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Fix for OpenVINO 2024.0

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Remove support OpenVINO lower than 2022.1 release
Remove legacy InferenceEngine wrappers
This commit is contained in:
Oleg Pipikin
2024-03-11 20:23:48 +04:00
parent 625eebad54
commit 6da2ddcf0e
48 changed files with 646 additions and 1677 deletions
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modules/dnn/test/test_ie_models.cpp
+62 -83
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@@ -9,7 +9,6 @@
#ifdef HAVE_INF_ENGINE
#include <opencv2/core/utils/filesystem.hpp>
//
// Synchronize headers include statements with src/op_inf_engine.hpp
//
@@ -26,14 +25,11 @@
#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
#include <openvino/runtime/core.hpp>
namespace opencv_test { namespace {
@@ -62,7 +58,6 @@ static void initDLDTDataPath()
using namespace cv;
using namespace cv::dnn;
using namespace InferenceEngine;
struct OpenVINOModelTestCaseInfo
{
@@ -161,27 +156,6 @@ inline static std::string getOpenVINOModel(const std::string &modelName, bool is
return std::string();
}
static inline void genData(const InferenceEngine::TensorDesc& desc, Mat& m, Blob::Ptr& dataPtr)
{
const std::vector<size_t>& dims = desc.getDims();
if (desc.getPrecision() == InferenceEngine::Precision::FP32)
{
m.create(std::vector<int>(dims.begin(), dims.end()), CV_32F);
randu(m, -1, 1);
dataPtr = make_shared_blob<float>(desc, (float*)m.data);
}
else if (desc.getPrecision() == InferenceEngine::Precision::I32)
{
m.create(std::vector<int>(dims.begin(), dims.end()), CV_32S);
randu(m, -100, 100);
dataPtr = make_shared_blob<int>(desc, (int*)m.data);
}
else
{
FAIL() << "Unsupported precision: " << desc.getPrecision();
}
}
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)
{
@@ -189,25 +163,12 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath
std::string device_name;
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2019010000)
Core ie;
#else
InferenceEnginePluginPtr enginePtr;
InferencePlugin plugin;
#endif
ov::Core core;
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2019030000)
CNNNetwork net = ie.ReadNetwork(xmlPath, binPath);
#else
CNNNetReader reader;
reader.ReadNetwork(xmlPath);
reader.ReadWeights(binPath);
auto model = core.read_model(xmlPath, binPath);
CNNNetwork net = reader.getNetwork();
#endif
ExecutableNetwork netExec;
InferRequest infRequest;
ov::CompiledModel compiledModel;
ov::InferRequest infRequest;
try
{
@@ -230,10 +191,6 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath
CV_Error(Error::StsNotImplemented, "Unknown target");
};
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
auto dispatcher = InferenceEngine::PluginDispatcher({""});
enginePtr = dispatcher.getPluginByDevice(device_name);
#endif
if (target == DNN_TARGET_CPU || target == DNN_TARGET_FPGA)
{
std::string suffixes[] = {"_avx2", "_sse4", ""};
@@ -255,68 +212,90 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath
#endif // _WIN32
try
{
IExtensionPtr extension = make_so_pointer<IExtension>(libName);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2019010000)
ie.AddExtension(extension, device_name);
#else
enginePtr->AddExtension(extension, 0);
#endif
core.add_extension(libName);
break;
}
catch(...) {}
}
// Some of networks can work without a library of extra layers.
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2019010000)
netExec = ie.LoadNetwork(net, device_name);
#else
plugin = InferencePlugin(enginePtr);
netExec = plugin.LoadNetwork(net, {});
#endif
infRequest = netExec.CreateInferRequest();
compiledModel = core.compile_model(model, device_name);
infRequest = compiledModel.create_infer_request();
}
catch (const std::exception& ex)
{
CV_Error(Error::StsAssert, format("Failed to initialize Inference Engine backend: %s", ex.what()));
}
// Fill input blobs.
// Fill input tensors.
inputsMap.clear();
BlobMap inputBlobs;
for (auto& it : net.getInputsInfo())
for (auto&& it : model->inputs())
{
const InferenceEngine::TensorDesc& desc = it.second->getTensorDesc();
genData(desc, inputsMap[it.first], inputBlobs[it.first]);
auto type = it.get_element_type();
auto shape = it.get_shape();
auto& m = inputsMap[it.get_any_name()];
auto tensor = ov::Tensor(type, shape);
if (type == ov::element::f32)
{
m.create(std::vector<int>(shape.begin(), shape.end()), CV_32F);
randu(m, -1, 1);
}
else if (type == ov::element::i32)
{
m.create(std::vector<int>(shape.begin(), shape.end()), CV_32S);
randu(m, -100, 100);
}
else
{
FAIL() << "Unsupported precision: " << type;
}
std::memcpy(tensor.data(), m.data, tensor.get_byte_size());
if (cvtest::debugLevel > 0)
{
const std::vector<size_t>& dims = desc.getDims();
std::cout << "Input: '" << it.first << "' precision=" << desc.getPrecision() << " dims=" << dims.size() << " [";
for (auto d : dims)
std::cout << "Input: '" << it.get_any_name() << "' precision=" << type << " dims=" << shape << " [";
for (auto d : shape)
std::cout << " " << d;
std::cout << "] ocv_mat=" << inputsMap[it.first].size << " of " << typeToString(inputsMap[it.first].type()) << std::endl;
std::cout << "] ocv_mat=" << inputsMap[it.get_any_name()].size << " of " << typeToString(inputsMap[it.get_any_name()].type()) << std::endl;
}
infRequest.set_tensor(it, tensor);
}
infRequest.SetInput(inputBlobs);
infRequest.infer();
// Fill output blobs.
// Fill output tensors.
outputsMap.clear();
BlobMap outputBlobs;
for (auto& it : net.getOutputsInfo())
for (const auto& it : model->outputs())
{
const InferenceEngine::TensorDesc& desc = it.second->getTensorDesc();
genData(desc, outputsMap[it.first], outputBlobs[it.first]);
auto type = it.get_element_type();
auto shape = it.get_shape();
auto& m = outputsMap[it.get_any_name()];
auto tensor = infRequest.get_tensor(it);
if (type == ov::element::f32)
{
m.create(std::vector<int>(shape.begin(), shape.end()), CV_32F);
}
else if (type == ov::element::i32)
{
m.create(std::vector<int>(shape.begin(), shape.end()), CV_32S);
}
else
{
FAIL() << "Unsupported precision: " << type;
}
std::memcpy(m.data, tensor.data(), tensor.get_byte_size());
if (cvtest::debugLevel > 0)
{
const std::vector<size_t>& dims = desc.getDims();
std::cout << "Output: '" << it.first << "' precision=" << desc.getPrecision() << " dims=" << dims.size() << " [";
for (auto d : dims)
std::cout << "Output: '" << it.get_any_name() << "' precision=" << type << " dims=" << shape << " [";
for (auto d : shape)
std::cout << " " << d;
std::cout << "] ocv_mat=" << outputsMap[it.first].size << " of " << typeToString(outputsMap[it.first].type()) << std::endl;
std::cout << "] ocv_mat=" << outputsMap[it.get_any_name()].size << " of " << typeToString(outputsMap[it.get_any_name()].type()) << std::endl;
}
}
infRequest.SetOutput(outputBlobs);
infRequest.Infer();
}
}
void runCV(Backend backendId, Target targetId, const std::string& xmlPath, const std::string& binPath,