potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-29 15:23:05 +04:00
Code Packages Releases Wiki Activity

MVN layer using Intel's Inference Engine backend

Browse Source
This commit is contained in:
Dmitry Kurtaev
2018-08-02 16:36:15 +03:00
parent 47e3e89e30
commit be08730cd6
8 changed files with 118 additions and 46 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/src/layers/mvn_layer.cpp
+43 -11
View File
@@ -42,6 +42,7 @@
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_inf_engine.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#ifdef HAVE_OPENCL
@@ -66,27 +67,25 @@ public:
fuse_batch_norm = false;
fuse_relu = false;
relu_slope = 0.f;
zeroDev = false;
}
Mat scale, shift;
bool fuse_batch_norm;
virtual bool tryFuse(Ptr<Layer>& top) CV_OVERRIDE
{
if (!fuse_batch_norm)
{
top->getScaleShift(scale, shift);
fuse_batch_norm = !scale.empty() || !shift.empty();
return fuse_batch_norm;
}
return false;
}
Ptr<ReLULayer> activ_relu;
float relu_slope;
bool fuse_relu;
bool zeroDev; // TODO: Doesn't considered in Intel's Inference Engine backend.
bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
{
if (!layer.empty() && !fuse_relu && !fuse_batch_norm)
{
layer->getScaleShift(scale, shift);
fuse_batch_norm = !scale.empty() || !shift.empty();
return fuse_batch_norm;
}
if (!layer.empty() && preferableTarget == DNN_TARGET_OPENCL)
{
activ_relu = layer.dynamicCast<ReLULayer>();
@@ -97,6 +96,23 @@ public:
return fuse_relu;
}
void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs) CV_OVERRIDE
{
int splitDim = (acrossChannels) ? 1 : 2;
int i, newRows = 1;
for( i = 0; i < splitDim; i++ )
newRows *= inputs[0]->size[i];
zeroDev = inputs[0]->total() == newRows;
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
return !zeroDev && (preferableTarget == DNN_TARGET_CPU || eps <= 1e-7f);
else
return backendId == DNN_BACKEND_OPENCV;
}
#ifdef HAVE_OPENCL
bool fast_forward_ocl(std::vector<UMat> &inputs, std::vector<UMat> &outputs)
{
@@ -324,6 +340,22 @@ public:
}
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "MVN";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::MVNLayer> ieLayer(new InferenceEngine::MVNLayer(lp));
ieLayer->params["across_channels"] = acrossChannels ? "1" : "0";
ieLayer->params["normalize_variance"] = normVariance ? "1" : "0";
ieLayer->params["eps"] = format("%f", eps);
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 CV_OVERRIDE
{