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提交 98c5fc6a 编写于 作者: A Alexander Alekhin
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Merge pull request #20410 from alalek:fix_dnn_dldt_output_layout

上级 eb9218a8 fbde0c6c
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Showing with 230 addition and 32 deletion
modules/dnn/src/dnn.cpp
浏览文件 @ 98c5fc6a
......@@ -1944,7 +1944,10 @@ struct Net::Impl : public detail::NetImplBase
Ptr<InfEngineNgraphNode> ieNode = node.dynamicCast<InfEngineNgraphNode>();
CV_Assert(!ieNode.empty());
ieNode->net->reset();
CV_Assert(ieNode->net);
InfEngineNgraphNet& ienet = *ieNode->net;
ienet.reset();
for (it = layers.begin(); it != layers.end(); ++it)
{
......@@ -1961,16 +1964,26 @@ struct Net::Impl : public detail::NetImplBase
{
for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
{
InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.outputBlobsWrappers[i]);
dataPtr->setName(ld.name);
auto it = ienet.outputsDesc.find(ld.name);
if (it != ienet.outputsDesc.end())
{
const InferenceEngine::TensorDesc& descriptor = it->second;
InferenceEngine::DataPtr dataPtr = ngraphDataOutputNode(ld.outputBlobsWrappers[i], descriptor, ld.name);
dataPtr->setName(ld.name);
}
else
{
InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.outputBlobsWrappers[i]);
dataPtr->setName(ld.name);
}
}
}
ieNode->net->addBlobs(ld.inputBlobsWrappers);
ieNode->net->addBlobs(ld.outputBlobsWrappers);
ienet.addBlobs(ld.inputBlobsWrappers);
ienet.addBlobs(ld.outputBlobsWrappers);
ld.skip = true;
}
layers[lastLayerId].skip = false;
ieNode->net->init((Target)preferableTarget);
ienet.init((Target)preferableTarget);
return;
}
......@@ -3719,8 +3732,8 @@ void Net::forward(OutputArrayOfArrays outputBlobs,
matvec.push_back(impl->getBlob(pins[i]));
}
std::vector<Mat> & outputvec = *(std::vector<Mat> *)outputBlobs.getObj();
outputvec = matvec;
outputBlobs.create((int)matvec.size(), 1, CV_32F/*FIXIT*/, -1); // allocate vector
outputBlobs.assign(matvec);
}
void Net::forward(std::vector<std::vector<Mat> >& outputBlobs,
......
modules/dnn/src/ie_ngraph.cpp
浏览文件 @ 98c5fc6a
......@@ -789,21 +789,32 @@ void NgraphBackendLayer::forward(InputArrayOfArrays inputs, OutputArrayOfArrays
}
static InferenceEngine::Layout estimateLayout(const Mat& m)
static InferenceEngine::Layout estimateLayout(int dims)
{
if (m.dims == 4)
if (dims == 4)
return InferenceEngine::Layout::NCHW;
else if (m.dims == 3)
else if (dims == 3)
return InferenceEngine::Layout::CHW;
else if (m.dims == 2)
else if (dims == 2)
return InferenceEngine::Layout::NC;
else if (m.dims == 1)
else if (dims == 1)
return InferenceEngine::Layout::C;
else if (m.dims == 5)
else if (dims == 5)
return InferenceEngine::Layout::NCDHW;
else
return InferenceEngine::Layout::ANY;
}
static inline
InferenceEngine::Layout estimateLayout(size_t dims)
{
return estimateLayout((int)dims);
}
static inline
InferenceEngine::Layout estimateLayout(const Mat& m)
{
return estimateLayout(m.dims);
}
static InferenceEngine::DataPtr wrapToInfEngineDataNode(const Mat& m, const std::string& name = "")
{
......@@ -839,6 +850,7 @@ InferenceEngine::Blob::Ptr wrapToNgraphBlob(const Mat& m, InferenceEngine::Layou
NgraphBackendWrapper::NgraphBackendWrapper(int targetId, const cv::Mat& m)
: BackendWrapper(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, targetId)
, host((Mat*)&m)
{
dataPtr = wrapToInfEngineDataNode(m);
blob = wrapToNgraphBlob(m, estimateLayout(m));
......@@ -890,7 +902,11 @@ InferenceEngine::Blob::Ptr copyBlob(const InferenceEngine::Blob::Ptr& blob)
copy = InferenceEngine::make_shared_blob<uint8_t>(description);
}
else
CV_Error(Error::StsNotImplemented, "Unsupported blob precision");
{
std::ostringstream msg;
msg << precision;
CV_Error_(Error::StsNotImplemented, ("Unsupported blob precision: %s", msg.str().c_str()));
}
copy->allocate();
return copy;
}
......@@ -903,6 +919,66 @@ InferenceEngine::DataPtr ngraphDataNode(const Ptr<BackendWrapper>& ptr)
return p->dataPtr;
}
static
InferenceEngine::Blob::Ptr reallocateBlob(Mat &m, const InferenceEngine::TensorDesc& description)
{
auto dims = description.getDims();
auto layout = estimateLayout(dims.size());
MatShape matShape(dims.begin(), dims.end());
if (description.getPrecision() == InferenceEngine::Precision::FP32)
{
m.create(matShape, CV_32FC1);
return InferenceEngine::make_shared_blob<float>(
{description.getPrecision(), dims, layout}, (float*)m.data);
}
else if (description.getPrecision() == InferenceEngine::Precision::I32)
{
m.create(matShape, CV_32SC1);
return InferenceEngine::make_shared_blob<int>(
{description.getPrecision(), dims, layout}, (int*)m.data);
}
else if (description.getPrecision() == InferenceEngine::Precision::U8)
{
m.create(matShape, CV_8UC1);
return InferenceEngine::make_shared_blob<uchar>(
{description.getPrecision(), dims, layout}, (uchar*)m.data);
}
std::ostringstream msg;
msg << "Unsupported IE precision: " << description.getPrecision();
CV_Error(Error::StsNotImplemented, msg.str());
}
InferenceEngine::DataPtr ngraphDataOutputNode(
const Ptr<BackendWrapper>& ptr,
const InferenceEngine::TensorDesc& description,
const std::string name)
{
CV_Assert(!ptr.empty());
Ptr<NgraphBackendWrapper> p = ptr.dynamicCast<NgraphBackendWrapper>();
CV_Assert(!p.empty());
NgraphBackendWrapper& w = *p;
const InferenceEngine::TensorDesc& blobDesc = w.blob.get()->getTensorDesc();
auto dims = description.getDims();
bool reallocate = false;
if (blobDesc.getPrecision() != description.getPrecision())
{
reallocate = true;
CV_LOG_WARNING(NULL, "Reallocate output '" << name << "' blob due to wrong precision: " << blobDesc.getPrecision() << " => " << description.getPrecision() << " ndims=" << dims.size());
}
if (dims.size() != blobDesc.getDims().size())
{
reallocate = true;
CV_LOG_WARNING(NULL, "Reallocate output '" << name << "' blob due to wrong dims: " << blobDesc.getDims().size() << " => " << dims.size());
}
if (reallocate)
{
auto layout = estimateLayout(dims.size());
w.dataPtr = InferenceEngine::DataPtr(new InferenceEngine::Data(name,
{description.getPrecision(), dims, layout}));
w.blob = reallocateBlob(*w.host, description);
}
return w.dataPtr;
}
void forwardNgraph(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
Ptr<BackendNode>& node, bool isAsync)
......@@ -918,6 +994,13 @@ void InfEngineNgraphNet::reset()
allBlobs.clear();
infRequests.clear();
isInit = false;
outputsDesc.clear();
for (const auto& it : cnn.getOutputsInfo())
{
const std::string& name = it.first;
outputsDesc.insert({name, it.second->getTensorDesc()});
}
}
void InfEngineNgraphNet::addBlobs(const std::vector<cv::Ptr<BackendWrapper> >& ptrs)
......
modules/dnn/src/ie_ngraph.hpp
浏览文件 @ 98c5fc6a
......@@ -54,7 +54,8 @@ public:
void setNodePtr(std::shared_ptr<ngraph::Node>* ptr);
void reset();
private:
//private:
detail::NetImplBase& netImpl_;
void release();
......@@ -89,6 +90,8 @@ private:
bool hasNetOwner;
std::vector<std::string> requestedOutputs;
std::unordered_set<std::shared_ptr<ngraph::Node>> unconnectedNodes;
std::map<std::string, InferenceEngine::TensorDesc> outputsDesc;
};
class InfEngineNgraphNode : public BackendNode
......@@ -121,12 +124,17 @@ public:
virtual void copyToHost() CV_OVERRIDE;
virtual void setHostDirty() CV_OVERRIDE;
Mat* host;
InferenceEngine::DataPtr dataPtr;
InferenceEngine::Blob::Ptr blob;
AsyncArray futureMat;
};
InferenceEngine::DataPtr ngraphDataNode(const Ptr<BackendWrapper>& ptr);
InferenceEngine::DataPtr ngraphDataOutputNode(
const Ptr<BackendWrapper>& ptr,
const InferenceEngine::TensorDesc& description,
const std::string name);
// This is a fake class to run networks from Model Optimizer. Objects of that
// class simulate responses of layers are imported by OpenCV and supported by
......
modules/dnn/test/test_ie_models.cpp
浏览文件 @ 98c5fc6a
......@@ -103,11 +103,34 @@ static const std::map<std::string, OpenVINOModelTestCaseInfo>& getOpenVINOTestMo
#if INF_ENGINE_RELEASE >= 2020010000
// Downloaded using these parameters for Open Model Zoo downloader (2020.1):
// ./downloader.py -o ${OPENCV_DNN_TEST_DATA_PATH}/omz_intel_models --cache_dir ${OPENCV_DNN_TEST_DATA_PATH}/.omz_cache/ \
// --name person-detection-retail-0013
// --name person-detection-retail-0013,age-gender-recognition-retail-0013
{ "person-detection-retail-0013", { // IRv10
"intel/person-detection-retail-0013/FP32/person-detection-retail-0013",
"intel/person-detection-retail-0013/FP16/person-detection-retail-0013"
}},
{ "age-gender-recognition-retail-0013", {
"intel/age-gender-recognition-retail-0013/FP16/age-gender-recognition-retail-0013",
"intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013"
}},
#endif
#if INF_ENGINE_RELEASE >= 2021020000
// OMZ: 2020.2
{ "face-detection-0105", {
"intel/face-detection-0105/FP32/face-detection-0105",
"intel/face-detection-0105/FP16/face-detection-0105"
}},
{ "face-detection-0106", {
"intel/face-detection-0106/FP32/face-detection-0106",
"intel/face-detection-0106/FP16/face-detection-0106"
}},
#endif
#if INF_ENGINE_RELEASE >= 2021040000
// OMZ: 2021.4
{ "person-vehicle-bike-detection-2004", {
"intel/person-vehicle-bike-detection-2004/FP32/person-vehicle-bike-detection-2004",
"intel/person-vehicle-bike-detection-2004/FP16/person-vehicle-bike-detection-2004"
//"intel/person-vehicle-bike-detection-2004/FP16-INT8/person-vehicle-bike-detection-2004"
}},
#endif
};
......@@ -123,13 +146,40 @@ static const std::vector<std::string> getOpenVINOTestModelsList()
return result;
}
inline static std::string getOpenVINOModel(const std::string &modelName, bool isFP16)
{
const std::map<std::string, OpenVINOModelTestCaseInfo>& models = getOpenVINOTestModels();
const auto it = models.find(modelName);
if (it != models.end())
{
OpenVINOModelTestCaseInfo modelInfo = it->second;
if (isFP16 && modelInfo.modelPathFP16)
return std::string(modelInfo.modelPathFP16);
else if (!isFP16 && modelInfo.modelPathFP32)
return std::string(modelInfo.modelPathFP32);
}
return std::string();
}
static inline void genData(const InferenceEngine::TensorDesc& desc, Mat& m, Blob::Ptr& dataPtr)
{
const std::vector<size_t>& dims = desc.getDims();
m.create(std::vector<int>(dims.begin(), dims.end()), CV_32F);
randu(m, -1, 1);
dataPtr = make_shared_blob<float>(desc, (float*)m.data);
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,
......@@ -235,7 +285,16 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath
BlobMap inputBlobs;
for (auto& it : net.getInputsInfo())
{
genData(it.second->getTensorDesc(), inputsMap[it.first], inputBlobs[it.first]);
const InferenceEngine::TensorDesc& desc = it.second->getTensorDesc();
genData(desc, inputsMap[it.first], inputBlobs[it.first]);
if (cvtest::debugLevel > 0)
{
const std::vector<size_t>& dims = desc.getDims();
std::cout << "Input: '" << it.first << "' precison=" << desc.getPrecision() << " dims=" << dims.size() << " [";
for (auto d : dims)
std::cout << " " << d;
std::cout << "] ocv_mat=" << inputsMap[it.first].size << " of " << typeToString(inputsMap[it.first].type()) << std::endl;
}
}
infRequest.SetInput(inputBlobs);
......@@ -244,7 +303,16 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath
BlobMap outputBlobs;
for (auto& it : net.getOutputsInfo())
{
genData(it.second->getTensorDesc(), outputsMap[it.first], outputBlobs[it.first]);
const InferenceEngine::TensorDesc& desc = it.second->getTensorDesc();
genData(desc, outputsMap[it.first], outputBlobs[it.first]);
if (cvtest::debugLevel > 0)
{
const std::vector<size_t>& dims = desc.getDims();
std::cout << "Output: '" << it.first << "' precison=" << desc.getPrecision() << " dims=" << dims.size() << " [";
for (auto d : dims)
std::cout << " " << d;
std::cout << "] ocv_mat=" << outputsMap[it.first].size << " of " << typeToString(outputsMap[it.first].type()) << std::endl;
}
}
infRequest.SetOutput(outputBlobs);
......@@ -265,6 +333,12 @@ void runCV(Backend backendId, Target targetId, const std::string& xmlPath, const
net.setPreferableTarget(targetId);
std::vector<String> outNames = net.getUnconnectedOutLayersNames();
if (cvtest::debugLevel > 0)
{
std::cout << "OpenCV output names: " << outNames.size() << std::endl;
for (auto name : outNames)
std::cout << "- " << name << std::endl;
}
std::vector<Mat> outs;
net.forward(outs, outNames);
......@@ -288,13 +362,26 @@ TEST_P(DNNTestOpenVINO, models)
ASSERT_FALSE(backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) <<
"Inference Engine backend is required";
#if INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (targetId == DNN_TARGET_MYRIAD && (
modelName == "person-detection-retail-0013" || // ncDeviceOpen:1013 Failed to find booted device after boot
modelName == "age-gender-recognition-retail-0013" // ncDeviceOpen:1013 Failed to find booted device after boot
#if INF_ENGINE_VER_MAJOR_GE(2021030000)
if (targetId == DNN_TARGET_MYRIAD && (false
|| modelName == "person-detection-retail-0013" // ncDeviceOpen:1013 Failed to find booted device after boot
|| modelName == "age-gender-recognition-retail-0013" // ncDeviceOpen:1013 Failed to find booted device after boot
|| modelName == "face-detection-0105" // get_element_type() must be called on a node with exactly one output
|| modelName == "face-detection-0106" // get_element_type() must be called on a node with exactly one output
|| modelName == "person-vehicle-bike-detection-2004" // 2021.4+: ncDeviceOpen:1013 Failed to find booted device after boot
)
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (targetId == DNN_TARGET_OPENCL && (false
|| modelName == "face-detection-0106" // Operation: 2278 of type ExperimentalDetectronPriorGridGenerator(op::v6) is not supported
)
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (targetId == DNN_TARGET_OPENCL_FP16 && (false
|| modelName == "face-detection-0106" // Operation: 2278 of type ExperimentalDetectronPriorGridGenerator(op::v6) is not supported
)
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if INF_ENGINE_VER_MAJOR_GE(2020020000)
......@@ -319,11 +406,8 @@ TEST_P(DNNTestOpenVINO, models)
bool isFP16 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == 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;
const std::string modelPath = getOpenVINOModel(modelName, isFP16);
ASSERT_FALSE(modelPath.empty()) << modelName;
std::string xmlPath = findDataFile(modelPath + ".xml", false);
std::string binPath = findDataFile(modelPath + ".bin", false);
......@@ -334,6 +418,8 @@ TEST_P(DNNTestOpenVINO, models)
if (targetId == DNN_TARGET_MYRIAD)
resetMyriadDevice();
EXPECT_NO_THROW(runIE(targetId, xmlPath, binPath, inputsMap, ieOutputsMap)) << "runIE";
if (targetId == DNN_TARGET_MYRIAD)
resetMyriadDevice();
EXPECT_NO_THROW(runCV(backendId, targetId, xmlPath, binPath, inputsMap, cvOutputsMap)) << "runCV";
double eps = 0;
......@@ -341,6 +427,14 @@ TEST_P(DNNTestOpenVINO, models)
if (targetId == DNN_TARGET_CPU && checkHardwareSupport(CV_CPU_AVX_512F))
eps = 1e-5;
#endif
#if INF_ENGINE_VER_MAJOR_GE(2021030000)
if (targetId == DNN_TARGET_CPU && modelName == "face-detection-0105")
eps = 2e-4;
#endif
#if INF_ENGINE_VER_MAJOR_GE(2021040000)
if (targetId == DNN_TARGET_CPU && modelName == "person-vehicle-bike-detection-2004")
eps = 1e-6;
#endif
EXPECT_EQ(ieOutputsMap.size(), cvOutputsMap.size());
for (auto& srcIt : ieOutputsMap)
......
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