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Open Source Computer Vision Library
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450 lines
16 KiB
450 lines
16 KiB
// This file is part of OpenCV project. |
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// It is subject to the license terms in the LICENSE file found in the top-level directory |
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// of this distribution and at http://opencv.org/license.html. |
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// |
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// Copyright (C) 2017, Intel Corporation, all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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#include "perf_precomp.hpp" |
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#include "opencv2/core/ocl.hpp" |
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#include "opencv2/dnn/shape_utils.hpp" |
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#include "../test/test_common.hpp" |
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namespace opencv_test { |
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class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<Backend, Target> > |
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{ |
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public: |
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dnn::Backend backend; |
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dnn::Target target; |
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dnn::Net net; |
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DNNTestNetwork() |
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{ |
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backend = (dnn::Backend)(int)get<0>(GetParam()); |
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target = (dnn::Target)(int)get<1>(GetParam()); |
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} |
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void processNet(std::string weights, std::string proto, std::string halide_scheduler, |
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const std::vector<std::tuple<Mat, std::string>>& inputs, const std::string& outputLayer = ""){ |
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weights = findDataFile(weights, false); |
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if (!proto.empty()) |
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proto = findDataFile(proto); |
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if (backend == DNN_BACKEND_HALIDE) |
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{ |
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if (halide_scheduler == "disabled") |
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throw cvtest::SkipTestException("Halide test is disabled"); |
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if (!halide_scheduler.empty()) |
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halide_scheduler = findDataFile(std::string("dnn/halide_scheduler_") + (target == DNN_TARGET_OPENCL ? "opencl_" : "") + halide_scheduler, true); |
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} |
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net = readNet(weights, proto); |
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// Set multiple inputs |
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for(auto &inp: inputs){ |
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net.setInput(std::get<0>(inp), std::get<1>(inp)); |
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} |
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net.setPreferableBackend(backend); |
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net.setPreferableTarget(target); |
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if (backend == DNN_BACKEND_HALIDE) |
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{ |
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net.setHalideScheduler(halide_scheduler); |
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} |
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// Calculate multiple inputs memory consumption |
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std::vector<MatShape> netMatShapes; |
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for(auto &inp: inputs){ |
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netMatShapes.push_back(shape(std::get<0>(inp))); |
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} |
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size_t weightsMemory = 0, blobsMemory = 0; |
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net.getMemoryConsumption(netMatShapes, weightsMemory, blobsMemory); |
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int64 flops = net.getFLOPS(netMatShapes); |
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CV_Assert(flops > 0); |
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net.forward(outputLayer); // warmup |
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std::cout << "Memory consumption:" << std::endl; |
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std::cout << " Weights(parameters): " << divUp(weightsMemory, 1u<<20) << " Mb" << std::endl; |
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std::cout << " Blobs: " << divUp(blobsMemory, 1u<<20) << " Mb" << std::endl; |
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std::cout << "Calculation complexity: " << flops * 1e-9 << " GFlops" << std::endl; |
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PERF_SAMPLE_BEGIN() |
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net.forward(); |
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PERF_SAMPLE_END() |
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SANITY_CHECK_NOTHING(); |
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} |
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void processNet(std::string weights, std::string proto, std::string halide_scheduler, |
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Mat &input, const std::string& outputLayer = "") |
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{ |
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processNet(weights, proto, halide_scheduler, {std::make_tuple(input, "")}, outputLayer); |
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} |
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void processNet(std::string weights, std::string proto, std::string halide_scheduler, |
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Size inpSize, const std::string& outputLayer = "") |
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{ |
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Mat input_data(inpSize, CV_32FC3); |
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randu(input_data, 0.0f, 1.0f); |
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Mat input = blobFromImage(input_data, 1.0, Size(), Scalar(), false); |
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processNet(weights, proto, halide_scheduler, input, outputLayer); |
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} |
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}; |
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PERF_TEST_P_(DNNTestNetwork, AlexNet) |
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{ |
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processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt", |
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"alexnet.yml", cv::Size(227, 227)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, GoogLeNet) |
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{ |
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processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt", |
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"", cv::Size(224, 224)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, ResNet_50) |
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{ |
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processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt", |
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"resnet_50.yml", cv::Size(224, 224)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, SqueezeNet_v1_1) |
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{ |
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processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt", |
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"squeezenet_v1_1.yml", cv::Size(227, 227)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, Inception_5h) |
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{ |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) throw SkipTestException(""); |
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processNet("dnn/tensorflow_inception_graph.pb", "", |
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"inception_5h.yml", |
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cv::Size(224, 224), "softmax2"); |
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} |
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PERF_TEST_P_(DNNTestNetwork, ENet) |
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{ |
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if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) || |
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(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)) |
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throw SkipTestException(""); |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021010000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
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throw SkipTestException(""); |
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#endif |
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processNet("dnn/Enet-model-best.net", "", "enet.yml", |
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cv::Size(512, 256)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, SSD) |
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{ |
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applyTestTag(CV_TEST_TAG_DEBUG_VERYLONG); |
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processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", "dnn/ssd_vgg16.prototxt", "disabled", |
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cv::Size(300, 300)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, OpenFace) |
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{ |
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if (backend == DNN_BACKEND_HALIDE) |
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throw SkipTestException(""); |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_HDDL)) |
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throw SkipTestException(""); |
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#endif |
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processNet("dnn/openface_nn4.small2.v1.t7", "", "", |
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cv::Size(96, 96)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_Caffe) |
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{ |
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if (backend == DNN_BACKEND_HALIDE) |
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throw SkipTestException(""); |
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processNet("dnn/MobileNetSSD_deploy_19e3ec3.caffemodel", "dnn/MobileNetSSD_deploy_19e3ec3.prototxt", "", |
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cv::Size(300, 300)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow) |
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{ |
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if (backend == DNN_BACKEND_HALIDE) |
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throw SkipTestException(""); |
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processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "ssd_mobilenet_v1_coco_2017_11_17.pbtxt", "", |
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cv::Size(300, 300)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow) |
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{ |
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if (backend == DNN_BACKEND_HALIDE) |
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throw SkipTestException(""); |
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processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "ssd_mobilenet_v2_coco_2018_03_29.pbtxt", "", |
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cv::Size(300, 300)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, DenseNet_121) |
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{ |
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if (backend == DNN_BACKEND_HALIDE) |
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throw SkipTestException(""); |
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processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", "", |
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cv::Size(224, 224)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages) |
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{ |
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applyTestTag(CV_TEST_TAG_DEBUG_VERYLONG); |
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if (backend == DNN_BACKEND_HALIDE || |
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(backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_HDDL))) |
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throw SkipTestException(""); |
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// The same .caffemodel but modified .prototxt |
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// See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp |
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processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi_faster_4_stages.prototxt", "", |
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cv::Size(368, 368)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, opencv_face_detector) |
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{ |
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if (backend == DNN_BACKEND_HALIDE) |
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throw SkipTestException(""); |
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processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt", "", |
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cv::Size(300, 300)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, Inception_v2_SSD_TensorFlow) |
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{ |
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applyTestTag(CV_TEST_TAG_DEBUG_VERYLONG); |
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if (backend == DNN_BACKEND_HALIDE) |
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throw SkipTestException(""); |
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processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "ssd_inception_v2_coco_2017_11_17.pbtxt", "", |
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cv::Size(300, 300)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, YOLOv3) |
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{ |
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applyTestTag( |
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CV_TEST_TAG_MEMORY_2GB, |
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CV_TEST_TAG_DEBUG_VERYLONG |
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); |
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if (backend == DNN_BACKEND_HALIDE) |
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throw SkipTestException(""); |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) |
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throw SkipTestException("Test is disabled in OpenVINO 2020.4"); |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) |
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throw SkipTestException("Test is disabled in OpenVINO 2020.4"); |
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#endif |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021010000) // nGraph compilation failure |
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if (target == DNN_TARGET_MYRIAD) |
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throw SkipTestException(""); |
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#endif |
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Mat sample = imread(findDataFile("dnn/dog416.png")); |
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Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(), Scalar(), true); |
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processNet("dnn/yolov3.weights", "dnn/yolov3.cfg", "", inp); |
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} |
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PERF_TEST_P_(DNNTestNetwork, YOLOv4) |
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{ |
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applyTestTag( |
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CV_TEST_TAG_MEMORY_2GB, |
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CV_TEST_TAG_DEBUG_VERYLONG |
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); |
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if (backend == DNN_BACKEND_HALIDE) |
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throw SkipTestException(""); |
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if (target == DNN_TARGET_MYRIAD) // not enough resources |
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throw SkipTestException(""); |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) |
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throw SkipTestException("Test is disabled in OpenVINO 2020.4"); |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) |
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throw SkipTestException("Test is disabled in OpenVINO 2020.4"); |
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#endif |
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Mat sample = imread(findDataFile("dnn/dog416.png")); |
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Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(), Scalar(), true); |
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processNet("dnn/yolov4.weights", "dnn/yolov4.cfg", "", inp); |
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} |
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PERF_TEST_P_(DNNTestNetwork, YOLOv4_tiny) |
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{ |
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if (backend == DNN_BACKEND_HALIDE) |
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throw SkipTestException(""); |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021010000) // nGraph compilation failure |
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if (target == DNN_TARGET_MYRIAD) |
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throw SkipTestException(""); |
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#endif |
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Mat sample = imread(findDataFile("dnn/dog416.png")); |
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Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(), Scalar(), true); |
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processNet("dnn/yolov4-tiny-2020-12.weights", "dnn/yolov4-tiny-2020-12.cfg", "", inp); |
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} |
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PERF_TEST_P_(DNNTestNetwork, YOLOv5) { |
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applyTestTag(CV_TEST_TAG_MEMORY_512MB); |
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Mat sample = imread(findDataFile("dnn/dog416.png")); |
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Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(640, 640), Scalar(), true); |
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processNet("dnn/yolov5n.onnx", "", "", inp); |
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} |
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PERF_TEST_P_(DNNTestNetwork, YOLOv8) |
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{ |
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applyTestTag( |
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CV_TEST_TAG_MEMORY_512MB, |
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CV_TEST_TAG_DEBUG_LONG |
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); |
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Mat sample = imread(findDataFile("dnn/dog416.png")); |
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Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(640, 640), Scalar(), true); |
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processNet("dnn/yolov8n.onnx", "", "", inp); |
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} |
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PERF_TEST_P_(DNNTestNetwork, YOLOX) { |
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applyTestTag( |
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CV_TEST_TAG_MEMORY_512MB, |
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CV_TEST_TAG_DEBUG_VERYLONG |
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); |
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Mat sample = imread(findDataFile("dnn/dog416.png")); |
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Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(640, 640), Scalar(), true); |
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processNet("dnn/yolox_s.onnx", "", "", inp); |
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} |
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PERF_TEST_P_(DNNTestNetwork, EAST_text_detection) |
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{ |
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applyTestTag(CV_TEST_TAG_DEBUG_VERYLONG); |
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if (backend == DNN_BACKEND_HALIDE) |
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throw SkipTestException(""); |
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processNet("dnn/frozen_east_text_detection.pb", "", "", cv::Size(320, 320)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, FastNeuralStyle_eccv16) |
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{ |
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applyTestTag(CV_TEST_TAG_DEBUG_VERYLONG); |
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if (backend == DNN_BACKEND_HALIDE) |
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throw SkipTestException(""); |
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processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", "", cv::Size(320, 240)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, Inception_v2_Faster_RCNN) |
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{ |
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applyTestTag(CV_TEST_TAG_DEBUG_VERYLONG); |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019010000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
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throw SkipTestException("Test is disabled in OpenVINO 2019R1"); |
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#endif |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) |
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throw SkipTestException("Test is disabled in OpenVINO 2019R2"); |
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#endif |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021010000) |
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if (target == DNN_TARGET_MYRIAD) |
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throw SkipTestException("Test is disabled in OpenVINO 2021.1+ / MYRIAD"); |
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#endif |
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if (backend == DNN_BACKEND_HALIDE || |
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(backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) || |
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(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)) |
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throw SkipTestException(""); |
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processNet("dnn/faster_rcnn_inception_v2_coco_2018_01_28.pb", |
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"dnn/faster_rcnn_inception_v2_coco_2018_01_28.pbtxt", "", |
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cv::Size(800, 600)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, EfficientDet) |
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{ |
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if (backend == DNN_BACKEND_HALIDE || target != DNN_TARGET_CPU) |
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throw SkipTestException(""); |
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Mat sample = imread(findDataFile("dnn/dog416.png")); |
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Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(512, 512), Scalar(), true); |
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processNet("dnn/efficientdet-d0.pb", "dnn/efficientdet-d0.pbtxt", "", inp); |
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} |
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PERF_TEST_P_(DNNTestNetwork, EfficientNet) |
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{ |
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Mat sample = imread(findDataFile("dnn/dog416.png")); |
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Mat inp = blobFromImage(sample, 1.0 / 255.0, Size(224, 224), Scalar(), true); |
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transposeND(inp, {0, 2, 3, 1}, inp); |
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processNet("dnn/efficientnet-lite4.onnx", "", "", inp); |
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} |
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PERF_TEST_P_(DNNTestNetwork, YuNet) { |
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processNet("dnn/onnx/models/yunet-202303.onnx", "", "", cv::Size(640, 640)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, SFace) { |
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processNet("dnn/face_recognition_sface_2021dec.onnx", "", "", cv::Size(112, 112)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, MPPalm) { |
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Mat inp(cv::Size(192, 192), CV_32FC3); |
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randu(inp, 0.0f, 1.0f); |
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inp = blobFromImage(inp, 1.0, Size(), Scalar(), false); |
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transposeND(inp, {0, 2, 3, 1}, inp); |
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processNet("dnn/palm_detection_mediapipe_2023feb.onnx", "", "", inp); |
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} |
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PERF_TEST_P_(DNNTestNetwork, MPHand) { |
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Mat inp(cv::Size(224, 224), CV_32FC3); |
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randu(inp, 0.0f, 1.0f); |
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inp = blobFromImage(inp, 1.0, Size(), Scalar(), false); |
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transposeND(inp, {0, 2, 3, 1}, inp); |
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processNet("dnn/handpose_estimation_mediapipe_2023feb.onnx", "", "", inp); |
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} |
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PERF_TEST_P_(DNNTestNetwork, MPPose) { |
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Mat inp(cv::Size(256, 256), CV_32FC3); |
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randu(inp, 0.0f, 1.0f); |
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inp = blobFromImage(inp, 1.0, Size(), Scalar(), false); |
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transposeND(inp, {0, 2, 3, 1}, inp); |
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processNet("dnn/pose_estimation_mediapipe_2023mar.onnx", "", "", inp); |
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} |
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PERF_TEST_P_(DNNTestNetwork, PPOCRv3) { |
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applyTestTag(CV_TEST_TAG_MEMORY_512MB); |
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processNet("dnn/onnx/models/PP_OCRv3_DB_text_det.onnx", "", "", cv::Size(736, 736)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, PPHumanSeg) { |
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processNet("dnn/human_segmentation_pphumanseg_2023mar.onnx", "", "", cv::Size(192, 192)); |
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} |
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PERF_TEST_P_(DNNTestNetwork, CRNN) { |
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Mat inp(cv::Size(100, 32), CV_32FC1); |
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randu(inp, 0.0f, 1.0f); |
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inp = blobFromImage(inp, 1.0, Size(), Scalar(), false); |
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processNet("dnn/text_recognition_CRNN_EN_2021sep.onnx", "", "", inp); |
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} |
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PERF_TEST_P_(DNNTestNetwork, VitTrack) { |
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Mat inp1(cv::Size(128, 128), CV_32FC3); |
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Mat inp2(cv::Size(256, 256), CV_32FC3); |
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randu(inp1, 0.0f, 1.0f); |
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randu(inp2, 0.0f, 1.0f); |
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inp1 = blobFromImage(inp1, 1.0, Size(), Scalar(), false); |
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inp2 = blobFromImage(inp2, 1.0, Size(), Scalar(), false); |
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processNet("dnn/onnx/models/object_tracking_vittrack_2023sep.onnx", "", "", |
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{std::make_tuple(inp1, "template"), std::make_tuple(inp2, "search")}); |
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} |
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PERF_TEST_P_(DNNTestNetwork, EfficientDet_int8) |
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{ |
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if (target != DNN_TARGET_CPU || (backend != DNN_BACKEND_OPENCV && |
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backend != DNN_BACKEND_TIMVX && backend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)) { |
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throw SkipTestException(""); |
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} |
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Mat inp = imread(findDataFile("dnn/dog416.png")); |
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inp = blobFromImage(inp, 1.0 / 255.0, Size(320, 320), Scalar(), true); |
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processNet("dnn/tflite/coco_efficientdet_lite0_v1_1.0_quant_2021_09_06.tflite", "", "", inp); |
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} |
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PERF_TEST_P_(DNNTestNetwork, VIT_B_32) |
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{ |
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applyTestTag(CV_TEST_TAG_DEBUG_VERYLONG); |
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processNet("dnn/onnx/models/vit_b_32.onnx", "", "", cv::Size(224, 224)); |
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} |
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INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, dnnBackendsAndTargets()); |
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} // namespace
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