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Refactored deep learning layers fusion

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This commit is contained in:
Dmitry Kurtaev
2018-02-13 12:07:56 +03:00
parent 7474ad81d9
commit 514e6df460
7 changed files with 167 additions and 295 deletions
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modules/dnn/src/layers/mvn_layer.cpp
+12 -18
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@@ -65,16 +65,18 @@ public:
relu_slope = 0.f;
}
Ptr<BatchNormLayer> bnorm;
Mat scale, shift;
UMat bnorm_weight, bnorm_bias;
bool fuse_batch_norm;
bool setBatchNorm(const Ptr<BatchNormLayer>& layer )
virtual bool tryFuse(Ptr<Layer>& top)
{
bnorm = layer;
fuse_batch_norm = !bnorm.empty() && (preferableTarget == DNN_TARGET_OPENCL);
return fuse_batch_norm;
if (preferableTarget == DNN_TARGET_OPENCL && !fuse_batch_norm)
{
top->getScaleShift(scale, shift);
fuse_batch_norm = !scale.empty() || !shift.empty();
return fuse_batch_norm;
}
return false;
}
Ptr<ReLULayer> activ_relu;
@@ -95,12 +97,8 @@ public:
#ifdef HAVE_OPENCL
bool fast_forward_ocl(std::vector<UMat> &inputs, std::vector<UMat> &outputs)
{
if( fuse_batch_norm && scale.empty())
{
bnorm->getScaleShift(scale, shift);
bnorm_weight = scale.getUMat(ACCESS_READ);
bnorm_bias = shift.getUMat(ACCESS_READ);
}
UMat bnorm_weight = scale.empty() ? UMat() : scale.getUMat(ACCESS_READ);
UMat bnorm_bias = shift.empty() ? UMat() : shift.getUMat(ACCESS_READ);
int splitDim = (acrossChannels) ? 1 : 2;
for (size_t inpIdx = 0; inpIdx < inputs.size(); inpIdx++)
@@ -171,12 +169,8 @@ public:
return ret;
}
if( fuse_batch_norm && scale.empty())
{
bnorm->getScaleShift(scale, shift);
bnorm_weight = scale.getUMat(ACCESS_READ);
bnorm_bias = shift.getUMat(ACCESS_READ);
}
UMat bnorm_weight = scale.empty() ? UMat() : scale.getUMat(ACCESS_READ);
UMat bnorm_bias = shift.empty() ? UMat() : shift.getUMat(ACCESS_READ);
for (size_t inpIdx = 0; inpIdx < inputs.size(); inpIdx++)
{