Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge pull request #17858 from vpisarev:dnn_depthwise_conv

Browse Source 
* added depth-wise convolution; gives ~20-30% performance improvement in MobileSSD networks

* hopefully, eliminated compile warnings, errors, as well as failure in one test

* * fixed a few typos
* decreased buffer size in some cases
* added more optimal im2row branch in the case of 1x1 convolutions
* tuned fastConv to reduce the number of passes over arrays
pull/18010/head
Vadim Pisarevsky 4 years ago committed by GitHub
parent f53f491cd2
commit 77b01deb80
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 499 additions and 36 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 261
      modules/dnn/src/layers/convolution_layer.cpp
  2. 274
      modules/dnn/src/layers/layers_common.simd.hpp

261
modules/dnn/src/layers/convolution_layer.cpp
Unescape View File

@ -829,6 +829,7 @@ public:
bool useAVX;
bool useAVX2;
bool useAVX512;
int blk_size_cn;
ParallelConv()
: input_(0), weights_(0), output_(0), ngroups_(0), nstripes_(0),
@ -885,12 +886,17 @@ public:
p.useAVX2 = checkHardwareSupport(CPU_AVX2) && isConv2D;
p.useAVX512 = CV_CPU_HAS_SUPPORT_AVX512_SKX && isConv2D;
int ncn = std::min(inpCn, (int)BLK_SIZE_CN);
int kernel_d = !isConv2D? kernel_size[0] : 1;
int kernel_h = kernel_size[kernel_size.size() - 2];
int kernel_w = kernel_size.back();
int blk_size_cn0 = cvCeil(800./(kernel_w*kernel_h));
int ncn = 16;
while (ncn*2 < blk_size_cn0 && ncn < inpCn)
ncn *= 2;
ncn = std::min(ncn, inpCn);
p.blk_size_cn = ncn;
int dil_d = !isConv2D? dilations[0] : 1;
int dil_h = dilations[dilations.size() - 2];
int dil_w = dilations.back();
@ -958,18 +964,26 @@ public:
int dilation_w = dilations.back();
int i, j, k, d;
size_t inpPlaneSize = input_->total(2);
size_t outPlaneSize = output_->total(2);
int inpPlaneSize = (int)input_->total(2);
int outPlaneSize = (int)output_->total(2);
bool is1x1 = is1x1_;
int stripesPerSample;
size_t stripeSize;
int stripeSize;
Range r = r0;
if( nstripes >= batchSize*2 )
bool depthWiseConvolution = !is1x1 && isConv2D && ngroups > 1 && inpCn == 1 &&
outCn == 1 && kernel_d == 1 && dilation_d == 1 && stride_d == 0 && pad_d == 0 &&
width >= 16 + dilation_w*(kernel_w - 1);
// for now only 3x3 depth-wise convolutions are supported
depthWiseConvolution = depthWiseConvolution && kernel_w == 3 && kernel_h == 3 &&
// computing at most 1 pixel from each side can involve padding
max(stride_w, dilation_w) >= pad_l && max(stride_h, dilation_h) >= pad_t &&
pad_l <= 1 && pad_t <= 1;
if( !depthWiseConvolution && nstripes >= batchSize*2 )
{
stripesPerSample = nstripes/batchSize;
stripeSize = alignSize((outPlaneSize + stripesPerSample - 1)/stripesPerSample, valign);
stripeSize = (int)alignSize((outPlaneSize + stripesPerSample - 1)/stripesPerSample, valign);
stripeSize = std::min(stripeSize, outPlaneSize);
}
else
@ -988,20 +1002,29 @@ public:
const float* biasptr_ = &biasvec_->at(0);
const float* reluptr_ = reluslope_->empty() ? 0 : &reluslope_->at(0);
float* data_out0_ = output_->ptr<float>();
size_t rowbufsz = (size_t)karea*BLK_SIZE_CN*BLK_SIZE;
AutoBuffer<float> rowbuf0_(rowbufsz + valign);
float* rowbuf0 = alignPtr(rowbuf0_.data(), (int)(valign*sizeof(float)));
// we clear the buffer once; ultimately, it lets us to avoid
// tail processing after running the unrolled/vectorized loop.
// the main idea is to make sure that the tail (a.k.a. padding) of each row
// (i.e. the elements with indices between vsz=karea*ncn and vsz_a)
// does not contain NaNs or Infs. Because the padding in the weights
// matrix is explicitly initialized with 0's, we handle all other
// cases nicely, i.e. we can skip expliciting re-initialization
// of the padding - we just retain elements from the previous iteration
// of the loop over channels (cn0).
memset(rowbuf0, 0, rowbufsz*sizeof(rowbuf0[0]) );
AutoBuffer<float> rowbuf0_;
float* rowbuf0 = 0;
bool use_rowbuf = !depthWiseConvolution;
int blk_size = depthWiseConvolution ? outPlaneSize : min((int)BLK_SIZE, stripeSize);
// im2row buffer is not used for depth-wise convolution
if(use_rowbuf)
{
size_t rowbufsz = alignSize(karea*blk_size_cn, valign)*min((int)BLK_SIZE, blk_size);
//printf("karea=%d, blk_size_cn=%d, rowbufsz=%d, stripeSize=%d\n", karea, blk_size_cn, (int)rowbufsz, stripeSize);
rowbuf0_.allocate(rowbufsz + valign);
rowbuf0 = alignPtr(rowbuf0_.data(), (int)(valign*sizeof(float)));
// we clear the buffer once; ultimately, it lets us to avoid
// tail processing after running the unrolled/vectorized loop.
// the main idea is to make sure that the tail (a.k.a. padding) of each row
// (i.e. the elements with indices between vsz=karea*ncn and vsz_a)
// does not contain NaNs or Infs. Because the padding in the weights
// matrix is explicitly initialized with 0's, we handle all other
// cases nicely, i.e. we can skip expliciting re-initialization
// of the padding - we just retain elements from the previous iteration
// of the loop over channels (cn0).
memset(rowbuf0, 0, rowbufsz*sizeof(rowbuf0[0]) );
}
for( int stripe = r.start; stripe < r.end; stripe++ )
{
@ -1016,28 +1039,213 @@ public:
const float* wptr_orig = wptr_orig_ + wstep*startOutCn;
const float* biasptr = biasptr_ + startOutCn;
for( int cn0 = 0; cn0 < inpCn; cn0 += BLK_SIZE_CN )
for( int cn0 = 0; cn0 < inpCn; cn0 += blk_size_cn )
{
int cn1 = std::min(cn0 + BLK_SIZE_CN, inpCn);
int cn1 = std::min(cn0 + blk_size_cn, inpCn);
int ncn = cn1 - cn0, vsz = karea*ncn;
int vsz_a = (int)alignSize(vsz, valign);
const float* wptr = wptr_orig + cn0*karea;
// we apply [Channels][P]ReLU (if any) during the final pass only.
const float* relu = cn1 == inpCn && reluptr_ ? reluptr_ + startOutCn : 0;
for( int ofs0 = stripeStart; ofs0 < stripeEnd; ofs0 += BLK_SIZE )
for( int ofs0 = stripeStart; ofs0 < stripeEnd; ofs0 += blk_size )
{
int ofs, ofs1 = std::min(ofs0 + BLK_SIZE, stripeEnd);
int ofs, ofs1 = std::min(ofs0 + blk_size, stripeEnd);
int bsz = ofs1 - ofs0;
int out_d = ofs0 / (outH * outW);
int out_i = (ofs0 - out_d * outH * outW) / outW;
int out_j = ofs0 % outW;
if (depthWiseConvolution)
{
CV_Assert(out_i == 0 && out_j == 0);
int in_d = out_d * stride_d - pad_d;
const float* inptr_ = data_inp0 + (cn0*depth*height + in_d*height)*width;
float* outptr_ = data_out0 + ofs0;
#if CV_TRY_AVX2
if(useAVX2)
opt_AVX2::fastDepthwiseConv(wptr, kernel_h, kernel_w,
stride_h, stride_w, dilation_h, dilation_w, pad_t, pad_l,
biasptr, relu, inptr_, height, width, outptr_, out_d, outH, outW);
else
#endif
#if CV_TRY_AVX
if(useAVX)
opt_AVX::fastDepthwiseConv(wptr, kernel_h, kernel_w,
stride_h, stride_w, dilation_h, dilation_w, pad_t, pad_l,
biasptr, relu, inptr_, height, width, outptr_, out_d, outH, outW);
else
#endif
{
const float w00_ = wptr[0], w01_ = wptr[1], w02_ = wptr[2],
w10 = wptr[3], w11 = wptr[4], w12 = wptr[5],
w20_ = wptr[6], w21_ = wptr[7], w22_ = wptr[8];
int outW1 = min(outW, (width - dilation_w*(kernel_w - 1) + pad_l)/stride_w);
float relu_coeff = relu ? relu[out_d] : 1.f, bias = biasptr[out_d];
for (int out_i = 0; out_i < outH; out_i++)
{
int in_i = out_i * stride_h - pad_t, out_j = 0;
const float* imgptr0 = inptr_ + in_i*width;
const float* imgptr1 = imgptr0 + dilation_h*width;
const float* imgptr2 = imgptr0 + (dilation_h*2)*width;
float out, w00 = w00_, w01 = w01_, w02 = w02_;
float w20 = w20_, w21 = w21_, w22 = w22_;
if (in_i < 0)
{
w00 = w01 = w02 = 0.f;
imgptr0 = imgptr1;
}
else if (in_i + dilation_h*(kernel_h-1) >= height)
{
w20 = w21 = w22 = 0.f;
imgptr2 = imgptr1;
}
float* outptr = outptr_ + out_i*outW;
if (pad_l > 0)
{
out = imgptr0[0]*w01 + imgptr0[dilation_w]*w02 +
imgptr1[0]*w11 + imgptr1[dilation_w]*w12 +
imgptr2[0]*w21 + imgptr2[dilation_w]*w22 + bias;
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[0] = out;
out_j = 1;
}
#if CV_SIMD
// maybe with AVX or AVX512 strided depthwise convolution
// can be accelerated with vector code, but with 4xfloat vectors
// it's hardly the case
if( stride_w == 1 )
{
const int VECSZ = v_float32::nlanes;
const int out_delta = VECSZ/stride_w;
v_float32 vw00 = vx_setall_f32(w00), vw01 = vx_setall_f32(w01), vw02 = vx_setall_f32(w02),
vw10 = vx_setall_f32(w10), vw11 = vx_setall_f32(w11), vw12 = vx_setall_f32(w12),
vw20 = vx_setall_f32(w20), vw21 = vx_setall_f32(w21), vw22 = vx_setall_f32(w22);
v_float32 z = vx_setzero_f32(), vbias = vx_setall_f32(bias), vrc = vx_setall_f32(relu_coeff);
for( ; out_j < outW1; out_j += out_delta )
{
if (out_j + out_delta > outW1)
{
if (out_j <= pad_l)
break;
out_j = outW1 - out_delta;
}
int in_j = out_j * stride_w - pad_l;
v_float32 v00 = vx_load(imgptr0 + in_j),
v01 = vx_load(imgptr0 + in_j + dilation_w),
v02 = vx_load(imgptr0 + in_j + dilation_w*2),
v10 = vx_load(imgptr1 + in_j),
v11 = vx_load(imgptr1 + in_j + dilation_w),
v12 = vx_load(imgptr1 + in_j + dilation_w*2),
v20 = vx_load(imgptr2 + in_j),
v21 = vx_load(imgptr2 + in_j + dilation_w),
v22 = vx_load(imgptr2 + in_j + dilation_w*2);
v_float32 vout = v00*vw00 + v01*vw01 + v02*vw02 +
v10*vw10 + v11*vw11 + v12*vw12 +
v20*vw20 + v21*vw21 + v22*vw22 + vbias;
if (relu)
vout = v_select(vout > z, vout, vout*vrc);
vx_store(outptr + out_j, vout);
}
}
#endif
for (; out_j < outW1; out_j++)
{
int in_j = out_j * stride_w - pad_l;
out = imgptr0[in_j]*w00 + imgptr0[in_j + dilation_w]*w01 + imgptr0[in_j + dilation_w*2]*w02 +
imgptr1[in_j]*w10 + imgptr1[in_j + dilation_w]*w11 + imgptr1[in_j + dilation_w*2]*w12 +
imgptr2[in_j]*w20 + imgptr2[in_j + dilation_w]*w21 + imgptr2[in_j + dilation_w*2]*w22 + bias;
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[out_j] = out;
}
for (; out_j < outW; out_j++ )
{
int in_j0 = out_j * stride_w - pad_l, in_j1 = in_j0 + dilation_w, in_j2 = in_j0 + dilation_w*2;
float s0 = 1.f, s1 = 1.f, s2 = 1.f;
if (in_j0 >= width)
{
in_j0 = 0;
s0 = 0.f;
}
if (in_j1 >= width)
{
in_j1 = 0;
s1 = 0.f;
}
if (in_j2 >= width)
{
in_j2 = 0;
s2 = 0.f;
}
out = imgptr0[in_j0]*w00*s0 + imgptr0[in_j1]*w01*s1 + imgptr0[in_j2]*w02*s2 +
imgptr1[in_j0]*w10*s0 + imgptr1[in_j1]*w11*s1 + imgptr1[in_j2]*w12*s2 +
imgptr2[in_j0]*w20*s0 + imgptr2[in_j1]*w21*s1 + imgptr2[in_j2]*w22*s2 + bias;
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[out_j] = out;
}
}
}
continue;
}
// do im2row for a part of input tensor
float* rowbuf = rowbuf0;
if (isConv2D)
{
if( is1x1 && stride_w == 1 && stride_h == 1 )
{
const float* imgptr = data_inp0 + (cn0*height + out_i)*width + out_j;
for( int j = 0; j < bsz; j++, rowbuf += vsz_a )
{
if( j + 4 <= bsz )
{
k = 0;
#if CV_SIMD128
for( ; k <= vsz - 4; k += 4 )
{
const float* inp = imgptr + j + k*inpPlaneSize;
v_float32x4 p0 = v_load(inp), p1 = v_load(inp + inpPlaneSize);
v_float32x4 p2 = v_load(inp + inpPlaneSize*2), p3 = v_load(inp + inpPlaneSize*3);
v_float32x4 r0, r1, r2, r3;
v_transpose4x4(p0, p1, p2, p3, r0, r1, r2, r3);
v_store(rowbuf + k, r0);
v_store(rowbuf + k + vsz_a, r1);
v_store(rowbuf + k + vsz_a*2, r2);
v_store(rowbuf + k + vsz_a*3, r3);
}
#endif
for( ; k < vsz; k++ )
{
const float* inp = imgptr + j + k*inpPlaneSize;
float v0 = inp[0], v1 = inp[1], v2 = inp[2], v3 = inp[3];
rowbuf[k] = v0;
rowbuf[k + vsz_a] = v1;
rowbuf[k + vsz_a*2] = v2;
rowbuf[k + vsz_a*3] = v3;
}
j += 3;
rowbuf += vsz_a*3;
}
else
{
for( k = 0; k < vsz; k++ )
{
rowbuf[k] = imgptr[j + k*inpPlaneSize];
}
}
}
}
else
for( ofs = ofs0; ofs < ofs1; out_j = 0, ++out_i )
{
int delta = std::min(ofs1 - ofs, outW - out_j);
@ -1157,7 +1365,6 @@ public:
// now compute dot product of the weights
// and im2row-transformed part of the tensor
int bsz = ofs1 - ofs0;
#if CV_TRY_AVX512_SKX
/* AVX512 convolution requires an alignment of 16, and ROI is only there for larger vector sizes */
if(useAVX512)

274
modules/dnn/src/layers/layers_common.simd.hpp
Unescape View File

@ -50,6 +50,16 @@ void fastConv( 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 fastDepthwiseConv( const float* weights,
int kernel_h, int kernel_w,
int stride_h, int stride_w,
int dilation_h, int dilation_w,
int pad_t, int pad_l,
const float* bias, const float* relu,
const float* inptr,
int height, int width,
float* outptr,
int out_d, int outH, int outW );
void fastGEMM1T( const float* vec, const float* weights,
size_t wstep, const float* bias,
float* dst, int nvecs, int vecsize );
@ -64,6 +74,8 @@ void fastGEMM( const float* aptr, size_t astep, const float* bptr,
#define _mm256_fmadd_ps(a, b, c) _mm256_add_ps(c, _mm256_mul_ps(a, b))
#endif
enum { FASCONV_BASE_VECSZ = 4 };
void fastConv( const float* weights, size_t wstep, const float* bias,
const float* rowbuf, float* output, const int* outShape,
int blockSize, int vecsize, int vecsize_aligned,
@ -73,6 +85,11 @@ void fastConv( const float* weights, size_t wstep, const float* bias,
size_t outPlaneSize = outShape[2]*outShape[3];
float r0 = 1.f, r1 = 1.f, r2 = 1.f;
__m128 vr0 = _mm_set1_ps(1.f), vr1 = vr0, vr2 = vr0, z = _mm_setzero_ps();
int CV_DECL_ALIGNED(16) maskbuf[FASCONV_BASE_VECSZ] = {0};
int rsz = blockSize % FASCONV_BASE_VECSZ;
for( int i = 0; i < rsz; i++ )
maskbuf[FASCONV_BASE_VECSZ - i - 1] = -1;
__m128 mask = _mm_loadu_ps((const float*)maskbuf);
// now compute dot product of the weights
// and im2row-transformed part of the tensor
@ -114,8 +131,16 @@ void fastConv( const float* weights, size_t wstep, const float* bias,
}
int j = 0;
for( ; j <= blockSize - 4; j += 4 )
for( ; j < blockSize; j += FASCONV_BASE_VECSZ )
{
bool tail = false;
if (j + FASCONV_BASE_VECSZ > blockSize)
{
if (j == 0)
break;
j = blockSize - FASCONV_BASE_VECSZ;
tail = true;
}
int k = 0;
const float* rptr = rowbuf + j*vecsize_aligned;
@ -243,9 +268,16 @@ void fastConv( const float* weights, size_t wstep, const float* bias,
__m128 m0 = _mm_cmp_ps(s0, z, _CMP_GT_OS);
__m128 m1 = _mm_cmp_ps(s1, z, _CMP_GT_OS);
__m128 m2 = _mm_cmp_ps(s2, z, _CMP_GT_OS);
s0 = _mm_xor_ps(s0, _mm_andnot_ps(m0, _mm_xor_ps(_mm_mul_ps(s0, vr0), s0)));
s1 = _mm_xor_ps(s1, _mm_andnot_ps(m1, _mm_xor_ps(_mm_mul_ps(s1, vr1), s1)));
s2 = _mm_xor_ps(s2, _mm_andnot_ps(m2, _mm_xor_ps(_mm_mul_ps(s2, vr2), s2)));
s0 = _mm_blendv_ps(_mm_mul_ps(s0, vr0), s0, m0);
s1 = _mm_blendv_ps(_mm_mul_ps(s1, vr1), s1, m1);
s2 = _mm_blendv_ps(_mm_mul_ps(s2, vr2), s2, m2);
}
if( tail )
{
s0 = _mm_blendv_ps(_mm_loadu_ps(outptr0 + j), s0, mask);
s1 = _mm_blendv_ps(_mm_loadu_ps(outptr1 + j), s1, mask);
s2 = _mm_blendv_ps(_mm_loadu_ps(outptr2 + j), s2, mask);
}
_mm_storeu_ps(outptr0 + j, s0);
@ -253,9 +285,55 @@ void fastConv( const float* weights, size_t wstep, const float* bias,
_mm_storeu_ps(outptr2 + j, s2);
}
for( ; j <= blockSize - 2; j += 2 )
{
const float* rptr0 = rowbuf + j*vecsize_aligned;
const float* rptr1 = rowbuf + (j+1)*vecsize_aligned;
float s00, s01, s10, s11, s20, s21;
if( initOutput )
{
s00 = s01 = bias0;
s10 = s11 = bias1;
s20 = s21 = bias2;
}
else
{
s00 = outptr0[j]; s01 = outptr0[j+1];
s10 = outptr1[j]; s11 = outptr1[j+1];
s20 = outptr2[j]; s21 = outptr2[j+1];
}
for( int k = 0; k < vecsize; k++ )
{
float w0 = wptr0[k], w1 = wptr1[k], w2 = wptr2[k];
float r = rptr0[k];
s00 += w0*r; s10 += w1*r; s20 += w2*r;
r = rptr1[k];
s01 += w0*r; s11 += w1*r; s21 += w2*r;
}
if( relu )
{
s00 = s00 > 0.f ? s00 : s00*r0;
s01 = s01 > 0.f ? s01 : s01*r0;
s10 = s10 > 0.f ? s10 : s10*r1;
s11 = s11 > 0.f ? s11 : s11*r1;
s20 = s20 > 0.f ? s20 : s20*r2;
s21 = s21 > 0.f ? s21 : s21*r2;
}
outptr0[j] = s00;
outptr0[j+1] = s01;
outptr1[j] = s10;
outptr1[j+1] = s11;
outptr2[j] = s20;
outptr2[j+1] = s21;
}
for( ; j < blockSize; j++ )
{
const float* rptr = rowbuf + j*vecsize_aligned;
const float* rptr0 = rowbuf + j*vecsize_aligned;
float s00, s10, s20;
if( initOutput )
@ -273,10 +351,9 @@ void fastConv( const float* weights, size_t wstep, const float* bias,
for( int k = 0; k < vecsize; k++ )
{
float r0 = rptr[k];
s00 += wptr0[k]*r0;
s10 += wptr1[k]*r0;
s20 += wptr2[k]*r0;
float w0 = wptr0[k], w1 = wptr1[k], w2 = wptr2[k];
float r = rptr0[k];
s00 += w0*r; s10 += w1*r; s20 += w2*r;
}
if( relu )
@ -294,6 +371,185 @@ void fastConv( const float* weights, size_t wstep, const float* bias,
_mm256_zeroupper();
}
static inline void _mm256_load_deinterleave(const float* ptr, __m256& a, __m256& b)
{
__m256 t0 = _mm256_loadu_ps(ptr);
__m256 t1 = _mm256_loadu_ps(ptr + 8);
__m256 lo = _mm256_permute2f128_ps(t0, t1, 0+2*16);
__m256 hi = _mm256_permute2f128_ps(t0, t1, 1+3*16);
a = _mm256_shuffle_ps(lo, hi, 0x88);
b = _mm256_shuffle_ps(lo, hi, 0xdd);
}
void fastDepthwiseConv( const float* wptr,
int kernel_h, int kernel_w,
int stride_h, int stride_w,
int dilation_h, int dilation_w,
int pad_t, int pad_l,
const float* biasptr, const float* relu,
const float* inptr_,
int height, int width,
float* outptr_,
int out_d, int outH, int outW )
{
const float w00_ = wptr[0], w01_ = wptr[1], w02_ = wptr[2],
w10 = wptr[3], w11 = wptr[4], w12 = wptr[5],
w20_ = wptr[6], w21_ = wptr[7], w22_ = wptr[8];
int outW1 = min(outW, (width - dilation_w*(kernel_w - 1) + pad_l)/stride_w);
float relu_coeff = relu ? relu[out_d] : 1.f, bias = biasptr[out_d];
for (int out_i = 0; out_i < outH; out_i++)
{
int in_i = out_i * stride_h - pad_t, out_j = 0;
const float* imgptr0 = inptr_ + in_i*width;
const float* imgptr1 = imgptr0 + dilation_h*width;
const float* imgptr2 = imgptr0 + (dilation_h*2)*width;
float out, w00 = w00_, w01 = w01_, w02 = w02_;
float w20 = w20_, w21 = w21_, w22 = w22_;
if (in_i < 0)
{
w00 = w01 = w02 = 0.f;
imgptr0 = imgptr1;
}
else if (in_i + dilation_h*(kernel_h-1) >= height)
{
w20 = w21 = w22 = 0.f;
imgptr2 = imgptr1;
}
float* outptr = outptr_ + out_i*outW;
if (pad_l > 0)
{
out = imgptr0[0]*w01 + imgptr0[dilation_w]*w02 +
imgptr1[0]*w11 + imgptr1[dilation_w]*w12 +
imgptr2[0]*w21 + imgptr2[dilation_w]*w22 + bias;
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[0] = out;
out_j = 1;
}
if (stride_w == 1 || (stride_w == 2 && dilation_w == 1))
{
const int VECSZ = 8;
__m256 vw00 = _mm256_set1_ps(w00), vw01 = _mm256_set1_ps(w01), vw02 = _mm256_set1_ps(w02),
vw10 = _mm256_set1_ps(w10), vw11 = _mm256_set1_ps(w11), vw12 = _mm256_set1_ps(w12),
vw20 = _mm256_set1_ps(w20), vw21 = _mm256_set1_ps(w21), vw22 = _mm256_set1_ps(w22);
__m256 z = _mm256_setzero_ps(), vbias = _mm256_set1_ps(bias), vrc = _mm256_set1_ps(relu_coeff);
if( stride_w == 1 )
for( ; out_j < outW1; out_j += VECSZ )
{
if (out_j + VECSZ > outW1 && out_j > pad_l)
out_j = outW1 - VECSZ;
int in_j = out_j * stride_w - pad_l;
__m256 v00 = _mm256_loadu_ps(imgptr0 + in_j),
v01 = _mm256_loadu_ps(imgptr0 + in_j + dilation_w),
v02 = _mm256_loadu_ps(imgptr0 + in_j + dilation_w*2),
v10 = _mm256_loadu_ps(imgptr1 + in_j),
v11 = _mm256_loadu_ps(imgptr1 + in_j + dilation_w),
v12 = _mm256_loadu_ps(imgptr1 + in_j + dilation_w*2),
v20 = _mm256_loadu_ps(imgptr2 + in_j),
v21 = _mm256_loadu_ps(imgptr2 + in_j + dilation_w),
v22 = _mm256_loadu_ps(imgptr2 + in_j + dilation_w*2);
__m256 vout0 = _mm256_fmadd_ps(v00, vw00, vbias);
__m256 vout1 = _mm256_mul_ps(v01, vw01);
__m256 vout2 = _mm256_mul_ps(v02, vw02);
vout0 = _mm256_fmadd_ps(v10, vw10, vout0);
vout1 = _mm256_fmadd_ps(v11, vw11, vout1);
vout2 = _mm256_fmadd_ps(v12, vw12, vout2);
vout0 = _mm256_fmadd_ps(v20, vw20, vout0);
vout1 = _mm256_fmadd_ps(v21, vw21, vout1);
vout2 = _mm256_fmadd_ps(v22, vw22, vout2);
vout0 = _mm256_add_ps(_mm256_add_ps(vout0, vout1), vout2);
if (relu)
{
__m256 m = _mm256_cmp_ps(vout0, z, _CMP_GT_OQ);
vout0 = _mm256_blendv_ps(_mm256_mul_ps(vout0, vrc), vout0, m);
}
_mm256_storeu_ps(outptr + out_j, vout0);
}
else
for( ; out_j < outW1; out_j += VECSZ )
{
if (out_j + VECSZ > outW1 && out_j > pad_l)
out_j = outW1 - VECSZ;
int in_j = out_j * stride_w - pad_l;
__m256 v00, v01, v02, v10, v11, v12, v20, v21, v22, unused;
_mm256_load_deinterleave(imgptr0 + in_j, v00, v01);
_mm256_load_deinterleave(imgptr0 + in_j + 2, v02, unused);
_mm256_load_deinterleave(imgptr1 + in_j, v10, v11);
_mm256_load_deinterleave(imgptr1 + in_j + 2, v12, unused);
_mm256_load_deinterleave(imgptr2 + in_j, v20, v21);
_mm256_load_deinterleave(imgptr2 + in_j + 2, v22, unused);
__m256 vout0 = _mm256_fmadd_ps(v00, vw00, vbias);
__m256 vout1 = _mm256_mul_ps(v01, vw01);
__m256 vout2 = _mm256_mul_ps(v02, vw02);
vout0 = _mm256_fmadd_ps(v10, vw10, vout0);
vout1 = _mm256_fmadd_ps(v11, vw11, vout1);
vout2 = _mm256_fmadd_ps(v12, vw12, vout2);
vout0 = _mm256_fmadd_ps(v20, vw20, vout0);
vout1 = _mm256_fmadd_ps(v21, vw21, vout1);
vout2 = _mm256_fmadd_ps(v22, vw22, vout2);
vout0 = _mm256_add_ps(_mm256_add_ps(vout0, vout1), vout2);
if (relu)
{
__m256 m = _mm256_cmp_ps(vout0, z, _CMP_GT_OQ);
vout0 = _mm256_blendv_ps(_mm256_mul_ps(vout0, vrc), vout0, m);
}
_mm256_storeu_ps(outptr + out_j, vout0);
}
}
for (; out_j < outW1; out_j++)
{
int in_j = out_j * stride_w - pad_l;
out = imgptr0[in_j]*w00 + imgptr0[in_j + dilation_w]*w01 + imgptr0[in_j + dilation_w*2]*w02 +
imgptr1[in_j]*w10 + imgptr1[in_j + dilation_w]*w11 + imgptr1[in_j + dilation_w*2]*w12 +
imgptr2[in_j]*w20 + imgptr2[in_j + dilation_w]*w21 + imgptr2[in_j + dilation_w*2]*w22 + bias;
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[out_j] = out;
}
for (; out_j < outW; out_j++ )
{
int in_j0 = out_j * stride_w - pad_l, in_j1 = in_j0 + dilation_w, in_j2 = in_j0 + dilation_w*2;
float s0 = 1.f, s1 = 1.f, s2 = 1.f;
if (in_j0 >= width)
{
in_j0 = 0;
s0 = 0.f;
}
if (in_j1 >= width)
{
in_j1 = 0;
s1 = 0.f;
}
if (in_j2 >= width)
{
in_j2 = 0;
s2 = 0.f;
}
out = imgptr0[in_j0]*w00*s0 + imgptr0[in_j1]*w01*s1 + imgptr0[in_j2]*w02*s2 +
imgptr1[in_j0]*w10*s0 + imgptr1[in_j1]*w11*s1 + imgptr1[in_j2]*w12*s2 +
imgptr2[in_j0]*w20*s0 + imgptr2[in_j1]*w21*s1 + imgptr2[in_j2]*w22*s2 + bias;
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[out_j] = out;
}
}
_mm256_zeroupper();
}
// dst = vec * weights^t + bias
void fastGEMM1T( const float* vec, const float* weights,
size_t wstep, const float* bias,

Write Preview
Loading…
Cancel
Save