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Merge pull request #13799 from dkurt:dnn_ie_future_2

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This commit is contained in:
Alexander Alekhin 2019-02-12 14:07:42 +00:00
commit 9e7014b59f
6 changed files with 45 additions and 11 deletions
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24
modules/dnn/src/dnn.cpp
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@ -1637,6 +1637,27 @@ struct Net::Impl
preferableTarget == DNN_TARGET_MYRIAD ||
preferableTarget == DNN_TARGET_FPGA) && !fused)
{
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R5)
bool hasWeights = false;
for (const std::string& name : {"weights", "biases"})
{
auto it = ieNode->layer.getParameters().find(name);
if (it != ieNode->layer.getParameters().end())
{
InferenceEngine::Blob::CPtr bp = it->second.as<InferenceEngine::Blob::CPtr>();
it->second = (InferenceEngine::Blob::CPtr)convertFp16(std::const_pointer_cast<InferenceEngine::Blob>(bp));
hasWeights = true;
}
}
if (!hasWeights)
{
InferenceEngine::Blob::Ptr blob = InferenceEngine::make_shared_blob<int16_t>(
InferenceEngine::Precision::FP16,
InferenceEngine::Layout::C, {1});
blob->allocate();
ieNode->layer.getParameters()["weights"] = (InferenceEngine::Blob::CPtr)blob;
}
#else
auto& blobs = ieNode->layer.getConstantData();
if (blobs.empty())
{
@ -1653,6 +1674,7 @@ struct Net::Impl
for (auto& it : blobs)
it.second = convertFp16(std::const_pointer_cast<InferenceEngine::Blob>(it.second));
}
#endif
}
if (!fused)
@ -1724,7 +1746,7 @@ struct Net::Impl
if (!ieNode->net->isInitialized())
{
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R3)
#if INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2018R4)
// For networks which is built in runtime we need to specify a
// version of it's hyperparameters.
std::string versionTrigger = "<net name=\"TestInput\" version=\"3\" batch=\"1\">"

20
modules/dnn/src/layers/normalize_bbox_layer.cpp
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@ -276,23 +276,29 @@ public:
InferenceEngine::Builder::Layer l = ieLayer;
const int numChannels = input->dims[2]; // NOTE: input->dims are reversed (whcn)
InferenceEngine::Blob::Ptr weights;
if (blobs.empty())
{
auto weights = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
InferenceEngine::Layout::C,
{(size_t)numChannels});
weights->allocate();
auto onesBlob = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
InferenceEngine::Layout::C,
{(size_t)numChannels});
onesBlob->allocate();
std::vector<float> ones(numChannels, 1);
weights->set(ones);
l.addConstantData("weights", weights);
onesBlob->set(ones);
weights = onesBlob;
l.getParameters()["channel_shared"] = false;
}
else
{
CV_Assert(numChannels == blobs[0].total());
l.addConstantData("weights", wrapToInfEngineBlob(blobs[0], {(size_t)numChannels}, InferenceEngine::Layout::C));
weights = wrapToInfEngineBlob(blobs[0], {(size_t)numChannels}, InferenceEngine::Layout::C);
l.getParameters()["channel_shared"] = blobs[0].total() == 1;
}
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R5)
l.getParameters()["weights"] = (InferenceEngine::Blob::CPtr)weights;
#else
l.addConstantData("weights", weights);
#endif
l.getParameters()["across_spatial"] = acrossSpatial;
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}

2
modules/dnn/src/layers/resize_layer.cpp
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@ -173,7 +173,7 @@ public:
ieLayer.getParameters()["antialias"] = false;
if (scaleWidth != scaleHeight)
CV_Error(Error::StsNotImplemented, "resample with sw != sh");
ieLayer.getParameters()["factor"] = 1.0 / scaleWidth;
ieLayer.getParameters()["factor"] = 1.0f / scaleWidth;
}
else if (interpolation == "bilinear")
{

2
modules/dnn/src/op_inf_engine.cpp
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@ -766,7 +766,7 @@ void InfEngineBackendLayer::forward(InputArrayOfArrays inputs, OutputArrayOfArra
CV_Error(Error::StsInternal, "Choose Inference Engine as a preferable backend.");
}
InferenceEngine::TBlob<int16_t>::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob)
InferenceEngine::Blob::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob)
{
auto halfs = InferenceEngine::make_shared_blob<int16_t>(InferenceEngine::Precision::FP16, blob->layout(), blob->dims());
halfs->allocate();

3
modules/dnn/src/op_inf_engine.hpp
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@ -36,6 +36,7 @@
#define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000))
#define INF_ENGINE_VER_MAJOR_GE(ver) (((INF_ENGINE_RELEASE) / 10000) >= ((ver) / 10000))
#define INF_ENGINE_VER_MAJOR_LT(ver) (((INF_ENGINE_RELEASE) / 10000) < ((ver) / 10000))
#define INF_ENGINE_VER_MAJOR_EQ(ver) (((INF_ENGINE_RELEASE) / 10000) == ((ver) / 10000))
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
#include <ie_builders.hpp>
@ -252,7 +253,7 @@ Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob);
// Convert Inference Engine blob with FP32 precision to FP16 precision.
// Allocates memory for a new blob.
InferenceEngine::TBlob<int16_t>::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob);
InferenceEngine::Blob::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob);
// 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

5
modules/dnn/test/test_halide_layers.cpp
View File

@ -694,6 +694,11 @@ TEST_P(Eltwise, Accuracy)
Backend backendId = get<0>(get<4>(GetParam()));
Target targetId = get<1>(get<4>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE > 2018050000
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_OPENCL)
throw SkipTestException("");
#endif
Net net;
std::vector<int> convLayerIds(numConv);