// 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) 2018-2019, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. #include "test_precomp.hpp" #include "opencv2/core/ocl.hpp" namespace opencv_test { namespace { class DNNTestNetwork : public DNNTestLayer { public: void processNet(const std::string& weights, const std::string& proto, Size inpSize, const std::string& outputLayer = "", const std::string& halideScheduler = "", double l1 = 0.0, double lInf = 0.0) { // Create a common input blob. int blobSize[] = {1, 3, inpSize.height, inpSize.width}; Mat inp(4, blobSize, CV_32FC1); randu(inp, 0.0f, 1.0f); processNet(weights, proto, inp, outputLayer, halideScheduler, l1, lInf); } void processNet(std::string weights, std::string proto, Mat inp, const std::string& outputLayer = "", std::string halideScheduler = "", double l1 = 0.0, double lInf = 0.0, double detectionConfThresh = 0.2, bool useWinograd = true) { checkBackend(); l1 = l1 ? l1 : default_l1; lInf = lInf ? lInf : default_lInf; weights = findDataFile(weights, false); if (!proto.empty()) proto = findDataFile(proto); // Create two networks - with default backend and target and a tested one. Net netDefault = readNet(weights, proto); netDefault.setPreferableBackend(DNN_BACKEND_OPENCV); netDefault.setInput(inp); Mat outDefault = netDefault.forward(outputLayer).clone(); net = readNet(weights, proto); net.setInput(inp); net.setPreferableBackend(backend); net.setPreferableTarget(target); if (target == DNN_TARGET_CPU_FP16) net.enableWinograd(false); if (backend == DNN_BACKEND_HALIDE && !halideScheduler.empty()) { halideScheduler = findDataFile(halideScheduler); net.setHalideScheduler(halideScheduler); } Mat out = net.forward(outputLayer).clone(); check(outDefault, out, outputLayer, l1, lInf, detectionConfThresh, "First run"); // Test 2: change input. float* inpData = (float*)inp.data; for (int i = 0; i < inp.size[0] * inp.size[1]; ++i) { Mat slice(inp.size[2], inp.size[3], CV_32F, inpData); cv::flip(slice, slice, 1); inpData += slice.total(); } netDefault.setInput(inp); net.setInput(inp); outDefault = netDefault.forward(outputLayer).clone(); out = net.forward(outputLayer).clone(); check(outDefault, out, outputLayer, l1, lInf, detectionConfThresh, "Second run"); } void check(Mat& ref, Mat& out, const std::string& outputLayer, double l1, double lInf, double detectionConfThresh, const char* msg) { if (outputLayer == "detection_out") { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) { // Inference Engine produces detections terminated by a row which starts from -1. out = out.reshape(1, out.total() / 7); int numDetections = 0; while (numDetections < out.rows && out.at<float>(numDetections, 0) != -1) { numDetections += 1; } out = out.rowRange(0, numDetections); } normAssertDetections(ref, out, msg, detectionConfThresh, l1, lInf); } else normAssert(ref, out, msg, l1, lInf); } Net net; }; TEST_P(DNNTestNetwork, AlexNet) { applyTestTag(CV_TEST_TAG_MEMORY_1GB); processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt", Size(227, 227), "prob", target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_alexnet.yml" : "dnn/halide_scheduler_alexnet.yml"); expectNoFallbacksFromIE(net); expectNoFallbacksFromCUDA(net); } TEST_P(DNNTestNetwork, ResNet_50) { applyTestTag( (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB), CV_TEST_TAG_DEBUG_VERYLONG ); processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt", Size(224, 224), "prob", target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_resnet_50.yml" : "dnn/halide_scheduler_resnet_50.yml"); expectNoFallbacksFromIE(net); expectNoFallbacksFromCUDA(net); } TEST_P(DNNTestNetwork, SqueezeNet_v1_1) { processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt", Size(227, 227), "prob", target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_squeezenet_v1_1.yml" : "dnn/halide_scheduler_squeezenet_v1_1.yml"); expectNoFallbacksFromIE(net); expectNoFallbacksFromCUDA(net); } TEST_P(DNNTestNetwork, GoogLeNet) { applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB); processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt", Size(224, 224), "prob"); expectNoFallbacksFromIE(net); expectNoFallbacksFromCUDA(net); } TEST_P(DNNTestNetwork, Inception_5h) { applyTestTag(CV_TEST_TAG_MEMORY_512MB); double l1 = default_l1, lInf = default_lInf; if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_CPU || target == DNN_TARGET_OPENCL)) { l1 = 1.72e-5; lInf = 8e-4; } processNet("dnn/tensorflow_inception_graph.pb", "", Size(224, 224), "softmax2", target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_inception_5h.yml" : "dnn/halide_scheduler_inception_5h.yml", l1, lInf); expectNoFallbacksFromIE(net); expectNoFallbacksFromCUDA(net); } TEST_P(DNNTestNetwork, ENet) { applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2023000000) 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_CUDA && target == DNN_TARGET_CUDA_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16); if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_CPU_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_FP16); processNet("dnn/Enet-model-best.net", "", Size(512, 512), "l367_Deconvolution", target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_enet.yml" : "dnn/halide_scheduler_enet.yml", 2e-5, 0.15); expectNoFallbacksFromCUDA(net); } TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe) { applyTestTag(CV_TEST_TAG_MEMORY_512MB); if (backend == DNN_BACKEND_HALIDE) applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); Mat sample = imread(findDataFile("dnn/street.png")); Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false); float scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) ? 1.5e-2 : 0.0; float iouDiff = (target == DNN_TARGET_MYRIAD) ? 0.063 : 0.0; float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.262 : FLT_MIN; processNet("dnn/MobileNetSSD_deploy_19e3ec3.caffemodel", "dnn/MobileNetSSD_deploy_19e3ec3.prototxt", inp, "detection_out", "", scoreDiff, iouDiff, detectionConfThresh); expectNoFallbacksFromIE(net); } TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height) { if (backend == DNN_BACKEND_HALIDE) applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // May hang on some configurations 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) // IE exception: Ngraph operation Transpose with name conv15_2_mbox_conf_perm 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 ); if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && 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, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #elif defined(INF_ENGINE_RELEASE) if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && 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, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif Mat sample = imread(findDataFile("dnn/street.png")); Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 560), Scalar(127.5, 127.5, 127.5), false); float scoreDiff = 0.0, iouDiff = 0.0; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) { scoreDiff = 0.029; iouDiff = 0.09; } else if (target == DNN_TARGET_CUDA_FP16) { scoreDiff = 0.03; iouDiff = 0.08; } processNet("dnn/MobileNetSSD_deploy_19e3ec3.caffemodel", "dnn/MobileNetSSD_deploy_19e3ec3.prototxt", inp, "detection_out", "", scoreDiff, iouDiff); expectNoFallbacksFromIE(net); } TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow) { applyTestTag((target == DNN_TARGET_CPU || target == DNN_TARGET_CPU_FP16) ? "" : CV_TEST_TAG_MEMORY_512MB); if (backend == DNN_BACKEND_HALIDE) applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); Mat sample = imread(findDataFile("dnn/street.png")); Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false); float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.216 : 0.2; float scoreDiff = 0.0, iouDiff = 0.0; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) { scoreDiff = 0.095; iouDiff = 0.09; } else if (target == DNN_TARGET_CUDA_FP16) { scoreDiff = 0.007; iouDiff = 0.08; } processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt", inp, "detection_out", "", scoreDiff, iouDiff, detectionConfThresh); expectNoFallbacksFromIE(net); } TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height) { if (backend == DNN_BACKEND_HALIDE) applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); #endif #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000) 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, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif Mat sample = imread(findDataFile("dnn/street.png")); Mat inp = blobFromImage(sample, 1.0f, Size(300, 560), Scalar(), false); float scoreDiff = 0.0, iouDiff = 0.0; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) { scoreDiff = 0.013; iouDiff = 0.06; } else if (target == DNN_TARGET_CUDA_FP16) { scoreDiff = 0.007; iouDiff = 0.06; } processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt", inp, "detection_out", "", scoreDiff, iouDiff); expectNoFallbacksFromIE(net); } TEST_P(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow) { applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB); if (backend == DNN_BACKEND_HALIDE) applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); Mat sample = imread(findDataFile("dnn/street.png")); Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false); float scoreDiff = 2e-5, iouDiff = 0.0; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) { scoreDiff = 0.013; iouDiff = 0.062; } else if (target == DNN_TARGET_CUDA_FP16) { scoreDiff = 0.02; iouDiff = 0.07; } processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt", inp, "detection_out", "", scoreDiff, iouDiff, 0.25); expectNoFallbacksFromIE(net); } TEST_P(DNNTestNetwork, SSD_VGG16) { applyTestTag( CV_TEST_TAG_MEMORY_2GB, CV_TEST_TAG_LONG, CV_TEST_TAG_DEBUG_VERYLONG ); if (backend == DNN_BACKEND_HALIDE && target == DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); // TODO HALIDE_CPU Mat sample = imread(findDataFile("dnn/street.png")); Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false); float scoreDiff = 0.0, iouDiff = 0.0; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CPU_FP16) { scoreDiff = 0.04; } else if (target == DNN_TARGET_MYRIAD) { scoreDiff = 0.0325; iouDiff = 0.032; } else if (target == DNN_TARGET_CUDA_FP16) { scoreDiff = 0.03; iouDiff = 0.13; } processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", "dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreDiff, iouDiff, 0.2, false); expectNoFallbacksFromIE(net); } TEST_P(DNNTestNetwork, OpenPose_pose_coco) { applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB), CV_TEST_TAG_DEBUG_LONG); if (backend == DNN_BACKEND_HALIDE) applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000) 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_VERSION); #endif const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.009 : 0.0; const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.09 : 0.0; processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt", Size(46, 46), "", "", l1, lInf); expectNoFallbacksFromIE(net); expectNoFallbacksFromCUDA(net); } TEST_P(DNNTestNetwork, OpenPose_pose_mpi) { applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB), CV_TEST_TAG_DEBUG_VERYLONG); if (backend == DNN_BACKEND_HALIDE) applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000) 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_VERSION); #endif // output range: [-0.001, 0.97] const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.02 : 0.0; const float lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CPU_FP16) ? 0.2 : 0.0; processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt", Size(46, 46), "", "", l1, lInf); expectNoFallbacksFromIE(net); expectNoFallbacksFromCUDA(net); } TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages) { applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_MEMORY_1GB); if (backend == DNN_BACKEND_HALIDE) applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000) 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_VERSION); #endif // The same .caffemodel but modified .prototxt // See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi_faster_4_stages.prototxt", Size(46, 46)); expectNoFallbacksFromIE(net); expectNoFallbacksFromCUDA(net); } TEST_P(DNNTestNetwork, OpenFace) { #if defined(INF_ENGINE_RELEASE) #if INF_ENGINE_VER_MAJOR_EQ(2018050000) 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_VERSION); #endif #endif if (backend == DNN_BACKEND_HALIDE) applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.0024 : 0.0; const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.0071 : 0.0; processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "", "", l1, lInf); expectNoFallbacksFromCUDA(net); } TEST_P(DNNTestNetwork, opencv_face_detector) { if (backend == DNN_BACKEND_HALIDE) applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); Mat img = imread(findDataFile("gpu/lbpcascade/er.png")); Mat inp = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false); processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt", inp, "detection_out"); expectNoFallbacksFromIE(net); } TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow) { applyTestTag( (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB), CV_TEST_TAG_DEBUG_VERYLONG ); #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) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); #endif #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000) 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, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif if (backend == DNN_BACKEND_HALIDE) applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); Mat sample = imread(findDataFile("dnn/street.png")); Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false); float scoreDiff = 0.0, iouDiff = 0.0; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) { scoreDiff = 0.02; iouDiff = 0.1; } else if (target == DNN_TARGET_CUDA_FP16) { scoreDiff = 0.015; iouDiff = 0.08; } processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "dnn/ssd_inception_v2_coco_2017_11_17.pbtxt", inp, "detection_out", "", scoreDiff, iouDiff); expectNoFallbacksFromIE(net); } TEST_P(DNNTestNetwork, DenseNet_121) { applyTestTag(CV_TEST_TAG_MEMORY_512MB); if (backend == DNN_BACKEND_HALIDE) applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); // Reference output values are in range [-3.807, 4.605] float l1 = 0.0, lInf = 0.0; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CPU_FP16) { l1 = 2e-2; lInf = 9e-2; if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) lInf = 0.1f; } else if (target == DNN_TARGET_MYRIAD) { l1 = 0.1; lInf = 0.6; } else if (target == DNN_TARGET_CUDA_FP16) { l1 = 0.008; lInf = 0.06; } processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", "", l1, lInf); if (target != DNN_TARGET_MYRIAD || getInferenceEngineVPUType() != CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) expectNoFallbacksFromIE(net); expectNoFallbacksFromCUDA(net); } TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16) { applyTestTag(CV_TEST_TAG_MEMORY_512MB, CV_TEST_TAG_DEBUG_VERYLONG); if (backend == DNN_BACKEND_HALIDE) applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); 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 defined(INF_ENGINE_RELEASE) #if INF_ENGINE_VER_MAJOR_LE(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_VERSION); #endif #endif Mat img = imread(findDataFile("dnn/googlenet_1.png")); Mat inp = blobFromImage(img, 1.0, Size(320, 240), Scalar(103.939, 116.779, 123.68), false, false); // Output image has values in range [-143.526, 148.539]. float l1 = 2e-4, lInf = 2.4e-3; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) { l1 = 0.4; lInf = 7.46; } else if (target == DNN_TARGET_CUDA_FP16) { l1 = 0.3; lInf = 7.6; } else if (target == DNN_TARGET_CPU_FP16) { l1 = 0.4; lInf = 22.; } else if (target == DNN_TARGET_VULKAN) { l1 = 0.4; lInf = 7.46; } #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) { l1 = 5e-3; lInf = 5e-3; } if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) { lInf = 25; } #endif processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", inp, "", "", l1, lInf); #if defined(HAVE_INF_ENGINE) && INF_ENGINE_VER_MAJOR_GE(2019010000) expectNoFallbacksFromIE(net); #endif expectNoFallbacksFromCUDA(net); } INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, dnnBackendsAndTargets(true, true, false, true, true)); /* Backend tests of layers */ static void testLayer(Mat& input, Net& net, Backend backendId, Target targetId, bool skipCheck = false, bool randInput = true, double l1 = 0.0, double lInf = 0.0) { DNNTestLayer::checkBackend(backendId, targetId); if (randInput) randu(input, -1.0f, 1.0f); net.setInput(input); net.setPreferableBackend(DNN_BACKEND_OPENCV); Mat outputDefault = net.forward().clone(); net.setPreferableBackend(backendId); net.setPreferableTarget(targetId); Mat output = net.forward().clone(); if (skipCheck) return; double default_l1, default_lInf; DNNTestLayer::getDefaultThresholds(backendId, targetId, &default_l1, &default_lInf); if (l1 == 0.0) l1 = default_l1; if (lInf == 0.0) lInf = default_lInf; normAssert(outputDefault, output, "", l1, lInf); if (cvtest::debugLevel > 0 || testing::Test::HasFailure()) { std::cout << "l1=" << l1 << " lInf=" << lInf << std::endl; std::cout << outputDefault.reshape(1, outputDefault.total()).t() << std::endl; std::cout << output.reshape(1, outputDefault.total()).t() << std::endl; } } static void testLayer(LayerParams& params, Mat& input, Backend backendId, Target targetId, bool skipCheck = false, double l1 = 0.0, double lInf = 0.0) { Net net; net.addLayerToPrev(params.name, params.type, params); testLayer(input, net, backendId, targetId, skipCheck, true, l1, lInf); } class Test_layers_backends : public DNNTestLayer {}; //////////////////////////////////////////////////////////////////////////////// // Padding //////////////////////////////////////////////////////////////////////////////// TEST_P(Test_layers_backends, Padding) { static const int kNumRuns = 10; std::vector<int> paddings(8); cv::RNG& rng = cv::theRNG(); for (int t = 0; t < kNumRuns; ++t) { for (int i = 0; i < paddings.size(); ++i) paddings[i] = rng(5); LayerParams lp; lp.set("paddings", DictValue::arrayInt<int*>(&paddings[0], paddings.size())); lp.type = "Padding"; lp.name = "testLayer"; int sz[] = {1 + (int)rng(10), 1 + (int)rng(10), 1 + (int)rng(10), 1 + (int)rng(10)}; Mat input(4, &sz[0], CV_32F); testLayer(lp, input, backend, target); } } //////////////////////////////////////////////////////////////////////////////// // Convolution //////////////////////////////////////////////////////////////////////////////// typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Size, bool, tuple<Backend, Target> > > Convolution; TEST_P(Convolution, Accuracy) { int inChannels = get<0>(GetParam())[0]; int outChannels = get<0>(GetParam())[1]; int group = get<0>(GetParam())[2]; Size inSize = get<1>(GetParam()); Size kernel = get<2>(GetParam()); Size stride = get<3>(GetParam()); Size pad = get<4>(GetParam()); Size dilation = get<5>(GetParam()); bool hasBias = get<6>(GetParam()); Backend backendId = get<0>(get<7>(GetParam())); Target targetId = get<1>(get<7>(GetParam())); bool skipCheck = false; int sz[] = {outChannels, inChannels / group, kernel.height, kernel.width}; Mat weights(4, &sz[0], CV_32F); randu(weights, -1.0f, 1.0f); LayerParams lp; lp.set("kernel_w", kernel.width); lp.set("kernel_h", kernel.height); lp.set("pad_w", pad.width); lp.set("pad_h", pad.height); lp.set("stride_w", stride.width); lp.set("stride_h", stride.height); lp.set("dilation_w", dilation.width); lp.set("dilation_h", dilation.height); lp.set("num_output", outChannels); lp.set("group", group); lp.set("bias_term", hasBias); lp.type = "Convolution"; lp.name = "testLayer"; lp.blobs.push_back(weights); if (hasBias) { Mat bias(1, outChannels, CV_32F); randu(bias, -1.0f, 1.0f); lp.blobs.push_back(bias); } int inpSz[] = {1, inChannels, inSize.height, inSize.width}; Mat input(4, &inpSz[0], CV_32F); testLayer(lp, input, backendId, targetId, skipCheck); if (skipCheck) throw SkipTestException("Skip checks in unstable test"); } INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Convolution, testing::Combine( /*in channels, out channels, group*/ testing::Values(Vec3i(6, 4, 1), Vec3i(6, 9, 1), Vec3i(6, 4, 2), Vec3i(6, 9, 3)), /*in size*/ testing::Values(Size(5, 6)), /*kernel*/ testing::Values(Size(3, 1), Size(1, 3)), /*stride*/ testing::Values(Size(1, 1), Size(2, 2)), /*pad*/ testing::Values(Size(1, 0), Size(0, 1)), /*dilation*/ testing::Values(Size(1, 1), Size(2, 2)), /*has bias*/ testing::Bool(), dnnBackendsAndTargets() )); //////////////////////////////////////////////////////////////////////////////// // Deconvolution //////////////////////////////////////////////////////////////////////////////// typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Vec4i, bool, tuple<Backend, Target> > > Deconvolution; TEST_P(Deconvolution, Accuracy) { int inChannels = get<0>(GetParam())[0]; int outChannels = get<0>(GetParam())[1]; int group = get<0>(GetParam())[2]; Size inSize = get<1>(GetParam()); Size kernel = get<2>(GetParam()); Size pad = get<3>(GetParam()); Size dilation = get<4>(GetParam()); Size stride = Size(get<5>(GetParam())[0], get<5>(GetParam())[1]); Size adjPad = Size(get<5>(GetParam())[2], get<5>(GetParam())[3]); bool hasBias = get<6>(GetParam()); Backend backendId = get<0>(get<7>(GetParam())); Target targetId = get<1>(get<7>(GetParam())); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16) && inChannels == 6 && outChannels == 4 && group == 1 && kernel == Size(3, 1) && pad == Size(0, 1) && stride == Size(1, 1) && dilation == Size(1, 1)) applyTestTag(targetId == 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 ); if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16) && inChannels == 6 && outChannels == 4 && group == 1 && kernel == Size(1, 3) && pad == Size(1, 0) && stride == Size(1, 1) && dilation == Size(1, 1)) applyTestTag(targetId == 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_GE(2019010000) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X && inChannels == 6 && outChannels == 4 && group == 1 && kernel == Size(1, 3) && pad == Size(1, 0) && stride == Size(1, 1) && dilation == Size(1, 1)) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); #endif if (targetId == DNN_TARGET_CUDA_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16); int sz[] = {inChannels, outChannels / group, kernel.height, kernel.width}; Mat weights(4, &sz[0], CV_32F); randu(weights, -1.0f, 1.0f); LayerParams lp; lp.set("kernel_w", kernel.width); lp.set("kernel_h", kernel.height); lp.set("pad_w", pad.width); lp.set("pad_h", pad.height); lp.set("stride_w", stride.width); lp.set("stride_h", stride.height); lp.set("dilation_w", dilation.width); lp.set("dilation_h", dilation.height); lp.set("adj_w", adjPad.width); lp.set("adj_h", adjPad.height); lp.set("num_output", outChannels); lp.set("group", group); lp.set("bias_term", hasBias); lp.type = "Deconvolution"; lp.name = "testLayer"; lp.blobs.push_back(weights); if (hasBias) { Mat bias(1, outChannels, CV_32F); randu(bias, -1.0f, 1.0f); lp.blobs.push_back(bias); } int inpSz[] = {1, inChannels, inSize.height, inSize.width}; Mat input(4, &inpSz[0], CV_32F); testLayer(lp, input, backendId, targetId); } INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Deconvolution, testing::Combine( /*in channels, out channels, group*/ testing::Values(Vec3i(6, 4, 1), Vec3i(6, 9, 3)), /*in size*/ testing::Values(Size(5, 6)), /*kernel*/ testing::Values(Size(3, 1), Size(1, 3)), /*pad*/ testing::Values(Size(1, 0), Size(0, 1)), /*dilation*/ testing::Values(Size(1, 1)), /*stride, adj. pad*/ testing::Values(Vec4i(1,1, 0,0), Vec4i(2,2, 1,0), Vec4i(1,2, 0,1)), /*has bias*/ testing::Bool(), dnnBackendsAndTargets() )); //////////////////////////////////////////////////////////////////////////////// // LRN //////////////////////////////////////////////////////////////////////////////// typedef TestWithParam<tuple<Vec3i, int, Vec3f, bool, std::string, tuple<Backend, Target> > > LRN; TEST_P(LRN, Accuracy) { int inChannels = get<0>(GetParam())[0]; Size inSize = Size(get<0>(GetParam())[1], get<0>(GetParam())[2]); int localSize = get<1>(GetParam()); float alpha = get<2>(GetParam())[0]; float beta = get<2>(GetParam())[1]; float bias = get<2>(GetParam())[2]; bool normBySize = get<3>(GetParam()); std::string nrmType = get<4>(GetParam()); Backend backendId = get<0>(get<5>(GetParam())); Target targetId = get<1>(get<5>(GetParam())); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if ((inSize.width == 5 || inSize.height == 5) && targetId == DNN_TARGET_MYRIAD && nrmType == "ACROSS_CHANNELS") applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD); #endif LayerParams lp; lp.set("norm_region", nrmType); lp.set("local_size", localSize); lp.set("alpha", alpha); lp.set("beta", beta); lp.set("bias", bias); lp.set("norm_by_size", normBySize); lp.type = "LRN"; lp.name = "testLayer"; int sz[] = {1, inChannels, inSize.height, inSize.width}; Mat input(4, &sz[0], CV_32F); double l1 = 0.0, lInf = 0.0; // The OpenCL kernels use the native_ math functions which have // implementation defined accuracy, so we use relaxed thresholds. See // https://github.com/opencv/opencv/issues/9821 for more details. if (targetId == DNN_TARGET_OPENCL) { l1 = 0.01; lInf = 0.01; } testLayer(lp, input, backendId, targetId, false, l1, lInf); } INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, LRN, testing::Combine( /*input ch,w,h*/ testing::Values(Vec3i(6, 5, 8), Vec3i(7, 11, 6)), /*local size*/ testing::Values(3, 5), testing::Values(Vec3f(0.9f, 1.0f, 1.1f), Vec3f(0.9f, 1.1f, 1.0f), /*alpha, beta, bias*/ Vec3f(1.0f, 0.9f, 1.1f), Vec3f(1.0f, 1.1f, 0.9f), Vec3f(1.1f, 0.9f, 1.0f), Vec3f(1.1f, 1.0f, 0.9f)), /*norm_by_size*/ testing::Bool(), /*norm_type*/ testing::Values("ACROSS_CHANNELS", "WITHIN_CHANNEL"), dnnBackendsAndTargets() )); //////////////////////////////////////////////////////////////////////////////// // Average pooling //////////////////////////////////////////////////////////////////////////////// typedef TestWithParam<tuple<int, Size, Size, Size, tuple<Backend, Target> > > AvePooling; TEST_P(AvePooling, Accuracy) { int inChannels = get<0>(GetParam()); Size outSize = get<1>(GetParam());; // Input size will be computed from parameters. Size kernel = get<2>(GetParam()); Size stride = get<3>(GetParam()); Backend backendId = get<0>(get<4>(GetParam())); Target targetId = get<1>(get<4>(GetParam())); #if defined(INF_ENGINE_RELEASE) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X && kernel == Size(1, 1) && (stride == Size(1, 1) || stride == Size(2, 2))) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); #endif const int inWidth = (outSize.width - 1) * stride.width + kernel.width; const int inHeight = (outSize.height - 1) * stride.height + kernel.height; LayerParams lp; lp.set("pool", "ave"); lp.set("kernel_w", kernel.width); lp.set("kernel_h", kernel.height); lp.set("stride_w", stride.width); lp.set("stride_h", stride.height); lp.type = "Pooling"; lp.name = "testLayer"; int sz[] = {1, inChannels, inHeight, inWidth}; Mat input(4, &sz[0], CV_32F); testLayer(lp, input, backendId, targetId); } INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, AvePooling, testing::Combine( /*in channels*/ testing::Values(3, 4), /*out size*/ testing::Values(Size(1, 1), Size(2, 2), Size(3, 2), Size(4, 7)), /*kernel*/ testing::Values(Size(1, 1), Size(2, 2), Size(3, 3), Size(3, 2)), /*stride*/ testing::Values(Size(1, 1), Size(2, 2), Size(3, 2)), dnnBackendsAndTargets() )); //////////////////////////////////////////////////////////////////////////////// // Maximum pooling //////////////////////////////////////////////////////////////////////////////// typedef TestWithParam<tuple<int, Size, Size, Size, Size, tuple<Backend, Target> > > MaxPooling; TEST_P(MaxPooling, Accuracy) { int inChannels = get<0>(GetParam()); Size inSize = get<1>(GetParam()); Size kernel = get<2>(GetParam()); Size stride = get<3>(GetParam()); Size pad = get<4>(GetParam()); Backend backendId = get<0>(get<5>(GetParam())); Target targetId = get<1>(get<5>(GetParam())); // https://github.com/openvinotoolkit/openvino/issues/18731 if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && stride != Size(1, 1)) { int ow = ceil(static_cast<float>(inSize.width + 2 * pad.width - kernel.width) / stride.width); int oh = ceil(static_cast<float>(inSize.height + 2 * pad.height - kernel.height) / stride.height); if (ow * stride.width >= inSize.width + pad.width || oh * stride.height >= inSize.height + pad.height) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); } #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X && (stride == Size(1, 1) || stride == Size(2, 2)) && (pad == Size(0, 1) || pad == Size(1, 1)) ) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == 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 LayerParams lp; lp.set("pool", "max"); lp.set("kernel_w", kernel.width); lp.set("kernel_h", kernel.height); lp.set("stride_w", stride.width); lp.set("stride_h", stride.height); lp.set("pad_w", pad.width); lp.set("pad_h", pad.height); lp.type = "Pooling"; lp.name = "testLayer"; int sz[] = {1, inChannels, inSize.height, inSize.width}; Mat input(4, &sz[0], CV_32F); testLayer(lp, input, backendId, targetId); } INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, MaxPooling, testing::Combine( /*in channels*/ testing::Values(3, 4), /*in size*/ testing::Values(Size(5, 5), Size(7, 6)), /*kernel*/ testing::Values(Size(2, 2), Size(3, 3), Size(3, 2)), /*stride*/ testing::Values(Size(1, 1), Size(2, 2), Size(3, 2)), /*pad*/ testing::Values(Size(0, 0), Size(1, 1), Size(0, 1)), dnnBackendsAndTargets() )); //////////////////////////////////////////////////////////////////////////////// // Fully-connected //////////////////////////////////////////////////////////////////////////////// typedef TestWithParam<tuple<int, int, Size, int, bool, tuple<Backend, Target> > > FullyConnected; TEST_P(FullyConnected, Accuracy) { int batch = get<0>(GetParam()); int inChannels = get<1>(GetParam()); Size inSize = get<2>(GetParam()); int outChannels = get<3>(GetParam()); bool hasBias = get<4>(GetParam()); Backend backendId = get<0>(get<5>(GetParam())); Target targetId = get<1>(get<5>(GetParam())); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && (targetId == DNN_TARGET_OPENCL_FP16 || (targetId == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X))) { applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16); applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); } #endif // https://github.com/openvinotoolkit/openvino/issues/19436 if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL_FP16 && batch == 16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2023000000) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL && batch == 16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL); #endif Mat weights(outChannels, inChannels * inSize.height * inSize.width, CV_32F); randu(weights, -1.0f, 1.0f); Mat bias(1, outChannels, CV_32F); randu(bias, -1.0f, 1.0f); LayerParams lp; lp.set("num_output", outChannels); lp.set("bias_term", hasBias); lp.blobs.push_back(weights); lp.blobs.push_back(bias); lp.type = "InnerProduct"; lp.name = "testLayer"; int sz[] = {batch, inChannels, inSize.height, inSize.width}; Mat input(4, &sz[0], CV_32F); double l1 = 0.0; double lInf = 0.0; #if defined(INF_ENGINE_RELEASE) if (targetId == DNN_TARGET_MYRIAD) { l1 = 0.015; lInf = 0.025; } if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL_FP16) { l1 = 0.01; if (INF_ENGINE_VER_MAJOR_GE(2023000000)) lInf = 0.016; } if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL) { l1 = 5e-3; lInf = INF_ENGINE_VER_MAJOR_GE(2023000000) ? 0.016 : 7e-3; } #endif if (targetId == DNN_TARGET_CUDA_FP16) l1 = 0.015; testLayer(lp, input, backendId, targetId, false, l1, lInf); } INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, FullyConnected, testing::Combine( /*batch*/ testing::Values(1, 2, 4, 8, 16), /*in channels*/ testing::Values(3, 4), /*in size*/ testing::Values(Size(5, 4), Size(4, 5), Size(1, 1)), /*out channels*/ testing::Values(3, 4), /*has bias*/ testing::Bool(), dnnBackendsAndTargets() )); //////////////////////////////////////////////////////////////////////////////// // SoftMax //////////////////////////////////////////////////////////////////////////////// typedef TestWithParam<tuple<int, tuple<Backend, Target> > > SoftMax; TEST_P(SoftMax, Accuracy) { int inChannels = get<0>(GetParam()); Backend backendId = get<0>(get<1>(GetParam())); Target targetId = get<1>(get<1>(GetParam())); LayerParams lp; lp.type = "Softmax"; lp.name = "testLayer"; int sz[] = {1, inChannels, 1, 1}; Mat input(4, &sz[0], CV_32F); testLayer(lp, input, backendId, targetId); } INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, SoftMax, testing::Combine( testing::Values(3, 4, 5, 1024), dnnBackendsAndTargets() )); ////////////////////////////////////////////////////////////////////////////// // Max pooling - unpooling ////////////////////////////////////////////////////////////////////////////// TEST_P(Test_layers_backends, MaxPoolUnpool) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2023000000) 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 LayerParams pool; pool.set("pool", "max"); pool.set("kernel_w", 2); pool.set("kernel_h", 2); pool.set("stride_w", 2); pool.set("stride_h", 2); pool.set("pad_w", 0); pool.set("pad_h", 0); pool.type = "Pooling"; pool.name = "testPool"; LayerParams unpool; unpool.set("pool_k_w", 2); unpool.set("pool_k_h", 2); unpool.set("pool_stride_w", 2); unpool.set("pool_stride_h", 2); unpool.set("pool_pad_w", 0); unpool.set("pool_pad_h", 0); unpool.type = "MaxUnpool"; unpool.name = "testUnpool"; Net net; int poolId = net.addLayer(pool.name, pool.type, pool); net.connect(0, 0, poolId, 0); int unpoolId = net.addLayer(unpool.name, unpool.type, unpool); net.connect(poolId, 0, unpoolId, 0); net.connect(poolId, 1, unpoolId, 1); int sz[] = {1, 1, 4, 4}; Mat input(4, &sz[0], CV_32F); testLayer(input, net, backend, target); } //////////////////////////////////////////////////////////////////////////////// // AvePooling + in-place layers //////////////////////////////////////////////////////////////////////////////// static const int kNumChannels = 3; void testInPlaceActivation(LayerParams& lp, Backend backendId, Target targetId, double l1 = 0.0, double lInf = 0.0) { EXPECT_FALSE(lp.name.empty()); LayerParams pool; pool.set("pool", "ave"); pool.set("kernel_w", 2); pool.set("kernel_h", 2); pool.set("stride_w", 2); pool.set("stride_h", 2); pool.type = "Pooling"; pool.name = "ave_pool"; Net net; int poolId = net.addLayer(pool.name, pool.type, pool); net.connect(0, 0, poolId, 0); net.addLayerToPrev(lp.name, lp.type, lp); int sz[] = {1, kNumChannels, 10, 10}; Mat input(4, &sz[0], CV_32F); testLayer(input, net, backendId, targetId, false, true, l1, lInf); } typedef TestWithParam<tuple<bool, bool, float, tuple<Backend, Target> > > BatchNorm; TEST_P(BatchNorm, Accuracy) { bool hasWeights = get<0>(GetParam()); bool hasBias = get<1>(GetParam()); float epsilon = get<2>(GetParam()); Backend backendId = get<0>(get<3>(GetParam())); Target targetId = get<1>(get<3>(GetParam())); LayerParams lp; lp.set("has_weight", hasWeights); lp.set("has_bias", hasBias); lp.set("eps", epsilon); lp.type = "BatchNorm"; lp.name = "testLayer"; lp.blobs.reserve(4); for (int i = 0; i < 3; ++i) lp.blobs.push_back(Mat(1, kNumChannels, CV_32F)); if (hasBias || hasWeights) lp.blobs.push_back(Mat(1, kNumChannels, CV_32F)); for (int i = 0; i < lp.blobs.size(); ++i) randu(lp.blobs[i], 0.0f, 1.0f); testInPlaceActivation(lp, backendId, targetId); } INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, BatchNorm, testing::Combine( /*has weights*/ testing::Bool(), /*has bias*/ testing::Bool(), /*epsilon*/ testing::Values(1e-3f, 1e-5f), dnnBackendsAndTargets() )); typedef TestWithParam<tuple<float, tuple<Backend, Target> > > ReLU; TEST_P(ReLU, Accuracy) { float negativeSlope = get<0>(GetParam()); Backend backendId = get<0>(get<1>(GetParam())); Target targetId = get<1>(get<1>(GetParam())); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019020000) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD && negativeSlope < 0) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif LayerParams lp; lp.set("negative_slope", negativeSlope); lp.type = "ReLU"; lp.name = "testLayer"; testInPlaceActivation(lp, backendId, targetId); } INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, ReLU, testing::Combine( /*negative slope*/ testing::Values(2.0f, 0.3f, -0.1f, 0.0f), dnnBackendsAndTargets() )); typedef TestWithParam<tuple<std::string, tuple<Backend, Target> > > NoParamActivation; TEST_P(NoParamActivation, Accuracy) { Backend backendId = get<0>(get<1>(GetParam())); Target targetId = get<1>(get<1>(GetParam())); std::string layer_type = get<0>(GetParam()); LayerParams lp; lp.type = layer_type; lp.name = "testLayer"; testInPlaceActivation(lp, backendId, targetId); } INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, NoParamActivation, testing::Combine( /*type*/ testing::Values("TanH", "Sigmoid", "AbsVal", "BNLL", "Swish", "Mish"), dnnBackendsAndTargets() )); typedef TestWithParam<tuple<Vec3f, tuple<Backend, Target> > > Power; TEST_P(Power, Accuracy) { float power = get<0>(GetParam())[0]; float scale = get<0>(GetParam())[1]; float shift = get<0>(GetParam())[2]; Backend backendId = get<0>(get<1>(GetParam())); Target targetId = get<1>(get<1>(GetParam())); LayerParams lp; lp.set("power", power); lp.set("scale", scale); lp.set("shift", shift); lp.type = "Power"; lp.name = "testLayer"; testInPlaceActivation(lp, backendId, targetId); } INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Power, testing::Combine( /*power, scale, shift*/ testing::Values(Vec3f(0.9f, 1.0f, 1.1f), Vec3f(0.9f, 1.1f, 1.0f), Vec3f(1.0f, 0.9f, 1.1f), Vec3f(1.0f, 1.1f, 0.9f), Vec3f(1.1f, 0.9f, 1.0f), Vec3f(1.1f, 1.0f, 0.9f)), dnnBackendsAndTargets() )); typedef TestWithParam<tuple<Vec3f, tuple<Backend, Target> > > Exp; TEST_P(Exp, Accuracy) { float base = get<0>(GetParam())[0]; float scale = get<0>(GetParam())[1]; float shift = get<0>(GetParam())[2]; Backend backendId = get<0>(get<1>(GetParam())); Target targetId = get<1>(get<1>(GetParam())); LayerParams lp; lp.set("base", base); lp.set("scale", scale); lp.set("shift", shift); lp.type = "Exp"; lp.name = "testLayer"; testInPlaceActivation(lp, backendId, targetId); } INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Exp, testing::Combine( /*base, scale, shift*/ testing::Values(Vec3f(0.9f, -1.0f, 1.1f), Vec3f(0.9f, 1.1f, -1.0f), Vec3f(-1.0f, 0.9f, 1.1f), Vec3f(-1.0f, 1.1f, 0.9f), Vec3f(1.1f, 0.9f, -1.0f), Vec3f(1.1f, -1.0f, 0.9f)), dnnBackendsAndTargets() )); TEST_P(Test_layers_backends, ChannelsPReLU) { LayerParams lp; lp.type = "ChannelsPReLU"; lp.name = "testLayer"; lp.blobs.push_back(Mat(1, kNumChannels, CV_32F)); randu(lp.blobs[0], -1.0f, 1.0f); testInPlaceActivation(lp, backend, target); } typedef TestWithParam<tuple<bool, tuple<Backend, Target> > > Scale; TEST_P(Scale, Accuracy) { bool hasBias = get<0>(GetParam()); Backend backendId = get<0>(get<1>(GetParam())); Target targetId = get<1>(get<1>(GetParam())); LayerParams lp; lp.set("bias_term", hasBias); lp.type = "Scale"; lp.name = "testLayer"; lp.blobs.push_back(Mat(1, kNumChannels, CV_32F)); randu(lp.blobs[0], -1.0f, 1.0f); if (hasBias) { lp.blobs.push_back(Mat(1, kNumChannels, CV_32F)); randu(lp.blobs[1], -1.0f, 1.0f); } testInPlaceActivation(lp, backendId, targetId); } INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Scale, testing::Combine( testing::Bool(), dnnBackendsAndTargets() )); //////////////////////////////////////////////////////////////////////////////// // Concat layer //////////////////////////////////////////////////////////////////////////////// // // input --- conv --- concat --- output // `--- conv ----^ ^ ^ // `---- ... ------' ' // `-----------------' typedef TestWithParam<tuple<Vec3i, Vec3i, tuple<Backend, Target> > > Concat; TEST_P(Concat, Accuracy) { Vec3i inSize = get<0>(GetParam()); Vec3i numChannels = get<1>(GetParam()); Backend backendId = get<0>(get<2>(GetParam())); Target targetId = get<1>(get<2>(GetParam())); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD && inSize == Vec3i(1, 4, 5) && numChannels == Vec3i(1, 6, 2) ) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); // crash #endif #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_CPU && inSize == Vec3i(1, 4, 5) && numChannels == Vec3i(1, 6, 2) ) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); // TODO: IE_CPU #endif Net net; std::vector<int> convLayerIds; convLayerIds.reserve(numChannels.channels); for (int i = 0, n = numChannels.channels; i < n; ++i) { if (!numChannels[i]) break; int sz[] = {numChannels[i], inSize[0], 1, 1}; Mat weights(4, &sz[0], CV_32F); randu(weights, -1.0f, 1.0f); LayerParams convParam; convParam.set("kernel_w", 1); convParam.set("kernel_h", 1); convParam.set("num_output", numChannels[i]); convParam.set("bias_term", false); convParam.type = "Convolution"; std::ostringstream ss; ss << "convLayer" << i; convParam.name = ss.str(); convParam.blobs.push_back(weights); int layerId = net.addLayer(convParam.name, convParam.type, convParam); convLayerIds.push_back(layerId); net.connect(0, 0, layerId, 0); } LayerParams concatParam; concatParam.type = "Concat"; concatParam.name = "testLayer"; int concatId = net.addLayer(concatParam.name, concatParam.type, concatParam); net.connect(0, 0, concatId, 0); for (int i = 0; i < convLayerIds.size(); ++i) { net.connect(convLayerIds[i], 0, concatId, i + 1); } int sz[] = {1, inSize[0], inSize[1], inSize[2]}; Mat input(4, &sz[0], CV_32F); testLayer(input, net, backendId, targetId); } INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Concat, testing::Combine( /*input size*/ testing::Values(Vec3i(1, 4, 5), Vec3i(2, 8, 6)), /*channels*/ testing::Values(Vec3i(2, 0, 0), Vec3i(3, 4, 0), Vec3i(1, 6, 2)), dnnBackendsAndTargets() )); //////////////////////////////////////////////////////////////////////////////// // Element-wise layers //////////////////////////////////////////////////////////////////////////////// // // input --- conv --- eltwise --- output // `--- conv ----^ ^ ^ // `---- ... ------' ' // `-----------------' typedef TestWithParam<tuple<Vec3i, std::string, int, bool, tuple<Backend, Target> > > Eltwise; TEST_P(Eltwise, Accuracy) { Vec3i inSize = get<0>(GetParam()); std::string op = get<1>(GetParam()); int numConv = get<2>(GetParam()); bool weighted = get<3>(GetParam()); Backend backendId = get<0>(get<4>(GetParam())); Target targetId = get<1>(get<4>(GetParam())); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // accuracy if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL && inSize == Vec3i(1, 4, 5) && op == "sum" && numConv == 1 && !weighted) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL && inSize == Vec3i(2, 8, 6) && op == "sum" && numConv == 1 && !weighted) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD && inSize == Vec3i(1, 4, 5)) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && numConv > 1) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_OPENCL && op == "sum" && numConv == 1 && !weighted) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); #endif #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && numConv > 1) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif bool convInputShift = 1; int numEltwiseInputs = numConv; if (op == "div") { numConv = 1; convInputShift = 0; // first input is convolution } Net net; std::vector<int> convLayerIds(numConv); for (int i = 0; i < numConv; ++i) { int sz[] = {inSize[0], inSize[0], 1, 1}; Mat weights(4, &sz[0], CV_32F); randu(weights, -1.0f, 1.0f); LayerParams convParam; convParam.set("kernel_w", 1); convParam.set("kernel_h", 1); convParam.set("num_output", inSize[0]); convParam.set("bias_term", false); convParam.type = "Convolution"; std::ostringstream ss; ss << "convLayer" << i; convParam.name = ss.str(); convParam.blobs.push_back(weights); convLayerIds[i] = net.addLayer(convParam.name, convParam.type, convParam); net.connect(0, 0, convLayerIds[i], 0); } LayerParams eltwiseParam; eltwiseParam.set("operation", op); if (op == "sum" && weighted) { RNG& rng = cv::theRNG(); std::vector<float> coeff(1 + numConv); for (int i = 0; i < coeff.size(); ++i) { coeff[i] = rng.uniform(-2.0f, 2.0f); } eltwiseParam.set("coeff", DictValue::arrayReal<float*>(&coeff[0], coeff.size())); } eltwiseParam.type = "Eltwise"; eltwiseParam.name = "testLayer"; int eltwiseId = net.addLayer(eltwiseParam.name, eltwiseParam.type, eltwiseParam); if (convInputShift == 1) net.connect(0, 0, eltwiseId, 0); for (int i = 0; i < numConv; ++i) { net.connect(convLayerIds[i], 0, eltwiseId, i + convInputShift); } if (convInputShift == 0) net.connect(0, 0, eltwiseId, numConv); for (int i = numConv; i < numEltwiseInputs; ++i) { net.connect(0, 0, eltwiseId, i + 1); } int sz[] = {1, inSize[0], inSize[1], inSize[2]}; Mat input(4, &sz[0], CV_32F); if (op == "div") randu(input, 1.0f, 1.0f); // ensure no divisor value has absouluate value of less than 0.5 testLayer(input, net, backendId, targetId, /*skipCheck*/false, (op == "div") ? false : true); } INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Eltwise, testing::Combine( /*input size*/ testing::Values(Vec3i(1, 4, 5), Vec3i(2, 8, 6)), /*operation*/ testing::Values("prod", "sum", "div", "max", "min"), /*num convs*/ testing::Values(1, 2, 3), /*weighted(for sum only)*/ testing::Bool(), dnnBackendsAndTargets() )); INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_layers_backends, dnnBackendsAndTargets()); }} // namespace