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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2018-2019, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
#include "test_precomp.hpp"
#ifdef HAVE_INF_ENGINE
#include <opencv2/core/utils/filesystem.hpp>
//
// Synchronize headers include statements with src/op_inf_engine.hpp
//
//#define INFERENCE_ENGINE_DEPRECATED // turn off deprecation warnings from IE
//there is no way to suppress warnigns from IE only at this moment, so we are forced to suppress warnings globally
#if defined(__GNUC__)
#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
#endif
#ifdef _MSC_VER
#pragma warning(disable: 4996) // was declared deprecated
#endif
#if defined(__GNUC__)
#pragma GCC visibility push(default)
#endif
#include <inference_engine.hpp>
#include <ie_icnn_network.hpp>
#include <ie_extension.h>
#if defined(__GNUC__)
#pragma GCC visibility pop
#endif
namespace opencv_test { namespace {
static void initDLDTDataPath()
{
#ifndef WINRT
static bool initialized = false;
if (!initialized)
{
#if INF_ENGINE_RELEASE <= 2018050000
const char* dldtTestDataPath = getenv("INTEL_CVSDK_DIR");
if (dldtTestDataPath)
cvtest::addDataSearchPath(dldtTestDataPath);
#else
const char* omzDataPath = getenv("OPENCV_OPEN_MODEL_ZOO_DATA_PATH");
if (omzDataPath)
cvtest::addDataSearchPath(omzDataPath);
const char* dnnDataPath = getenv("OPENCV_DNN_TEST_DATA_PATH");
if (dnnDataPath)
cvtest::addDataSearchPath(std::string(dnnDataPath) + "/omz_intel_models");
#endif
initialized = true;
}
#endif
}
using namespace cv;
using namespace cv::dnn;
using namespace InferenceEngine;
struct OpenVINOModelTestCaseInfo
{
const char* modelPathFP32;
const char* modelPathFP16;
};
static const std::map<std::string, OpenVINOModelTestCaseInfo>& getOpenVINOTestModels()
{
static std::map<std::string, OpenVINOModelTestCaseInfo> g_models {
#if INF_ENGINE_RELEASE <= 2018050000
{ "age-gender-recognition-retail-0013", {
"deployment_tools/intel_models/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013",
"deployment_tools/intel_models/age-gender-recognition-retail-0013/FP16/age-gender-recognition-retail-0013"
}},
{ "face-person-detection-retail-0002", {
"deployment_tools/intel_models/face-person-detection-retail-0002/FP32/face-person-detection-retail-0002",
"deployment_tools/intel_models/face-person-detection-retail-0002/FP16/face-person-detection-retail-0002"
}},
{ "head-pose-estimation-adas-0001", {
"deployment_tools/intel_models/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001",
"deployment_tools/intel_models/head-pose-estimation-adas-0001/FP16/head-pose-estimation-adas-0001"
}},
{ "person-detection-retail-0002", {
"deployment_tools/intel_models/person-detection-retail-0002/FP32/person-detection-retail-0002",
"deployment_tools/intel_models/person-detection-retail-0002/FP16/person-detection-retail-0002"
}},
{ "vehicle-detection-adas-0002", {
"deployment_tools/intel_models/vehicle-detection-adas-0002/FP32/vehicle-detection-adas-0002",
"deployment_tools/intel_models/vehicle-detection-adas-0002/FP16/vehicle-detection-adas-0002"
}}
#else
// layout is defined by open_model_zoo/model_downloader
// Downloaded using these parameters for Open Model Zoo downloader (2019R1):
// ./downloader.py -o ${OPENCV_DNN_TEST_DATA_PATH}/omz_intel_models --cache_dir ${OPENCV_DNN_TEST_DATA_PATH}/.omz_cache/ \
// --name face-person-detection-retail-0002,face-person-detection-retail-0002-fp16,age-gender-recognition-retail-0013,age-gender-recognition-retail-0013-fp16,head-pose-estimation-adas-0001,head-pose-estimation-adas-0001-fp16,person-detection-retail-0002,person-detection-retail-0002-fp16,vehicle-detection-adas-0002,vehicle-detection-adas-0002-fp16
{ "age-gender-recognition-retail-0013", {
"Retail/object_attributes/age_gender/dldt/age-gender-recognition-retail-0013",
"Retail/object_attributes/age_gender/dldt/age-gender-recognition-retail-0013-fp16"
}},
{ "face-person-detection-retail-0002", {
"Retail/object_detection/face_pedestrian/rmnet-ssssd-2heads/0002/dldt/face-person-detection-retail-0002",
"Retail/object_detection/face_pedestrian/rmnet-ssssd-2heads/0002/dldt/face-person-detection-retail-0002-fp16"
}},
{ "head-pose-estimation-adas-0001", {
"Transportation/object_attributes/headpose/vanilla_cnn/dldt/head-pose-estimation-adas-0001",
"Transportation/object_attributes/headpose/vanilla_cnn/dldt/head-pose-estimation-adas-0001-fp16"
}},
{ "person-detection-retail-0002", {
"Retail/object_detection/pedestrian/hypernet-rfcn/0026/dldt/person-detection-retail-0002",
"Retail/object_detection/pedestrian/hypernet-rfcn/0026/dldt/person-detection-retail-0002-fp16"
}},
{ "vehicle-detection-adas-0002", {
"Transportation/object_detection/vehicle/mobilenet-reduced-ssd/dldt/vehicle-detection-adas-0002",
"Transportation/object_detection/vehicle/mobilenet-reduced-ssd/dldt/vehicle-detection-adas-0002-fp16"
}}
#endif
};
return g_models;
}
static const std::vector<std::string> getOpenVINOTestModelsList()
{
std::vector<std::string> result;
const std::map<std::string, OpenVINOModelTestCaseInfo>& models = getOpenVINOTestModels();
for (const auto& it : models)
result.push_back(it.first);
return result;
}
static inline void genData(const std::vector<size_t>& dims, Mat& m, Blob::Ptr& dataPtr)
{
std::vector<int> reversedDims(dims.begin(), dims.end());
std::reverse(reversedDims.begin(), reversedDims.end());
m.create(reversedDims, CV_32F);
randu(m, -1, 1);
dataPtr = make_shared_blob<float>(Precision::FP32, dims, (float*)m.data);
}
void runIE(Target target, const std::string& xmlPath, const std::string& binPath,
std::map<std::string, cv::Mat>& inputsMap, std::map<std::string, cv::Mat>& outputsMap)
{
CNNNetReader reader;
reader.ReadNetwork(xmlPath);
reader.ReadWeights(binPath);
CNNNetwork net = reader.getNetwork();
InferenceEnginePluginPtr enginePtr;
InferencePlugin plugin;
ExecutableNetwork netExec;
InferRequest infRequest;
try
{
auto dispatcher = InferenceEngine::PluginDispatcher({""});
switch (target)
{
case DNN_TARGET_CPU:
enginePtr = dispatcher.getSuitablePlugin(TargetDevice::eCPU);
break;
case DNN_TARGET_OPENCL:
case DNN_TARGET_OPENCL_FP16:
enginePtr = dispatcher.getSuitablePlugin(TargetDevice::eGPU);
break;
case DNN_TARGET_MYRIAD:
enginePtr = dispatcher.getSuitablePlugin(TargetDevice::eMYRIAD);
break;
case DNN_TARGET_FPGA:
enginePtr = dispatcher.getPluginByDevice("HETERO:FPGA,CPU");
break;
default:
CV_Error(Error::StsNotImplemented, "Unknown target");
};
if (target == DNN_TARGET_CPU || target == DNN_TARGET_FPGA)
{
std::string suffixes[] = {"_avx2", "_sse4", ""};
bool haveFeature[] = {
checkHardwareSupport(CPU_AVX2),
checkHardwareSupport(CPU_SSE4_2),
true
};
for (int i = 0; i < 3; ++i)
{
if (!haveFeature[i])
continue;
#ifdef _WIN32
std::string libName = "cpu_extension" + suffixes[i] + ".dll";
#elif defined(__APPLE__)
std::string libName = "libcpu_extension" + suffixes[i] + ".dylib";
#else
std::string libName = "libcpu_extension" + suffixes[i] + ".so";
#endif // _WIN32
try
{
IExtensionPtr extension = make_so_pointer<IExtension>(libName);
enginePtr->AddExtension(extension, 0);
break;
}
catch(...) {}
}
// Some of networks can work without a library of extra layers.
}
plugin = InferencePlugin(enginePtr);
netExec = plugin.LoadNetwork(net, {});
infRequest = netExec.CreateInferRequest();
}
catch (const std::exception& ex)
{
CV_Error(Error::StsAssert, format("Failed to initialize Inference Engine backend: %s", ex.what()));
}
// Fill input blobs.
inputsMap.clear();
BlobMap inputBlobs;
for (auto& it : net.getInputsInfo())
{
genData(it.second->getDims(), inputsMap[it.first], inputBlobs[it.first]);
}
infRequest.SetInput(inputBlobs);
// Fill output blobs.
outputsMap.clear();
BlobMap outputBlobs;
for (auto& it : net.getOutputsInfo())
{
genData(it.second->dims, outputsMap[it.first], outputBlobs[it.first]);
}
infRequest.SetOutput(outputBlobs);
infRequest.Infer();
}
std::vector<String> getOutputsNames(const Net& net)
{
std::vector<String> names;
if (names.empty())
{
std::vector<int> outLayers = net.getUnconnectedOutLayers();
std::vector<String> layersNames = net.getLayerNames();
names.resize(outLayers.size());
for (size_t i = 0; i < outLayers.size(); ++i)
names[i] = layersNames[outLayers[i] - 1];
}
return names;
}
void runCV(Target target, const std::string& xmlPath, const std::string& binPath,
const std::map<std::string, cv::Mat>& inputsMap,
std::map<std::string, cv::Mat>& outputsMap)
{
Net net = readNet(xmlPath, binPath);
for (auto& it : inputsMap)
net.setInput(it.second, it.first);
net.setPreferableTarget(target);
std::vector<String> outNames = getOutputsNames(net);
std::vector<Mat> outs;
net.forward(outs, outNames);
outputsMap.clear();
EXPECT_EQ(outs.size(), outNames.size());
for (int i = 0; i < outs.size(); ++i)
{
EXPECT_TRUE(outputsMap.insert({outNames[i], outs[i]}).second);
}
}
typedef TestWithParam<tuple<Target, std::string> > DNNTestOpenVINO;
TEST_P(DNNTestOpenVINO, models)
{
initDLDTDataPath();
Target target = (dnn::Target)(int)get<0>(GetParam());
std::string modelName = get<1>(GetParam());
bool isFP16 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD);
const std::map<std::string, OpenVINOModelTestCaseInfo>& models = getOpenVINOTestModels();
const auto it = models.find(modelName);
ASSERT_TRUE(it != models.end()) << modelName;
OpenVINOModelTestCaseInfo modelInfo = it->second;
std::string modelPath = isFP16 ? modelInfo.modelPathFP16 : modelInfo.modelPathFP32;
std::string xmlPath = findDataFile(modelPath + ".xml");
std::string binPath = findDataFile(modelPath + ".bin");
std::map<std::string, cv::Mat> inputsMap;
std::map<std::string, cv::Mat> ieOutputsMap, cvOutputsMap;
// Single Myriad device cannot be shared across multiple processes.
if (target == DNN_TARGET_MYRIAD)
resetMyriadDevice();
runIE(target, xmlPath, binPath, inputsMap, ieOutputsMap);
runCV(target, xmlPath, binPath, inputsMap, cvOutputsMap);
EXPECT_EQ(ieOutputsMap.size(), cvOutputsMap.size());
for (auto& srcIt : ieOutputsMap)
{
auto dstIt = cvOutputsMap.find(srcIt.first);
CV_Assert(dstIt != cvOutputsMap.end());
double normInf = cvtest::norm(srcIt.second, dstIt->second, cv::NORM_INF);
EXPECT_EQ(normInf, 0);
}
}
INSTANTIATE_TEST_CASE_P(/**/,
DNNTestOpenVINO,
Combine(testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE)),
testing::ValuesIn(getOpenVINOTestModelsList())
)
);
}}
#endif // HAVE_INF_ENGINE