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Merge pull request #13841 from dkurt:dnn_ie_future_3

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This commit is contained in:
Alexander Alekhin
2019-02-19 14:19:36 +00:00
parent 6e34d2778f ca5976e3d4
commit 8cedc052ca
12 changed files with 114 additions and 49 deletions
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modules/dnn/src/layers/batch_norm_layer.cpp
+3 -4
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@@ -350,11 +350,10 @@ public:
{
#ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::ScaleShiftLayer ieLayer(name);
InferenceEngine::Builder::Layer ieLayer = InferenceEngine::Builder::ScaleShiftLayer(name);
const size_t numChannels = weights_.total();
ieLayer.setWeights(wrapToInfEngineBlob(weights_, {numChannels}, InferenceEngine::Layout::C));
ieLayer.setBiases(wrapToInfEngineBlob(bias_, {numChannels}, InferenceEngine::Layout::C));
addConstantData("weights", wrapToInfEngineBlob(weights_, {numChannels}, InferenceEngine::Layout::C), ieLayer);
addConstantData("biases", wrapToInfEngineBlob(bias_, {numChannels}, InferenceEngine::Layout::C), ieLayer);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
InferenceEngine::LayerParams lp;
modules/dnn/src/layers/blank_layer.cpp
+3 -1
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@@ -125,7 +125,9 @@ public:
ieLayer.getParameters()["axis"] = input->dims.size() - 1;
ieLayer.getParameters()["out_sizes"] = input->dims[0];
}
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
std::vector<size_t> shape(input->dims);
std::reverse(shape.begin(), shape.end());
ieLayer.setInputPorts({InferenceEngine::Port(shape)});
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
modules/dnn/src/layers/convolution_layer.cpp
+8 -9
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@@ -493,11 +493,11 @@ public:
ieLayer.setGroup((size_t)group);
ieLayer.setOutDepth((size_t)outCn);
ieLayer.setWeights(ieWeights);
if (ieBiases)
ieLayer.setBiases(ieBiases);
InferenceEngine::Builder::Layer l = ieLayer;
addConstantData("weights", ieWeights, l);
if (ieBiases)
addConstantData("biases", ieBiases, l);
if (!padMode.empty())
l.getParameters()["auto_pad"] = padMode == "VALID" ? std::string("valid") : std::string("same_upper");
@@ -1725,12 +1725,11 @@ public:
ieLayer.setGroup((size_t)group);
ieLayer.setOutDepth((size_t)numOutput);
ieLayer.setWeights(wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW));
InferenceEngine::Builder::Layer l = ieLayer;
addConstantData("weights", wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW), l);
if (hasBias())
{
ieLayer.setBiases(wrapToInfEngineBlob(blobs[1], {(size_t)numOutput}, InferenceEngine::Layout::C));
}
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
addConstantData("biases", wrapToInfEngineBlob(blobs[1], {(size_t)numOutput}, InferenceEngine::Layout::C), l);
return Ptr<BackendNode>(new InfEngineBackendNode(l));
#else
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout
const int group = numOutput / outGroupCn;
modules/dnn/src/layers/elementwise_layers.cpp
+3 -3
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@@ -1134,10 +1134,10 @@ struct ChannelsPReLUFunctor
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
InferenceEngine::Builder::PReLULayer ieLayer("");
InferenceEngine::Builder::Layer l = InferenceEngine::Builder::PReLULayer("");
const size_t numChannels = scale.total();
ieLayer.setWeights(wrapToInfEngineBlob(scale, {numChannels}, InferenceEngine::Layout::C));
return ieLayer;
addConstantData("weights", wrapToInfEngineBlob(scale, {numChannels}, InferenceEngine::Layout::C), l);
return l;
}
#else
InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp)
modules/dnn/src/layers/fully_connected_layer.cpp
+4 -3
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@@ -448,11 +448,12 @@ public:
const int outNum = blobs[0].size[0];
ieLayer.setOutputNum(outNum);
ieLayer.setWeights(wrapToInfEngineBlob(blobs[0], {(size_t)blobs[0].size[0], (size_t)blobs[0].size[1], 1, 1}, InferenceEngine::Layout::OIHW));
InferenceEngine::Builder::Layer l = ieLayer;
addConstantData("weights", wrapToInfEngineBlob(blobs[0], {(size_t)blobs[0].size[0], (size_t)blobs[0].size[1], 1, 1}, InferenceEngine::Layout::OIHW), l);
if (blobs.size() > 1)
ieLayer.setBiases(wrapToInfEngineBlob(blobs[1], {(size_t)outNum}, InferenceEngine::Layout::C));
addConstantData("biases", wrapToInfEngineBlob(blobs[1], {(size_t)outNum}, InferenceEngine::Layout::C), l);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
return Ptr<BackendNode>(new InfEngineBackendNode(l));
#else
InferenceEngine::LayerParams lp;
lp.name = name;
modules/dnn/src/layers/normalize_bbox_layer.cpp
+1 -1
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@@ -291,7 +291,7 @@ public:
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;
l.getParameters()["weights"] = weights;
#else
l.addConstantData("weights", weights);
#endif
modules/dnn/src/layers/prior_box_layer.cpp
+3 -3
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@@ -524,12 +524,12 @@ public:
if (_stepX == _stepY)
{
l.getParameters()["step"] = _stepX;
l.getParameters()["step_h"] = 0.0;
l.getParameters()["step_w"] = 0.0;
l.getParameters()["step_h"] = 0.0f;
l.getParameters()["step_w"] = 0.0f;
}
else
{
l.getParameters()["step"] = 0.0;
l.getParameters()["step"] = 0.0f;
l.getParameters()["step_h"] = _stepY;
l.getParameters()["step_w"] = _stepX;
}
modules/dnn/src/layers/scale_layer.cpp
+5 -5
View File
@@ -198,13 +198,13 @@ public:
{
#ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::ScaleShiftLayer ieLayer(name);
InferenceEngine::Builder::Layer l = InferenceEngine::Builder::ScaleShiftLayer(name);
CV_Assert(!blobs.empty());
const size_t numChannels = blobs[0].total();
if (hasWeights)
{
ieLayer.setWeights(wrapToInfEngineBlob(blobs[0], {numChannels}, InferenceEngine::Layout::C));
addConstantData("weights", wrapToInfEngineBlob(blobs[0], {numChannels}, InferenceEngine::Layout::C), l);
}
else
{
@@ -214,11 +214,11 @@ public:
std::vector<float> ones(numChannels, 1);
weights->set(ones);
ieLayer.setWeights(weights);
addConstantData("weights", weights, l);
}
if (hasBias)
ieLayer.setBiases(wrapToInfEngineBlob(blobs.back(), {numChannels}, InferenceEngine::Layout::C));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
addConstantData("biases", wrapToInfEngineBlob(blobs.back(), {numChannels}, InferenceEngine::Layout::C), l);
return Ptr<BackendNode>(new InfEngineBackendNode(l));
#else
InferenceEngine::LayerParams lp;
lp.name = name;