diff --git a/modules/dnn/src/dnn.cpp b/modules/dnn/src/dnn.cpp
index fc5548cd8d11be82a34076e8801c7c24b94ebcad..5b65a6c50aea2daaf3541309d269501799a98b0d 100644
--- a/modules/dnn/src/dnn.cpp
+++ b/modules/dnn/src/dnn.cpp
@@ -730,9 +730,9 @@ struct DataLayer : public Layer
biases->set(biasesVec);
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
- InferenceEngine::Builder::ScaleShiftLayer ieLayer(name);
- ieLayer.setWeights(weights);
- ieLayer.setBiases(biases);
+ InferenceEngine::Builder::Layer ieLayer = InferenceEngine::Builder::ScaleShiftLayer(name);
+ addConstantData("weights", weights, ieLayer);
+ addConstantData("biases", biases, ieLayer);
#else
InferenceEngine::LayerParams lp;
lp.name = name;
@@ -1638,25 +1638,15 @@ struct Net::Impl
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;
+ InferenceEngine::Blob::Ptr bp = it->second.as<InferenceEngine::Blob::Ptr>();
+ it->second = convertFp16(std::const_pointer_cast<InferenceEngine::Blob>(bp));
}
}
- 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())
diff --git a/modules/dnn/src/layers/batch_norm_layer.cpp b/modules/dnn/src/layers/batch_norm_layer.cpp
index 522d0229bacc84b85c6bcf1f882adc93a26abf6f..4c69c247c41a3424748d790bddb4e54ab0b89965 100644
--- a/modules/dnn/src/layers/batch_norm_layer.cpp
+++ b/modules/dnn/src/layers/batch_norm_layer.cpp
@@ -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;
diff --git a/modules/dnn/src/layers/blank_layer.cpp b/modules/dnn/src/layers/blank_layer.cpp
index 96336808a0ba74e088f516fa2a88e8149dfa20a8..c3a68a2a420fe02d8079bf62a850a72ae68005bd 100644
--- a/modules/dnn/src/layers/blank_layer.cpp
+++ b/modules/dnn/src/layers/blank_layer.cpp
@@ -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
diff --git a/modules/dnn/src/layers/convolution_layer.cpp b/modules/dnn/src/layers/convolution_layer.cpp
index 31665d7dcc9d2317fde119f6a1ba0adf3d6f0fc5..60611b52b2490c2b8373eaba10d79a430df7d707 100644
--- a/modules/dnn/src/layers/convolution_layer.cpp
+++ b/modules/dnn/src/layers/convolution_layer.cpp
@@ -493,11 +493,11 @@ public:
ieLayer.setGroup((size_t)group);
ieLayer.setOutDepth((size_t)outCn);
- ieLayer.setWeights(ieWeights);
+ InferenceEngine::Builder::Layer l = ieLayer;
+ addConstantData("weights", ieWeights, l);
if (ieBiases)
- ieLayer.setBiases(ieBiases);
+ addConstantData("biases", ieBiases, l);
- InferenceEngine::Builder::Layer l = ieLayer;
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;
diff --git a/modules/dnn/src/layers/elementwise_layers.cpp b/modules/dnn/src/layers/elementwise_layers.cpp
index b2e0621d6d45f5da479e5adb236ab00729b96d30..a18cce6fa9123cae59a637aa158bafa380e80ef2 100644
--- a/modules/dnn/src/layers/elementwise_layers.cpp
+++ b/modules/dnn/src/layers/elementwise_layers.cpp
@@ -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)
diff --git a/modules/dnn/src/layers/fully_connected_layer.cpp b/modules/dnn/src/layers/fully_connected_layer.cpp
index 3a71a872fe5eeb86cb4fadebeaad2e5a81a61b5e..dcfa7d1dacae1d446a9bd3be0bea8aaf97bfde51 100644
--- a/modules/dnn/src/layers/fully_connected_layer.cpp
+++ b/modules/dnn/src/layers/fully_connected_layer.cpp
@@ -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;
diff --git a/modules/dnn/src/layers/normalize_bbox_layer.cpp b/modules/dnn/src/layers/normalize_bbox_layer.cpp
index 8e21f116e4b21138099b2f5c793d51450ae4fb58..cf968f823fe7975d979ed7aabcda4d863822db89 100644
--- a/modules/dnn/src/layers/normalize_bbox_layer.cpp
+++ b/modules/dnn/src/layers/normalize_bbox_layer.cpp
@@ -295,7 +295,7 @@ 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
diff --git a/modules/dnn/src/layers/prior_box_layer.cpp b/modules/dnn/src/layers/prior_box_layer.cpp
index ac11fe7adadc13fa40d9dbbffde931a73b71072a..b2907b7b8b5d377400efd4efac40a4bac5f384aa 100644
--- a/modules/dnn/src/layers/prior_box_layer.cpp
+++ b/modules/dnn/src/layers/prior_box_layer.cpp
@@ -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;
}
diff --git a/modules/dnn/src/layers/scale_layer.cpp b/modules/dnn/src/layers/scale_layer.cpp
index a11fd379a2e51e1d05600848f4a61be52e598a5d..d911905d36d36d45e2617a703689694abda1ecdd 100644
--- a/modules/dnn/src/layers/scale_layer.cpp
+++ b/modules/dnn/src/layers/scale_layer.cpp
@@ -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;
diff --git a/modules/dnn/src/op_inf_engine.cpp b/modules/dnn/src/op_inf_engine.cpp
index a45206433705fc715232b0fd1d00cf0059431d41..ddaab41cdc994294cd421893b62f0d4b66bc1c01 100644
--- a/modules/dnn/src/op_inf_engine.cpp
+++ b/modules/dnn/src/op_inf_engine.cpp
@@ -18,6 +18,11 @@ 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";
+
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InfEngineBackendNode::InfEngineBackendNode(const InferenceEngine::Builder::Layer& _layer)
: BackendNode(DNN_BACKEND_INFERENCE_ENGINE), layer(_layer) {}
@@ -90,7 +95,7 @@ void InfEngineBackendNet::connect(const std::vector<Ptr<BackendWrapper> >& input
it = layers.find(inpName);
if (it == layers.end())
{
- InferenceEngine::Builder::InputLayer inpLayer(inpName);
+ InferenceEngine::Builder::InputLayer inpLayer(!inpName.empty() ? inpName : kDefaultInpLayerName);
std::vector<size_t> shape(inp->blob->dims());
std::reverse(shape.begin(), shape.end());
@@ -119,6 +124,14 @@ void InfEngineBackendNet::init(int targetId)
for (int id : unconnectedLayersIds)
{
InferenceEngine::Builder::OutputLayer outLayer("myconv1");
+#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R5)
+ // 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);
}
cnn = InferenceEngine::CNNNetwork(InferenceEngine::Builder::convertToICNNNetwork(netBuilder.build()));
@@ -167,12 +180,56 @@ void InfEngineBackendNet::init(int targetId)
initPlugin(cnn);
}
-void InfEngineBackendNet::addLayer(const InferenceEngine::Builder::Layer& layer)
+void InfEngineBackendNet::addLayer(InferenceEngine::Builder::Layer& layer)
{
+#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R5)
+ // 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_GT(INF_ENGINE_RELEASE_2018R5)
+ // 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, portIds[i]});
+ }
+#endif
}
void InfEngineBackendNet::addOutput(const std::string& name)
@@ -705,7 +762,7 @@ void InfEngineBackendNet::addBlobs(const std::vector<Ptr<BackendWrapper> >& ptrs
{
std::string name = wrapper->dataPtr->name;
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
- name = name.empty() ? "id1" : name; // TODO: drop the magic input name.
+ name = name.empty() ? kDefaultInpLayerName : name;
#endif
allBlobs.insert({name, wrapper->blob});
}
@@ -776,6 +833,18 @@ InferenceEngine::Blob::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob)
return halfs;
}
+#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
+void addConstantData(const std::string& name, InferenceEngine::Blob::Ptr data,
+ InferenceEngine::Builder::Layer& l)
+{
+#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R5)
+ l.getParameters()[name] = data;
+#else
+ l.addConstantData(name, data);
+#endif
+}
+#endif
+
#endif // HAVE_INF_ENGINE
bool haveInfEngine()
diff --git a/modules/dnn/src/op_inf_engine.hpp b/modules/dnn/src/op_inf_engine.hpp
index e912725296f29df17caf8d8c32290a785b7a0840..ac72c0c69c341da18f022cae6b393edc055d7ed4 100644
--- a/modules/dnn/src/op_inf_engine.hpp
+++ b/modules/dnn/src/op_inf_engine.hpp
@@ -162,7 +162,7 @@ public:
InfEngineBackendNet(InferenceEngine::CNNNetwork& net);
- void addLayer(const InferenceEngine::Builder::Layer& layer);
+ void addLayer(InferenceEngine::Builder::Layer& layer);
void addOutput(const std::string& name);
@@ -255,6 +255,10 @@ Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob);
// Allocates memory for a new blob.
InferenceEngine::Blob::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob);
+#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
+void addConstantData(const std::string& name, InferenceEngine::Blob::Ptr data, InferenceEngine::Builder::Layer& l);
+#endif
+
// 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
// Inference Engine. The main difference is that they do not perform forward pass.
diff --git a/modules/dnn/test/test_halide_layers.cpp b/modules/dnn/test/test_halide_layers.cpp
index 879dd7bbf03781280b7940f56f5c66b702695d7c..92af2e94ee53dcc364a540d77b47199b4d99fbd2 100644
--- a/modules/dnn/test/test_halide_layers.cpp
+++ b/modules/dnn/test/test_halide_layers.cpp
@@ -695,7 +695,8 @@ TEST_P(Eltwise, Accuracy)
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)
+ if (backendId == DNN_BACKEND_INFERENCE_ENGINE &&
+ (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
#endif