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opencv/modules/dnn/src/op_inf_engine.cpp

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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 "precomp.hpp"
#include "op_inf_engine.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#ifdef HAVE_INF_ENGINE
#include <ie_extension.h>
#include <ie_plugin_dispatcher.hpp>
#endif // HAVE_INF_ENGINE
#include <opencv2/core/utils/configuration.private.hpp>
#include <opencv2/core/utils/logger.hpp>
namespace cv { namespace dnn {
#ifdef HAVE_INF_ENGINE
// For networks with input layer which has an empty name, IE generates a name id[some_number].
// OpenCV lets users use an empty input name and to prevent unexpected naming,
// we can use some predefined name.
static std::string kDefaultInpLayerName = "empty_inp_layer_name";
static std::string kOpenCVLayersType = "OpenCVLayer";
static std::string shapesToStr(const std::vector<Mat>& mats)
{
std::ostringstream shapes;
shapes << mats.size() << " ";
for (const Mat& m : mats)
{
shapes << m.dims << " ";
for (int i = 0; i < m.dims; ++i)
shapes << m.size[i] << " ";
}
return shapes.str();
}
static void strToShapes(const std::string& str, std::vector<std::vector<size_t> >& shapes)
{
std::istringstream ss(str);
int num, dims;
ss >> num;
shapes.resize(num);
for (int i = 0; i < num; ++i)
{
ss >> dims;
shapes[i].resize(dims);
for (int j = 0; j < dims; ++j)
ss >> shapes[i][j];
}
}
class InfEngineCustomLayer : public InferenceEngine::ILayerExecImpl
{
public:
explicit InfEngineCustomLayer(const InferenceEngine::CNNLayer& layer) : cnnLayer(layer)
{
std::istringstream iss(layer.GetParamAsString("impl"));
size_t ptr;
iss >> ptr;
cvLayer = (Layer*)ptr;
std::vector<std::vector<size_t> > shapes;
strToShapes(layer.GetParamAsString("internals"), shapes);
internals.resize(shapes.size());
for (int i = 0; i < shapes.size(); ++i)
internals[i].create(std::vector<int>(shapes[i].begin(), shapes[i].end()), CV_32F);
}
virtual InferenceEngine::StatusCode execute(std::vector<InferenceEngine::Blob::Ptr>& inputs,
std::vector<InferenceEngine::Blob::Ptr>& outputs,
InferenceEngine::ResponseDesc *resp) noexcept
{
std::vector<Mat> inpMats, outMats;
infEngineBlobsToMats(inputs, inpMats);
infEngineBlobsToMats(outputs, outMats);
try
{
cvLayer->forward(inpMats, outMats, internals);
return InferenceEngine::StatusCode::OK;
}
catch (...)
{
return InferenceEngine::StatusCode::GENERAL_ERROR;
}
}
virtual InferenceEngine::StatusCode
getSupportedConfigurations(std::vector<InferenceEngine::LayerConfig>& conf,
InferenceEngine::ResponseDesc* resp) noexcept
{
std::vector<InferenceEngine::DataConfig> inDataConfig;
std::vector<InferenceEngine::DataConfig> outDataConfig;
for (auto& it : cnnLayer.insData)
{
InferenceEngine::DataConfig conf;
conf.desc = it.lock()->getTensorDesc();
inDataConfig.push_back(conf);
}
for (auto& it : cnnLayer.outData)
{
InferenceEngine::DataConfig conf;
conf.desc = it->getTensorDesc();
outDataConfig.push_back(conf);
}
InferenceEngine::LayerConfig layerConfig;
layerConfig.inConfs = inDataConfig;
layerConfig.outConfs = outDataConfig;
conf.push_back(layerConfig);
return InferenceEngine::StatusCode::OK;
}
InferenceEngine::StatusCode init(InferenceEngine::LayerConfig& config,
InferenceEngine::ResponseDesc *resp) noexcept
{
return InferenceEngine::StatusCode::OK;
}
private:
InferenceEngine::CNNLayer cnnLayer;
dnn::Layer* cvLayer;
std::vector<Mat> internals;
};
class InfEngineCustomLayerShapeInfer : public InferenceEngine::IShapeInferImpl
{
public:
InferenceEngine::StatusCode
inferShapes(const std::vector<InferenceEngine::Blob::CPtr>& inBlobs,
const std::map<std::string, std::string>& params,
const std::map<std::string, InferenceEngine::Blob::Ptr>& blobs,
std::vector<InferenceEngine::SizeVector>& outShapes,
InferenceEngine::ResponseDesc* desc) noexcept override
{
strToShapes(params.at("outputs"), outShapes);
return InferenceEngine::StatusCode::OK;
}
};
class InfEngineCustomLayerFactory : public InferenceEngine::ILayerImplFactory {
public:
explicit InfEngineCustomLayerFactory(const InferenceEngine::CNNLayer* layer) : cnnLayer(*layer) {}
InferenceEngine::StatusCode
getImplementations(std::vector<InferenceEngine::ILayerImpl::Ptr>& impls,
InferenceEngine::ResponseDesc* resp) noexcept override {
impls.push_back(std::make_shared<InfEngineCustomLayer>(cnnLayer));
return InferenceEngine::StatusCode::OK;
}
private:
InferenceEngine::CNNLayer cnnLayer;
};
class InfEngineExtension : public InferenceEngine::IExtension
{
public:
virtual void SetLogCallback(InferenceEngine::IErrorListener&) noexcept {}
virtual void Unload() noexcept {}
virtual void Release() noexcept {}
virtual void GetVersion(const InferenceEngine::Version*&) const noexcept {}
virtual InferenceEngine::StatusCode getPrimitiveTypes(char**&, unsigned int&,
InferenceEngine::ResponseDesc*) noexcept
{
return InferenceEngine::StatusCode::OK;
}
InferenceEngine::StatusCode getFactoryFor(InferenceEngine::ILayerImplFactory*& factory,
const InferenceEngine::CNNLayer* cnnLayer,
InferenceEngine::ResponseDesc* resp) noexcept
{
if (cnnLayer->type != kOpenCVLayersType)
return InferenceEngine::StatusCode::NOT_IMPLEMENTED;
factory = new InfEngineCustomLayerFactory(cnnLayer);
return InferenceEngine::StatusCode::OK;
}
};
InfEngineBackendNode::InfEngineBackendNode(const InferenceEngine::Builder::Layer& _layer)
: BackendNode(DNN_BACKEND_INFERENCE_ENGINE), layer(_layer) {}
InfEngineBackendNode::InfEngineBackendNode(Ptr<Layer>& cvLayer_, std::vector<Mat*>& inputs,
std::vector<Mat>& outputs,
std::vector<Mat>& internals)
: BackendNode(DNN_BACKEND_INFERENCE_ENGINE), layer(cvLayer_->name),
cvLayer(cvLayer_)
{
CV_Assert(!cvLayer->name.empty());
layer.setName(cvLayer->name);
layer.setType(kOpenCVLayersType);
layer.getParameters()["impl"] = (size_t)cvLayer.get();
layer.getParameters()["outputs"] = shapesToStr(outputs);
layer.getParameters()["internals"] = shapesToStr(internals);
layer.setInputPorts(std::vector<InferenceEngine::Port>(inputs.size()));
layer.setOutputPorts(std::vector<InferenceEngine::Port>(outputs.size()));
}
static std::vector<Ptr<InfEngineBackendWrapper> >
infEngineWrappers(const std::vector<Ptr<BackendWrapper> >& ptrs)
{
std::vector<Ptr<InfEngineBackendWrapper> > wrappers(ptrs.size());
for (int i = 0; i < ptrs.size(); ++i)
{
CV_Assert(!ptrs[i].empty());
wrappers[i] = ptrs[i].dynamicCast<InfEngineBackendWrapper>();
CV_Assert(!wrappers[i].empty());
}
return wrappers;
}
InfEngineBackendNet::InfEngineBackendNet() : netBuilder("")
{
hasNetOwner = false;
device_name = "CPU";
}
InfEngineBackendNet::InfEngineBackendNet(InferenceEngine::CNNNetwork& net) : netBuilder(""), cnn(net)
{
hasNetOwner = true;
device_name = "CPU";
}
void InfEngineBackendNet::connect(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendWrapper> >& outputs,
const std::string& layerName)
{
std::vector<Ptr<InfEngineBackendWrapper> > inpWrappers = infEngineWrappers(inputs);
std::map<std::string, int>::iterator it = layers.find(layerName);
CV_Assert(it != layers.end());
const int layerId = it->second;
for (size_t i = 0; i < inpWrappers.size(); ++i)
{
const auto& inp = inpWrappers[i];
const std::string& inpName = inp->dataPtr->getName();
int inpId;
it = layers.find(inpName);
if (it == layers.end())
{
InferenceEngine::Builder::InputLayer inpLayer(!inpName.empty() ? inpName : kDefaultInpLayerName);
std::vector<size_t> shape(inp->blob->getTensorDesc().getDims());
inpLayer.setPort(InferenceEngine::Port(shape));
inpId = netBuilder.addLayer(inpLayer);
layers.insert({inpName, inpId});
}
else
inpId = it->second;
netBuilder.connect((size_t)inpId, {(size_t)layerId, i});
unconnectedLayersIds.erase(inpId);
}
CV_Assert(!outputs.empty());
InferenceEngine::DataPtr dataPtr = infEngineDataNode(outputs[0]);
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
dataPtr->name = layerName;
#else
dataPtr->setName(layerName);
#endif
}
void InfEngineBackendNet::init(int targetId)
{
if (!hasNetOwner)
{
CV_Assert(!unconnectedLayersIds.empty());
for (int id : unconnectedLayersIds)
{
InferenceEngine::Builder::OutputLayer outLayer("myconv1");
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
// Inference Engine determines network precision by ports.
InferenceEngine::Precision p = (targetId == DNN_TARGET_MYRIAD ||
targetId == DNN_TARGET_OPENCL_FP16) ?
InferenceEngine::Precision::FP16 :
InferenceEngine::Precision::FP32;
outLayer.setPort(InferenceEngine::Port({}, p));
#endif
netBuilder.addLayer({InferenceEngine::PortInfo(id)}, outLayer);
}
netBuilder.getContext().addShapeInferImpl(kOpenCVLayersType,
std::make_shared<InfEngineCustomLayerShapeInfer>());
cnn = InferenceEngine::CNNNetwork(InferenceEngine::Builder::convertToICNNNetwork(netBuilder.build()));
}
switch (targetId)
{
case DNN_TARGET_CPU:
device_name = "CPU";
break;
case DNN_TARGET_OPENCL:
case DNN_TARGET_OPENCL_FP16:
device_name = "GPU";
break;
case DNN_TARGET_MYRIAD:
device_name = "MYRIAD";
break;
case DNN_TARGET_FPGA:
device_name = "FPGA";
break;
default:
CV_Error(Error::StsNotImplemented, "Unknown target");
};
for (const auto& name : requestedOutputs)
{
cnn.addOutput(name);
}
for (const auto& it : cnn.getInputsInfo())
{
const std::string& name = it.first;
auto blobIt = allBlobs.find(name);
CV_Assert(blobIt != allBlobs.end());
it.second->setPrecision(blobIt->second->getTensorDesc().getPrecision());
}
for (const auto& it : cnn.getOutputsInfo())
{
const std::string& name = it.first;
auto blobIt = allBlobs.find(name);
CV_Assert(blobIt != allBlobs.end());
it.second->setPrecision(blobIt->second->getTensorDesc().getPrecision()); // Should be always FP32
}
initPlugin(cnn);
}
void InfEngineBackendNet::addLayer(InferenceEngine::Builder::Layer& layer)
{
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
// Add weights to network and connect them after input blobs.
std::map<std::string, InferenceEngine::Parameter>& params = layer.getParameters();
std::vector<int> blobsIds;
std::vector<int> portIds;
for (const std::string& name : {"weights", "biases"})
{
bool asInput = false;
int portId = 0;
for (int i = 0; i < layer.getInputPorts().size(); ++i)
{
const auto& port = layer.getInputPorts()[i];
auto it = port.getParameters().find("type");
if (it != port.getParameters().end() && it->second == name)
{
portId = i;
asInput = true;
break;
}
}
if (!asInput)
continue;
auto it = params.find(name);
if (it != params.end())
{
InferenceEngine::Blob::Ptr blob = it->second.as<InferenceEngine::Blob::Ptr>();
params.erase(it);
int blobId = netBuilder.addLayer(InferenceEngine::Builder::ConstLayer(name).setData(blob));
blobsIds.push_back(blobId);
portIds.push_back(portId);
}
}
#endif
int id = netBuilder.addLayer(layer);
const std::string& layerName = layer.getName();
CV_Assert(layers.insert({layerName, id}).second);
unconnectedLayersIds.insert(id);
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
// By default, all the weights are connected to last ports ids.
for (int i = 0; i < blobsIds.size(); ++i)
{
netBuilder.connect((size_t)blobsIds[i], {(size_t)id, (size_t)portIds[i]});
}
#endif
}
void InfEngineBackendNet::addOutput(const std::string& name)
{
requestedOutputs.push_back(name);
}
static InferenceEngine::Layout estimateLayout(const Mat& m)
{
if (m.dims == 4)
return InferenceEngine::Layout::NCHW;
else if (m.dims == 2)
return InferenceEngine::Layout::NC;
else
return InferenceEngine::Layout::ANY;
}
static InferenceEngine::DataPtr wrapToInfEngineDataNode(const Mat& m, const std::string& name = "")
{
std::vector<size_t> shape(&m.size[0], &m.size[0] + m.dims);
if (m.type() == CV_32F)
return InferenceEngine::DataPtr(new InferenceEngine::Data(name,
{InferenceEngine::Precision::FP32, shape, estimateLayout(m)}));
else if (m.type() == CV_8U)
return InferenceEngine::DataPtr(new InferenceEngine::Data(name,
{InferenceEngine::Precision::U8, shape, estimateLayout(m)}));
else
CV_Error(Error::StsNotImplemented, format("Unsupported data type %d", m.type()));
}
InferenceEngine::Blob::Ptr wrapToInfEngineBlob(const Mat& m, const std::vector<size_t>& shape,
InferenceEngine::Layout layout)
{
if (m.type() == CV_32F)
return InferenceEngine::make_shared_blob<float>(
{InferenceEngine::Precision::FP32, shape, layout}, (float*)m.data);
else if (m.type() == CV_8U)
return InferenceEngine::make_shared_blob<uint8_t>(
{InferenceEngine::Precision::U8, shape, layout}, (uint8_t*)m.data);
else
CV_Error(Error::StsNotImplemented, format("Unsupported data type %d", m.type()));
}
InferenceEngine::Blob::Ptr wrapToInfEngineBlob(const Mat& m, InferenceEngine::Layout layout)
{
std::vector<size_t> shape(&m.size[0], &m.size[0] + m.dims);
return wrapToInfEngineBlob(m, shape, layout);
}
InferenceEngine::Blob::Ptr cloneBlob(const InferenceEngine::Blob::Ptr& blob)
{
InferenceEngine::Blob::Ptr copy;
auto description = blob->getTensorDesc();
InferenceEngine::Precision precision = description.getPrecision();
if (precision == InferenceEngine::Precision::FP32)
{
copy = InferenceEngine::make_shared_blob<float>(description);
}
else if (precision == InferenceEngine::Precision::U8)
{
copy = InferenceEngine::make_shared_blob<uint8_t>(description);
}
else
CV_Error(Error::StsNotImplemented, "Unsupported blob precision");
copy->allocate();
return copy;
}
InferenceEngine::DataPtr infEngineDataNode(const Ptr<BackendWrapper>& ptr)
{
CV_Assert(!ptr.empty());
Ptr<InfEngineBackendWrapper> p = ptr.dynamicCast<InfEngineBackendWrapper>();
CV_Assert(!p.empty());
return p->dataPtr;
}
InfEngineBackendWrapper::InfEngineBackendWrapper(int targetId, const cv::Mat& m)
: BackendWrapper(DNN_BACKEND_INFERENCE_ENGINE, targetId)
{
dataPtr = wrapToInfEngineDataNode(m);
blob = wrapToInfEngineBlob(m, estimateLayout(m));
}
InfEngineBackendWrapper::InfEngineBackendWrapper(Ptr<BackendWrapper> wrapper)
: BackendWrapper(DNN_BACKEND_INFERENCE_ENGINE, wrapper->targetId)
{
Ptr<InfEngineBackendWrapper> ieWrapper = wrapper.dynamicCast<InfEngineBackendWrapper>();
CV_Assert(!ieWrapper.empty());
InferenceEngine::DataPtr srcData = ieWrapper->dataPtr;
dataPtr = InferenceEngine::DataPtr(new InferenceEngine::Data(srcData->getName(), srcData->getTensorDesc()));
blob = ieWrapper->blob;
}
Ptr<BackendWrapper> InfEngineBackendWrapper::create(Ptr<BackendWrapper> wrapper)
{
return Ptr<BackendWrapper>(new InfEngineBackendWrapper(wrapper));
}
InfEngineBackendWrapper::~InfEngineBackendWrapper()
{
}
void InfEngineBackendWrapper::copyToHost()
{
}
void InfEngineBackendWrapper::setHostDirty()
{
}
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
static std::map<std::string, InferenceEngine::InferenceEnginePluginPtr>& getSharedPlugins()
{
static std::map<std::string, InferenceEngine::InferenceEnginePluginPtr> sharedPlugins;
return sharedPlugins;
}
#else
static InferenceEngine::Core& getCore()
{
static InferenceEngine::Core core;
return core;
}
#endif
#if !defined(OPENCV_DNN_IE_VPU_TYPE_DEFAULT)
static bool detectMyriadX_()
{
InferenceEngine::Builder::Network builder("");
InferenceEngine::idx_t inpId = builder.addLayer(
InferenceEngine::Builder::InputLayer().setPort(InferenceEngine::Port({1})));
#if INF_ENGINE_RELEASE <= 2018050000
InferenceEngine::idx_t clampId;
{
InferenceEngine::Builder::Layer l = InferenceEngine::Builder::ClampLayer();
auto& blobs = l.getConstantData();
auto blob = InferenceEngine::make_shared_blob<int16_t>(
InferenceEngine::Precision::FP16,
InferenceEngine::Layout::C, {1});
blob->allocate();
blobs[""] = blob;
clampId = builder.addLayer({inpId}, l);
}
builder.addLayer({InferenceEngine::PortInfo(clampId)}, InferenceEngine::Builder::OutputLayer());
#else
InferenceEngine::idx_t clampId = builder.addLayer({inpId}, InferenceEngine::Builder::ClampLayer());
builder.addLayer({InferenceEngine::PortInfo(clampId)},
InferenceEngine::Builder::OutputLayer().setPort(InferenceEngine::Port({},
InferenceEngine::Precision::FP16)));
#endif
InferenceEngine::CNNNetwork cnn = InferenceEngine::CNNNetwork(
InferenceEngine::Builder::convertToICNNNetwork(builder.build()));
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
InferenceEngine::InferenceEnginePluginPtr enginePtr;
{
AutoLock lock(getInitializationMutex());
auto& sharedPlugins = getSharedPlugins();
auto pluginIt = sharedPlugins.find("MYRIAD");
if (pluginIt != sharedPlugins.end()) {
enginePtr = pluginIt->second;
} else {
auto dispatcher = InferenceEngine::PluginDispatcher({""});
enginePtr = dispatcher.getPluginByDevice("MYRIAD");
sharedPlugins["MYRIAD"] = enginePtr;
}
}
auto plugin = InferenceEngine::InferencePlugin(enginePtr);
try
{
auto netExec = plugin.LoadNetwork(cnn, {{"VPU_PLATFORM", "VPU_2480"}});
#else
try
{
auto netExec = getCore().LoadNetwork(cnn, "MYRIAD", {{"VPU_PLATFORM", "VPU_2480"}});
#endif
auto infRequest = netExec.CreateInferRequest();
} catch(...) {
return false;
}
return true;
}
#endif // !defined(OPENCV_DNN_IE_VPU_TYPE_DEFAULT)
void InfEngineBackendNet::initPlugin(InferenceEngine::CNNNetwork& net)
{
CV_Assert(!isInitialized());
try
{
AutoLock lock(getInitializationMutex());
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
auto& sharedPlugins = getSharedPlugins();
auto pluginIt = sharedPlugins.find(device_name);
if (pluginIt != sharedPlugins.end())
{
enginePtr = pluginIt->second;
}
else
#else
InferenceEngine::Core& ie = getCore();
#endif
{
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
auto dispatcher = InferenceEngine::PluginDispatcher({""});
if (device_name == "FPGA")
enginePtr = dispatcher.getPluginByDevice("HETERO:FPGA,CPU");
else
enginePtr = dispatcher.getPluginByDevice(device_name);
sharedPlugins[device_name] = enginePtr;
#else
isInit = true;
#endif
std::vector<std::string> candidates;
std::string param_pluginPath = utils::getConfigurationParameterString("OPENCV_DNN_IE_EXTRA_PLUGIN_PATH", "");
if (!param_pluginPath.empty())
{
candidates.push_back(param_pluginPath);
}
if (device_name == "CPU" || device_name == "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
candidates.push_back("cpu_extension" + suffixes[i] + ".dll");
#elif defined(__APPLE__)
candidates.push_back("libcpu_extension" + suffixes[i] + ".so"); // built as loadable module
candidates.push_back("libcpu_extension" + suffixes[i] + ".dylib"); // built as shared library
#else
candidates.push_back("libcpu_extension" + suffixes[i] + ".so");
#endif // _WIN32
}
}
bool found = false;
for (size_t i = 0; i != candidates.size(); ++i)
{
const std::string& libName = candidates[i];
try
{
InferenceEngine::IExtensionPtr extension =
InferenceEngine::make_so_pointer<InferenceEngine::IExtension>(libName);
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
enginePtr->AddExtension(extension, 0);
#else
ie.AddExtension(extension, "CPU");
// OpenCV fallbacks as extensions.
ie.AddExtension(std::make_shared<InfEngineExtension>(), "CPU");
#endif
CV_LOG_INFO(NULL, "DNN-IE: Loaded extension plugin: " << libName);
found = true;
break;
}
catch(...) {}
}
if (!found && !candidates.empty())
{
CV_LOG_WARNING(NULL, "DNN-IE: Can't load extension plugin (extra layers for some networks). Specify path via OPENCV_DNN_IE_EXTRA_PLUGIN_PATH parameter");
}
// Some of networks can work without a library of extra layers.
#ifndef _WIN32
// Limit the number of CPU threads.
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
enginePtr->SetConfig({{
InferenceEngine::PluginConfigParams::KEY_CPU_THREADS_NUM, format("%d", getNumThreads()),
}}, 0);
#else
if (device_name == "CPU")
ie.SetConfig({{
InferenceEngine::PluginConfigParams::KEY_CPU_THREADS_NUM, format("%d", getNumThreads()),
}}, device_name);
#endif
#endif
}
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
plugin = InferenceEngine::InferencePlugin(enginePtr);
netExec = plugin.LoadNetwork(net, {});
#else
bool isHetero = false;
if (device_name != "CPU")
{
isHetero = device_name == "FPGA";
for (auto& layer : net)
{
if (layer->type == kOpenCVLayersType)
{
layer->affinity = "CPU";
isHetero = true;
}
else
layer->affinity = device_name;
}
}
if (isHetero)
netExec = ie.LoadNetwork(net, "HETERO:" + device_name + ",CPU");
else
netExec = ie.LoadNetwork(net, device_name);
#endif
}
catch (const std::exception& ex)
{
CV_Error(Error::StsAssert, format("Failed to initialize Inference Engine backend: %s", ex.what()));
}
}
bool InfEngineBackendNet::isInitialized()
{
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
return (bool)enginePtr;
#else
return isInit;
#endif
}
void InfEngineBackendNet::addBlobs(const std::vector<cv::Ptr<BackendWrapper> >& ptrs)
{
auto wrappers = infEngineWrappers(ptrs);
for (const auto& wrapper : wrappers)
{
std::string name = wrapper->dataPtr->getName();
name = name.empty() ? kDefaultInpLayerName : name;
allBlobs.insert({name, wrapper->blob});
}
}
void InfEngineBackendNet::InfEngineReqWrapper::makePromises(const std::vector<Ptr<BackendWrapper> >& outsWrappers)
{
auto outs = infEngineWrappers(outsWrappers);
outProms.clear();
outProms.resize(outs.size());
outsNames.resize(outs.size());
for (int i = 0; i < outs.size(); ++i)
{
outs[i]->futureMat = outProms[i].getArrayResult();
outsNames[i] = outs[i]->dataPtr->getName();
}
}
void InfEngineBackendNet::forward(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
bool isAsync)
{
// Look for finished requests.
Ptr<InfEngineReqWrapper> reqWrapper;
for (auto& wrapper : infRequests)
{
if (wrapper->isReady)
{
reqWrapper = wrapper;
break;
}
}
if (reqWrapper.empty())
{
reqWrapper = Ptr<InfEngineReqWrapper>(new InfEngineReqWrapper());
try
{
reqWrapper->req = netExec.CreateInferRequest();
}
catch (const std::exception& ex)
{
CV_Error(Error::StsAssert, format("Failed to initialize Inference Engine backend: %s", ex.what()));
}
infRequests.push_back(reqWrapper);
InferenceEngine::BlobMap inpBlobs, outBlobs;
for (const auto& it : cnn.getInputsInfo())
{
const std::string& name = it.first;
auto blobIt = allBlobs.find(name);
CV_Assert(blobIt != allBlobs.end());
inpBlobs[name] = isAsync ? cloneBlob(blobIt->second) : blobIt->second;
}
for (const auto& it : cnn.getOutputsInfo())
{
const std::string& name = it.first;
auto blobIt = allBlobs.find(name);
CV_Assert(blobIt != allBlobs.end());
outBlobs[name] = isAsync ? cloneBlob(blobIt->second) : blobIt->second;
}
reqWrapper->req.SetInput(inpBlobs);
reqWrapper->req.SetOutput(outBlobs);
InferenceEngine::IInferRequest::Ptr infRequestPtr = reqWrapper->req;
infRequestPtr->SetUserData(reqWrapper.get(), 0);
infRequestPtr->SetCompletionCallback(
[](InferenceEngine::IInferRequest::Ptr request, InferenceEngine::StatusCode status)
{
InfEngineReqWrapper* wrapper;
request->GetUserData((void**)&wrapper, 0);
CV_Assert(wrapper && "Internal error");
size_t processedOutputs = 0;
try
{
for (; processedOutputs < wrapper->outProms.size(); ++processedOutputs)
{
const std::string& name = wrapper->outsNames[processedOutputs];
Mat m = infEngineBlobToMat(wrapper->req.GetBlob(name));
try
{
CV_Assert(status == InferenceEngine::StatusCode::OK);
wrapper->outProms[processedOutputs].setValue(m.clone());
}
catch (...)
{
try {
wrapper->outProms[processedOutputs].setException(std::current_exception());
} catch(...) {
CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation");
}
}
}
}
catch (...)
{
std::exception_ptr e = std::current_exception();
for (; processedOutputs < wrapper->outProms.size(); ++processedOutputs)
{
try {
wrapper->outProms[processedOutputs].setException(e);
} catch(...) {
CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation");
}
}
}
wrapper->isReady = true;
}
);
}
if (isAsync)
{
// Copy actual data to infer request's input blobs.
for (const auto& it : cnn.getInputsInfo())
{
const std::string& name = it.first;
auto blobIt = allBlobs.find(name);
Mat srcMat = infEngineBlobToMat(blobIt->second);
Mat dstMat = infEngineBlobToMat(reqWrapper->req.GetBlob(name));
srcMat.copyTo(dstMat);
}
// Set promises to output blobs wrappers.
reqWrapper->makePromises(outBlobsWrappers);
reqWrapper->isReady = false;
reqWrapper->req.StartAsync();
}
else
{
reqWrapper->req.Infer();
}
}
Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob)
{
// NOTE: Inference Engine sizes are reversed.
std::vector<size_t> dims = blob->getTensorDesc().getDims();
std::vector<int> size(dims.begin(), dims.end());
auto precision = blob->getTensorDesc().getPrecision();
int type = -1;
switch (precision)
{
case InferenceEngine::Precision::FP32: type = CV_32F; break;
case InferenceEngine::Precision::U8: type = CV_8U; break;
default:
CV_Error(Error::StsNotImplemented, "Unsupported blob precision");
}
return Mat(size, type, (void*)blob->buffer());
}
void infEngineBlobsToMats(const std::vector<InferenceEngine::Blob::Ptr>& blobs,
std::vector<Mat>& mats)
{
mats.resize(blobs.size());
for (int i = 0; i < blobs.size(); ++i)
mats[i] = infEngineBlobToMat(blobs[i]);
}
bool InfEngineBackendLayer::getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const
{
InferenceEngine::ICNNNetwork::InputShapes inShapes = t_net.getInputShapes();
InferenceEngine::ICNNNetwork::InputShapes::iterator itr;
bool equal_flag = true;
size_t i = 0;
for (itr = inShapes.begin(); itr != inShapes.end(); ++itr)
{
InferenceEngine::SizeVector currentInShape(inputs[i].begin(), inputs[i].end());
if (itr->second != currentInShape)
{
itr->second = currentInShape;
equal_flag = false;
}
i++;
}
if (!equal_flag)
{
InferenceEngine::CNNNetwork curr_t_net(t_net);
curr_t_net.reshape(inShapes);
}
std::vector<size_t> dims = t_net.getOutputsInfo()[name]->getDims();
outputs.push_back(MatShape(dims.begin(), dims.end()));
return false;
}
bool InfEngineBackendLayer::supportBackend(int backendId)
{
return backendId == DNN_BACKEND_DEFAULT ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
}
void InfEngineBackendLayer::forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs,
OutputArrayOfArrays internals)
{
CV_Error(Error::StsInternal, "Choose Inference Engine as a preferable backend.");
}
InferenceEngine::Blob::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob)
{
auto halfs = InferenceEngine::make_shared_blob<int16_t>({
InferenceEngine::Precision::FP16, blob->getTensorDesc().getDims(),
blob->getTensorDesc().getLayout()
});
halfs->allocate();
Mat floatsData(1, blob->size(), CV_32F, blob->buffer());
Mat halfsData(1, blob->size(), CV_16SC1, halfs->buffer());
convertFp16(floatsData, halfsData);
return halfs;
}
void addConstantData(const std::string& name, InferenceEngine::Blob::Ptr data,
InferenceEngine::Builder::Layer& l)
{
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
l.getParameters()[name] = data;
#else
l.addConstantData(name, data);
#endif
}
#endif // HAVE_INF_ENGINE
bool haveInfEngine()
{
#ifdef HAVE_INF_ENGINE
return true;
#else
return false;
#endif // HAVE_INF_ENGINE
}
void forwardInfEngine(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
Ptr<BackendNode>& node, bool isAsync)
{
CV_Assert(haveInfEngine());
#ifdef HAVE_INF_ENGINE
CV_Assert(!node.empty());
Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
CV_Assert(!ieNode.empty());
ieNode->net->forward(outBlobsWrappers, isAsync);
#endif // HAVE_INF_ENGINE
}
CV__DNN_INLINE_NS_BEGIN
void resetMyriadDevice()
{
#ifdef HAVE_INF_ENGINE
AutoLock lock(getInitializationMutex());
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
getSharedPlugins().erase("MYRIAD");
#else
// To unregister both "MYRIAD" and "HETERO:MYRIAD,CPU" plugins
getCore() = InferenceEngine::Core();
#endif
#endif // HAVE_INF_ENGINE
}
#ifdef HAVE_INF_ENGINE
bool isMyriadX()
{
static bool myriadX = getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X;
return myriadX;
}
static std::string getInferenceEngineVPUType_()
{
static std::string param_vpu_type = utils::getConfigurationParameterString("OPENCV_DNN_IE_VPU_TYPE", "");
if (param_vpu_type == "")
{
#if defined(OPENCV_DNN_IE_VPU_TYPE_DEFAULT)
param_vpu_type = OPENCV_DNN_IE_VPU_TYPE_DEFAULT;
#else
CV_LOG_INFO(NULL, "OpenCV-DNN: running Inference Engine VPU autodetection: Myriad2/X. In case of other accelerator types specify 'OPENCV_DNN_IE_VPU_TYPE' parameter");
try {
bool isMyriadX_ = detectMyriadX_();
if (isMyriadX_)
{
param_vpu_type = CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X;
}
else
{
param_vpu_type = CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2;
}
}
catch (...)
{
CV_LOG_WARNING(NULL, "OpenCV-DNN: Failed Inference Engine VPU autodetection. Specify 'OPENCV_DNN_IE_VPU_TYPE' parameter.");
param_vpu_type.clear();
}
#endif
}
CV_LOG_INFO(NULL, "OpenCV-DNN: Inference Engine VPU type='" << param_vpu_type << "'");
return param_vpu_type;
}
cv::String getInferenceEngineVPUType()
{
static cv::String vpu_type = getInferenceEngineVPUType_();
return vpu_type;
}
#else // HAVE_INF_ENGINE
cv::String getInferenceEngineVPUType()
{
CV_Error(Error::StsNotImplemented, "This OpenCV build doesn't include InferenceEngine support");
}
#endif // HAVE_INF_ENGINE
CV__DNN_INLINE_NS_END
}} // namespace dnn, namespace cv
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