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MobileNet-SSD from TensorFlow 1.3 and Inception-V2-SSD using Inference Engine backend

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This commit is contained in:
Dmitry Kurtaev
2018-02-07 11:28:45 +03:00
parent 090ee46f4a
commit 7fe97376c2
10 changed files with 188 additions and 73 deletions
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modules/dnn/src/layers/shift_layer.cpp
+53
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@@ -10,6 +10,7 @@ Implementation of shift layer, which adds up const values to blob.
*/
#include "../precomp.hpp"
#include "op_inf_engine.hpp"
#include <opencv2/dnn/shape_utils.hpp>
namespace cv
@@ -26,6 +27,12 @@ public:
CV_Assert(blobs.size() == 1);
}
virtual bool supportBackend(int backendId)
{
return backendId == DNN_BACKEND_DEFAULT ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
@@ -83,6 +90,52 @@ public:
}
}
virtual Ptr<BackendNode> tryAttach(const Ptr<BackendNode>& node)
{
switch (node->backendId)
{
case DNN_BACKEND_INFERENCE_ENGINE:
{
#ifdef HAVE_INF_ENGINE
auto base = node.dynamicCast<InfEngineBackendNode>();
auto conv = std::dynamic_pointer_cast<InferenceEngine::ConvolutionLayer>(base->layer);
if (conv)
{
fuseConvWeights(conv, Mat(), blobs[0]);
return base;
}
#endif // HAVE_INF_ENGINE
break;
}
}
return Ptr<BackendNode>();
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&)
{
#ifdef HAVE_INF_ENGINE
// Inference Engine has no layer just for biases. Create a linear
// transformation layer with ones weights.
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "ScaleShift";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::ScaleShiftLayer> ieLayer(new InferenceEngine::ScaleShiftLayer(lp));
auto weights = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
{blobs[0].total()});
weights->allocate();
std::vector<float> ones(blobs[0].total(), 1);
weights->set(ones);
ieLayer->_weights = weights;
ieLayer->_biases = wrapToInfEngineBlob(blobs[0]);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const
{