// This file is part of OpenCV project. // It is subject to the license terms in the LICENSE file found in the top-level directory // of this distribution and at http://opencv.org/license.html. // Copyright (C) 2017-2019, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. /* Test for Tensorflow models loading */ #include "test_precomp.hpp" #include "npy_blob.hpp" #include // CV_DNN_REGISTER_LAYER_CLASS #include namespace opencv_test { using namespace cv; using namespace cv::dnn; template static std::string _tf(TString filename) { return (getOpenCVExtraDir() + "/dnn/") + filename; } TEST(Test_TensorFlow, read_inception) { Net net; { const string model = findDataFile("dnn/tensorflow_inception_graph.pb", false); net = readNetFromTensorflow(model); ASSERT_FALSE(net.empty()); } net.setPreferableBackend(DNN_BACKEND_OPENCV); Mat sample = imread(_tf("grace_hopper_227.png")); ASSERT_TRUE(!sample.empty()); Mat input; resize(sample, input, Size(224, 224)); input -= Scalar::all(117); // mean sub Mat inputBlob = blobFromImage(input); net.setInput(inputBlob, "input"); Mat out = net.forward("softmax2"); std::cout << out.dims << std::endl; } TEST(Test_TensorFlow, inception_accuracy) { Net net; { const string model = findDataFile("dnn/tensorflow_inception_graph.pb", false); net = readNetFromTensorflow(model); ASSERT_FALSE(net.empty()); } net.setPreferableBackend(DNN_BACKEND_OPENCV); Mat sample = imread(_tf("grace_hopper_227.png")); ASSERT_TRUE(!sample.empty()); Mat inputBlob = blobFromImage(sample, 1.0, Size(224, 224), Scalar(), /*swapRB*/true); net.setInput(inputBlob, "input"); Mat out = net.forward("softmax2"); Mat ref = blobFromNPY(_tf("tf_inception_prob.npy")); normAssert(ref, out); } static std::string path(const std::string& file) { return findDataFile("dnn/tensorflow/" + file); } class Test_TensorFlow_layers : public DNNTestLayer { public: void runTensorFlowNet(const std::string& prefix, bool hasText = false, double l1 = 0.0, double lInf = 0.0, bool memoryLoad = false, const std::string& groupPrefix = "") { if (cvtest::debugLevel > 0) { std::cout << prefix << groupPrefix << std::endl; } std::string netPath = path(prefix + groupPrefix + "_net.pb"); std::string netConfig = (hasText ? path(prefix + groupPrefix + "_net.pbtxt") : ""); std::string inpPath = path(prefix + "_in.npy"); std::string outPath = path(prefix + groupPrefix + "_out.npy"); cv::Mat input = blobFromNPY(inpPath); cv::Mat ref = blobFromNPY(outPath); checkBackend(&input, &ref); Net net; if (memoryLoad) { // Load files into a memory buffers std::vector dataModel; readFileContent(netPath, dataModel); std::vector dataConfig; if (hasText) { readFileContent(netConfig, dataConfig); } net = readNetFromTensorflow(dataModel.data(), dataModel.size(), dataConfig.data(), dataConfig.size()); } else net = readNetFromTensorflow(netPath, netConfig); ASSERT_FALSE(net.empty()); net.setPreferableBackend(backend); net.setPreferableTarget(target); net.setInput(input); cv::Mat output = net.forward(); normAssert(ref, output, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf); if (cvtest::debugLevel > 0 || HasFailure()) { std::cout << "input: " << input.size << std::endl; std::cout << input.reshape(1, 1) << std::endl; std::cout << "ref " << ref.size << std::endl; std::cout << ref.reshape(1, 1) << std::endl; std::cout << "output: " << output.size << std::endl; std::cout << output.reshape(1, 1) << std::endl; } } }; TEST_P(Test_TensorFlow_layers, reduce_mean) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); runTensorFlowNet("global_pool_by_axis"); } TEST_P(Test_TensorFlow_layers, reduce_max) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); runTensorFlowNet("max_pool_by_axis", false, 0.0f, 0.0f); } TEST_P(Test_TensorFlow_layers, reduce_sum) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); runTensorFlowNet("sum_pool_by_axis"); } TEST_P(Test_TensorFlow_layers, reduce_max_channel) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) // incorrect result applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif runTensorFlowNet("reduce_max_channel", false, 0.0f, 0.0f); } TEST_P(Test_TensorFlow_layers, reduce_sum_channel) { runTensorFlowNet("reduce_sum_channel"); } TEST_P(Test_TensorFlow_layers, reduce_max_channel_keep_dims) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) // incorrect result applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif runTensorFlowNet("reduce_max_channel", false, 0.0, 0.0, false, "_keep_dims"); } TEST_P(Test_TensorFlow_layers, reduce_sum_channel_keep_dims) { runTensorFlowNet("reduce_sum_channel", false, 0.0, 0.0, false, "_keep_dims"); } TEST_P(Test_TensorFlow_layers, ArgLayer) { if (backend != DNN_BACKEND_OPENCV || target != DNN_TARGET_CPU) throw SkipTestException("Only CPU is supported"); // FIXIT use tags runTensorFlowNet("argmax"); runTensorFlowNet("argmin"); } TEST_P(Test_TensorFlow_layers, conv_single_conv) { runTensorFlowNet("single_conv"); } TEST_P(Test_TensorFlow_layers, conv_atrous_conv2d_valid) { runTensorFlowNet("atrous_conv2d_valid"); } TEST_P(Test_TensorFlow_layers, conv_atrous_conv2d_same) { runTensorFlowNet("atrous_conv2d_same"); } TEST_P(Test_TensorFlow_layers, conv_depthwise_conv2d) { runTensorFlowNet("depthwise_conv2d"); } TEST_P(Test_TensorFlow_layers, conv_keras_atrous_conv2d_same) { runTensorFlowNet("keras_atrous_conv2d_same"); } TEST_P(Test_TensorFlow_layers, conv_pool_nchw) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // [ GENERAL_ERROR ] AssertionFailed: !expired() 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); #elif 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 runTensorFlowNet("conv_pool_nchw"); } TEST_P(Test_TensorFlow_layers, Convolution3D) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported #endif runTensorFlowNet("conv3d"); } TEST_P(Test_TensorFlow_layers, padding) { runTensorFlowNet("padding_valid"); runTensorFlowNet("spatial_padding"); runTensorFlowNet("mirror_pad"); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019020000) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (target == DNN_TARGET_MYRIAD) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); 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 runTensorFlowNet("keras_pad_concat"); } TEST_P(Test_TensorFlow_layers, padding_asymmetric_1) { runTensorFlowNet("conv2d_asymmetric_pads_nchw"); } TEST_P(Test_TensorFlow_layers, padding_asymmetric_2) { runTensorFlowNet("conv2d_asymmetric_pads_nhwc"); } TEST_P(Test_TensorFlow_layers, padding_asymmetric_3) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) // Exception: Unsupported pad value applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) // Exception: Unsupported pad value applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif runTensorFlowNet("max_pool2d_asymmetric_pads_nchw"); } TEST_P(Test_TensorFlow_layers, padding_asymmetric_4) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // Unsupported pad value 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); // accuracy if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) // Exception: Unsupported pad value applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) // Exception: Unsupported pad value applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) // Exception: Unsupported pad value applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif #endif runTensorFlowNet("max_pool2d_asymmetric_pads_nhwc"); } TEST_P(Test_TensorFlow_layers, padding_asymmetric_5) { runTensorFlowNet("conv2d_backprop_input_asymmetric_pads_nchw"); } TEST_P(Test_TensorFlow_layers, padding_asymmetric_6) { runTensorFlowNet("conv2d_backprop_input_asymmetric_pads_nhwc"); } TEST_P(Test_TensorFlow_layers, padding_same) { // Reference output values are in range [0.0006, 2.798] runTensorFlowNet("padding_same"); } TEST_P(Test_TensorFlow_layers, eltwise) { runTensorFlowNet("eltwise_add_mul"); runTensorFlowNet("eltwise_sub"); } TEST_P(Test_TensorFlow_layers, eltwise_add_vec) { runTensorFlowNet("eltwise_add_vec"); } TEST_P(Test_TensorFlow_layers, eltwise_mul_vec) { runTensorFlowNet("eltwise_mul_vec"); } TEST_P(Test_TensorFlow_layers, tf_reshape_nhwc) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); runTensorFlowNet("tf_reshape_nhwc"); } TEST_P(Test_TensorFlow_layers, channel_broadcast) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); runTensorFlowNet("channel_broadcast"); } TEST_P(Test_TensorFlow_layers, pad_and_concat) { runTensorFlowNet("pad_and_concat"); } TEST_P(Test_TensorFlow_layers, concat_axis_1) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // IE Exception: Ngraph operation Transpose with name Flatten_1/flatten/Reshape/nhwc has dynamic output shape on 0 port, but CPU plug-in supports only static shape if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); #endif #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception 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); // exception #endif runTensorFlowNet("concat_axis_1"); } TEST_P(Test_TensorFlow_layers, concat_3d) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU) { if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16); if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL); } if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH || 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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); #endif runTensorFlowNet("concat_3d"); } TEST_P(Test_TensorFlow_layers, batch_norm_1) { runTensorFlowNet("batch_norm"); } TEST_P(Test_TensorFlow_layers, batch_norm_2) { runTensorFlowNet("batch_norm", false, 0.0, 0.0, true); } TEST_P(Test_TensorFlow_layers, batch_norm_3) { runTensorFlowNet("fused_batch_norm"); } TEST_P(Test_TensorFlow_layers, batch_norm_4) { runTensorFlowNet("fused_batch_norm", false, 0.0, 0.0, true); } TEST_P(Test_TensorFlow_layers, batch_norm_5) { runTensorFlowNet("batch_norm_text", true); } TEST_P(Test_TensorFlow_layers, batch_norm_6) { runTensorFlowNet("batch_norm_text", true, 0.0, 0.0, true); } TEST_P(Test_TensorFlow_layers, batch_norm_7) { runTensorFlowNet("unfused_batch_norm"); } TEST_P(Test_TensorFlow_layers, batch_norm_8) { runTensorFlowNet("fused_batch_norm_no_gamma"); } TEST_P(Test_TensorFlow_layers, batch_norm_9) { runTensorFlowNet("unfused_batch_norm_no_gamma"); } TEST_P(Test_TensorFlow_layers, batch_norm_10) { runTensorFlowNet("mvn_batch_norm"); } TEST_P(Test_TensorFlow_layers, batch_norm_11) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // nan #endif 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); runTensorFlowNet("mvn_batch_norm_1x1"); } TEST_P(Test_TensorFlow_layers, batch_norm_12) { runTensorFlowNet("switch_identity"); } TEST_P(Test_TensorFlow_layers, batch_norm_13) { runTensorFlowNet("keras_batch_norm_training"); } TEST_P(Test_TensorFlow_layers, batch_norm3D) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) { if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); throw SkipTestException(""); } runTensorFlowNet("batch_norm3d"); } TEST_P(Test_TensorFlow_layers, slim_batch_norm) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) 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); #endif // Output values range: [-40.0597, 207.827] double l1 = default_l1; double lInf = default_lInf; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) { l1 = 0.041; lInf = 0.33; } #if defined(INF_ENGINE_RELEASE) else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) { lInf = 0.0002; } #endif else if (target == DNN_TARGET_CUDA_FP16) { l1 = 0.005; lInf = 0.33; } else if (target == DNN_TARGET_CPU_FP16) { l1 = 0.041; lInf = 0.37; } runTensorFlowNet("slim_batch_norm", false, l1, lInf); } TEST_P(Test_TensorFlow_layers, pooling_max_pool_even) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // [ GENERAL_ERROR ] AssertionFailed: !expired() 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); #elif 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 runTensorFlowNet("max_pool_even"); } TEST_P(Test_TensorFlow_layers, pooling_max_pool_odd_valid) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // [ GENERAL_ERROR ] AssertionFailed: !expired() 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); #elif 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 runTensorFlowNet("max_pool_odd_valid"); } TEST_P(Test_TensorFlow_layers, pooling_max_pool_odd_same) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // [ GENERAL_ERROR ] AssertionFailed: !expired() 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); #elif 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 runTensorFlowNet("max_pool_odd_same"); } TEST_P(Test_TensorFlow_layers, pooling_reduce_mean) { runTensorFlowNet("reduce_mean"); // an average pooling over all spatial dimensions. } TEST_P(Test_TensorFlow_layers, pooling_reduce_max) { runTensorFlowNet("reduce_max"); // a MAX pooling over all spatial dimensions. } TEST_P(Test_TensorFlow_layers, pooling_reduce_sum) { runTensorFlowNet("reduce_sum"); // a SUM pooling over all spatial dimensions. } TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_0_false) { runTensorFlowNet("reduce_sum_0_False"); } TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_1_false) { runTensorFlowNet("reduce_sum_1_False"); } TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_2_false) { runTensorFlowNet("reduce_sum_2_False"); } TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_3_false) { runTensorFlowNet("reduce_sum_3_False"); } TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_1_2_false) { #if defined(INF_ENGINE_RELEASE) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) { default_l1 = 0.01f; } if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) { default_l1 = 0.01f; } #endif runTensorFlowNet("reduce_sum_1_2_False"); } TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_0_true) { runTensorFlowNet("reduce_sum_0_True"); } TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_1_true) { runTensorFlowNet("reduce_sum_1_True"); } TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_2_true) { runTensorFlowNet("reduce_sum_2_True"); } TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_3_true) { runTensorFlowNet("reduce_sum_3_True"); } TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_1_2_true) { #if defined(INF_ENGINE_RELEASE) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) { default_l1 = 0.01f; } if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) { default_l1 = 0.01f; } #endif runTensorFlowNet("reduce_sum_1_2_True"); } TEST_P(Test_TensorFlow_layers, max_pool_grad) { 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("max_pool_grad"); } // TODO: fix tests and replace to pooling TEST_P(Test_TensorFlow_layers, ave_pool_same) { // Reference output values are in range [-0.519531, 0.112976] #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); } #endif runTensorFlowNet("ave_pool_same"); } TEST_P(Test_TensorFlow_layers, MaxPooling3D) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // IE exception: [ GENERAL_ERROR ] AssertionFailed: !expired() 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); // accuracy if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { // accuracy if (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); // IE exception: [ GENERAL_ERROR ] AssertionFailed: !expired() if (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); } #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported #endif if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU) throw SkipTestException("Only CPU is supported"); // FIXIT use tags if (backend == DNN_BACKEND_VKCOM) applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); runTensorFlowNet("max_pool3d"); } TEST_P(Test_TensorFlow_layers, AvePooling3D) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported #endif if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU) throw SkipTestException("Only CPU is supported"); // FIXIT use tags if (backend == DNN_BACKEND_VKCOM) applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); 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); if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_CPU_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_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"); runTensorFlowNet("two_inputs_matmul"); } TEST_P(Test_TensorFlow_layers, batch_matmul) { if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_CPU_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_FP16); runTensorFlowNet("batch_matmul"); } TEST_P(Test_TensorFlow_layers, square) { if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_CPU_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_FP16); runTensorFlowNet("square"); } TEST_P(Test_TensorFlow_layers, reshape) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) 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); #endif 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, reshape_layer) { runTensorFlowNet("reshape_layer"); } TEST_P(Test_TensorFlow_layers, reshape_nchw) { runTensorFlowNet("reshape_nchw"); } TEST_P(Test_TensorFlow_layers, reshape_conv) { runTensorFlowNet("reshape_conv"); } 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"); 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"); } TEST_P(Test_TensorFlow_layers, BiasAdd) { #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("bias_add_1"); } TEST_P(Test_TensorFlow_layers, ExpandDims) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Layout::ANY is broken on CPU #endif #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("expand_dims_1"); runTensorFlowNet("expand_dims_2"); } // 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(2022010000) // accuracy 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); #elif 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_diagnostics : public DNNTestLayer { public: Test_TensorFlow_diagnostics() { enableModelDiagnostics(true); skipModelImport(true); } ~Test_TensorFlow_diagnostics() { enableModelDiagnostics(false); skipModelImport(false); } void runFailingTensorFlowNet(const std::string& prefix, bool hasText = false) { std::string netPath = path(prefix + "_net.pb"); std::string netConfig = (hasText ? path(prefix + "_net.pbtxt") : ""); Net net = readNetFromTensorflow(netPath, netConfig); } }; TEST_P(Test_TensorFlow_diagnostics, not_implemented_layer) { runFailingTensorFlowNet("not_implemented_layer"); } TEST_P(Test_TensorFlow_diagnostics, broken_parameters) { runFailingTensorFlowNet("broken_layer"); } INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_diagnostics, dnnBackendsAndTargets()); 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 || target == DNN_TARGET_CPU_FP16) { scoreDiff = 0.01; iouDiff = 0.1; } 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_(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 || target == DNN_TARGET_CPU_FP16) { 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 || target == DNN_TARGET_CPU_FP16) { 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_inception_v2_coco_2018_01_28) { applyTestTag( (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB), CV_TEST_TAG_LONG, CV_TEST_TAG_DEBUG_VERYLONG ); #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 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) // 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); #endif if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16); if (target == DNN_TARGET_CPU_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_FP16); checkBackend(); double scoresDiff = 1e-5; double iouDiff = 1e-4; if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { scoresDiff = 0.02; iouDiff = 0.1; } std::string name = "faster_rcnn_inception_v2_coco_2018_01_28"; { std::string proto = findDataFile("dnn/" + name + ".pbtxt"); std::string model = findDataFile("dnn/" + name + ".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/" + name + ".detection_out.npy")); // accuracy (both OpenCV & IE) if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); if (target == DNN_TARGET_CPU_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_FP16); normAssertDetections(ref, out, name.c_str(), 0.3, scoresDiff, iouDiff); } } TEST_P(Test_TensorFlow_nets, Faster_RCNN_resnet50_coco_2018_01_28) { applyTestTag( (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB), CV_TEST_TAG_LONG, CV_TEST_TAG_DEBUG_VERYLONG ); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // [ GENERAL_ERROR ] AssertionFailed: subgraphTopoSortsStep < subgraphs.size() 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); // [ GENERAL_ERROR ] AssertionFailed: subgraphTopoSortsStep < subgraphs.size() if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // [ GENERAL_ERROR ] AssertionFailed: subgraphTopoSortsStep++ < subgraphs.size() 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); // IE exception: Ngraph operation Transpose with name FirstStageBoxPredictor/ClassPredictor/reshape_1/nhwc has dynamic output shape on 0 port, but CPU plug-in supports only static shape if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); #elif defined(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); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) // 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); #endif #endif 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) { scoresDiff = 0.06; iouDiff = 0.08; } std::string name = "faster_rcnn_resnet50_coco_2018_01_28"; { std::string proto = findDataFile("dnn/" + name + ".pbtxt"); std::string model = findDataFile("dnn/" + name + ".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/" + name + ".detection_out.npy")); // accuracy if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); if (target == DNN_TARGET_CPU_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_FP16); normAssertDetections(ref, out, name.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 || target == DNN_TARGET_CPU_FP16) { 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_(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 || target == DNN_TARGET_CPU_FP16) { 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 outs; std::vector 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 if (target == DNN_TARGET_CPU_FP16) { lInf_scores = 0.1; l1_geometry = 0.28; lInf_geometry = 5.94; } else { l1_geometry = 1e-4, lInf_geometry = 4.3e-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_EQ(2022010000) // [ GENERAL_ERROR ] AssertionFailed: !expired() 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); #elif 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; if (target == DNN_TARGET_CPU_FP16) l1 = 0.00083; 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_EQ(2022010000) // [ GENERAL_ERROR ] AssertionFailed: !expired() 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); #elif 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_EQ(2022010000) // [ GENERAL_ERROR ] AssertionFailed: !expired() 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); #elif 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) { float l1 = 7e-4, lInf = 4e-3; if (target == DNN_TARGET_CPU_FP16) lInf = 5e-3; // Reference output values are in range [-3.504, -0.002] runTensorFlowNet("fp16_padding_same", false, l1, lInf); } 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 defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // Xlink, Failed to allocate graph: NC_ERROR 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); #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { // Exception: Ngraph operation Reshape with name Reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape if (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); // Xlink if (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); } #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) 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); #endif if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_CPU_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_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"); } TEST_P(Test_TensorFlow_layers, resize_nearest_neighbor_keras_upsampling2d) { runTensorFlowNet("keras_upsampling2d"); } TEST_P(Test_TensorFlow_layers, resize_nearest_neighbor_align_corners) { runTensorFlowNet("resize_nearest_neighbor", false, 0.0, 0.0, false, "_align_corners"); } TEST_P(Test_TensorFlow_layers, resize_nearest_neighbor_half_pixel) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); #endif runTensorFlowNet("resize_nearest_neighbor", false, 0.0, 0.0, false, "_half_pixel"); } TEST_P(Test_TensorFlow_layers, fused_resize_conv) { runTensorFlowNet("fused_resize_conv"); } TEST_P(Test_TensorFlow_layers, slice_crop2d) { double l1 = target == DNN_TARGET_MYRIAD ? 4.9e-3 : default_l1; runTensorFlowNet("crop2d", false, l1); } TEST_P(Test_TensorFlow_layers, slice_4d) { runTensorFlowNet("slice_4d"); } TEST_P(Test_TensorFlow_layers, slice_strided) { runTensorFlowNet("strided_slice"); } TEST_P(Test_TensorFlow_layers, softmax_keras) { runTensorFlowNet("keras_softmax"); } TEST_P(Test_TensorFlow_layers, softmax_slim) { runTensorFlowNet("slim_softmax"); } TEST_P(Test_TensorFlow_layers, softmax_slim_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 defined(INF_ENGINE_RELEASE) 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); #endif runTensorFlowNet("subpixel"); } TEST_P(Test_TensorFlow_layers, keras_mobilenet_head) { runTensorFlowNet("keras_mobilenet_head"); runTensorFlowNet("keras_learning_phase"); } // TF case: align_corners=False, half_pixel_centers=False TEST_P(Test_TensorFlow_layers, resize_bilinear) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000) 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); // exception #endif runTensorFlowNet("resize_bilinear"); } // TF case: align_corners=True, half_pixel_centers=False TEST_P(Test_TensorFlow_layers, resize_bilinear_align_corners) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000) 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); // exception #endif runTensorFlowNet("resize_bilinear", false, 0.0, 0.0, false, // default parameters "_align_corners"); } // TF case: align_corners=False, half_pixel_centers=True TEST_P(Test_TensorFlow_layers, resize_bilinear_half_pixel) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); runTensorFlowNet("resize_bilinear", false, 0.0, 0.0, false, "_half_pixel"); } // TF case: align_corners=False, half_pixel_centers=False TEST_P(Test_TensorFlow_layers, resize_bilinear_factor) { runTensorFlowNet("resize_bilinear_factor"); } // TF case: align_corners=False, half_pixel_centers=True TEST_P(Test_TensorFlow_layers, resize_bilinear_factor_half_pixel) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); runTensorFlowNet("resize_bilinear_factor", false, 0.0, 0.0, false, "_half_pixel"); } // TF case: align_corners=True, half_pixel_centers=False TEST_P(Test_TensorFlow_layers, resize_bilinear_factor_align_corners) { runTensorFlowNet("resize_bilinear_factor", false, 0.0, 0.0, false, "_align_corners"); } // TF case: align_corners=False, half_pixel_centers=False TEST_P(Test_TensorFlow_layers, resize_bilinear_down) { runTensorFlowNet("resize_bilinear_down"); } TEST_P(Test_TensorFlow_layers, resize_concat_optimization) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) // Exception: Function contains several inputs and outputs with one friendly name! (HETERO bug?) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif runTensorFlowNet("resize_concat_optimization"); } 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) { double l1 = 0, lInf = 0; if (backend == DNN_BACKEND_CUDA) applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported; only across channels is supported #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // Eltwise executor got invalid input/output dims configuration if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); // Input prelu:StatefulPartitionedCall/StatefulPartitionedCall/sequential/p_re_lu/add hasn't been found in primitiveIDs map if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2023000000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) { l1 = 1e-4; lInf = 1e-3; } #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { // IE exception: Input prelu:StatefulPartitionedCall/StatefulPartitionedCall/sequential/p_re_lu/add hasn't been found in primitiveIDs map if (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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); // IE exception: Eltwise node with name `StatefulPartitionedCall/StatefulPartitionedCall/sequential/p_re_lu/add` has invalid input/output dims configuration if (target == DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); } #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) 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); #endif runTensorFlowNet("tf2_prelu", false, l1, lInf); } 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); // Mask-RCNN predicts bounding boxes and segmentation masks. std::vector outNames(2); outNames[0] = "detection_out_final"; outNames[1] = "detection_masks"; std::vector outs; net.forward(outs, outNames); Mat outDetections = outs[0]; Mat outMasks = outs[1]; double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) ? 0.2 : 2e-5; double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) ? 0.018 : default_lInf; if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { scoreDiff = std::max(scoreDiff, 0.06); iouDiff = std::max(iouDiff, 0.01); } 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 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 srcRanges(4, cv::Range::all()); std::vector 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(outDetections.at(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, (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) ? 0.98 : 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_CPU_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_FP16); 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_(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 = 0.005; } normAssertDetections(ref, out, "", 0.5, scoreDiff, iouDiff); expectNoFallbacksFromIE(net); } TEST(Test_TensorFlow_Importer, tf_graph_simplifier_buffer_overflow_21852) { uint8_t payload[] = {0x08, 0x08, 0x0a, 0x00, 0x0a, 0x00}; EXPECT_ANY_THROW(readNetFromTensorflow(reinterpret_cast(payload), sizeof(payload) / sizeof(payload[0]))); } // can be triggered with -fsanitize=address TEST(Test_TensorFlow_Importer, tf_graph_simplifier_buffer_overflow_21947) { uint8_t payload[] = {0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0xba, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0xbd, 0x00, 0x1a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0xba, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0xba, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0xba, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x2a, 0x00, 0xba, 0x0a, 0x00, 0x0a, 0x00, 0x5d, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x40}; EXPECT_ANY_THROW(readNetFromTensorflow(reinterpret_cast(payload), sizeof(payload) / sizeof(payload[0]))); } }