mirror of https://github.com/opencv/opencv.git
Merge pull request #12037 from dkurt:test_openvino_models
Alexander Alekhin
7 years ago
commit
236f383969
5 changed files with 278 additions and 11 deletions
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// 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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#ifdef HAVE_INF_ENGINE |
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#include <opencv2/core/utils/filesystem.hpp> |
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#include <inference_engine.hpp> |
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#include <ie_icnn_network.hpp> |
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#include <ie_extension.h> |
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namespace opencv_test { namespace { |
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static void initDLDTDataPath() |
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{ |
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#ifndef WINRT |
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static bool initialized = false; |
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if (!initialized) |
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{ |
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const char* dldtTestDataPath = getenv("INTEL_CVSDK_DIR"); |
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if (dldtTestDataPath) |
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cvtest::addDataSearchPath(cv::utils::fs::join(dldtTestDataPath, "deployment_tools")); |
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initialized = true; |
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} |
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#endif |
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} |
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using namespace cv; |
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using namespace cv::dnn; |
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using namespace InferenceEngine; |
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static inline void genData(const std::vector<size_t>& dims, Mat& m, Blob::Ptr& dataPtr) |
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{ |
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std::vector<int> reversedDims(dims.begin(), dims.end()); |
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std::reverse(reversedDims.begin(), reversedDims.end()); |
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m.create(reversedDims, CV_32F); |
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randu(m, -1, 1); |
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dataPtr = make_shared_blob<float>(Precision::FP32, dims, (float*)m.data); |
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} |
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void runIE(Target target, const std::string& xmlPath, const std::string& binPath, |
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std::map<std::string, cv::Mat>& inputsMap, std::map<std::string, cv::Mat>& outputsMap) |
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{ |
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CNNNetReader reader; |
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reader.ReadNetwork(xmlPath); |
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reader.ReadWeights(binPath); |
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CNNNetwork net = reader.getNetwork(); |
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InferenceEnginePluginPtr enginePtr; |
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InferencePlugin plugin; |
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ExecutableNetwork netExec; |
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InferRequest infRequest; |
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TargetDevice targetDevice; |
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switch (target) |
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{ |
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case DNN_TARGET_CPU: |
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targetDevice = TargetDevice::eCPU; |
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break; |
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case DNN_TARGET_OPENCL: |
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case DNN_TARGET_OPENCL_FP16: |
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targetDevice = TargetDevice::eGPU; |
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break; |
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case DNN_TARGET_MYRIAD: |
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targetDevice = TargetDevice::eMYRIAD; |
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break; |
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default: |
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CV_Error(Error::StsNotImplemented, "Unknown target"); |
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}; |
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try |
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{ |
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enginePtr = PluginDispatcher({""}).getSuitablePlugin(targetDevice); |
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if (targetDevice == TargetDevice::eCPU) |
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{ |
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std::string suffixes[] = {"_avx2", "_sse4", ""}; |
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bool haveFeature[] = { |
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checkHardwareSupport(CPU_AVX2), |
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checkHardwareSupport(CPU_SSE4_2), |
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true |
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}; |
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for (int i = 0; i < 3; ++i) |
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{ |
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if (!haveFeature[i]) |
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continue; |
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#ifdef _WIN32 |
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std::string libName = "cpu_extension" + suffixes[i] + ".dll"; |
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#else |
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std::string libName = "libcpu_extension" + suffixes[i] + ".so"; |
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#endif // _WIN32
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try |
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{ |
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IExtensionPtr extension = make_so_pointer<IExtension>(libName); |
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enginePtr->AddExtension(extension, 0); |
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break; |
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} |
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catch(...) {} |
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} |
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// Some of networks can work without a library of extra layers.
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} |
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plugin = InferencePlugin(enginePtr); |
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netExec = plugin.LoadNetwork(net, {}); |
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infRequest = netExec.CreateInferRequest(); |
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} |
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catch (const std::exception& ex) |
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{ |
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CV_Error(Error::StsAssert, format("Failed to initialize Inference Engine backend: %s", ex.what())); |
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} |
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// Fill input blobs.
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inputsMap.clear(); |
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BlobMap inputBlobs; |
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for (auto& it : net.getInputsInfo()) |
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{ |
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genData(it.second->getDims(), inputsMap[it.first], inputBlobs[it.first]); |
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} |
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infRequest.SetInput(inputBlobs); |
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// Fill output blobs.
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outputsMap.clear(); |
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BlobMap outputBlobs; |
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for (auto& it : net.getOutputsInfo()) |
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{ |
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genData(it.second->dims, outputsMap[it.first], outputBlobs[it.first]); |
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} |
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infRequest.SetOutput(outputBlobs); |
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infRequest.Infer(); |
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} |
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std::vector<String> getOutputsNames(const Net& net) |
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{ |
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std::vector<String> names; |
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if (names.empty()) |
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{ |
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std::vector<int> outLayers = net.getUnconnectedOutLayers(); |
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std::vector<String> layersNames = net.getLayerNames(); |
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names.resize(outLayers.size()); |
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for (size_t i = 0; i < outLayers.size(); ++i) |
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names[i] = layersNames[outLayers[i] - 1]; |
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} |
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return names; |
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} |
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void runCV(Target target, const std::string& xmlPath, const std::string& binPath, |
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const std::map<std::string, cv::Mat>& inputsMap, |
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std::map<std::string, cv::Mat>& outputsMap) |
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{ |
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Net net = readNet(xmlPath, binPath); |
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for (auto& it : inputsMap) |
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net.setInput(it.second, it.first); |
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net.setPreferableTarget(target); |
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std::vector<String> outNames = getOutputsNames(net); |
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std::vector<Mat> outs; |
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net.forward(outs, outNames); |
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outputsMap.clear(); |
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EXPECT_EQ(outs.size(), outNames.size()); |
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for (int i = 0; i < outs.size(); ++i) |
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{ |
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EXPECT_TRUE(outputsMap.insert({outNames[i], outs[i]}).second); |
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} |
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} |
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typedef TestWithParam<tuple<Target, String> > DNNTestOpenVINO; |
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TEST_P(DNNTestOpenVINO, models) |
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{ |
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Target target = (dnn::Target)(int)get<0>(GetParam()); |
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std::string modelName = get<1>(GetParam()); |
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if (modelName == "semantic-segmentation-adas-0001" && target == DNN_TARGET_OPENCL_FP16) |
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throw SkipTestException(""); |
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std::string precision = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? "FP16" : "FP32"; |
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std::string prefix = utils::fs::join("intel_models", |
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utils::fs::join(modelName, |
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utils::fs::join(precision, modelName))); |
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std::string xmlPath = findDataFile(prefix + ".xml"); |
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std::string binPath = findDataFile(prefix + ".bin"); |
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std::map<std::string, cv::Mat> inputsMap; |
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std::map<std::string, cv::Mat> ieOutputsMap, cvOutputsMap; |
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runIE(target, xmlPath, binPath, inputsMap, ieOutputsMap); |
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runCV(target, xmlPath, binPath, inputsMap, cvOutputsMap); |
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EXPECT_EQ(ieOutputsMap.size(), cvOutputsMap.size()); |
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for (auto& srcIt : ieOutputsMap) |
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{ |
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auto dstIt = cvOutputsMap.find(srcIt.first); |
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CV_Assert(dstIt != cvOutputsMap.end()); |
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double normInf = cvtest::norm(srcIt.second, dstIt->second, cv::NORM_INF); |
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EXPECT_EQ(normInf, 0); |
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} |
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} |
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static testing::internal::ParamGenerator<String> intelModels() |
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{ |
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initDLDTDataPath(); |
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std::vector<String> modelsNames; |
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std::string path; |
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try |
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{ |
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path = findDataDirectory("intel_models", false); |
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} |
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catch (...) |
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{ |
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std::cerr << "ERROR: Can't find OpenVINO models. Check INTEL_CVSDK_DIR environment variable (run setup.sh)" << std::endl; |
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return ValuesIn(modelsNames); // empty list
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} |
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cv::utils::fs::glob_relative(path, "", modelsNames, false, true); |
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modelsNames.erase( |
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std::remove_if(modelsNames.begin(), modelsNames.end(), |
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[&](const String& dir){ return !utils::fs::isDirectory(utils::fs::join(path, dir)); }), |
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modelsNames.end() |
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); |
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CV_Assert(!modelsNames.empty()); |
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return ValuesIn(modelsNames); |
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} |
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INSTANTIATE_TEST_CASE_P(/**/, DNNTestOpenVINO, Combine( |
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Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16), intelModels() |
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)); |
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}} |
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#endif // HAVE_INF_ENGINE
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