[opencv] 33/251: reuse AVX2-optimized kernels for AVX1 CPUs (like IvyBridge)
Nobuhiro Iwamatsu
iwamatsu at moszumanska.debian.org
Sun Aug 27 23:27:21 UTC 2017
This is an automated email from the git hooks/post-receive script.
iwamatsu pushed a commit to annotated tag 3.3.0
in repository opencv.
commit ed9564106cac5c28472135b7a71b676122d52dd7
Author: Vadim Pisarevsky <vadim.pisarevsky at gmail.com>
Date: Thu Jul 6 21:36:59 2017 +0300
reuse AVX2-optimized kernels for AVX1 CPUs (like IvyBridge)
---
modules/dnn/src/layers/convolution_layer.cpp | 17 +-
modules/dnn/src/layers/fully_connected_layer.cpp | 9 +-
.../{layers_common.hpp => layers_common.avx.cpp} | 47 +---
modules/dnn/src/layers/layers_common.avx2.cpp | 308 +--------------------
modules/dnn/src/layers/layers_common.hpp | 13 +
...yers_common.avx2.cpp => layers_common.simd.hpp} | 27 +-
6 files changed, 64 insertions(+), 357 deletions(-)
diff --git a/modules/dnn/src/layers/convolution_layer.cpp b/modules/dnn/src/layers/convolution_layer.cpp
index f5c782f..12e38c5 100644
--- a/modules/dnn/src/layers/convolution_layer.cpp
+++ b/modules/dnn/src/layers/convolution_layer.cpp
@@ -285,11 +285,12 @@ public:
const std::vector<float>* reluslope_;
const ActivationLayer* activ_;
bool is1x1_;
+ bool useAVX;
bool useAVX2;
ParallelConv()
: input_(0), weights_(0), output_(0), ngroups_(0), nstripes_(0),
- biasvec_(0), reluslope_(0), activ_(0), is1x1_(false), useAVX2(false)
+ biasvec_(0), reluslope_(0), activ_(0), is1x1_(false), useAVX(false), useAVX2(false)
{}
static void run( const Mat& input, Mat& output, const Mat& weights,
@@ -322,6 +323,7 @@ public:
int inpCnAll = input.size[1], width = input.size[3], height = input.size[2];
int inpCn = inpCnAll / ngroups;
p.is1x1_ = kernel == Size(0,0) && pad == Size(0, 0);
+ p.useAVX = checkHardwareSupport(CPU_AVX);
p.useAVX2 = checkHardwareSupport(CPU_AVX2);
int ncn = std::min(inpCn, (int)BLK_SIZE_CN);
@@ -508,6 +510,12 @@ public:
outShape, bsz, vsz, vsz_a, relu, cn0 == 0);
else
#endif
+ #if CV_TRY_AVX
+ if(useAVX)
+ fastConv_avx(wptr, wstep, biasptr, rowbuf0, data_out0 + ofs0,
+ outShape, bsz, vsz, vsz_a, relu, cn0 == 0);
+ else
+ #endif
for( int i = 0; i < outCn; i += 2 )
{
const float* wptr0 = wptr + i*wstep;
@@ -795,6 +803,7 @@ public:
b_ = &b;
c_ = &c;
nstripes_ = nstripes;
+ useAVX = checkHardwareSupport(CPU_AVX);
useAVX2 = checkHardwareSupport(CPU_AVX2);
}
@@ -818,6 +827,11 @@ public:
fastGEMM_avx2( aptr, astep, bptr, bstep, cptr, cstep, mmax, kmax, nmax );
else
#endif
+ #if CV_TRY_AVX
+ if( useAVX )
+ fastGEMM_avx( aptr, astep, bptr, bstep, cptr, cstep, mmax, kmax, nmax );
+ else
+ #endif
for( m = 0; m < mmax; m += 2 )
{
float* dst0 = cptr + cstep*m;
@@ -910,6 +924,7 @@ public:
const Mat *a_, *b_;
Mat* c_;
int nstripes_;
+ bool useAVX;
bool useAVX2;
};
diff --git a/modules/dnn/src/layers/fully_connected_layer.cpp b/modules/dnn/src/layers/fully_connected_layer.cpp
index 071593d..f27f39c 100644
--- a/modules/dnn/src/layers/fully_connected_layer.cpp
+++ b/modules/dnn/src/layers/fully_connected_layer.cpp
@@ -119,7 +119,7 @@ public:
class FullyConnected : public ParallelLoopBody
{
public:
- FullyConnected() : srcMat(0), weights(0), biasMat(0), activ(0), dstMat(0), nstripes(0), useAVX2(false) {}
+ FullyConnected() : srcMat(0), weights(0), biasMat(0), activ(0), dstMat(0), nstripes(0), useAVX(false), useAVX2(false) {}
static void run(const Mat& srcMat, const Mat& weights, const Mat& biasMat,
Mat& dstMat, const ActivationLayer* activ, int nstripes)
@@ -139,6 +139,7 @@ public:
p.dstMat = &dstMat;
p.nstripes = nstripes;
p.activ = activ;
+ p.useAVX = checkHardwareSupport(CPU_AVX);
p.useAVX2 = checkHardwareSupport(CPU_AVX2);
parallel_for_(Range(0, nstripes), p, nstripes);
@@ -179,6 +180,11 @@ public:
fastGEMM1T_avx2( sptr, wptr, wstep, biasptr, dptr, nw, vecsize);
else
#endif
+ #if CV_TRY_AVX
+ if( useAVX )
+ fastGEMM1T_avx( sptr, wptr, wstep, biasptr, dptr, nw, vecsize);
+ else
+ #endif
{
int i = 0;
@@ -228,6 +234,7 @@ public:
const ActivationLayer* activ;
Mat* dstMat;
int nstripes;
+ bool useAVX;
bool useAVX2;
};
diff --git a/modules/dnn/src/layers/layers_common.hpp b/modules/dnn/src/layers/layers_common.avx.cpp
similarity index 56%
copy from modules/dnn/src/layers/layers_common.hpp
copy to modules/dnn/src/layers/layers_common.avx.cpp
index 06f7825..4e0c034 100644
--- a/modules/dnn/src/layers/layers_common.hpp
+++ b/modules/dnn/src/layers/layers_common.avx.cpp
@@ -40,44 +40,15 @@
//
//M*/
-#ifndef __OPENCV_DNN_LAYERS_LAYERS_COMMON_HPP__
-#define __OPENCV_DNN_LAYERS_LAYERS_COMMON_HPP__
-#include <opencv2/dnn.hpp>
-#include <opencv2/dnn/shape_utils.hpp>
+#include "precomp.hpp"
+#include "layers_common.hpp"
+#include "opencv2/core/hal/intrin.hpp"
-namespace cv
-{
-namespace dnn
-{
+#define fastConv_some_avx fastConv_avx
+#define fastGEMM1T_some_avx fastGEMM1T_avx
+#define fastGEMM_some_avx fastGEMM_avx
-void getConvolutionKernelParams(const LayerParams ¶ms, int &kernelH, int &kernelW, int &padH, int &padW,
- int &strideH, int &strideW, int &dilationH, int &dilationW, cv::String& padMode);
+#undef _mm256_fmadd_ps
+#define _mm256_fmadd_ps(a, b, c) _mm256_add_ps(c, _mm256_mul_ps(a, b))
-void getPoolingKernelParams(const LayerParams ¶ms, int &kernelH, int &kernelW, bool &globalPooling,
- int &padH, int &padW, int &strideH, int &strideW, cv::String& padMode);
-
-void getConvPoolOutParams(const Size& inp, const Size &kernel,
- const Size &stride, const String &padMode,
- Size& out);
-
-void getConvPoolPaddings(const Size& inp, const Size& out,
- const Size &kernel, const Size &stride,
- const String &padMode, Size &pad);
-
-#if CV_TRY_AVX2
-void fastConv_avx2(const float* weights, size_t wstep, const float* bias,
- const float* rowbuf, float* output, const int* outShape,
- int blockSize, int vecsize, int vecsize_aligned,
- const float* relu, bool initOutput);
-void fastGEMM1T_avx2( const float* vec, const float* weights,
- size_t wstep, const float* bias,
- float* dst, int nvecs, int vecsize );
-void fastGEMM_avx2( const float* aptr, size_t astep, const float* bptr0,
- size_t bstep, float* cptr, size_t cstep,
- int ma, int na, int nb );
-#endif
-
-}
-}
-
-#endif
+#include "layers_common.simd.hpp"
diff --git a/modules/dnn/src/layers/layers_common.avx2.cpp b/modules/dnn/src/layers/layers_common.avx2.cpp
index 4f0c15f..ef8108c 100644
--- a/modules/dnn/src/layers/layers_common.avx2.cpp
+++ b/modules/dnn/src/layers/layers_common.avx2.cpp
@@ -44,308 +44,8 @@
#include "layers_common.hpp"
#include "opencv2/core/hal/intrin.hpp"
-namespace cv {
-namespace dnn {
+#define fastConv_some_avx fastConv_avx2
+#define fastGEMM1T_some_avx fastGEMM1T_avx2
+#define fastGEMM_some_avx fastGEMM_avx2
-void fastConv_avx2( const float* weights, size_t wstep, const float* bias,
- const float* rowbuf, float* output, const int* outShape,
- int blockSize, int vecsize, int vecsize_aligned,
- const float* relu, bool initOutput )
-{
- int outCn = outShape[1];
- size_t outPlaneSize = outShape[2]*outShape[3];
- float r0 = 1.f, r1 = 1.f, r2 = 1.f;
- __m256 vr0 = _mm256_set1_ps(1.f), vr1 = vr0, vr2 = vr0, z = _mm256_setzero_ps();
-
- // now compute dot product of the weights
- // and im2row-transformed part of the tensor
- for( int i = 0; i < outCn; i += 3 )
- {
- const float* wptr0 = weights + i*wstep;
- const float* wptr1 = wptr0 + wstep;
- const float* wptr2 = wptr1 + wstep;
- float* outptr0 = output + i*outPlaneSize;
- float* outptr1 = outptr0 + outPlaneSize;
- float* outptr2 = outptr1 + outPlaneSize;
- float bias0 = bias[i], bias1 = bias[i+1], bias2 = bias[i+2];
-
- if( i+2 >= outCn )
- {
- wptr2 = wptr1;
- outptr2 = outptr1;
- bias2 = bias1;
- if( i+1 >= outCn )
- {
- wptr2 = wptr1 = wptr0;
- outptr2 = outptr1 = outptr0;
- bias2 = bias1 = bias0;
- }
- }
-
- if( relu )
- {
- r0 = relu[i];
- r1 = relu[i+1];
- r2 = relu[i+2];
- vr0 = _mm256_set1_ps(r0);
- vr1 = _mm256_set1_ps(r1);
- vr2 = _mm256_set1_ps(r2);
- }
-
- int j = 0;
- for( ; j <= blockSize - 4; j += 4 )
- {
- const float* rptr = rowbuf + j*vecsize_aligned;
-
- __m256 vs00 = _mm256_setzero_ps(), vs01 = _mm256_setzero_ps(),
- vs02 = _mm256_setzero_ps(), vs03 = _mm256_setzero_ps(),
- vs10 = _mm256_setzero_ps(), vs11 = _mm256_setzero_ps(),
- vs12 = _mm256_setzero_ps(), vs13 = _mm256_setzero_ps(),
- vs20 = _mm256_setzero_ps(), vs21 = _mm256_setzero_ps(),
- vs22 = _mm256_setzero_ps(), vs23 = _mm256_setzero_ps();
-
- for( int k = 0; k < vecsize; k += 8, rptr += 8 )
- {
- __m256 w0 = _mm256_load_ps(wptr0 + k);
- __m256 w1 = _mm256_load_ps(wptr1 + k);
- __m256 w2 = _mm256_load_ps(wptr2 + k);
- __m256 r0 = _mm256_load_ps(rptr);
-
- vs00 = _mm256_fmadd_ps(w0, r0, vs00);
- vs10 = _mm256_fmadd_ps(w1, r0, vs10);
- vs20 = _mm256_fmadd_ps(w2, r0, vs20);
-
- r0 = _mm256_load_ps(rptr + vecsize_aligned);
- vs01 = _mm256_fmadd_ps(w0, r0, vs01);
- vs11 = _mm256_fmadd_ps(w1, r0, vs11);
- vs21 = _mm256_fmadd_ps(w2, r0, vs21);
-
- r0 = _mm256_load_ps(rptr + vecsize_aligned*2);
- vs02 = _mm256_fmadd_ps(w0, r0, vs02);
- vs12 = _mm256_fmadd_ps(w1, r0, vs12);
- vs22 = _mm256_fmadd_ps(w2, r0, vs22);
-
- r0 = _mm256_load_ps(rptr + vecsize_aligned*3);
- vs03 = _mm256_fmadd_ps(w0, r0, vs03);
- vs13 = _mm256_fmadd_ps(w1, r0, vs13);
- vs23 = _mm256_fmadd_ps(w2, r0, vs23);
- }
-
- __m256 t0 = _mm256_hadd_ps(_mm256_hadd_ps(vs00, vs01), _mm256_hadd_ps(vs02, vs03));
- __m256 t1 = _mm256_hadd_ps(_mm256_hadd_ps(vs10, vs11), _mm256_hadd_ps(vs12, vs13));
- __m256 t2 = _mm256_hadd_ps(_mm256_hadd_ps(vs20, vs21), _mm256_hadd_ps(vs22, vs23));
-
- t0 = _mm256_add_ps(t0, _mm256_permute2f128_ps(t0, t0, 1));
- t1 = _mm256_add_ps(t1, _mm256_permute2f128_ps(t1, t1, 1));
- t2 = _mm256_add_ps(t2, _mm256_permute2f128_ps(t2, t2, 1));
-
- __m256 s0, s1, s2;
-
- if( initOutput )
- {
- s0 = _mm256_set1_ps(bias0);
- s1 = _mm256_set1_ps(bias1);
- s2 = _mm256_set1_ps(bias2);
- }
- else
- {
- s0 = _mm256_castps128_ps256(_mm_loadu_ps(outptr0 + j));
- s1 = _mm256_castps128_ps256(_mm_loadu_ps(outptr1 + j));
- s2 = _mm256_castps128_ps256(_mm_loadu_ps(outptr2 + j));
- }
-
- s0 = _mm256_add_ps(s0, t0);
- s1 = _mm256_add_ps(s1, t1);
- s2 = _mm256_add_ps(s2, t2);
-
- if( relu )
- {
- __m256 m0 = _mm256_cmp_ps(s0, z, _CMP_GT_OS);
- __m256 m1 = _mm256_cmp_ps(s1, z, _CMP_GT_OS);
- __m256 m2 = _mm256_cmp_ps(s2, z, _CMP_GT_OS);
- s0 = _mm256_xor_ps(s0, _mm256_andnot_ps(m0, _mm256_xor_ps(_mm256_mul_ps(s0, vr0), s0)));
- s1 = _mm256_xor_ps(s1, _mm256_andnot_ps(m1, _mm256_xor_ps(_mm256_mul_ps(s1, vr1), s1)));
- s2 = _mm256_xor_ps(s2, _mm256_andnot_ps(m2, _mm256_xor_ps(_mm256_mul_ps(s2, vr2), s2)));
- }
-
- _mm_storeu_ps(outptr0 + j, _mm256_castps256_ps128(s0));
- _mm_storeu_ps(outptr1 + j, _mm256_castps256_ps128(s1));
- _mm_storeu_ps(outptr2 + j, _mm256_castps256_ps128(s2));
- }
-
- for( ; j < blockSize; j++ )
- {
- const float* rptr = rowbuf + j*vecsize_aligned;
- float s00, s10, s20;
-
- if( initOutput )
- {
- s00 = bias0;
- s10 = bias1;
- s20 = bias2;
- }
- else
- {
- s00 = outptr0[j];
- s10 = outptr1[j];
- s20 = outptr2[j];
- }
-
- for( int k = 0; k < vecsize; k++ )
- {
- float r0 = rptr[k];
- s00 += wptr0[k]*r0;
- s10 += wptr1[k]*r0;
- s20 += wptr2[k]*r0;
- }
-
- if( relu )
- {
- s00 = s00 > 0.f ? s00 : s00*r0;
- s10 = s10 > 0.f ? s10 : s10*r1;
- s20 = s20 > 0.f ? s20 : s20*r2;
- }
-
- outptr0[j] = s00;
- outptr1[j] = s10;
- outptr2[j] = s20;
- }
- }
- _mm256_zeroupper();
-}
-
-// dst = vec * weights^t + bias
-void fastGEMM1T_avx2( const float* vec, const float* weights,
- size_t wstep, const float* bias,
- float* dst, int nvecs, int vecsize )
-{
- int i = 0;
-
- for( ; i <= nvecs - 8; i += 8 )
- {
- const float* wptr = weights + i*wstep;
- __m256 vs0 = _mm256_setzero_ps(), vs1 = _mm256_setzero_ps(),
- vs2 = _mm256_setzero_ps(), vs3 = _mm256_setzero_ps(),
- vs4 = _mm256_setzero_ps(), vs5 = _mm256_setzero_ps(),
- vs6 = _mm256_setzero_ps(), vs7 = _mm256_setzero_ps();
-
- for( int k = 0; k < vecsize; k += 8, wptr += 8 )
- {
- __m256 v = _mm256_load_ps(vec + k);
-
- vs0 = _mm256_fmadd_ps(_mm256_load_ps(wptr), v, vs0);
- vs1 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep), v, vs1);
- vs2 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*2), v, vs2);
- vs3 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*3), v, vs3);
- vs4 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*4), v, vs4);
- vs5 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*5), v, vs5);
- vs6 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*6), v, vs6);
- vs7 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*7), v, vs7);
- }
-
- __m256 s0 = _mm256_hadd_ps(_mm256_hadd_ps(vs0, vs1), _mm256_hadd_ps(vs2, vs3));
- __m256 s1 = _mm256_hadd_ps(_mm256_hadd_ps(vs4, vs5), _mm256_hadd_ps(vs6, vs7));
-
- s0 = _mm256_add_ps(s0, _mm256_permute2f128_ps(s0, s0, 1));
- s1 = _mm256_add_ps(s1, _mm256_permute2f128_ps(s1, s1, 1));
-
- s0 = _mm256_add_ps(s0, _mm256_castps128_ps256(_mm_loadu_ps(bias + i)));
- s1 = _mm256_add_ps(s1, _mm256_castps128_ps256(_mm_loadu_ps(bias + i + 4)));
-
- _mm_storeu_ps(dst + i, _mm256_castps256_ps128(s0));
- _mm_storeu_ps(dst + i + 4, _mm256_castps256_ps128(s1));
- }
-
- float temp = 0.f;
- for( ; i < nvecs; i++ )
- {
- const float* wptr = weights + i*wstep;
- __m256 vs0 = _mm256_setzero_ps();
-
- for( int k = 0; k < vecsize; k += 8, wptr += 8 )
- {
- __m256 v = _mm256_load_ps(vec + k);
- vs0 = _mm256_fmadd_ps(_mm256_load_ps(wptr), v, vs0);
- }
-
- __m256 s0 = _mm256_hadd_ps(_mm256_hadd_ps(vs0, vs0), vs0);
- s0 = _mm256_add_ps(s0, _mm256_permute2f128_ps(s0, s0, 1));
- _mm_store_ss(&temp, _mm256_castps256_ps128(s0));
- dst[i] = temp + bias[i];
- }
-
- _mm256_zeroupper();
-}
-
-void fastGEMM_avx2( const float* aptr, size_t astep, const float* bptr,
- size_t bstep, float* cptr, size_t cstep,
- int ma, int na, int nb )
-{
- int n = 0;
- for( ; n <= nb - 16; n += 16 )
- {
- for( int m = 0; m < ma; m += 4 )
- {
- const float* aptr0 = aptr + astep*m;
- const float* aptr1 = aptr + astep*std::min(m+1, ma-1);
- const float* aptr2 = aptr + astep*std::min(m+2, ma-1);
- const float* aptr3 = aptr + astep*std::min(m+3, ma-1);
-
- float* cptr0 = cptr + cstep*m;
- float* cptr1 = cptr + cstep*std::min(m+1, ma-1);
- float* cptr2 = cptr + cstep*std::min(m+2, ma-1);
- float* cptr3 = cptr + cstep*std::min(m+3, ma-1);
-
- __m256 d00 = _mm256_setzero_ps(), d01 = _mm256_setzero_ps();
- __m256 d10 = _mm256_setzero_ps(), d11 = _mm256_setzero_ps();
- __m256 d20 = _mm256_setzero_ps(), d21 = _mm256_setzero_ps();
- __m256 d30 = _mm256_setzero_ps(), d31 = _mm256_setzero_ps();
-
- for( int k = 0; k < na; k++ )
- {
- __m256 a0 = _mm256_set1_ps(aptr0[k]);
- __m256 a1 = _mm256_set1_ps(aptr1[k]);
- __m256 a2 = _mm256_set1_ps(aptr2[k]);
- __m256 a3 = _mm256_set1_ps(aptr3[k]);
- __m256 b0 = _mm256_loadu_ps(bptr + k*bstep + n);
- __m256 b1 = _mm256_loadu_ps(bptr + k*bstep + n + 8);
- d00 = _mm256_fmadd_ps(a0, b0, d00);
- d01 = _mm256_fmadd_ps(a0, b1, d01);
- d10 = _mm256_fmadd_ps(a1, b0, d10);
- d11 = _mm256_fmadd_ps(a1, b1, d11);
- d20 = _mm256_fmadd_ps(a2, b0, d20);
- d21 = _mm256_fmadd_ps(a2, b1, d21);
- d30 = _mm256_fmadd_ps(a3, b0, d30);
- d31 = _mm256_fmadd_ps(a3, b1, d31);
- }
-
- _mm256_storeu_ps(cptr0 + n, d00);
- _mm256_storeu_ps(cptr0 + n + 8, d01);
- _mm256_storeu_ps(cptr1 + n, d10);
- _mm256_storeu_ps(cptr1 + n + 8, d11);
- _mm256_storeu_ps(cptr2 + n, d20);
- _mm256_storeu_ps(cptr2 + n + 8, d21);
- _mm256_storeu_ps(cptr3 + n, d30);
- _mm256_storeu_ps(cptr3 + n + 8, d31);
- }
- }
-
- for( ; n < nb; n++ )
- {
- for( int m = 0; m < ma; m++ )
- {
- const float* aptr0 = aptr + astep*m;
- float* cptr0 = cptr + cstep*m;
- float d0 = 0.f;
-
- for( int k = 0; k < na; k++ )
- d0 += aptr0[k]*bptr[k*bstep + n];
-
- cptr0[n] = d0;
- }
- }
- _mm256_zeroupper();
-}
-
-}
-}
+#include "layers_common.simd.hpp"
diff --git a/modules/dnn/src/layers/layers_common.hpp b/modules/dnn/src/layers/layers_common.hpp
index 06f7825..bbab275 100644
--- a/modules/dnn/src/layers/layers_common.hpp
+++ b/modules/dnn/src/layers/layers_common.hpp
@@ -64,6 +64,19 @@ void getConvPoolPaddings(const Size& inp, const Size& out,
const Size &kernel, const Size &stride,
const String &padMode, Size &pad);
+#if CV_TRY_AVX
+void fastConv_avx(const float* weights, size_t wstep, const float* bias,
+ const float* rowbuf, float* output, const int* outShape,
+ int blockSize, int vecsize, int vecsize_aligned,
+ const float* relu, bool initOutput);
+void fastGEMM1T_avx( const float* vec, const float* weights,
+ size_t wstep, const float* bias,
+ float* dst, int nvecs, int vecsize );
+void fastGEMM_avx( const float* aptr, size_t astep, const float* bptr0,
+ size_t bstep, float* cptr, size_t cstep,
+ int ma, int na, int nb );
+#endif
+
#if CV_TRY_AVX2
void fastConv_avx2(const float* weights, size_t wstep, const float* bias,
const float* rowbuf, float* output, const int* outShape,
diff --git a/modules/dnn/src/layers/layers_common.avx2.cpp b/modules/dnn/src/layers/layers_common.simd.hpp
similarity index 94%
copy from modules/dnn/src/layers/layers_common.avx2.cpp
copy to modules/dnn/src/layers/layers_common.simd.hpp
index 4f0c15f..1110ed0 100644
--- a/modules/dnn/src/layers/layers_common.avx2.cpp
+++ b/modules/dnn/src/layers/layers_common.simd.hpp
@@ -40,17 +40,16 @@
//
//M*/
-#include "precomp.hpp"
-#include "layers_common.hpp"
-#include "opencv2/core/hal/intrin.hpp"
+#ifndef __DNN_LAYERS_COMMON_SIMD_HPP__
+#define __DNN_LAYERS_COMMON_SIMD_HPP__
namespace cv {
namespace dnn {
-void fastConv_avx2( const float* weights, size_t wstep, const float* bias,
- const float* rowbuf, float* output, const int* outShape,
- int blockSize, int vecsize, int vecsize_aligned,
- const float* relu, bool initOutput )
+void fastConv_some_avx( const float* weights, size_t wstep, const float* bias,
+ const float* rowbuf, float* output, const int* outShape,
+ int blockSize, int vecsize, int vecsize_aligned,
+ const float* relu, bool initOutput )
{
int outCn = outShape[1];
size_t outPlaneSize = outShape[2]*outShape[3];
@@ -215,9 +214,9 @@ void fastConv_avx2( const float* weights, size_t wstep, const float* bias,
}
// dst = vec * weights^t + bias
-void fastGEMM1T_avx2( const float* vec, const float* weights,
- size_t wstep, const float* bias,
- float* dst, int nvecs, int vecsize )
+void fastGEMM1T_some_avx( const float* vec, const float* weights,
+ size_t wstep, const float* bias,
+ float* dst, int nvecs, int vecsize )
{
int i = 0;
@@ -277,9 +276,9 @@ void fastGEMM1T_avx2( const float* vec, const float* weights,
_mm256_zeroupper();
}
-void fastGEMM_avx2( const float* aptr, size_t astep, const float* bptr,
- size_t bstep, float* cptr, size_t cstep,
- int ma, int na, int nb )
+void fastGEMM_some_avx( const float* aptr, size_t astep, const float* bptr,
+ size_t bstep, float* cptr, size_t cstep,
+ int ma, int na, int nb )
{
int n = 0;
for( ; n <= nb - 16; n += 16 )
@@ -349,3 +348,5 @@ void fastGEMM_avx2( const float* aptr, size_t astep, const float* bptr,
}
}
+
+#endif
--
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