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Further optimization of Conv2D, fused Conv_Add_Activation, bring latest code from ficus OpConv.fx. (#22401)

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This commit is contained in:
Zihao Mu
2022-08-26 17:57:25 +08:00
committed by GitHub
parent 67fa8a2f47
commit bb64db98d8
12 changed files with 1194 additions and 905 deletions
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modules/dnn/src/layers/convolution_layer.cpp
+17 -25
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@@ -118,6 +118,9 @@ public:
fusedWeights = false;
fusedBias = false;
if (kernel_size.size() == 2)
isConv2D = true;
}
virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
@@ -188,6 +191,9 @@ public:
virtual bool tryFuse(Ptr<Layer>& top) CV_OVERRIDE
{
if (fusedAdd) // If the Conv layer has fused Add layer, it cannot fuse other layers.
return false;
Ptr<BlankLayer> blank_layer = top.dynamicCast<BlankLayer>();
if (blank_layer)
return true;
@@ -260,7 +266,6 @@ public:
std::vector<float> reluslope;
Ptr<ActivationLayer> activ;
Mat fastWeights; // Used to store weight params. It will be used for layer fusion and without memory alignment.
Ptr<FastConv2d> fastConv2dImpl;
#ifdef HAVE_OPENCL
@@ -438,7 +443,6 @@ public:
wm.copyTo(wm_aligned);
wm = wm_aligned;
}
fastWeights = blobs[0].reshape(1, numOutput);
weightsMat = wm;
}
else
@@ -584,11 +588,15 @@ public:
}
}
#endif
return !activ.empty();
fusedActivation = !activ.empty();
return fusedActivation;
}
virtual bool tryFuse(Ptr<Layer>& top) CV_OVERRIDE
{
if (fusedAdd) // If the Conv layer has fused Add layer, it cannot fuse other layers.
return false;
#ifdef HAVE_CUDA
if(IS_DNN_CUDA_TARGET(preferableTarget))
{
@@ -634,26 +642,14 @@ public:
if (weightsMat.data == blobs[0].data)
weightsMat = weightsMat.clone();
// If fastWeights is the same as weightsMat, we don't need to allocate more space for fastWeights.
bool sameFastWeights = false;
if (fastWeights.step1() == weightsMat.step1()) // If weightsMat is realigned, it is not the same as fastWeights.
sameFastWeights = true;
if (!sameFastWeights && fastWeights.data == blobs[0].data)
fastWeights = fastWeights.clone();
Mat originWeights = blobs[0].reshape(1, outCn);
for (int i = 0; i < outCn; ++i)
{
double wi = w.at<float>(i);
weightsMultipliers[i] *= wi;
cv::multiply(originWeights.row(i), weightsMultipliers[i], weightsMat.row(i));
if (!sameFastWeights)
cv::multiply(originWeights.row(i), weightsMultipliers[i], fastWeights.row(i));
biasvec[i] *= wi;
}
if (sameFastWeights)
fastWeights = weightsMat;
}
if (!b.empty())
@@ -1970,9 +1966,6 @@ public:
if (blobs.empty())
{
variableWeight = true;
if (fastWeights.data != inputs[1].data)
fastWeights = inputs[1].clone();
Mat wm = inputs[1].reshape(1, outCn);
if (wm.data != weightsMat.data)
{
@@ -2089,7 +2082,7 @@ public:
{
int nstripes = std::max(getNumThreads(), 1);
// Initialization of FastCovn2d
// Initialization of FastCovn2d, pack weight.
if ((!fastConv2dImpl || variableWeight) && inputs[0].dims == 4)
{
int K = outputs[0].size[1];
@@ -2103,23 +2096,22 @@ public:
int dilation_h = dilations[dilations.size() - 2];
int dilation_w = dilations.back();
float* weightsPtr = fastWeights.ptr<float>();
CV_Assert(weightsPtr);
fastConv2dImpl = initFastConv2d(ngroups, K, C, Hk, Wk, stride_w, stride_h,
dilation_w, dilation_h, pads_begin, pads_end, weightsPtr, &biasvec[0]);
fastConv2dImpl = initFastConv2d(ngroups, K, C, Hk, Wk, stride_w, stride_h, dilation_w,
dilation_h, pads_begin, pads_end, weightsMat, &biasvec[0]);
}
if (fastConv2dImpl)
{
runFastConv2d(inputs[0], outputs[0], fastConv2dImpl, nstripes, activ);
runFastConv2d(inputs[0], outputs[0], fastConv2dImpl, nstripes, activ, fusedAdd);
return;
}
//TODO: Add support of Conv1D and Conv3D to fastConv, and remove the old Conv branch.
// Use only for Conv1D and Conv3D.
CV_Assert(!fusedAdd);
ParallelConv::run(inputs[0], outputs[0], weightsMat, biasvec, reluslope,
kernel_size, strides, pads_begin, pads_end, dilations, activ.get(), ngroups, nstripes);
}
}