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266 lines
9.3 KiB
C++
266 lines
9.3 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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//
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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 Mat& input, const std::string& outputLayer = "")
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{
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randu(input, 0.0f, 1.0f);
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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(proto, weights);
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net.setInput(blobFromImage(input, 1.0, Size(), Scalar(), false));
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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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MatShape netInputShape = shape(1, 3, input.rows, input.cols);
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size_t weightsMemory = 0, blobsMemory = 0;
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net.getMemoryConsumption(netInputShape, weightsMemory, blobsMemory);
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int64 flops = net.getFLOPS(netInputShape);
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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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};
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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", Mat(cv::Size(227, 227), CV_32FC3));
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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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"", Mat(cv::Size(224, 224), CV_32FC3));
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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", Mat(cv::Size(224, 224), CV_32FC3));
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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", Mat(cv::Size(227, 227), CV_32FC3));
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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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Mat(cv::Size(224, 224), CV_32FC3), "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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processNet("dnn/Enet-model-best.net", "", "enet.yml",
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Mat(cv::Size(512, 256), CV_32FC3));
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}
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PERF_TEST_P_(DNNTestNetwork, SSD)
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{
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processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", "dnn/ssd_vgg16.prototxt", "disabled",
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Mat(cv::Size(300, 300), CV_32FC3));
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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)
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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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Mat(cv::Size(96, 96), CV_32FC3));
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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.caffemodel", "dnn/MobileNetSSD_deploy.prototxt", "",
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Mat(cv::Size(300, 300), CV_32FC3));
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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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Mat(cv::Size(300, 300), CV_32FC3));
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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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Mat(cv::Size(300, 300), CV_32FC3));
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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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Mat(cv::Size(224, 224), CV_32FC3));
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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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if (backend == DNN_BACKEND_HALIDE ||
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(backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD))
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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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Mat(cv::Size(368, 368), CV_32FC3));
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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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Mat(cv::Size(300, 300), CV_32FC3));
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}
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PERF_TEST_P_(DNNTestNetwork, Inception_v2_SSD_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_inception_v2_coco_2017_11_17.pb", "ssd_inception_v2_coco_2017_11_17.pbtxt", "",
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Mat(cv::Size(300, 300), CV_32FC3));
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}
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PERF_TEST_P_(DNNTestNetwork, YOLOv3)
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{
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if (backend == DNN_BACKEND_HALIDE)
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throw SkipTestException("");
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Mat sample = imread(findDataFile("dnn/dog416.png"));
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cvtColor(sample, sample, COLOR_BGR2RGB);
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Mat inp;
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sample.convertTo(inp, CV_32FC3, 1.0f / 255, 0);
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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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if (backend == DNN_BACKEND_HALIDE)
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throw SkipTestException("");
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if (target == DNN_TARGET_MYRIAD)
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throw SkipTestException("");
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Mat sample = imread(findDataFile("dnn/dog416.png"));
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cvtColor(sample, sample, COLOR_BGR2RGB);
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Mat inp;
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sample.convertTo(inp, CV_32FC3, 1.0f / 255, 0);
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processNet("dnn/yolov4.weights", "dnn/yolov4.cfg", "", inp);
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}
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PERF_TEST_P_(DNNTestNetwork, EAST_text_detection)
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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/frozen_east_text_detection.pb", "", "", Mat(cv::Size(320, 320), CV_32FC3));
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}
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PERF_TEST_P_(DNNTestNetwork, FastNeuralStyle_eccv16)
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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/fast_neural_style_eccv16_starry_night.t7", "", "", Mat(cv::Size(320, 240), CV_32FC3));
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}
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PERF_TEST_P_(DNNTestNetwork, Inception_v2_Faster_RCNN)
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{
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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 (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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Mat(cv::Size(800, 600), CV_32FC3));
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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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resize(sample, sample, Size(512, 512));
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Mat inp;
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sample.convertTo(inp, CV_32FC3, 1.0/255);
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processNet("dnn/efficientdet-d0.pb", "dnn/efficientdet-d0.pbtxt", "", inp);
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}
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INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, dnnBackendsAndTargets());
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} // namespace
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