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Merge pull request #21910 from zihaomu:fast_conv_ARM

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DNN: Accelerating convolution

* Fast Conv of ARM, X86 and universal intrinsics.

* improve code style.

* error fixed.

* improve the License

* optimize memory allocated and Adjust the threshold.

* change FasterRCNN_vgg16 to 2GB memory.
This commit is contained in:
Zihao Mu
2022-07-01 18:03:15 +08:00
committed by GitHub
parent b91f173680
commit 59b870a87a
11 changed files with 3286 additions and 12 deletions
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modules/dnn/src/layers/convolution_layer.cpp
+53 -1
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@@ -71,6 +71,8 @@ using namespace cv::dnn::ocl4dnn;
using namespace cv::dnn::cuda4dnn;
#endif
#include "fast_convolution/fast_convolution.hpp"
namespace cv
{
namespace dnn
@@ -253,11 +255,14 @@ class ConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl
{
public:
enum { VEC_ALIGN = 8, DFT_TYPE = CV_32F };
Mat weightsMat;
Mat weightsMat; // Used to store weight params. It will be used for layer fusion and memory alignment.
std::vector<float> biasvec;
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
Ptr<OCL4DNNConvSpatial<float> > convolutionOp;
std::vector<UMat> umat_blobs;
@@ -433,6 +438,7 @@ public:
wm.copyTo(wm_aligned);
wm = wm_aligned;
}
fastWeights = blobs[0].reshape(1, numOutput);
weightsMat = wm;
}
else
@@ -628,14 +634,26 @@ 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())
@@ -1948,8 +1966,13 @@ public:
int outCn = blobs.empty() ? inputs[1].size[0] : blobs[0].size[0];
// Need to align non-const blobs
bool variableWeight = false;
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)
{
@@ -2066,8 +2089,37 @@ public:
{
int nstripes = std::max(getNumThreads(), 1);
// Initialization of FastCovn2d
if ((!fastConv2dImpl || variableWeight) && inputs[0].dims == 4)
{
int K = outputs[0].size[1];
int C = inputs[0].size[1];
int Hk = kernel_size[kernel_size.size() - 2];
int Wk = kernel_size.back();
CV_Assert(outputs[0].size[1] % ngroups == 0);
int stride_h = strides[strides.size() - 2];
int stride_w = strides.back();
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]);
}
if (fastConv2dImpl)
{
runFastConv2d(inputs[0], outputs[0], fastConv2dImpl, nstripes, activ);
return;
}
// Use only for Conv1D and Conv3D.
ParallelConv::run(inputs[0], outputs[0], weightsMat, biasvec, reluslope,
kernel_size, strides, pads_begin, pads_end, dilations, activ.get(), ngroups, nstripes);
}
}