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259 lines
10 KiB
C++
259 lines
10 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) 2018, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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#include "test_precomp.hpp"
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#include "opencv2/core/ocl.hpp"
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namespace opencv_test { namespace {
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CV_ENUM(DNNBackend, DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE, DNN_BACKEND_INFERENCE_ENGINE)
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CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL)
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static void loadNet(const std::string& weights, const std::string& proto,
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const std::string& framework, Net* net)
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{
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if (framework == "caffe")
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*net = cv::dnn::readNetFromCaffe(proto, weights);
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else if (framework == "torch")
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*net = cv::dnn::readNetFromTorch(weights);
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else if (framework == "tensorflow")
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*net = cv::dnn::readNetFromTensorflow(weights, proto);
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else
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CV_Error(Error::StsNotImplemented, "Unknown framework " + framework);
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}
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class DNNTestNetwork : public TestWithParam <tuple<DNNBackend, DNNTarget> >
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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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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(const std::string& weights, const std::string& proto,
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Size inpSize, const std::string& outputLayer,
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const std::string& framework, const std::string& halideScheduler = "",
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double l1 = 1e-5, double lInf = 1e-4)
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{
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// Create a common input blob.
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int blobSize[] = {1, 3, inpSize.height, inpSize.width};
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Mat inp(4, blobSize, CV_32FC1);
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randu(inp, 0.0f, 1.0f);
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processNet(weights, proto, inp, outputLayer, framework, halideScheduler, l1, lInf);
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}
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void processNet(std::string weights, std::string proto,
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Mat inp, const std::string& outputLayer,
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const std::string& framework, std::string halideScheduler = "",
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double l1 = 1e-5, double lInf = 1e-4)
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{
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if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL)
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{
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#ifdef HAVE_OPENCL
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if (!cv::ocl::useOpenCL())
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#endif
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{
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throw SkipTestException("OpenCL is not available/disabled in OpenCV");
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}
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}
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weights = findDataFile(weights, false);
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if (!proto.empty())
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proto = findDataFile(proto, false);
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// Create two networks - with default backend and target and a tested one.
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Net netDefault, net;
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loadNet(weights, proto, framework, &netDefault);
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loadNet(weights, proto, framework, &net);
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netDefault.setInput(inp);
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Mat outDefault = netDefault.forward(outputLayer).clone();
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net.setInput(inp);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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if (backend == DNN_BACKEND_HALIDE && !halideScheduler.empty())
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{
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halideScheduler = findDataFile(halideScheduler, false);
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net.setHalideScheduler(halideScheduler);
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}
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Mat out = net.forward(outputLayer).clone();
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if (outputLayer == "detection_out")
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checkDetections(outDefault, out, "First run", l1, lInf);
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else
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normAssert(outDefault, out, "First run", l1, lInf);
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// Test 2: change input.
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inp *= 0.1f;
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netDefault.setInput(inp);
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net.setInput(inp);
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outDefault = netDefault.forward(outputLayer).clone();
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out = net.forward(outputLayer).clone();
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if (outputLayer == "detection_out")
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checkDetections(outDefault, out, "Second run", l1, lInf);
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else
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normAssert(outDefault, out, "Second run", l1, lInf);
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}
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void checkDetections(const Mat& out, const Mat& ref, const std::string& msg,
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float l1, float lInf, int top = 5)
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{
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top = std::min(std::min(top, out.size[2]), out.size[3]);
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std::vector<cv::Range> range(4, cv::Range::all());
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range[2] = cv::Range(0, top);
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normAssert(out(range), ref(range));
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}
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};
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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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Size(227, 227), "prob", "caffe",
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target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_alexnet.yml" :
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"dnn/halide_scheduler_alexnet.yml");
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}
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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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Size(224, 224), "prob", "caffe",
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target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_resnet_50.yml" :
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"dnn/halide_scheduler_resnet_50.yml");
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}
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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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Size(227, 227), "prob", "caffe",
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target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_squeezenet_v1_1.yml" :
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"dnn/halide_scheduler_squeezenet_v1_1.yml");
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}
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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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Size(224, 224), "prob", "caffe");
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}
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TEST_P(DNNTestNetwork, Inception_5h)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE) throw SkipTestException("");
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processNet("dnn/tensorflow_inception_graph.pb", "", Size(224, 224), "softmax2", "tensorflow",
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target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_inception_5h.yml" :
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"dnn/halide_scheduler_inception_5h.yml");
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}
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TEST_P(DNNTestNetwork, ENet)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE) throw SkipTestException("");
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processNet("dnn/Enet-model-best.net", "", Size(512, 512), "l367_Deconvolution", "torch",
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target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_enet.yml" :
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"dnn/halide_scheduler_enet.yml",
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2e-5, 0.15);
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}
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TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
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{
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if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
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Mat sample = imread(findDataFile("dnn/street.png", false));
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Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
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processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
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inp, "detection_out", "caffe");
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}
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TEST_P(DNNTestNetwork, MobileNet_SSD_TensorFlow)
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{
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if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
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Mat sample = imread(findDataFile("dnn/street.png", false));
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Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
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processNet("dnn/ssd_mobilenet_v1_coco.pb", "dnn/ssd_mobilenet_v1_coco.pbtxt",
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inp, "detection_out", "tensorflow");
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}
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TEST_P(DNNTestNetwork, SSD_VGG16)
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{
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if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL ||
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backend == DNN_BACKEND_HALIDE && target == DNN_TARGET_CPU ||
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backend == DNN_BACKEND_INFERENCE_ENGINE)
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throw SkipTestException("");
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processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
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"dnn/ssd_vgg16.prototxt", Size(300, 300), "detection_out", "caffe");
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}
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TEST_P(DNNTestNetwork, OpenPose_pose_coco)
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{
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if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
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processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
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Size(368, 368), "", "caffe");
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}
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TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
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{
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if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
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processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
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Size(368, 368), "", "caffe");
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}
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TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
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{
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if (backend == DNN_BACKEND_HALIDE) 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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Size(368, 368), "", "caffe");
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}
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TEST_P(DNNTestNetwork, OpenFace)
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{
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if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
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processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "", "torch");
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}
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TEST_P(DNNTestNetwork, opencv_face_detector)
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{
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if (backend == DNN_BACKEND_HALIDE ||
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backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL)
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throw SkipTestException("");
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Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
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Mat inp = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
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processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt",
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inp, "detection_out", "caffe");
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}
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TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
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{
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if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL ||
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backend == DNN_BACKEND_HALIDE)
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throw SkipTestException("");
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Mat sample = imread(findDataFile("dnn/street.png", false));
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Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
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processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "dnn/ssd_inception_v2_coco_2017_11_17.pbtxt",
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inp, "detection_out", "tensorflow");
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}
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const tuple<DNNBackend, DNNTarget> testCases[] = {
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#ifdef HAVE_HALIDE
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tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
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tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
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#endif
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#ifdef HAVE_INF_ENGINE
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tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
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#endif
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tuple<DNNBackend, DNNTarget>(DNN_BACKEND_DEFAULT, DNN_TARGET_OPENCL)
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};
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INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, testing::ValuesIn(testCases));
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}} // namespace
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