetFromTensorflow(model, proto);
|
|
Mat img = imread(findDataFile("gpu/lbpcascade/er.png"));
|
|
Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
|
|
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
net.setInput(blob);
|
|
// Output has shape 1x1xNx7 where N - number of detections.
|
|
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
|
|
Mat out = net.forward();
|
|
|
|
// References are from test for Caffe model.
|
|
Mat ref = (Mat_<float>(6, 7) << 0, 1, 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631,
|
|
0, 1, 0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168,
|
|
0, 1, 0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290,
|
|
0, 1, 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477,
|
|
0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
|
|
0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
|
|
double scoreDiff = 3.4e-3, iouDiff = 1e-2;
|
|
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
|
|
{
|
|
scoreDiff = 4e-3;
|
|
iouDiff = 0.024;
|
|
}
|
|
else if (target == DNN_TARGET_CUDA_FP16)
|
|
{
|
|
scoreDiff = 4e-3;
|
|
iouDiff = 0.02;
|
|
}
|
|
normAssertDetections(ref, out, "", 0.9, scoreDiff, iouDiff);
|
|
expectNoFallbacksFromIE(net);
|
|
}
|
|
|
|
// inp = cv.imread('opencv_extra/testdata/cv/ximgproc/sources/08.png')
|
|
// inp = inp[:,:,[2, 1, 0]].astype(np.float32).reshape(1, 512, 512, 3)
|
|
// outs = sess.run([sess.graph.get_tensor_by_name('feature_fusion/Conv_7/Sigmoid:0'),
|
|
// sess.graph.get_tensor_by_name('feature_fusion/concat_3:0')],
|
|
// feed_dict={'input_images:0': inp})
|
|
// scores = np.ascontiguousarray(outs[0].transpose(0, 3, 1, 2))
|
|
// geometry = np.ascontiguousarray(outs[1].transpose(0, 3, 1, 2))
|
|
// np.save('east_text_detection.scores.npy', scores)
|
|
// np.save('east_text_detection.geometry.npy', geometry)
|
|
TEST_P(Test_TensorFlow_nets, EAST_text_detection)
|
|
{
|
|
applyTestTag(
|
|
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
|
|
CV_TEST_TAG_DEBUG_LONG
|
|
);
|
|
|
|
#if defined(INF_ENGINE_RELEASE)
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
|
|
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL_FP16 &&
|
|
(INF_ENGINE_VER_MAJOR_EQ(2019020000) || INF_ENGINE_VER_MAJOR_GE(2020010000))
|
|
)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
|
#endif
|
|
|
|
checkBackend();
|
|
|
|
std::string netPath = findDataFile("dnn/frozen_east_text_detection.pb", false);
|
|
std::string imgPath = findDataFile("cv/ximgproc/sources/08.png");
|
|
std::string refScoresPath = findDataFile("dnn/east_text_detection.scores.npy");
|
|
std::string refGeometryPath = findDataFile("dnn/east_text_detection.geometry.npy");
|
|
|
|
Net net = readNet(netPath);
|
|
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
Mat img = imread(imgPath);
|
|
Mat inp = blobFromImage(img, 1.0, Size(), Scalar(123.68, 116.78, 103.94), true, false);
|
|
net.setInput(inp);
|
|
|
|
std::vector<Mat> outs;
|
|
std::vector<String> outNames(2);
|
|
outNames[0] = "feature_fusion/Conv_7/Sigmoid";
|
|
outNames[1] = "feature_fusion/concat_3";
|
|
net.forward(outs, outNames);
|
|
|
|
Mat scores = outs[0];
|
|
Mat geometry = outs[1];
|
|
|
|
// Scores are in range [0, 1]. Geometry values are in range [-0.23, 290]
|
|
double l1_scores = default_l1, lInf_scores = default_lInf;
|
|
double l1_geometry = default_l1, lInf_geometry = default_lInf;
|
|
if (target == DNN_TARGET_OPENCL_FP16)
|
|
{
|
|
lInf_scores = backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ? 0.16 : 0.11;
|
|
l1_geometry = 0.28; lInf_geometry = 5.94;
|
|
}
|
|
else if (target == DNN_TARGET_MYRIAD)
|
|
{
|
|
lInf_scores = 0.41;
|
|
l1_geometry = 0.28; lInf_geometry = 5.94;
|
|
}
|
|
else if (target == DNN_TARGET_CUDA_FP16)
|
|
{
|
|
lInf_scores = 0.1;
|
|
l1_geometry = 0.3; lInf_geometry = 7;
|
|
}
|
|
else
|
|
{
|
|
l1_geometry = 1e-4, lInf_geometry = 3e-3;
|
|
}
|
|
normAssert(scores, blobFromNPY(refScoresPath), "scores", l1_scores, lInf_scores);
|
|
normAssert(geometry, blobFromNPY(refGeometryPath), "geometry", l1_geometry, lInf_geometry);
|
|
expectNoFallbacksFromIE(net);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_nets, dnnBackendsAndTargets());
|
|
|
|
|
|
TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_single_conv)
|
|
{
|
|
float l1 = 0.00078, lInf = 0.012;
|
|
runTensorFlowNet("fp16_single_conv", false, l1, lInf);
|
|
}
|
|
TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_max_pool_odd_same)
|
|
{
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
|
#endif
|
|
float l1 = 0.00078, lInf = 0.012;
|
|
runTensorFlowNet("fp16_max_pool_odd_same", false, l1, lInf);
|
|
}
|
|
TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_eltwise_add_mul)
|
|
{
|
|
float l1 = 0.00078, lInf = 0.012;
|
|
runTensorFlowNet("fp16_eltwise_add_mul", false, l1, lInf);
|
|
}
|
|
TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_pad_and_concat)
|
|
{
|
|
float l1 = 0.00078, lInf = 0.012;
|
|
runTensorFlowNet("fp16_pad_and_concat", false, l1, lInf);
|
|
}
|
|
TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_padding_valid)
|
|
{
|
|
float l1 = 0.00078, lInf = 0.012;
|
|
runTensorFlowNet("fp16_padding_valid", false, l1, lInf);
|
|
}
|
|
TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_max_pool_even)
|
|
{
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
|
#endif
|
|
float l1 = 0.00078, lInf = 0.012;
|
|
// Reference output values are in range [0.0889, 1.651]
|
|
runTensorFlowNet("fp16_max_pool_even", false, (target == DNN_TARGET_MYRIAD) ? 0.003 : l1, lInf);
|
|
}
|
|
TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_deconvolution)
|
|
{
|
|
float l1 = 0.00078, lInf = 0.012;
|
|
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);
|
|
}
|
|
TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_max_pool_odd_valid)
|
|
{
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
|
#endif
|
|
float l1 = 0.00078, lInf = 0.012;
|
|
if (target == DNN_TARGET_MYRIAD) {
|
|
l1 = 0.0041;
|
|
lInf = 0.024;
|
|
}
|
|
// 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_CUDA)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); /* not supported */
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
|
|
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_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
|
|
runTensorFlowNet("split");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, split_equals)
|
|
{
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
|
|
runTensorFlowNet("split_equals");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, resize_nearest_neighbor)
|
|
{
|
|
runTensorFlowNet("resize_nearest_neighbor");
|
|
runTensorFlowNet("keras_upsampling2d");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, fused_resize_conv)
|
|
{
|
|
runTensorFlowNet("fused_resize_conv");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, slice)
|
|
{
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
|
|
(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,
|
|
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
double l1 = target == DNN_TARGET_MYRIAD ? 4.9e-3 : default_l1;
|
|
runTensorFlowNet("crop2d", false, l1);
|
|
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_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD &&
|
|
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2
|
|
)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
#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_NN_BUILDER_2019)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
|
|
runTensorFlowNet("subpixel");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, keras_mobilenet_head)
|
|
{
|
|
runTensorFlowNet("keras_mobilenet_head");
|
|
runTensorFlowNet("keras_learning_phase");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, resize_bilinear)
|
|
{
|
|
runTensorFlowNet("resize_bilinear");
|
|
runTensorFlowNet("resize_bilinear_factor");
|
|
runTensorFlowNet("resize_bilinear_down");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, tf2_dense)
|
|
{
|
|
runTensorFlowNet("tf2_dense");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, clip_by_value)
|
|
{
|
|
runTensorFlowNet("clip_by_value");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, tf2_prelu)
|
|
{
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
|
|
if (backend == DNN_BACKEND_CUDA)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported; only across channels is supported
|
|
runTensorFlowNet("tf2_prelu");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, tf2_permute_nhwc_ncwh)
|
|
{
|
|
runTensorFlowNet("tf2_permute_nhwc_ncwh");
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_layers, squeeze)
|
|
{
|
|
#if defined(INF_ENGINE_RELEASE)
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
|
|
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2
|
|
)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
#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_P(Test_TensorFlow_nets, Mask_RCNN)
|
|
{
|
|
static const double kMaskThreshold = 0.5;
|
|
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
|
|
if (target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
|
|
|
|
if (target == DNN_TARGET_CUDA_FP16)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
|
|
|
|
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(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
net.setInput(blob);
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|
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// Mask-RCNN predicts bounding boxes and segmentation masks.
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std::vector<String> outNames(2);
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outNames[0] = "detection_out_final";
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outNames[1] = "detection_masks";
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|
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std::vector<Mat> outs;
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net.forward(outs, outNames);
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|
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Mat outDetections = outs[0];
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Mat outMasks = outs[1];
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|
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double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.019 : 2e-5;
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double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.018 : default_lInf;
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normAssertDetections(refDetections, outDetections, "", /*threshold for zero confidence*/1e-5, scoreDiff, iouDiff);
|
|
|
|
// Output size of masks is NxCxHxW where
|
|
// N - number of detected boxes
|
|
// C - number of classes (excluding background)
|
|
// HxW - segmentation shape
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|
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()};
|
|
refMasks = refMasks(&topRefMasks[0]);
|
|
|
|
// make binary masks
|
|
cv::threshold(masks.reshape(1, 1), masks, kMaskThreshold, 1, THRESH_BINARY);
|
|
cv::threshold(refMasks.reshape(1, 1), refMasks, kMaskThreshold, 1, THRESH_BINARY);
|
|
|
|
double inter = cv::countNonZero(masks & refMasks);
|
|
double area = cv::countNonZero(masks | refMasks);
|
|
EXPECT_GE(inter / area, 0.99);
|
|
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
expectNoFallbacks(net);
|
|
}
|
|
|
|
TEST_P(Test_TensorFlow_nets, EfficientDet)
|
|
{
|
|
if (target != DNN_TARGET_CPU)
|
|
{
|
|
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
|
|
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
|
|
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
|
|
}
|
|
checkBackend();
|
|
std::string proto = findDataFile("dnn/efficientdet-d0.pbtxt");
|
|
std::string model = findDataFile("dnn/efficientdet-d0.pb", false);
|
|
|
|
Net net = readNetFromTensorflow(model, proto);
|
|
Mat img = imread(findDataFile("dnn/dog416.png"));
|
|
Mat blob = blobFromImage(img, 1.0/255, Size(512, 512), Scalar(123.675, 116.28, 103.53));
|
|
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
net.setInput(blob);
|
|
// Output has shape 1x1xNx7 where N - number of detections.
|
|
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
|
|
Mat out = net.forward();
|
|
|
|
// References are from test for TensorFlow model.
|
|
Mat ref = (Mat_<float>(3, 7) << 0, 1, 0.8437444, 0.153996080160141, 0.20534580945968628, 0.7463544607162476, 0.7414066195487976,
|
|
0, 17, 0.8245924, 0.16657517850399017, 0.3996818959712982, 0.4111558794975281, 0.9306337833404541,
|
|
0, 7, 0.8039304, 0.6118435263633728, 0.13175517320632935, 0.9065558314323425, 0.2943994700908661);
|
|
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 4e-3 : 1e-5;
|
|
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 2e-3 : 1e-4;
|
|
if (target == DNN_TARGET_CUDA_FP16)
|
|
{
|
|
scoreDiff = 0.002;
|
|
iouDiff = 0.004;
|
|
}
|
|
normAssertDetections(ref, out, "", 0.5, scoreDiff, iouDiff);
|
|
expectNoFallbacksFromIE(net);
|
|
}
|
|
|
|
}
|