remove old convolution branch, and optimize conv3d and conv1d.

pull/22905/head
zihaomu 2 years ago
parent 281b790618
commit 71c6339af0
  1. 841
      modules/dnn/src/layers/convolution_layer.cpp
  2. 623
      modules/dnn/src/layers/fast_convolution/depthwise_convolution.cpp
  3. 329
      modules/dnn/src/layers/fast_convolution/fast_convolution.avx2.cpp
  4. 753
      modules/dnn/src/layers/fast_convolution/fast_convolution.cpp
  5. 60
      modules/dnn/src/layers/fast_convolution/fast_convolution.hpp
  6. 280
      modules/dnn/src/layers/fast_convolution/fast_convolution.simd.hpp
  7. 4
      modules/dnn/src/layers/fast_convolution/winograd_3x3s1_f63.cpp
  8. 806
      modules/dnn/src/layers/layers_common.simd.hpp

@ -259,7 +259,7 @@ public:
std::vector<float> reluslope;
Ptr<ActivationLayer> activ;
Ptr<FastConv2d> fastConv2dImpl;
Ptr<FastConv> fastConvImpl;
#ifdef HAVE_OPENCL
Ptr<OCL4DNNConvSpatial<float> > convolutionOp;
@ -967,808 +967,6 @@ public:
}
#endif // HAVE_WEBNN
class ParallelConv : public cv::ParallelLoopBody
{
public:
enum { BLK_SIZE = 32, BLK_SIZE_CN = 64 };
const Mat* input_;
const Mat* weights_;
Mat* output_;
int outShape[4]; // used only for conv2d
std::vector<size_t> kernel_size, pads_begin, pads_end, strides, dilations;
int ngroups_, nstripes_;
std::vector<int> ofstab_;
const std::vector<float>* biasvec_;
const std::vector<float>* reluslope_;
const ActivationLayer* activ_;
bool is1x1_;
bool useAVX;
bool useAVX2;
bool useAVX512;
bool useRVV;
bool useLASX;
int blk_size_cn;
ParallelConv()
: input_(0), weights_(0), output_(0), ngroups_(0), nstripes_(0),
biasvec_(0), reluslope_(0), activ_(0), is1x1_(false), useAVX(false), useAVX2(false), useAVX512(false), useRVV(false)
, useLASX(false), blk_size_cn(0)
{}
static void run( const Mat& input, Mat& output, const Mat& weights,
const std::vector<float>& biasvec,
const std::vector<float>& reluslope,
const std::vector<size_t>& kernel_size, const std::vector<size_t>& strides,
const std::vector<size_t>& pads_begin, const std::vector<size_t>& pads_end,
const std::vector<size_t>& dilations,
const ActivationLayer* activ, int ngroups, int nstripes )
{
size_t karea = std::accumulate(kernel_size.begin(), kernel_size.end(),
1, std::multiplies<size_t>());
bool isConv1D = input.dims == 3;
bool isConv2D = input.dims == 4;
bool isConv3D = input.dims == 5;
CV_CheckEQ(static_cast<int>(kernel_size.size()), input.dims - 2, "");
CV_Assert_N(input.dims == output.dims,
input.size[0] == output.size[0],
weights.rows == output.size[1],
weights.cols == (input.size[1]/ngroups)*karea,
input.type() == output.type(),
input.type() == weights.type(),
input.type() == CV_32FC1,
input.isContinuous(),
output.isContinuous(),
biasvec.size() == (size_t)output.size[1]+2);
CV_Check(weights.step1(), weights.step1() % VEC_ALIGN == 0, "");
CV_CheckType(weights.type(), CV_32FC1, "");
ParallelConv p;
p.input_ = &input;
p.weights_ = &weights;
p.output_ = &output;
int max_ind = isConv1D? 3: 4;
for( int i = 0; i < max_ind; i++ ) p.outShape[i] = output.size[i];
p.outShape[1] /= ngroups;
p.kernel_size = kernel_size; p.strides = strides; p.dilations = dilations;
p.pads_begin = pads_begin; p.pads_end = pads_end;
p.ngroups_ = ngroups;
p.nstripes_ = nstripes;
int inpCnAll = input.size[1];
int depth = (input.dims == 5) ? input.size[2] : 1;
int width = input.size[input.dims - 1];
int height = isConv1D? 1 : input.size[input.dims - 2];
int inpCn = inpCnAll / ngroups;
p.is1x1_ = (isConv2D && kernel_size[0] == 1 && kernel_size[1] == 1 &&
pads_begin[0] == 0 && pads_begin[1] == 0) ||
(isConv1D && pads_begin[0] == 0 && kernel_size[0] == 1);
p.useAVX = checkHardwareSupport(CPU_AVX) && isConv2D;
p.useAVX2 = checkHardwareSupport(CPU_AVX2) && isConv2D;
p.useAVX512 = CV_CPU_HAS_SUPPORT_AVX512_SKX && isConv2D;
p.useRVV = checkHardwareSupport(CPU_RVV) && isConv2D;
p.useLASX = checkHardwareSupport(CPU_LASX) && isConv2D;
int kernel_d = isConv3D? kernel_size[0] : 1;
int kernel_h = isConv1D? 1 : 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 = isConv3D? dilations[0] : 1;
int dil_h = isConv1D? 1 : dilations[dilations.size() - 2];
int dil_w = dilations.back();
p.ofstab_.resize(karea * ncn);
int* ofstab = &p.ofstab_[0];
if (isConv1D)
{
for( int k = 0; k < ncn; k++ )
for( int k_c = 0; k_c < kernel_w; k_c++ )
ofstab[k*kernel_w + k_c] = k*width + k_c*dil_w;
}
else if (isConv2D)
{
for( int k = 0; k < ncn; k++ )
for( int k_r = 0; k_r < kernel_h; k_r++ )
for( int k_c = 0; k_c < kernel_w; k_c++ )
ofstab[(k*kernel_h + k_r)*kernel_w + k_c] =
(k*height + k_r*dil_h)*width + k_c*dil_w;
}
else
{
for( int k = 0; k < ncn; k++ )
for (int k_d = 0; k_d < kernel_d; k_d++)
for( int k_r = 0; k_r < kernel_h; k_r++ )
for( int k_c = 0; k_c < kernel_w; k_c++ )
ofstab[(k*kernel_d*kernel_h + k_d*kernel_h + k_r)*kernel_w + k_c] =
(k*depth*height + k_d*dil_d*height + k_r*dil_h)*width + k_c*dil_w;
}
p.biasvec_ = &biasvec;
p.reluslope_ = &reluslope;
p.activ_ = p.reluslope_->empty() ? activ : 0;
parallel_for_(Range(0, nstripes), p, nstripes);
}
virtual void operator ()(const Range &r0) const CV_OVERRIDE
{
const int valign = ConvolutionLayerImpl::VEC_ALIGN;
int ngroups = ngroups_, batchSize = input_->size[0]*ngroups;
bool isConv1D = input_->dims == 3;
bool isConv2D = input_->dims == 4;
bool isConv3D = input_->dims == 5;
int outW = output_->size[output_->dims - 1];
int outH = isConv1D? 1 : output_->size[output_->dims - 2];
int outCn = output_->size[1]/ngroups;
int depth = isConv3D? input_->size[2] : 1;
int height = isConv1D? 1 : input_->size[input_->dims - 2];
int width = input_->size[input_->dims - 1];
int inpCn = input_->size[1]/ngroups;
const int nstripes = nstripes_;
int kernel_d = isConv3D? kernel_size[0] : 1;
int kernel_h = isConv1D? 1 : kernel_size[kernel_size.size() - 2];
int kernel_w = kernel_size.back();
int karea = kernel_w*kernel_h*kernel_d;
int pad_d = isConv3D? pads_begin[0] : 0;
int pad_t = isConv1D? 0 : pads_begin[pads_begin.size() - 2];
int pad_l = pads_begin.back();
int stride_d = isConv3D? strides[0] : 0;
int stride_h = isConv1D? 0 : strides[strides.size() - 2];
int stride_w = strides.back();
int dilation_d = isConv3D? dilations[0] : 1;
int dilation_h = isConv1D? 1 : dilations[dilations.size() - 2];
int dilation_w = dilations.back();
int i, j, k, d;
int inpPlaneSize = (int)input_->total(2);
int outPlaneSize = (int)output_->total(2);
bool is1x1 = is1x1_;
int stripesPerSample;
int stripeSize;
Range r = r0;
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 = (int)alignSize((outPlaneSize + stripesPerSample - 1)/stripesPerSample, valign);
stripeSize = std::min(stripeSize, outPlaneSize);
}
else
{
stripesPerSample = 1;
int samplesPerStripe = std::max((batchSize + nstripes - 1)/nstripes, 1);
r.start *= samplesPerStripe;
r.end *= samplesPerStripe;
stripeSize = outPlaneSize;
}
const float* data_inp0_ = input_->ptr<float>();
const int* ofstab = &ofstab_[0];
const float* wptr_orig_ = weights_->ptr<float>();
size_t wstep = weights_->step1();
const float* biasptr_ = &biasvec_->at(0);
const float* reluptr_ = reluslope_->empty() ? 0 : &reluslope_->at(0);
float* data_out0_ = output_->ptr<float>();
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++ )
{
int subsampleIdx = stripe/stripesPerSample;
if( subsampleIdx >= batchSize )
break;
int stripeStart = (int)((stripe - subsampleIdx*stripesPerSample)*stripeSize);
int stripeEnd = (int)std::min(stripeStart + stripeSize, outPlaneSize);
const float* data_inp0 = data_inp0_ + subsampleIdx*inpPlaneSize*inpCn;
float* data_out0 = data_out0_ + subsampleIdx*outPlaneSize*outCn;
int startOutCn = (subsampleIdx % ngroups)*outCn;
const float* wptr_orig = wptr_orig_ + wstep*startOutCn;
const float* biasptr = biasptr_ + startOutCn;
for( int cn0 = 0; cn0 < inpCn; cn0 += blk_size_cn )
{
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 )
{
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
#if CV_TRY_RVV
if(useRVV)
opt_RVV::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_LASX
if(useLASX)
opt_LASX::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);
v_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 (isConv1D)
{
for( ofs = ofs0; ofs < ofs1; out_j = 0, ++out_i )
{
int delta = std::min(ofs1 - ofs, outW - out_j);
int out_j1 = out_j + delta;
int in_j = out_j * stride_w - pad_l;
const float* imgptr = data_inp0 + cn0*width + in_j;
ofs += delta;
// do im2row for a part of input tensor
if( is1x1 )
{
for( ; out_j < out_j1; out_j++, rowbuf += vsz_a, imgptr += stride_w )
{
for( k = 0; k < vsz; k++ )
rowbuf[k] = imgptr[k*inpPlaneSize];
}
}
else
{
for( ; out_j < out_j1; out_j++, rowbuf += vsz_a, imgptr += stride_w, in_j += stride_w )
{
// this condition should be true for most of the tensor elements, i.e.
// most of the time the kernel aperture is inside the tensor X-Y plane.
if( out_j + 2 <= out_j1 && 0 <= in_j && in_j + stride_w*2 <= width - (kernel_w-1)*dilation_w )
{
for( k = 0; k < vsz; k++ )
{
int k1 = ofstab[k];
float v0 = imgptr[k1];
float v1 = imgptr[k1 + stride_w];
rowbuf[k] = v0;
rowbuf[k+vsz_a] = v1;
}
out_j++;
rowbuf += vsz_a;
imgptr += stride_w;
in_j += stride_w;
}
else
{
int i0 = std::max(0, (-in_j + dilation_w-1)/dilation_w);
int i1 = std::min(kernel_w, (width - in_j + dilation_w-1)/dilation_w);
// here some non-continuous sub-row of the row will not be
// filled from the tensor; we need to make sure that the uncovered
// elements are explicitly set to 0's. the easiest way is to
// set all the elements to 0's before the loop.
memset(rowbuf, 0, vsz*sizeof(rowbuf[0]));
for( k = 0; k < ncn; k++ )
{
for( i = i0; i < i1; i++ )
{
int imgofs = k*width + i*dilation_w;
rowbuf[k*kernel_w + i] = imgptr[imgofs];
}
}
}
}
}
}
}
else 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);
int out_j1 = out_j + delta;
int in_i = out_i * stride_h - pad_t;
int in_j = out_j * stride_w - pad_l;
const float* imgptr = data_inp0 + (cn0*height + in_i)*width + in_j;
ofs += delta;
// do im2row for a part of input tensor
if( is1x1 )
{
for( ; out_j < out_j1; out_j++, rowbuf += vsz_a, imgptr += stride_w )
{
for( k = 0; k < vsz; k++ )
rowbuf[k] = imgptr[k*inpPlaneSize];
}
}
else
{
bool ok_i = 0 <= in_i && in_i < height - (kernel_h-1)*dilation_h;
int i0 = std::max(0, (-in_i + dilation_h-1)/dilation_h);
int i1 = std::min(kernel_h, (height - in_i + dilation_h-1)/dilation_h);
for( ; out_j < out_j1; out_j++, rowbuf += vsz_a, imgptr += stride_w, in_j += stride_w )
{
// this condition should be true for most of the tensor elements, i.e.
// most of the time the kernel aperture is inside the tensor X-Y plane.
if( ok_i && out_j + 2 <= out_j1 && 0 <= in_j && in_j + stride_w*2 <= width - (kernel_w-1)*dilation_w )
{
for( k = 0; k < vsz; k++ )
{
int k1 = ofstab[k];
float v0 = imgptr[k1];
float v1 = imgptr[k1 + stride_w];
rowbuf[k] = v0;
rowbuf[k+vsz_a] = v1;
}
out_j++;
rowbuf += vsz_a;
imgptr += stride_w;
in_j += stride_w;
}
else
{
int j0 = std::max(0, (-in_j + dilation_w-1)/dilation_w);
int j1 = std::min(kernel_w, (width - in_j + dilation_w-1)/dilation_w);
// here some non-continuous sub-row of the row will not be
// filled from the tensor; we need to make sure that the uncovered
// elements are explicitly set to 0's. the easiest way is to
// set all the elements to 0's before the loop.
memset(rowbuf, 0, vsz*sizeof(rowbuf[0]));
for( k = 0; k < ncn; k++ )
{
for( i = i0; i < i1; i++ )
{
for( j = j0; j < j1; j++ )
{
int imgofs = k*(width*height) + i*(dilation_h*width) + j*dilation_w;
rowbuf[(k*kernel_h + i)*kernel_w + j] = imgptr[imgofs];
}
}
}
}
}
}
}
}
else
{
for( ofs = ofs0; ofs < ofs1; out_d += (out_i + 1) / outH, out_i = (out_i + 1) % outH, out_j = 0 )
{
int delta = std::min(ofs1 - ofs, outW - out_j);
int out_j1 = out_j + delta;
int in_d = out_d * stride_d - pad_d;
int in_i = out_i * stride_h - pad_t;
int in_j = out_j * stride_w - pad_l;
const float* imgptr = data_inp0 + (cn0*depth*height + in_d*height + in_i)*width + in_j;
ofs += delta;
int d0 = std::max(0, (-in_d + dilation_d - 1) / dilation_d);
int d1 = std::min(kernel_d, (depth - in_d + dilation_d - 1) / dilation_d);
int i0 = std::max(0, (-in_i + dilation_h-1)/dilation_h);
int i1 = std::min(kernel_h, (height - in_i + dilation_h-1)/dilation_h);
for( ; out_j < out_j1; out_j++, rowbuf += vsz_a, imgptr += stride_w, in_j += stride_w )
{
int j0 = std::max(0, (-in_j + dilation_w-1)/dilation_w);
int j1 = std::min(kernel_w, (width - in_j + dilation_w-1)/dilation_w);
// here some non-continuous sub-row of the row will not be
// filled from the tensor; we need to make sure that the uncovered
// elements are explicitly set to 0's. the easiest way is to
// set all the elements to 0's before the loop.
memset(rowbuf, 0, vsz*sizeof(rowbuf[0]));
for( k = 0; k < ncn; k++ )
{
for ( d = d0; d < d1; d++)
{
for( i = i0; i < i1; i++ )
{
for( j = j0; j < j1; j++ )
{
int imgofs = k*(depth*width*height) + d*dilation_d*width*height + i*(dilation_h*width) + j*dilation_w;
rowbuf[(k*kernel_d*kernel_h + d*kernel_h + i)*kernel_w + j] = imgptr[imgofs];
}
}
}
}
}
}
}
// now compute dot product of the weights
// and im2row-transformed part of the tensor
#if CV_TRY_AVX512_SKX
/* AVX512 convolution requires an alignment of 16, and ROI is only there for larger vector sizes */
if(useAVX512)
opt_AVX512_SKX::fastConv(wptr, wstep, biasptr, rowbuf0, data_out0 + ofs0,
outShape, bsz, vsz, vsz_a, relu, cn0 == 0);
else
#endif
#if CV_TRY_AVX2
if(useAVX2)
opt_AVX2::fastConv(wptr, wstep, biasptr, rowbuf0, data_out0 + ofs0,
outShape, bsz, vsz, vsz_a, relu, cn0 == 0);
else
#endif
#if CV_TRY_AVX
if(useAVX)
opt_AVX::fastConv(wptr, wstep, biasptr, rowbuf0, data_out0 + ofs0,
outShape, bsz, vsz, vsz_a, relu, cn0 == 0);
else
#endif
#if CV_TRY_RVV
if(useRVV)
opt_RVV::fastConv(wptr, wstep, biasptr, rowbuf0, data_out0 + ofs0,
outShape, bsz, vsz, vsz_a, relu, cn0 == 0);
else
#endif
#if CV_TRY_LASX
if(useLASX)
opt_LASX::fastConv(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;
const float* wptr1 = wptr0 + wstep;
float* outptr0 = data_out0 + ofs0 + i*outPlaneSize;
float* outptr1 = outptr0 + outPlaneSize;
float bias0 = biasptr[i], bias1 = biasptr[i+1];
float r0 = 1.f, r1 = 1.f;
if( i+1 >= outCn )
{
wptr1 = wptr0;
outptr1 = outptr0;
bias1 = bias0;
}
if( relu )
{
r0 = relu[i]; r1 = relu[i+1];
if( i+1 >= outCn )
r1 = r0;
}
int j = 0;
#if CV_SIMD128
v_float32x4 vr0 = v_setall_f32(r0), vr1 = v_setall_f32(r1), z = v_setzero_f32();
for( ; j <= bsz - 4; j += 4 )
{
const float* rptr = rowbuf0 + j*vsz_a;
v_float32x4 s0, s1;
if( cn0 == 0 )
{
s0 = v_setall_f32(bias0);
s1 = v_setall_f32(bias1);
}
else
{
s0 = v_load(outptr0 + j);
s1 = v_load(outptr1 + j);
}
v_float32x4 vs00 = v_setzero_f32(), vs01 = v_setzero_f32(),
vs02 = v_setzero_f32(), vs03 = v_setzero_f32(),
vs10 = v_setzero_f32(), vs11 = v_setzero_f32(),
vs12 = v_setzero_f32(), vs13 = v_setzero_f32();
for( k = 0; k < vsz; k += 4, rptr += 4 )
{
v_float32x4 w0 = v_load_aligned(wptr0 + k);
v_float32x4 w1 = v_load_aligned(wptr1 + k);
v_float32x4 r0 = v_load_aligned(rptr);
v_float32x4 r1 = v_load_aligned(rptr + vsz_a);
v_float32x4 r2 = v_load_aligned(rptr + vsz_a*2);
v_float32x4 r3 = v_load_aligned(rptr + vsz_a*3);
vs00 = v_fma(w0, r0, vs00);
vs01 = v_fma(w0, r1, vs01);
vs02 = v_fma(w0, r2, vs02);
vs03 = v_fma(w0, r3, vs03);
vs10 = v_fma(w1, r0, vs10);
vs11 = v_fma(w1, r1, vs11);
vs12 = v_fma(w1, r2, vs12);
vs13 = v_fma(w1, r3, vs13);
}
s0 += v_reduce_sum4(vs00, vs01, vs02, vs03);
s1 += v_reduce_sum4(vs10, vs11, vs12, vs13);
if( relu )
{
s0 = v_select(s0 > z, s0, s0*vr0);
s1 = v_select(s1 > z, s1, s1*vr1);
}
v_store(outptr0 + j, s0);
v_store(outptr1 + j, s1);
}
#endif
for( ; j < bsz; j++ )
{
const float* rptr = rowbuf0 + j*vsz_a;
float s00, s10;
if( cn0 == 0 )
{
s00 = bias0;
s10 = bias1;
}
else
{
s00 = outptr0[j];
s10 = outptr1[j];
}
for( k = 0; k < vsz; k++ )
{
float r0 = rptr[k];
s00 += wptr0[k]*r0;
s10 += wptr1[k]*r0;
}
if( relu )
{
s00 = s00 > 0.f ? s00 : s00*r0;
s10 = s10 > 0.f ? s10 : s10*r1;
}
outptr0[j] = s00;
outptr1[j] = s10;
}
}
}
}
if( activ_ )
activ_->forwardSlice(data_out0 + stripeStart, data_out0 + stripeStart,
(int)(stripeEnd - stripeStart),
outPlaneSize, startOutCn, startOutCn + outCn);
}
}
};
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
@ -2096,40 +1294,27 @@ public:
#endif
{
int nstripes = std::max(getNumThreads(), 1);
int conv_dim = CONV_2D;
if (inputs[0].dims == 3)
conv_dim = CONV_1D;
if (inputs[0].dims == 5)
conv_dim = CONV_3D;
// Initialization of FastCovn2d, pack weight.
if ((!fastConv2dImpl || variableWeight) && inputs[0].dims == 4)
if (!fastConvImpl || variableWeight)
{
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();
// Winograd only works when input h and w >= 12.
bool canUseWinograd = useWinograd && conv_dim == CONV_2D && inputs[0].size[2] >= 12 && inputs[0].size[3] >= 12;
// Winograd only works well on input h and w >12.
bool canUseWinograd = useWinograd && inputs[0].size[2] >= 12 && inputs[0].size[3] >= 12;
fastConv2dImpl = initFastConv2d(ngroups, K, C, Hk, Wk, stride_w, stride_h, dilation_w,
dilation_h, pads_begin, pads_end, weightsMat, &biasvec[0], canUseWinograd);
}
if (fastConv2dImpl)
{
runFastConv2d(inputs[0], outputs[0], fastConv2dImpl, nstripes, activ, fusedAdd);
return;
CV_Assert(outputs[0].size[1] % ngroups == 0);
fastConvImpl = initFastConv(weightsMat, &biasvec[0], ngroups, K, C, kernel_size, strides,
dilations, pads_begin, pads_end, conv_dim, canUseWinograd);
}
//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);
runFastConv(inputs[0], outputs[0], fastConvImpl, nstripes, activ, reluslope, fusedAdd);
}
}

@ -11,381 +11,404 @@
#include "../../precomp.hpp"
#include "fast_convolution.hpp"
#include "../layers_common.hpp"
namespace cv { namespace dnn {
static void depthWiseBlock(const float *inptr, float *outptr, const float *weights, float biasval, int *ofstab, int *yxtab,
float minval, float maxval, int Hi, int Wi, int H0, int W0, int ksize, int pad_top, int pad_left,
int dilation_y, int stride_x, int stride_y, int inner_xleft, int inner_xright, int inner_ytop,
int inner_ybottom, bool ifMinMaxAct, bool useSIMD, bool is3x3)
static void depthWiseBlockConv2D(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)
{
#if CV_SIMD128
const int VEC_NLANES = 4;
v_float32x4 vminval = v_setall_f32(minval), vmaxval = v_setall_f32(maxval);
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];
v_float32x4 w0 = v_setall_f32(
0.f), w1 = w0, w2 = w0, w3 = w0, w4 = w0, w5 = w0, w6 = w0, w7 = w0, w8 = w0, vbias = w0;
if (useSIMD)
for (int out_i = 0; out_i < outH; out_i++)
{
vbias = v_setall_f32(biasval);
if (is3x3)
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)
{
w0 = v_setall_f32(weights[0]);
w1 = v_setall_f32(weights[1]);
w2 = v_setall_f32(weights[2]);
w3 = v_setall_f32(weights[3]);
w4 = v_setall_f32(weights[4]);
w5 = v_setall_f32(weights[5]);
w6 = v_setall_f32(weights[6]);
w7 = v_setall_f32(weights[7]);
w8 = v_setall_f32(weights[8]);
w00 = w01 = w02 = 0.f;
imgptr0 = imgptr1;
}
else if (in_i + dilation_h*(kernel_h-1) >= height)
{
w20 = w21 = w22 = 0.f;
imgptr2 = imgptr1;
}
}
#endif
int dy0 = 1;
for (int y0 = 0; y0 < H0; y0 += dy0, outptr += W0 * dy0)
{
#if CV_SIMD128
dy0 = inner_ytop <= y0 && y0 + 3 < inner_ybottom && is3x3 && stride_y == 1 && dilation_y == 1
? 3 : 1;
#endif
int x0 = 0, x1 = y0 >= inner_ytop && y0 < inner_ybottom ? inner_xleft : W0;
int yi_ = y0 * stride_y - pad_top;
for (;;)
float* outptr = outptr_ + out_i*outW;
if (pad_l > 0)
{
float s_0, s_1, s_2;
if (dy0 == 3)
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_SIMD128
const int VEC_NLANES = 4;
v_float32x4 vw00 = v_setall_f32(w00);
v_float32x4 vw01 = v_setall_f32(w01);
v_float32x4 vw02 = v_setall_f32(w02);
v_float32x4 vw10 = v_setall_f32(w10);
v_float32x4 vw11 = v_setall_f32(w11);
v_float32x4 vw12 = v_setall_f32(w12);
v_float32x4 vw20 = v_setall_f32(w20);
v_float32x4 vw21 = v_setall_f32(w21);
v_float32x4 vw22 = v_setall_f32(w22);
v_float32x4 z = v_setzero_f32();
v_float32x4 vbias = v_setall_f32(bias);
v_float32x4 vrc = v_setall_f32(relu_coeff);
if (stride_w == 1 || (stride_w == 2 && dilation_w == 1))
{
if( stride_w == 1 )
{
for (; x0 < x1; x0++)
for( ; out_j < outW1; out_j += VEC_NLANES )
{
int xi_ = x0 * stride_x - pad_left;
s_0 = s_1 = s_2 = biasval;
for (int k = 0; k < ksize; k++)
if (out_j + VEC_NLANES > outW1)
{
int dy = yxtab[k * 2];
int yi = yi_ + dy;
int xi = xi_ + yxtab[k * 2 + 1];
float w = weights[k];
if ((unsigned) xi < (unsigned) Wi)
{
s_0 += inptr[yi * Wi + xi] * w;
s_1 += inptr[(yi + 1) * Wi + xi] * w;
s_2 += inptr[(yi + 2) * Wi + xi] * w;
}
}
s_0 = std::min(std::max(s_0, minval), maxval);
s_1 = std::min(std::max(s_1, minval), maxval);
s_2 = std::min(std::max(s_2, minval), maxval);
outptr[x0] = s_0;
outptr[x0 + W0] = s_1;
outptr[x0 + W0 * 2] = s_2;
}
}
else
{
for (; x0 < x1; x0++)
{
int xi_ = x0 * stride_x - pad_left;
s_0 = biasval;
for (int k = 0; k < ksize; k++) {
int dy = yxtab[k * 2];
int yi = yi_ + dy;
int xi = xi_ + yxtab[k * 2 + 1];
float w = weights[k];
if (((unsigned) yi < (unsigned) Hi) & ((unsigned) xi < (unsigned) Wi))
s_0 += inptr[yi * Wi + xi] * w;
if (out_j <= pad_l || outW1 - VEC_NLANES < 0)
break;
out_j = outW1 - VEC_NLANES;
}
s_0 = std::min(std::max(s_0, minval), maxval);
outptr[x0] = s_0;
int in_j = out_j * stride_w - pad_l;
v_float32x4 v00 = v_load(imgptr0 + in_j),
v01 = v_load(imgptr0 + in_j + dilation_w),
v02 = v_load(imgptr0 + in_j + dilation_w*2),
v10 = v_load(imgptr1 + in_j),
v11 = v_load(imgptr1 + in_j + dilation_w),
v12 = v_load(imgptr1 + in_j + dilation_w*2),
v20 = v_load(imgptr2 + in_j),
v21 = v_load(imgptr2 + in_j + dilation_w),
v22 = v_load(imgptr2 + in_j + dilation_w*2);
v_float32x4 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);
v_store(outptr + out_j, vout);
}
}
if (x0 == W0)
break;
x1 = inner_xright;
#if CV_SIMD128
if (useSIMD)
else // (stride_w == 2 && dilation_w == 1)
{
if (is3x3)
{
if (dy0 == 3)
{
for (; x0 <= x1 - VEC_NLANES; x0 += VEC_NLANES)
{
int xi_ = x0 * stride_x - pad_left;
const float *inptr_xi = inptr + Wi * yi_ + xi_;
v_float32x4 s0, s1, s2;
v_float32x4 x00 = v_load(inptr_xi);
v_float32x4 x01 = v_load(inptr_xi + 1);
v_float32x4 x02 = v_load(inptr_xi + 2);
v_float32x4 x10 = v_load(inptr_xi + Wi);
v_float32x4 x11 = v_load(inptr_xi + Wi + 1);
v_float32x4 x12 = v_load(inptr_xi + Wi + 2);
v_float32x4 x20 = v_load(inptr_xi + Wi * 2);
v_float32x4 x21 = v_load(inptr_xi + Wi * 2 + 1);
v_float32x4 x22 = v_load(inptr_xi + Wi * 2 + 2);
v_float32x4 x30 = v_load(inptr_xi + Wi * 3);
v_float32x4 x31 = v_load(inptr_xi + Wi * 3 + 1);
v_float32x4 x32 = v_load(inptr_xi + Wi * 3 + 2);
v_float32x4 x40 = v_load(inptr_xi + Wi * 4);
v_float32x4 x41 = v_load(inptr_xi + Wi * 4 + 1);
v_float32x4 x42 = v_load(inptr_xi + Wi * 4 + 2);
s0 = v_fma(x00, w0, vbias);
s1 = v_fma(x10, w0, vbias);
s2 = v_fma(x20, w0, vbias);
s0 = v_fma(x01, w1, s0);
s1 = v_fma(x11, w1, s1);
s2 = v_fma(x21, w1, s2);
s0 = v_fma(x02, w2, s0);
s1 = v_fma(x12, w2, s1);
s2 = v_fma(x22, w2, s2);
s0 = v_fma(x10, w3, s0);
s1 = v_fma(x20, w3, s1);
s2 = v_fma(x30, w3, s2);
s0 = v_fma(x11, w4, s0);
s1 = v_fma(x21, w4, s1);
s2 = v_fma(x31, w4, s2);
s0 = v_fma(x12, w5, s0);
s1 = v_fma(x22, w5, s1);
s2 = v_fma(x32, w5, s2);
s0 = v_fma(x20, w6, s0);
s1 = v_fma(x30, w6, s1);
s2 = v_fma(x40, w6, s2);
s0 = v_fma(x21, w7, s0);
s1 = v_fma(x31, w7, s1);
s2 = v_fma(x41, w7, s2);
s0 = v_fma(x22, w8, s0);
s1 = v_fma(x32, w8, s1);
s2 = v_fma(x42, w8, s2);
if (ifMinMaxAct)
{
s0 = v_min(v_max(s0, vminval), vmaxval);
s1 = v_min(v_max(s1, vminval), vmaxval);
s2 = v_min(v_max(s2, vminval), vmaxval);
}
v_store(outptr + x0, s0);
v_store(outptr + W0 + x0, s1);
v_store(outptr + W0 * 2 + x0, s2);
}
}
else
{
for (; x0 <= x1 - VEC_NLANES; x0 += VEC_NLANES)
{
int xi_ = x0 * stride_x - pad_left;
const float *inptr_xi = inptr + Wi * yi_ + xi_;
v_float32x4 s0 = v_fma(v_load(inptr_xi + ofstab[0]), w0, vbias);
v_float32x4 s1 = v_load(inptr_xi + ofstab[1]) * w1;
v_float32x4 s2 = v_load(inptr_xi + ofstab[2]) * w2;
s0 = v_fma(v_load(inptr_xi + ofstab[3]), w3, s0);
s1 = v_fma(v_load(inptr_xi + ofstab[4]), w4, s1);
s2 = v_fma(v_load(inptr_xi + ofstab[5]), w5, s2);
s0 = v_fma(v_load(inptr_xi + ofstab[6]), w6, s0);
s1 = v_fma(v_load(inptr_xi + ofstab[7]), w7, s1);
s2 = v_fma(v_load(inptr_xi + ofstab[8]), w8, s2);
s0 = s0 + s1 + s2;
if (ifMinMaxAct)
s0 = v_min(v_max(s0, vminval), vmaxval);
v_store(outptr + x0, s0);
}
}
}
else
for( ; out_j < outW1; out_j += VEC_NLANES )
{
for (; x0 <= x1 - VEC_NLANES; x0 += VEC_NLANES)
if (out_j + VEC_NLANES > outW1 && out_j > pad_l)
{
int xi_ = x0 * stride_x - pad_left, k = 0;
const float *inptr_xi = inptr + Wi * yi_ + xi_;
v_float32x4 s0 = vbias;
for (; k <= ksize - 4; k += 4)
{
v_float32x4 v0 = v_load(inptr_xi + ofstab[k]);
v_float32x4 v1 = v_load(inptr_xi + ofstab[k + 1]);
v_float32x4 v2 = v_load(inptr_xi + ofstab[k + 2]);
v_float32x4 v3 = v_load(inptr_xi + ofstab[k + 3]);
v_float32x4 ww0 = v_setall_f32(weights[k]);
v_float32x4 ww1 = v_setall_f32(weights[k+1]);
v_float32x4 ww2 = v_setall_f32(weights[k+2]);
v_float32x4 ww3 = v_setall_f32(weights[k+3]);
s0 = v_fma(v0, ww0, s0);
s0 = v_fma(v1, ww1, s0);
s0 = v_fma(v2, ww2, s0);
s0 = v_fma(v3, ww3, s0);
}
for (; k < ksize; k++)
s0 = v_fma(v_load(inptr_xi + ofstab[k]),
v_setall_f32(weights[k]), s0);
if (ifMinMaxAct)
s0 = v_min(v_max(s0, vminval), vmaxval);
v_store(outptr + x0, s0);
if (outW1 - VEC_NLANES < 0)
break;
out_j = outW1 - VEC_NLANES;
}
int in_j = out_j * stride_w - pad_l;
v_float32x4 v00, v01, v02, v10, v11, v12, v20, v21, v22, unused;
v_load_deinterleave(imgptr0 + in_j, v00, v01);
v_load_deinterleave(imgptr0 + in_j + 2, v02, unused);
v_load_deinterleave(imgptr1 + in_j, v10, v11);
v_load_deinterleave(imgptr1 + in_j + 2, v12, unused);
v_load_deinterleave(imgptr2 + in_j, v20, v21);
v_load_deinterleave(imgptr2 + in_j + 2, v22, unused);
v_float32x4 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);
v_store(outptr + out_j, vout);
}
}
}
#endif
if (dy0 == 3)
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)
{
for (; x0 < x1; x0++)
{
int xi_ = x0 * stride_x - pad_left;
const float *inptr_xi = inptr + W0 * yi_ + xi_;
s_0 = s_1 = s_2 = biasval;
for (int k = 0; k < ksize; k++)
{
int inp_ofs = ofstab[k];
float w = weights[k];
s_0 += inptr_xi[inp_ofs] * w;
s_1 += inptr_xi[inp_ofs + Wi] * w;
s_2 += inptr_xi[inp_ofs + Wi * 2] * w;
}
if (ifMinMaxAct)
{
s_0 = std::min(std::max(s_0, minval), maxval);
s_1 = std::min(std::max(s_1, minval), maxval);
s_2 = std::min(std::max(s_2, minval), maxval);
}
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;
}
}
}
static void depthWiseBlockConv1D(const float* wptr,
int kernel_w, int stride_w, int dilation_w, int pad_l,
const float* biasptr, const float* relu,
const float* inptr_, int width,
float* outptr_,
int out_d, int outW)
{
const float w00_ = wptr[0], w01_ = wptr[1], w02_ = wptr[2];
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];
int out_j = 0;
const float* imgptr0 = inptr_;
float out, w00 = w00_, w01 = w01_, w02 = w02_;
float* outptr = outptr_;
if (pad_l > 0)
{
out = imgptr0[0]*w01 + imgptr0[dilation_w]*w02 + bias;
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[0] = out;
out_j = 1;
}
outptr[x0] = s_0;
outptr[x0 + W0] = s_1;
outptr[x0 + W0 * 2] = s_2;
#if CV_SIMD128
const int VEC_NLANES = 4;
v_float32x4 vw00 = v_setall_f32(w00);
v_float32x4 vw01 = v_setall_f32(w01);
v_float32x4 vw02 = v_setall_f32(w02);
v_float32x4 z = v_setzero_f32();
v_float32x4 vbias = v_setall_f32(bias);
v_float32x4 vrc = v_setall_f32(relu_coeff);
if (stride_w == 1 || (stride_w == 2 && dilation_w == 1))
{
if( stride_w == 1 )
{
for( ; out_j < outW1; out_j += VEC_NLANES )
{
if (out_j + VEC_NLANES > outW1)
{
if (out_j <= pad_l || outW1 - VEC_NLANES < 0)
break;
out_j = outW1 - VEC_NLANES;
}
int in_j = out_j * stride_w - pad_l;
v_float32x4 v00 = v_load(imgptr0 + in_j),
v01 = v_load(imgptr0 + in_j + dilation_w),
v02 = v_load(imgptr0 + in_j + dilation_w*2);
v_float32x4 vout = v00*vw00 + v01*vw01 + v02*vw02 + vbias;
if (relu)
vout = v_select(vout > z, vout, vout*vrc);
v_store(outptr + out_j, vout);
}
else
}
else // (stride_w == 2 && dilation_w == 1)
{
for( ; out_j < outW1; out_j += VEC_NLANES )
{
for (; x0 < x1; x0++)
if (out_j + VEC_NLANES > outW1)
{
int xi_ = x0 * stride_x - pad_left;
const float *inptr_xi = inptr + Wi * yi_ + xi_;
s_0 = biasval;
for (int k = 0; k < ksize; k++)
{
s_0 += inptr_xi[ofstab[k]] * weights[k];
}
if (ifMinMaxAct)
s_0 = std::min(std::max(s_0, minval), maxval);
outptr[x0] = s_0;
if (out_j <= pad_l || outW1 - VEC_NLANES < 0)
break;
out_j = outW1 - VEC_NLANES;
}
int in_j = out_j * stride_w - pad_l;
v_float32x4 v00, v01, v02, unused;
v_load_deinterleave(imgptr0 + in_j, v00, v01);
v_load_deinterleave(imgptr0 + in_j + 2, v02, unused);
v_float32x4 vout = v00 * vw00 + v01 * vw01 + v02 * vw02 + vbias;
if (relu)
vout = v_select(vout > z, vout, vout*vrc);
v_store(outptr + out_j, vout);
}
x1 = W0;
}
}
#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 + 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 + bias;
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[out_j] = out;
}
}
void runDepthwise(InputArray _input, OutputArray _output, const Ptr<FastConv2d>& conv, float minval, float maxval, ActivationLayer* activ, bool ifMinMaxAct) {
void runDepthwise(InputArray _input, OutputArray _output, const Ptr<FastConv>& conv, ActivationLayer* activ_,
const std::vector<float>& reluslope)
{
Mat input = _input.getMat();
Mat output = _output.getMat();
MatShape inputShape = shape(input);
MatShape outputShape = shape(output);
CV_Assert(inputShape.size() == 4 && outputShape.size() == 4);
int N = inputShape[0], C = inputShape[1], Hi = inputShape[2], Wi = inputShape[3]; // [N, C, H, W]
CV_Assert(inputShape.size() == 3 || inputShape.size() == 4);
CV_Assert(inputShape.size() == outputShape.size());
int conv_dim = conv->conv_dim;
CV_Assert((conv_dim == CONV_2D || conv_dim == CONV_1D) &&
"DNN: Currently we do not support depth-wise for Convolution 3D!");
ActivationLayer* activ = reluslope.empty() ? activ_ : nullptr;
int N = inputShape[0], C = inputShape[1];
int Hi = conv_dim == CONV_1D ? 1 : inputShape[inputShape.size() - 2];
int Wi = inputShape[inputShape.size() - 1];
int K = conv->K, Hk = conv->Hk, Wk = conv->Wk;
int H0 = outputShape[2], W0 = outputShape[3], ngroups = conv->ngroups;
int H0 = conv_dim == CONV_1D ? 1 : outputShape[outputShape.size() - 2];
int W0 = outputShape[outputShape.size() - 1];
int ngroups = conv->ngroups;
const size_t inp_planesize = (size_t) Hi * Wi;
const size_t out_planesize = (size_t) H0 * W0;
CV_Assert(ngroups > 1 && ngroups == K && ngroups == C);
int stride_y = conv->stride_y, stride_x = conv->stride_x;
int dilation_y = conv->dilation_y, dilation_x = conv->dilation_x;
int stride_h = conv->stride_h, stride_w = conv->stride_w;
int dilation_h = conv->dilation_h, dilation_w = conv->dilation_w;
int pad_top = conv->pad_top, pad_bottom = conv->pad_bottom;
int pad_left = conv->pad_left, pad_right = conv->pad_right;
int VEC_NLANES = 4;
#if CV_TRY_AVX2
if (conv->useAVX2)
VEC_NLANES = 8;
#endif
int ksize = Hk * Wk, padded_ksize = ((ksize + VEC_NLANES - 1) / VEC_NLANES) * VEC_NLANES;
int ksize = Hk * Wk;
const int VEC_NLANES = 32;
int padded_ksize = ((ksize + VEC_NLANES-1) / VEC_NLANES) * VEC_NLANES;
const float *inp = input.ptr<float>();
float *out = output.ptr<float>();
std::vector<int> ofstab_(3 * padded_ksize, 0);
#if CV_TRY_AVX2 || CV_TRY_AVX || CV_TRY_RVV
// TODO: remove the following limitation, need change code in layers_common.simd.hpp.
bool canRunOpt = Wi >= 16 + dilation_w*(Wk - 1);
#endif
std::vector<int> ofstab_(3 * ksize, 0);
int *ofstab = ofstab_.data();
int *yxtab = ofstab + padded_ksize;
int *yxtab = ofstab + ksize;
for (int k = 0; k < padded_ksize; k++)
for (int k = 0; k < ksize; k++)
{
int y = k < ksize ? k / Wk : 0;
int x = k < ksize ? k % Wk : 0;
int dy = y * dilation_y, dx = x * dilation_x;
int dy = y * dilation_h, dx = x * dilation_w;
yxtab[k * 2] = dy;
yxtab[k * 2 + 1] = dx;
ofstab[k] = dy * Wi + dx;
}
const float *weights0 = conv->weightsBufPtr, *bias = conv->biasBuf.data();
int inner_ytop = (pad_bottom + stride_y - 1) / stride_y, inner_ybottom = 3;
int inner_xleft = (pad_left + stride_x - 1) / stride_x, inner_xright = 4;
const float* relu = reluslope.data();
CV_Assert(ksize > 1 || (pad_left == 0 && pad_right == 0 && pad_top == 0 && pad_bottom == 0));
inner_xright = (Wi - (Wk - 1) * dilation_x + pad_left) / stride_x;
inner_xright += inner_xright * stride_x - pad_left + (Wk - 1) * dilation_x < Wi;
inner_ybottom = (Hi - (Hk - 1) * dilation_y + pad_top) / stride_y;
inner_ybottom += inner_ybottom * stride_y - pad_top + (Hk - 1) * dilation_y < Hi;
if (inner_xleft >= inner_xright || inner_ytop >= inner_ybottom)
parallel_for_(Range(0, N * C), [&](const Range &r0) {
for (int nc = r0.start; nc < r0.end; nc++)
{
inner_xleft = W0;
inner_ytop = H0;
}
inner_ybottom = inner_ybottom < H0 ? inner_ybottom : H0;
int c = nc % C;
const float *inptr0 = inp + inp_planesize * nc;
float *outptr0 = out + out_planesize * nc;
bool useSIMD = stride_x == 1 && inner_xleft < W0;
bool is3x3 = Hk == 3 && Wk == 3;
const float *weights = weights0 + c * padded_ksize;
parallel_for_(Range(0, N * C), [&](const Range &r0) {
for (int nc = r0.start; nc < r0.end; nc++)
if (conv_dim == CONV_2D)
{
int c = nc % C;
const float *inptr = inp + inp_planesize * nc;
float *outptr0 = out + out_planesize * nc;
float biasval = bias[c];
const float *weights = weights0 + c * padded_ksize;
#if CV_TRY_AVX2
if (conv->useAVX2)
opt_AVX2::depthWiseBlock_AVX2(inptr, outptr0, weights, biasval, ofstab, yxtab, minval, maxval, Hi, Wi, H0, W0, ksize,
pad_top, pad_left, dilation_y, stride_x, stride_y, inner_xleft, inner_xright, inner_ytop,
inner_ybottom, ifMinMaxAct, useSIMD, is3x3);
if(canRunOpt && conv->useAVX2)
opt_AVX2::fastDepthwiseConv(weights, Hk, Wk, stride_h, stride_w, dilation_h, dilation_w,
pad_top, pad_left, bias, relu, inptr0, Hi, Wi, outptr0, c, H0, W0);
else
#endif
depthWiseBlock(inptr, outptr0, weights, biasval, ofstab, yxtab, minval, maxval, Hi, Wi, H0, W0, ksize,
pad_top, pad_left, dilation_y, stride_x, stride_y, inner_xleft, inner_xright, inner_ytop,
inner_ybottom, ifMinMaxAct, useSIMD, is3x3);
if (activ)
activ->forwardSlice(outptr0, outptr0, (int) out_planesize, out_planesize, c, c+1);
#if CV_TRY_AVX
if(canRunOpt && conv->useAVX)
opt_AVX::fastDepthwiseConv(weights, Hk, Wk, stride_h, stride_w, dilation_h, dilation_w,
pad_top, pad_left, bias, relu, inptr0, Hi, Wi, outptr0, c, H0, W0);
else
#endif
#if CV_TRY_RVV
if(canRunOpt && conv->useRVV)
opt_RVV::fastDepthwiseConv(weights, Hk, Wk, stride_h, stride_w, dilation_h, dilation_w,
pad_top, pad_left, bias, relu, inptr0, Hi, Wi, outptr0, c, H0, W0);
else
#endif
depthWiseBlockConv2D(weights, Hk, Wk, stride_h, stride_w, dilation_h, dilation_w,
pad_top, pad_left, bias, relu, inptr0, Hi, Wi, outptr0, c, H0, W0);
}
});
else // conv_dim == CONV_1D, spatial branch for depth-wise Conv1D.
{
depthWiseBlockConv1D(weights, Wk, stride_w, dilation_w, pad_left, bias, relu, inptr0, Wi, outptr0, c, W0);
}
if (activ)
activ->forwardSlice(outptr0, outptr0, (int) out_planesize, out_planesize, c, c+1);
}});
}
}} // namespace cv::dnn
}} // namespace cv::dnn

@ -6,9 +6,52 @@
#include "fast_convolution.hpp"
namespace cv {
namespace dnn {
namespace opt_AVX2
{
#if CV_TRY_AVX2
void convBlockMR1(int np, const float* a, const float* b, float *c, const float bias, bool init_c,
const float minval, const float maxval, bool ifMinMaxAct)
{
#if CONV_NR == 24
__m256 c0 = _mm256_set1_ps(bias), c1 = c0, c2 = c0;
for (int p = 0; p < np; p++, a++, b += CONV_NR)
{
__m256 a0 = _mm256_set1_ps(a[0]);
__m256 b0 = _mm256_loadu_ps(b), b1 = _mm256_loadu_ps(b + 8), b2 = _mm256_loadu_ps(b + 16);
c0 = _mm256_fmadd_ps(b0, a0, c0);
c1 = _mm256_fmadd_ps(b1, a0, c1);
c2 = _mm256_fmadd_ps(b2, a0, c2);
}
if (init_c)
{
c0 = _mm256_add_ps(_mm256_loadu_ps(c), c0);
c1 = _mm256_add_ps(_mm256_loadu_ps(c + 8), c1);
c2 = _mm256_add_ps(_mm256_loadu_ps(c + 16), c2);
}
if (ifMinMaxAct)
{
__m256 vmax = _mm256_set1_ps(maxval);
__m256 vmin = _mm256_set1_ps(minval);
c0 = _mm256_min_ps(_mm256_max_ps(c0, vmin), vmax);
c1 = _mm256_min_ps(_mm256_max_ps(c1, vmin), vmax);
c2 = _mm256_min_ps(_mm256_max_ps(c2, vmin), vmax);
}
_mm256_storeu_ps(c, c0);
_mm256_storeu_ps(c + 8, c1);
_mm256_storeu_ps(c + 16, c2);
_mm256_zeroupper();
#else
#error "unsupported CONV_NR in convBlockMR1."
#endif
}
void convBlock_AVX2(int np, const float* a, const float* b, float* c, int ldc, bool init_c)
{
#if CONV_MR == 4 && CONV_NR == 24
@ -73,291 +116,6 @@ void convBlock_AVX2(int np, const float* a, const float* b, float* c, int ldc, b
#endif
}
void depthWiseBlock_AVX2(const float *inptr, float *outptr, const float *weights, float biasval, int *ofstab, int *yxtab,
float minval, float maxval, int Hi, int Wi, int H0, int W0, int ksize, int pad_top, int pad_left,
int dilation_y, int stride_x, int stride_y, int inner_xleft, int inner_xright, int inner_ytop,
int inner_ybottom, bool ifMinMaxAct, bool useSIMD, bool is3x3)
{
const int VEC_NLANES = 8;
__m256 vminval = _mm256_set1_ps(minval);
__m256 vmaxval = _mm256_set1_ps(maxval);
__m256 w0 = _mm256_setzero_ps(),
w1 = w0, w2 = w0, w3 = w0, w4 = w0, w5 = w0, w6 = w0, w7 = w0, w8 = w0, vbias = w0;
if (useSIMD)
{
vbias = _mm256_set1_ps(biasval);
if (is3x3)
{
w0 = _mm256_set1_ps(weights[0]);
w1 = _mm256_set1_ps(weights[1]);
w2 = _mm256_set1_ps(weights[2]);
w3 = _mm256_set1_ps(weights[3]);
w4 = _mm256_set1_ps(weights[4]);
w5 = _mm256_set1_ps(weights[5]);
w6 = _mm256_set1_ps(weights[6]);
w7 = _mm256_set1_ps(weights[7]);
w8 = _mm256_set1_ps(weights[8]);
}
}
int dy0 = 1;
for (int y0 = 0; y0 < H0; y0 += dy0, outptr += W0 * dy0)
{
dy0 = inner_ytop <= y0 && y0 + 3 < inner_ybottom && is3x3 && stride_y == 1 && dilation_y == 1
? 3 : 1;
int x0 = 0, x1 = y0 >= inner_ytop && y0 < inner_ybottom ? inner_xleft : W0;
int yi_ = y0 * stride_y - pad_top;
for (;;)
{
float s_0, s_1, s_2;
if (dy0 == 3)
{
for (; x0 < x1; x0++)
{
int xi_ = x0 * stride_x - pad_left;
s_0 = s_1 = s_2 = biasval;
for (int k = 0; k < ksize; k++)
{
int dy = yxtab[k * 2];
int yi = yi_ + dy;
int xi = xi_ + yxtab[k * 2 + 1];
float w = weights[k];
if ((unsigned) xi < (unsigned) Wi)
{
s_0 += inptr[yi * Wi + xi] * w;
s_1 += inptr[(yi + 1) * Wi + xi] * w;
s_2 += inptr[(yi + 2) * Wi + xi] * w;
}
}
if (ifMinMaxAct)
{
s_0 = std::min(std::max(s_0, minval), maxval);
s_1 = std::min(std::max(s_1, minval), maxval);
s_2 = std::min(std::max(s_2, minval), maxval);
}
outptr[x0] = s_0;
outptr[x0 + W0] = s_1;
outptr[x0 + W0 * 2] = s_2;
}
}
else
{
for (; x0 < x1; x0++)
{
int xi_ = x0 * stride_x - pad_left;
s_0 = biasval;
for (int k = 0; k < ksize; k++) {
int dy = yxtab[k * 2];
int yi = yi_ + dy;
int xi = xi_ + yxtab[k * 2 + 1];
float w = weights[k];
if (((unsigned) yi < (unsigned) Hi) & ((unsigned) xi < (unsigned) Wi))
s_0 += inptr[yi * Wi + xi] * w;
}
if (ifMinMaxAct)
s_0 = std::min(std::max(s_0, minval), maxval);
outptr[x0] = s_0;
}
}
if (x0 == W0)
break;
x1 = inner_xright;
if (useSIMD)
{
if (is3x3)
{
if (dy0 == 3)
{
for (; x0 <= x1 - VEC_NLANES; x0 += VEC_NLANES)
{
int xi_ = x0 * stride_x - pad_left;
const float *inptr_xi = inptr + Wi * yi_ + xi_;
__m256 s0, s1, s2;
__m256 x00 = _mm256_loadu_ps(inptr_xi);
__m256 x01 = _mm256_loadu_ps(inptr_xi + 1);
__m256 x02 = _mm256_loadu_ps(inptr_xi + 2);
__m256 x10 = _mm256_loadu_ps(inptr_xi + Wi);
__m256 x11 = _mm256_loadu_ps(inptr_xi + Wi + 1);
__m256 x12 = _mm256_loadu_ps(inptr_xi + Wi + 2);
__m256 x20 = _mm256_loadu_ps(inptr_xi + Wi * 2);
__m256 x21 = _mm256_loadu_ps(inptr_xi + Wi * 2 + 1);
__m256 x22 = _mm256_loadu_ps(inptr_xi + Wi * 2 + 2);
__m256 x30 = _mm256_loadu_ps(inptr_xi + Wi * 3);
__m256 x31 = _mm256_loadu_ps(inptr_xi + Wi * 3 + 1);
__m256 x32 = _mm256_loadu_ps(inptr_xi + Wi * 3 + 2);
__m256 x40 = _mm256_loadu_ps(inptr_xi + Wi * 4);
__m256 x41 = _mm256_loadu_ps(inptr_xi + Wi * 4 + 1);
__m256 x42 = _mm256_loadu_ps(inptr_xi + Wi * 4 + 2);
s0 = _mm256_fmadd_ps(x00, w0, vbias);
s1 = _mm256_fmadd_ps(x10, w0, vbias);
s2 = _mm256_fmadd_ps(x20, w0, vbias);
s0 = _mm256_fmadd_ps(x01, w1, s0);
s1 = _mm256_fmadd_ps(x11, w1, s1);
s2 = _mm256_fmadd_ps(x21, w1, s2);
s0 = _mm256_fmadd_ps(x02, w2, s0);
s1 = _mm256_fmadd_ps(x12, w2, s1);
s2 = _mm256_fmadd_ps(x22, w2, s2);
s0 = _mm256_fmadd_ps(x10, w3, s0);
s1 = _mm256_fmadd_ps(x20, w3, s1);
s2 = _mm256_fmadd_ps(x30, w3, s2);
s0 = _mm256_fmadd_ps(x11, w4, s0);
s1 = _mm256_fmadd_ps(x21, w4, s1);
s2 = _mm256_fmadd_ps(x31, w4, s2);
s0 = _mm256_fmadd_ps(x12, w5, s0);
s1 = _mm256_fmadd_ps(x22, w5, s1);
s2 = _mm256_fmadd_ps(x32, w5, s2);
s0 = _mm256_fmadd_ps(x20, w6, s0);
s1 = _mm256_fmadd_ps(x30, w6, s1);
s2 = _mm256_fmadd_ps(x40, w6, s2);
s0 = _mm256_fmadd_ps(x21, w7, s0);
s1 = _mm256_fmadd_ps(x31, w7, s1);
s2 = _mm256_fmadd_ps(x41, w7, s2);
s0 = _mm256_fmadd_ps(x22, w8, s0);
s1 = _mm256_fmadd_ps(x32, w8, s1);
s2 = _mm256_fmadd_ps(x42, w8, s2);
if (ifMinMaxAct)
{
s0 = _mm256_min_ps(_mm256_max_ps(s0, vminval), vmaxval);
s1 = _mm256_min_ps(_mm256_max_ps(s1, vminval), vmaxval);
s2 = _mm256_min_ps(_mm256_max_ps(s2, vminval), vmaxval);
}
_mm256_storeu_ps(outptr + x0, s0);
_mm256_storeu_ps(outptr + W0 + x0, s1);
_mm256_storeu_ps(outptr + W0 * 2 + x0, s2);
}
}
else
{
for (; x0 <= x1 - VEC_NLANES; x0 += VEC_NLANES)
{
int xi_ = x0 * stride_x - pad_left;
const float *inptr_xi = inptr + Wi * yi_ + xi_;
__m256 s0 = _mm256_fmadd_ps(_mm256_loadu_ps(inptr_xi + ofstab[0]), w0, vbias);
__m256 s1 = _mm256_mul_ps(_mm256_loadu_ps(inptr_xi + ofstab[1]), w1);
__m256 s2 = _mm256_mul_ps(_mm256_loadu_ps(inptr_xi + ofstab[2]), w2);
s0 = _mm256_fmadd_ps(_mm256_loadu_ps(inptr_xi + ofstab[3]), w3, s0);
s1 = _mm256_fmadd_ps(_mm256_loadu_ps(inptr_xi + ofstab[4]), w4, s1);
s2 = _mm256_fmadd_ps(_mm256_loadu_ps(inptr_xi + ofstab[5]), w5, s2);
s0 = _mm256_fmadd_ps(_mm256_loadu_ps(inptr_xi + ofstab[6]), w6, s0);
s1 = _mm256_fmadd_ps(_mm256_loadu_ps(inptr_xi + ofstab[7]), w7, s1);
s2 = _mm256_fmadd_ps(_mm256_loadu_ps(inptr_xi + ofstab[8]), w8, s2);
s0 = _mm256_add_ps(_mm256_add_ps(s0, s1), s2);
if (ifMinMaxAct)
s0 = _mm256_min_ps(_mm256_max_ps(s0, vminval), vmaxval);
_mm256_storeu_ps(outptr + x0, s0);
}
}
}
else
{
for (; x0 <= x1 - VEC_NLANES; x0 += VEC_NLANES)
{
int xi_ = x0 * stride_x - pad_left, k = 0;
const float *inptr_xi = inptr + Wi * yi_ + xi_;
__m256 s0 = vbias;
for (; k <= ksize - 4; k += 4)
{
__m256 v0 = _mm256_loadu_ps(inptr_xi + ofstab[k]);
__m256 v1 = _mm256_loadu_ps(inptr_xi + ofstab[k + 1]);
__m256 v2 = _mm256_loadu_ps(inptr_xi + ofstab[k + 2]);
__m256 v3 = _mm256_loadu_ps(inptr_xi + ofstab[k + 3]);
__m256 ww0 = _mm256_set1_ps(weights[k]);
__m256 ww1 = _mm256_set1_ps(weights[k+1]);
__m256 ww2 = _mm256_set1_ps(weights[k+2]);
__m256 ww3 = _mm256_set1_ps(weights[k+3]);
s0 = _mm256_fmadd_ps(v0, ww0, s0);
s0 = _mm256_fmadd_ps(v1, ww1, s0);
s0 = _mm256_fmadd_ps(v2, ww2, s0);
s0 = _mm256_fmadd_ps(v3, ww3, s0);
}
for (; k < ksize; k++)
s0 = _mm256_fmadd_ps(_mm256_loadu_ps(inptr_xi + ofstab[k]),
_mm256_set1_ps(weights[k]), s0);
if (ifMinMaxAct)
s0 = _mm256_min_ps(_mm256_max_ps(s0, vminval), vmaxval);
_mm256_storeu_ps(outptr + x0, s0);
}
}
}
if (dy0 == 3)
{
for (; x0 < x1; x0++)
{
int xi_ = x0 * stride_x - pad_left;
const float *inptr_xi = inptr + W0 * yi_ + xi_;
s_0 = s_1 = s_2 = biasval;
for (int k = 0; k < ksize; k++) {
int inp_ofs = ofstab[k];
float w = weights[k];
s_0 += inptr_xi[inp_ofs] * w;
s_1 += inptr_xi[inp_ofs + Wi] * w;
s_2 += inptr_xi[inp_ofs + Wi * 2] * w;
}
if (ifMinMaxAct)
{
s_0 = std::min(std::max(s_0, minval), maxval);
s_1 = std::min(std::max(s_1, minval), maxval);
s_2 = std::min(std::max(s_2, minval), maxval);
}
outptr[x0] = s_0;
outptr[x0 + W0] = s_1;
outptr[x0 + W0 * 2] = s_2;
}
}
else
{
for (; x0 < x1; x0++)
{
int xi_ = x0 * stride_x - pad_left;
const float *inptr_xi = inptr + Wi * yi_ + xi_;
s_0 = biasval;
for (int k = 0; k < ksize; k++)
{
s_0 += inptr_xi[ofstab[k]] * weights[k];
}
if (ifMinMaxAct)
s_0 = std::min(std::max(s_0, minval), maxval);
outptr[x0] = s_0;
}
}
x1 = W0;
}
}
_mm256_zeroupper();
}
void _fx_winograd_accum_f32(const float* inwptr, const float* wptr,
float* outbuf, int Cg, int iblock)
{
@ -737,4 +495,5 @@ void _fx_winograd_AtXA_8x8_f32(const float* inptr, int inpstep,
#endif
} // namespace opt_AVX2
} // namespace dnn
} // namespace cv

@ -42,19 +42,20 @@ enum {
_FX_WINO_NATOMS_F32 = _FX_WINO_AREA / _FX_WINO_ATOM_F32, // for AVX2, it is 8, otherwise, it's 16.
};
enum { _FX_CONV_TYPE_GENERIC=0, _FX_CONV_TYPE_DEPTHWISE=1, _FX_CONV_TYPE_WINOGRAD3X3=2 };
enum { _FX_CONV_TYPE_GENERIC=0, _FX_CONV_TYPE_DEPTHWISE=1, _FX_CONV_TYPE_WINOGRAD3X3=2, _FX_CONV_TYPE_DEPTHWISE_REMAIN=3 };
enum { CONV_1D = 0, CONV_2D = 1, CONV_3D = 2 };
#endif
namespace cv {
namespace dnn {
struct FastConv2d
struct FastConv
{
int ngroups;
int K, C, Hk, Wk;
int stride_y, stride_x;
int dilation_y, dilation_x;
int pad_top, pad_bottom, pad_left, pad_right;
int K, C, Hk, Wk, Dk;
int stride_h, stride_w, stride_d;
int dilation_h, dilation_w, dilation_d;
int pad_top, pad_bottom, pad_left, pad_right, pad_front, pad_behind;
std::vector<float> weightsBuf; // For generic Conv 2D
float* weightsBufPtr;
@ -62,57 +63,55 @@ struct FastConv2d
float* weightsWinoBufPtr;
std::vector<float> biasBuf;
int conv_type;
int conv_dim; // Flag for conv1d, conv2d, or conv3d.
#if CV_SIMD128
bool useSIMD128 = true;
#else
bool useSIMD128 = false;
#endif
#if CV_TRY_AVX2
bool useAVX2 = checkHardwareSupport(CPU_AVX2);
#else
bool useAVX2 = false;
#endif
#if CV_NEON
bool useNEON = checkHardwareSupport(CPU_NEON);
#else
bool useNEON = false;
#endif
bool useAVX = checkHardwareSupport(CPU_AVX);
bool useAVX2 = checkHardwareSupport(CPU_AVX2);
bool useRVV = checkHardwareSupport(CPU_RVV);
};
// return a FastConv2d instance.
Ptr<FastConv2d> initFastConv2d(
// return a FastConv instance.
Ptr<FastConv> initFastConv(
InputArray weightsMat,
float* srcBias,
int ngroups,
int K, int C, int Hk, int Wk,
int stride_x, int stride_y,
int dilation_x, int dilation_y,
int K, int C,
const std::vector<size_t>& kernel_size,
const std::vector<size_t>& strides,
const std::vector<size_t>& dilations,
const std::vector<size_t>& pads_begin,
const std::vector<size_t>& pads_end,
InputArray weightsMat,
float* srcBias, bool useWinograd);
int conv_dim,
bool useWinograd);
// It contains different computing branches, like winograd, 1x1 conv.
void runFastConv2d(InputArray _input, OutputArray _output, const Ptr<FastConv2d>& conv, int ntasks,
const Ptr<ActivationLayer>& actLayer, bool fusedAdd);
void runFastConv(InputArray _input, OutputArray _output, const Ptr<FastConv>& conv, int ntasks,
const Ptr<ActivationLayer>& actLayer, const std::vector<float>& reluslope, bool fusedAdd);
void runDepthwise(InputArray _input, OutputArray _output, const Ptr<FastConv2d>& conv, float minval, float maxval,
ActivationLayer* activ, bool ifMinMaxAct);
void runDepthwise(InputArray _input, OutputArray _output, const Ptr<FastConv>& conv, ActivationLayer* activ,
const std::vector<float>& reluslope);
int runWinograd63(InputArray _input, InputArray _fusedAddMat, OutputArray _output, const Ptr<FastConv2d>& conv, int ntasks,
int runWinograd63(InputArray _input, InputArray _fusedAddMat, OutputArray _output, const Ptr<FastConv>& conv, int ntasks,
float minval, float maxval, ActivationLayer* activ, bool ifMinMaxAct);
} // namespace dnn
namespace opt_AVX2
{
#if CV_TRY_AVX2
void convBlock_AVX2(int np, const float* a, const float* b, float* c, int ldc, bool init_c);
void depthWiseBlock_AVX2(const float *inptr, float *outptr, const float *weights, float biasval, int *ofstab, int *yxtab,
float minval, float maxval, int Hi, int Wi, int H0, int W0, int ksize, int pad_top, int pad_left,
int dilation_y, int stride_x, int stride_y, int inner_xleft, int inner_xright, int inner_ytop,
int inner_ybottom, bool ifMinMaxAct, bool useSIMD, bool is3x3);
void convBlockMR1(int np, const float* a, const float* b, float *c, const float bias, bool init_c, const float minval,
const float maxval, bool ifMinMaxAct);
void _fx_winograd_accum_f32(const float* inwptr, const float* wptr, float* outbuf, int Cg, int iblock);
void _fx_winograd_BtXB_8x8_f32(const float* inptr, int inpstep, float* outptr, int Cg);
@ -122,6 +121,7 @@ void _fx_winograd_AtXA_8x8_f32(const float* inptr, int inpstep, float* bpptr, in
#endif
} // namespace opt_AVX2
} // namespace dnn
} // namespace cv
#endif //OPENCV_FAST_CONVOLUTION_HPP

@ -11,9 +11,132 @@
namespace cv {
namespace dnn {
void convBlock(int np, const float* a, const float* b, float* c, int ldc, bool init_c)
static void convBlockMR1NoSIMD(int np, const float* a, const float* b, float *c, const float bias, bool init_c,
const float minval, const float maxval, bool ifMinMaxAct, const int outLen)
{
std::vector<float> cbuffer(outLen, 0);
float* cbuf = cbuffer.data();
for( int p = 0; p < np; p++ )
{
float ai = a[p];
for( int j = 0; j < outLen; j++ )
cbuf[j] += b[CONV_NR*p + j] * ai;
}
if (init_c)
{
for(int j = 0; j < outLen; j++)
{
c[j] += cbuf[j] + bias;
if (ifMinMaxAct)
c[j] = std::min(std::max(c[j], minval), maxval);
}
}
else
{
for(int j = 0; j < outLen; j++)
{
c[j] = cbuf[j] + bias;
if (ifMinMaxAct)
c[j] = std::min(std::max(c[j], minval), maxval);
}
}
}
void convBlockMR1(int np, const float* a, const float* b, float *c, const float bias, bool init_c,
const float minval, const float maxval, bool ifMinMaxAct, const int outLen)
{
#if CV_SIMD128
// The outLen represents the valid output value in CONV_NR length.
// When outLen is very small, we use the no-SIMD branch.
const int CONV_NRby3 = CONV_NR/3;
if (outLen > CONV_NRby3)
{
v_float32x4 c0 = v_setall_f32(bias), c1 = c0, c2 = c0; // CONV_NR == 12
#if CONV_NR == 28 || CONV_NR == 24
v_float32x4 c3 = c0, c4 = c0, c5 = c0;
#endif
#if CONV_NR == 28
v_float32x4 c6 = c0;
#endif
for (int p = 0; p < np; p++, a++, b += CONV_NR)
{
v_float32x4 a0 = v_setall_f32(a[0]);
v_float32x4 b0 = v_load(b), b1 = v_load(b + 4), b2 = v_load(b + 8);
#if CONV_NR == 28 || CONV_NR == 24
v_float32x4 b3 = v_load(b + 12), b4 = v_load(b + 16), b5 = v_load(b + 20);
#endif
#if CONV_NR == 28
v_float32x4 b6 = v_load(b + 24);
#endif
c0 = v_fma(b0, a0, c0);
c1 = v_fma(b1, a0, c1);
c2 = v_fma(b2, a0, c2);
#if CONV_NR == 28 || CONV_NR == 24
c3 = v_fma(b3, a0, c3);
c4 = v_fma(b4, a0, c4);
c5 = v_fma(b5, a0, c5);
#endif
#if CONV_NR == 28
c6 = v_fma(b6, a0, c6);
#endif
}
if (init_c)
{
c0 += v_load(c);
c1 += v_load(c + 4);
c2 += v_load(c + 8);
#if CONV_NR == 28 || CONV_NR == 24
c3 += v_load(c + 12);
c4 += v_load(c + 16);
c5 += v_load(c + 20);
#endif
#if CONV_NR == 28
c6 += v_load(c + 24);
#endif
}
if (ifMinMaxAct)
{
v_float32x4 vmax = v_setall_f32(maxval), vmin = v_setall_f32(minval);
c0 = v_min(v_max(c0, vmin), vmax);
c1 = v_min(v_max(c1, vmin), vmax);
c2 = v_min(v_max(c2, vmin), vmax);
#if CONV_NR == 28 || CONV_NR == 24
c3 = v_min(v_max(c3, vmin), vmax);
c4 = v_min(v_max(c4, vmin), vmax);
c5 = v_min(v_max(c5, vmin), vmax);
#endif
#if CONV_NR == 28
c6 = v_min(v_max(c6, vmin), vmax);
#endif
}
v_store(c, c0);
v_store(c + 4, c1);
v_store(c + 8, c2);
#if CONV_NR == 28 || CONV_NR == 24
v_store(c + 12, c3);
v_store(c + 16, c4);
v_store(c + 20, c5);
#endif
#if CONV_NR == 28
v_store(c + 24, c6);
#endif
}
else
convBlockMR1NoSIMD(np, a, b, c, bias, init_c, minval, maxval, ifMinMaxAct, outLen);
#else
convBlockMR1NoSIMD(np, a, b, c, bias, init_c, minval, maxval, ifMinMaxAct, outLen);
#endif
}
#if CV_SIMD128
#if CONV_MR == 4 && CONV_NR == 24
static void convBlock4x24(int np, const float* a, const float* b, float* c, int ldc, bool init_c)
{
#if CV_SIMD128 && CONV_MR == 4 && CONV_NR == 24
v_float32x4 c0 = v_setzero_f32(), c1 = c0, c2 = c0, c3 = c0, c4 = c0, c5 = c0;
v_float32x4 c6 = v_setzero_f32(), c7 = c6, c8 = c6, c9 = c6, c10 = c6, c11 = c6;
v_float32x4 c12 = v_setzero_f32(), c13 = c12, c14 = c12, c15 = c12, c16 = c12, c17 = c12;
@ -115,29 +238,156 @@ void convBlock(int np, const float* a, const float* b, float* c, int ldc, bool i
v_store(c + ldc * 3 + 12, c21);
v_store(c + ldc * 3 + 16, c22);
v_store(c + ldc * 3 + 20, c23);
#else
float cbuf[CONV_MR * CONV_NR];
memset(cbuf, 0, sizeof(cbuf));
}
#endif
static void convBlock4x8(int np, const float* a, const float* b, float* c, int ldc, bool init_c)
{
CV_Assert(CONV_NR >= 4);
v_float32x4 c0 = v_setzero_f32(), c1 = c0, c2 = c0, c3 = c0;
v_float32x4 c4 = c0, c5 = c0, c6 = c0, c7 = c0;
for (int p = 0; p < np; p++, a += CONV_MR, b += CONV_NR)
{
v_float32x4 a0 = v_setall_f32(a[0]);
v_float32x4 a1 = v_setall_f32(a[1]);
v_float32x4 a2 = v_setall_f32(a[2]);
v_float32x4 a3 = v_setall_f32(a[3]);
v_float32x4 b0 = v_load(b), b1 = v_load(b + 4);
c0 = v_fma(b0, a0, c0);
c1 = v_fma(b1, a0, c1);
c2 = v_fma(b0, a1, c2);
c3 = v_fma(b1, a1, c3);
c4 = v_fma(b0, a2, c4);
c5 = v_fma(b1, a2, c5);
c6 = v_fma(b0, a3, c6);
c7 = v_fma(b1, a3, c7);
}
if (!init_c)
{
c0 += v_load(c);
c1 += v_load(c + 4);
c2 += v_load(c + ldc);
c3 += v_load(c + ldc + 4);
c4 += v_load(c + ldc*2);
c5 += v_load(c + ldc*2 + 4);
c6 += v_load(c + ldc*3);
c7 += v_load(c + ldc*3 + 4);
}
v_store(c, c0);
v_store(c + 4, c1);
v_store(c + ldc, c2);
v_store(c + ldc + 4, c3);
v_store(c + ldc * 2, c4);
v_store(c + ldc * 2 + 4, c5);
v_store(c + ldc * 3, c6);
v_store(c + ldc * 3 + 4, c7);
}
static void convBlock4x4(int np, const float* a, const float* b, float* c, int ldc, bool init_c)
{
CV_Assert(CONV_NR >= 4);
v_float32x4 c0 = v_setzero_f32(), c1 = c0, c2 = c0, c3 = c0;
for (int p = 0; p < np; p++, a += CONV_MR, b += CONV_NR)
{
v_float32x4 a0 = v_setall_f32(a[0]);
v_float32x4 a1 = v_setall_f32(a[1]);
v_float32x4 a2 = v_setall_f32(a[2]);
v_float32x4 a3 = v_setall_f32(a[3]);
v_float32x4 b0 = v_load(b);
c0 = v_fma(b0, a0, c0);
c1 = v_fma(b0, a1, c1);
c2 = v_fma(b0, a2, c2);
c3 = v_fma(b0, a3, c3);
}
if (!init_c)
{
c0 += v_load(c);
c1 += v_load(c + ldc);
c2 += v_load(c + ldc*2);
c3 += v_load(c + ldc*3);
}
v_store(c, c0);
v_store(c + ldc, c1);
v_store(c + ldc * 2, c2);
v_store(c + ldc * 3, c3);
}
#endif
static void convBlockNoSIMD(int np, const float* a, const float* b, float* c, int ldc, bool init_c, const int outLen)
{
std::vector<float> cbuffer(CONV_MR * outLen, 0);
float* cbuf = cbuffer.data();
for( int p = 0; p < np; p++ )
{
for( int i = 0; i < CONV_MR; i++ )
{
float ai = a[CONV_MR*p + i];
for( int j = 0; j < CONV_NR; j++ )
cbuf[i * CONV_NR+j] += b[CONV_NR*p + j] * ai;
for( int j = 0; j < outLen; j++ )
cbuf[i * outLen+j] += b[CONV_NR*p + j] * ai;
}
}
if (!init_c) {
for(int i = 0; i < CONV_MR; i++) {
for(int j = 0; j < CONV_NR; j++)
c[i*ldc + j] += cbuf[i*CONV_NR + j];
if (!init_c)
{
for(int i = 0; i < CONV_MR; i++)
{
for(int j = 0; j < outLen; j++)
c[i*ldc + j] += cbuf[i*outLen + j];
}
} else {
for(int i = 0; i < CONV_MR; i++) {
for(int j = 0; j < CONV_NR; j++)
c[i*ldc + j] = cbuf[i*CONV_NR + j];
}
else
{
for(int i = 0; i < CONV_MR; i++)
{
for(int j = 0; j < outLen; j++)
c[i*ldc + j] = cbuf[i*outLen + j];
}
}
}
void convBlock(int np, const float* a, const float* b, float* c, int ldc, bool init_c, const int outLen)
{
// The possible outLen range is [24, 8~1].
#if CV_SIMD128
#if CONV_MR == 4 && CONV_NR == 24
const int CONV_NRby3 = CONV_NR/3;
if (outLen > CONV_NRby3)
{
convBlock4x24(np, a, b, c, ldc, init_c);
return;
}
#endif
if (outLen <= 8 && outLen > 4)
{
convBlock4x8(np, a, b, c, ldc, init_c);
return;
}
if (outLen <= 4 && outLen > 1)
{
convBlock4x4(np, a, b, c, ldc, init_c);
return;
}
convBlockNoSIMD(np, a, b, c, ldc, init_c, outLen);
#else
convBlockNoSIMD(np, a, b, c, ldc, init_c, outLen);
#endif
}
} // namespace dnn

@ -920,7 +920,7 @@ _fx_winograd_AtXA_8x8_f32(const float* inptr, int inpstep,
#endif
}
int runWinograd63(InputArray _input, InputArray _fusedAddMat, OutputArray _output, const Ptr<FastConv2d>& conv,
int runWinograd63(InputArray _input, InputArray _fusedAddMat, OutputArray _output, const Ptr<FastConv>& conv,
int ntasks, float minval, float maxval, ActivationLayer* activ, bool ifMinMaxAct)
{
Mat input = _input.getMat();
@ -1144,7 +1144,7 @@ int runWinograd63(InputArray _input, InputArray _fusedAddMat, OutputArray _outpu
#else
int runWinograd63(InputArray _input, InputArray _fusedAddMat, OutputArray _output, const Ptr<FastConv2d>& conv,
int runWinograd63(InputArray _input, InputArray _fusedAddMat, OutputArray _output, const Ptr<FastConv>& conv,
int ntasks, float minval, float maxval, ActivationLayer* activ, bool ifMinMaxAct)
{
return 0;

@ -46,10 +46,6 @@ namespace cv {
namespace dnn {
CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN
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,
@ -74,305 +70,6 @@ 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,
const float* relu, bool initOutput )
{
CV_Assert(isAligned<32>(weights));
int outCn = outShape[1];
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
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];
if( i+2 >= outCn )
{
r2 = r1;
if( i+1 >= outCn )
r2 = r1 = r0;
}
vr0 = _mm_set1_ps(r0);
vr1 = _mm_set1_ps(r1);
vr2 = _mm_set1_ps(r2);
}
int j = 0;
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;
__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();
#if CV_AVX512_SKX // AVX512VL is necessary to avoid register spilling
if (vecsize >= 32)
{
__m512 vs00_5 = _mm512_setzero_ps(), vs01_5 = _mm512_setzero_ps(),
vs02_5 = _mm512_setzero_ps(), vs03_5 = _mm512_setzero_ps(),
vs10_5 = _mm512_setzero_ps(), vs11_5 = _mm512_setzero_ps(),
vs12_5 = _mm512_setzero_ps(), vs13_5 = _mm512_setzero_ps(),
vs20_5 = _mm512_setzero_ps(), vs21_5 = _mm512_setzero_ps(),
vs22_5 = _mm512_setzero_ps(), vs23_5 = _mm512_setzero_ps();
for (; k <= vecsize - 16; k += 16, rptr += 16)
{
__m512 w0 = _mm512_loadu_ps(wptr0 + k);
__m512 w1 = _mm512_loadu_ps(wptr1 + k);
__m512 w2 = _mm512_loadu_ps(wptr2 + k);
__m512 r0 = _mm512_loadu_ps(rptr);
vs00_5 = _mm512_fmadd_ps(w0, r0, vs00_5);
vs10_5 = _mm512_fmadd_ps(w1, r0, vs10_5);
vs20_5 = _mm512_fmadd_ps(w2, r0, vs20_5);
r0 = _mm512_loadu_ps(rptr + vecsize_aligned);
vs01_5 = _mm512_fmadd_ps(w0, r0, vs01_5);
vs11_5 = _mm512_fmadd_ps(w1, r0, vs11_5);
vs21_5 = _mm512_fmadd_ps(w2, r0, vs21_5);
r0 = _mm512_loadu_ps(rptr + vecsize_aligned*2);
vs02_5 = _mm512_fmadd_ps(w0, r0, vs02_5);
vs12_5 = _mm512_fmadd_ps(w1, r0, vs12_5);
vs22_5 = _mm512_fmadd_ps(w2, r0, vs22_5);
r0 = _mm512_loadu_ps(rptr + vecsize_aligned*3);
vs03_5 = _mm512_fmadd_ps(w0, r0, vs03_5);
vs13_5 = _mm512_fmadd_ps(w1, r0, vs13_5);
vs23_5 = _mm512_fmadd_ps(w2, r0, vs23_5);
}
/*
* now fold the 512 bit accumulator vectors into 256 bit vectors so that the AVX2 code can finish
* the tail of the vector
*/
vs00 = _mm256_add_ps( _mm512_extractf32x8_ps(vs00_5, 0), _mm512_extractf32x8_ps(vs00_5, 1));
vs10 = _mm256_add_ps( _mm512_extractf32x8_ps(vs10_5, 0), _mm512_extractf32x8_ps(vs10_5, 1));
vs20 = _mm256_add_ps( _mm512_extractf32x8_ps(vs20_5, 0), _mm512_extractf32x8_ps(vs20_5, 1));
vs01 = _mm256_add_ps( _mm512_extractf32x8_ps(vs01_5, 0), _mm512_extractf32x8_ps(vs01_5, 1));
vs11 = _mm256_add_ps( _mm512_extractf32x8_ps(vs11_5, 0), _mm512_extractf32x8_ps(vs11_5, 1));
vs21 = _mm256_add_ps( _mm512_extractf32x8_ps(vs21_5, 0), _mm512_extractf32x8_ps(vs21_5, 1));
vs02 = _mm256_add_ps( _mm512_extractf32x8_ps(vs02_5, 0), _mm512_extractf32x8_ps(vs02_5, 1));
vs12 = _mm256_add_ps( _mm512_extractf32x8_ps(vs12_5, 0), _mm512_extractf32x8_ps(vs12_5, 1));
vs22 = _mm256_add_ps( _mm512_extractf32x8_ps(vs22_5, 0), _mm512_extractf32x8_ps(vs22_5, 1));
vs03 = _mm256_add_ps( _mm512_extractf32x8_ps(vs03_5, 0), _mm512_extractf32x8_ps(vs03_5, 1));
vs13 = _mm256_add_ps( _mm512_extractf32x8_ps(vs13_5, 0), _mm512_extractf32x8_ps(vs13_5, 1));
vs23 = _mm256_add_ps( _mm512_extractf32x8_ps(vs23_5, 0), _mm512_extractf32x8_ps(vs23_5, 1));
}
#endif
for (; 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));
__m128 s0, s1, s2;
if( initOutput )
{
s0 = _mm_set1_ps(bias0);
s1 = _mm_set1_ps(bias1);
s2 = _mm_set1_ps(bias2);
}
else
{
s0 = _mm_loadu_ps(outptr0 + j);
s1 = _mm_loadu_ps(outptr1 + j);
s2 = _mm_loadu_ps(outptr2 + j);
}
s0 = _mm_add_ps(s0, _mm256_castps256_ps128(t0));
s1 = _mm_add_ps(s1, _mm256_castps256_ps128(t1));
s2 = _mm_add_ps(s2, _mm256_castps256_ps128(t2));
if( relu )
{
__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_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);
_mm_storeu_ps(outptr1 + j, s1);
_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* rptr0 = 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 w0 = wptr0[k], w1 = wptr1[k], w2 = wptr2[k];
float r = rptr0[k];
s00 += w0*r; s10 += w1*r; s20 += w2*r;
}
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();
}
static inline void _mm256_load_deinterleave(const float* ptr, __m256& a, __m256& b)
{
__m256 t0 = _mm256_loadu_ps(ptr);
@ -957,198 +654,6 @@ void fastGEMM1T( const float* vec, const float* weights,
}
}
enum { FASCONV_BASE_VECSZ = 8 };
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 )
{
const int vlm1 = vsetvlmax_e32m1();
int outCn = outShape[1];
size_t outPlaneSize = outShape[2]*outShape[3];
// now compute dot product of the weights
// and im2row-transformed part of the tensor
for( int i = 0; i < outCn; i += 3 )
{
int unroll_tail = FASCONV_BASE_VECSZ;
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;
}
}
int j = 0;
for( ; j < blockSize; j += FASCONV_BASE_VECSZ )
{
const float* rptr = rowbuf + j*vecsize_aligned;
const float *rptr1 = rptr + vecsize_aligned*1,
*rptr2 = rptr + vecsize_aligned*2,
*rptr3 = rptr + vecsize_aligned*3,
*rptr4 = rptr + vecsize_aligned*4,
*rptr5 = rptr + vecsize_aligned*5,
*rptr6 = rptr + vecsize_aligned*6,
*rptr7 = rptr + vecsize_aligned*7;
if (j + FASCONV_BASE_VECSZ > blockSize)
{
unroll_tail = blockSize - j;
rptr1 = rptr + vecsize_aligned*std::min(1, unroll_tail-1),
rptr2 = rptr + vecsize_aligned*std::min(2, unroll_tail-1),
rptr3 = rptr + vecsize_aligned*std::min(3, unroll_tail-1),
rptr4 = rptr + vecsize_aligned*std::min(4, unroll_tail-1),
rptr5 = rptr + vecsize_aligned*std::min(5, unroll_tail-1),
rptr6 = rptr + vecsize_aligned*std::min(6, unroll_tail-1),
rptr7 = rptr + vecsize_aligned*std::min(7, unroll_tail-1);
}
int vl, avl = vecsize;
vfloat32m1_t
vs00 = vfmv_v_f_f32m1(0, vlm1), vs10 = vfmv_v_f_f32m1(0, vlm1), vs20 = vfmv_v_f_f32m1(0, vlm1),
vs01 = vfmv_v_f_f32m1(0, vlm1), vs11 = vfmv_v_f_f32m1(0, vlm1), vs21 = vfmv_v_f_f32m1(0, vlm1),
vs02 = vfmv_v_f_f32m1(0, vlm1), vs12 = vfmv_v_f_f32m1(0, vlm1), vs22 = vfmv_v_f_f32m1(0, vlm1),
vs03 = vfmv_v_f_f32m1(0, vlm1), vs13 = vfmv_v_f_f32m1(0, vlm1), vs23 = vfmv_v_f_f32m1(0, vlm1),
vs04 = vfmv_v_f_f32m1(0, vlm1), vs14 = vfmv_v_f_f32m1(0, vlm1), vs24 = vfmv_v_f_f32m1(0, vlm1),
vs05 = vfmv_v_f_f32m1(0, vlm1), vs15 = vfmv_v_f_f32m1(0, vlm1), vs25 = vfmv_v_f_f32m1(0, vlm1),
vs06 = vfmv_v_f_f32m1(0, vlm1), vs16 = vfmv_v_f_f32m1(0, vlm1), vs26 = vfmv_v_f_f32m1(0, vlm1),
vs07 = vfmv_v_f_f32m1(0, vlm1), vs17 = vfmv_v_f_f32m1(0, vlm1), vs27 = vfmv_v_f_f32m1(0, vlm1);
for (int k = 0; k < vecsize; k += vl, avl -= vl)
{
vl = vsetvl_e32m1(avl);
vfloat32m1_t w0 = vle32_v_f32m1(wptr0 + k, vl);
vfloat32m1_t w1 = vle32_v_f32m1(wptr1 + k, vl);
vfloat32m1_t w2 = vle32_v_f32m1(wptr2 + k, vl);
vfloat32m1_t r0 = vle32_v_f32m1(rptr, vl);
vs00 = vfmacc_vv_f32m1(vs00, w0, r0, vl);
vs10 = vfmacc_vv_f32m1(vs10, w1, r0, vl);
vs20 = vfmacc_vv_f32m1(vs20, w2, r0, vl);
r0 = vle32_v_f32m1(rptr1, vl);
vs01 = vfmacc_vv_f32m1(vs01, w0, r0, vl);
vs11 = vfmacc_vv_f32m1(vs11, w1, r0, vl);
vs21 = vfmacc_vv_f32m1(vs21, w2, r0, vl);
r0 = vle32_v_f32m1(rptr2, vl);
vs02 = vfmacc_vv_f32m1(vs02, w0, r0, vl);
vs12 = vfmacc_vv_f32m1(vs12, w1, r0, vl);
vs22 = vfmacc_vv_f32m1(vs22, w2, r0, vl);
r0 = vle32_v_f32m1(rptr3, vl);
vs03 = vfmacc_vv_f32m1(vs03, w0, r0, vl);
vs13 = vfmacc_vv_f32m1(vs13, w1, r0, vl);
vs23 = vfmacc_vv_f32m1(vs23, w2, r0, vl);
r0 = vle32_v_f32m1(rptr4, vl);
vs04 = vfmacc_vv_f32m1(vs04, w0, r0, vl);
vs14 = vfmacc_vv_f32m1(vs14, w1, r0, vl);
vs24 = vfmacc_vv_f32m1(vs24, w2, r0, vl);
r0 = vle32_v_f32m1(rptr5, vl);
vs05 = vfmacc_vv_f32m1(vs05, w0, r0, vl);
vs15 = vfmacc_vv_f32m1(vs15, w1, r0, vl);
vs25 = vfmacc_vv_f32m1(vs25, w2, r0, vl);
r0 = vle32_v_f32m1(rptr6, vl);
vs06 = vfmacc_vv_f32m1(vs06, w0, r0, vl);
vs16 = vfmacc_vv_f32m1(vs16, w1, r0, vl);
vs26 = vfmacc_vv_f32m1(vs26, w2, r0, vl);
r0 = vle32_v_f32m1(rptr7, vl);
vs07 = vfmacc_vv_f32m1(vs07, w0, r0, vl);
vs17 = vfmacc_vv_f32m1(vs17, w1, r0, vl);
vs27 = vfmacc_vv_f32m1(vs27, w2, r0, vl);
rptr += vl; rptr1 += vl; rptr2 += vl; rptr3 += vl;
rptr4 += vl; rptr5 += vl; rptr6 += vl; rptr7 += vl;
}
// compute sum of each vs
vfloat32m1_t zero = vfmv_v_f_f32m1(0, vlm1);
// unroll_tail(vl) is required here to be at least FASCONV_BASE_VECSZ, aka 8.
float sum0[FASCONV_BASE_VECSZ], sum1[FASCONV_BASE_VECSZ], sum2[FASCONV_BASE_VECSZ];
sum0[0] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs00, zero, vlm1));
sum0[1] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs01, zero, vlm1));
sum0[2] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs02, zero, vlm1));
sum0[3] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs03, zero, vlm1));
sum0[4] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs04, zero, vlm1));
sum0[5] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs05, zero, vlm1));
sum0[6] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs06, zero, vlm1));
sum0[7] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs07, zero, vlm1));
sum1[0] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs10, zero, vlm1));
sum1[1] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs11, zero, vlm1));
sum1[2] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs12, zero, vlm1));
sum1[3] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs13, zero, vlm1));
sum1[4] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs14, zero, vlm1));
sum1[5] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs15, zero, vlm1));
sum1[6] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs16, zero, vlm1));
sum1[7] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs17, zero, vlm1));
sum2[0] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs20, zero, vlm1));
sum2[1] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs21, zero, vlm1));
sum2[2] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs22, zero, vlm1));
sum2[3] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs23, zero, vlm1));
sum2[4] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs24, zero, vlm1));
sum2[5] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs25, zero, vlm1));
sum2[6] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs26, zero, vlm1));
sum2[7] = vfmv_f_s_f32m1_f32(vfredosum_vs_f32m1_f32m1(zero, vs27, zero, vlm1));
// if VLEN = 128, so LMUL = 2 for unroll_tail(vl) = 8.
// otherwise, VLEN >=256, we only use fist 8 element of the vReg.
vfloat32m2_t s0, s1, s2;
if( initOutput )
{
s0 = vfmv_v_f_f32m2(bias0, unroll_tail);
s1 = vfmv_v_f_f32m2(bias1, unroll_tail);
s2 = vfmv_v_f_f32m2(bias2, unroll_tail);
}
else
{
s0 = vle32_v_f32m2(outptr0 + j, unroll_tail);
s1 = vle32_v_f32m2(outptr1 + j, unroll_tail);
s2 = vle32_v_f32m2(outptr2 + j, unroll_tail);
}
s0 = vfadd_vv_f32m2(vle32_v_f32m2(sum0, unroll_tail), s0, unroll_tail);
s1 = vfadd_vv_f32m2(vle32_v_f32m2(sum1, unroll_tail), s1, unroll_tail);
s2 = vfadd_vv_f32m2(vle32_v_f32m2(sum2, unroll_tail), s2, unroll_tail);
if( relu )
{
float r0 = relu[i], r1 = relu[i+1], r2 = relu[i+2];
if( i+2 >= outCn )
{
r2 = r1;
if( i+1 >= outCn )
r2 = r1 = r0;
}
vbool16_t m0 = vmfgt_vf_f32m2_b16(s0, 0, unroll_tail);
vbool16_t m1 = vmfgt_vf_f32m2_b16(s1, 0, unroll_tail);
vbool16_t m2 = vmfgt_vf_f32m2_b16(s2, 0, unroll_tail);
s0 = vmerge_vvm_f32m2(m0, vfmul_vf_f32m2(s0, r0, unroll_tail), s0, unroll_tail);
s1 = vmerge_vvm_f32m2(m1, vfmul_vf_f32m2(s1, r1, unroll_tail), s1, unroll_tail);
s2 = vmerge_vvm_f32m2(m2, vfmul_vf_f32m2(s2, r2, unroll_tail), s2, unroll_tail);
}
vse32_v_f32m2(outptr0 + j, s0, unroll_tail);
vse32_v_f32m2(outptr1 + j, s1, unroll_tail);
vse32_v_f32m2(outptr2 + j, s2, unroll_tail);
}
}
}
/*
Example for load_deinterleave:
input: ptr[16] = {1,2,3, ... ,14,15,16}
@ -1345,317 +850,6 @@ void fastDepthwiseConv( const float* wptr,
#if !defined(CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY) && CV_LASX
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,
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 t1 = _v256_setall_ps(1.f), t2 = _v256_setall_ps(0.f);
__m128 vr0 = *(__m128*)&t1, vr1 = vr0, vr2 = vr0, z = *(__m128*)&t2;
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;
__m128i mask = __lsx_vld((const float*)maskbuf, 0);
// 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];
if( i+2 >= outCn )
{
r2 = r1;
if( i+1 >= outCn )
r2 = r1 = r0;
}
vr0 = _v256_extract_low(_v256_setall_ps(r0));
vr1 = _v256_extract_low(_v256_setall_ps(r1));
vr2 = _v256_extract_low(_v256_setall_ps(r2));
}
int j = 0;
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;
__m256i tmp;
__m256 vs00 = (__m256)__lasx_xvxor_v(tmp, tmp), vs01 = (__m256)__lasx_xvxor_v(tmp, tmp),
vs02 = (__m256)__lasx_xvxor_v(tmp, tmp), vs03 = (__m256)__lasx_xvxor_v(tmp, tmp),
vs10 = (__m256)__lasx_xvxor_v(tmp, tmp), vs11 = (__m256)__lasx_xvxor_v(tmp, tmp),
vs12 = (__m256)__lasx_xvxor_v(tmp, tmp), vs13 = (__m256)__lasx_xvxor_v(tmp, tmp),
vs20 = (__m256)__lasx_xvxor_v(tmp, tmp), vs21 = (__m256)__lasx_xvxor_v(tmp, tmp),
vs22 = (__m256)__lasx_xvxor_v(tmp, tmp), vs23 = (__m256)__lasx_xvxor_v(tmp, tmp);
for (; k < vecsize; k += 8, rptr += 8 )
{
__m256 w0 = (__m256)__lasx_xvld(wptr0 + k, 0);
__m256 w1 = (__m256)__lasx_xvld(wptr1 + k, 0);
__m256 w2 = (__m256)__lasx_xvld(wptr2 + k, 0);
__m256 r0 = (__m256)__lasx_xvld(rptr, 0);
vs00 = __lasx_xvfmadd_s(w0, r0, vs00);
vs10 = __lasx_xvfmadd_s(w1, r0, vs10);
vs20 = __lasx_xvfmadd_s(w2, r0, vs20);
r0 = (__m256)__lasx_xvld(rptr + vecsize_aligned, 0);
vs01 = __lasx_xvfmadd_s(w0, r0, vs01);
vs11 = __lasx_xvfmadd_s(w1, r0, vs11);
vs21 = __lasx_xvfmadd_s(w2, r0, vs21);
r0 = (__m256)__lasx_xvld(rptr + vecsize_aligned*2, 0);
vs02 = __lasx_xvfmadd_s(w0, r0, vs02);
vs12 = __lasx_xvfmadd_s(w1, r0, vs12);
vs22 = __lasx_xvfmadd_s(w2, r0, vs22);
r0 = (__m256)__lasx_xvld(rptr + vecsize_aligned*3, 0);
vs03 = __lasx_xvfmadd_s(w0, r0, vs03);
vs13 = __lasx_xvfmadd_s(w1, r0, vs13);
vs23 = __lasx_xvfmadd_s(w2, r0, vs23);
}
/*t0*/
__m256 vs00_perm = (__m256)__lasx_xvpermi_d(vs00, (2<<6) + (3<<4) + (0<<2) + 1);
__m256 vs00_add_2w = __lasx_xvfadd_s(vs00, vs00_perm);
__m256 tmp00_srl = (__m256)__lasx_xvsrli_d(vs00_add_2w, 32);
__m256 vs00_add_4w = __lasx_xvfadd_s(vs00_add_2w, tmp00_srl);
__m256 vs01_perm = (__m256)__lasx_xvpermi_d(vs01, (2<<6) + (3<<4) + (0<<2) + 1);
__m256 vs01_add_2w = __lasx_xvfadd_s(vs01, vs01_perm);
__m256 tmp01_srl = (__m256)__lasx_xvsrli_d(vs01_add_2w, 32);
__m256 vs01_add_4w = __lasx_xvfadd_s(vs01_add_2w, tmp01_srl);
__m256 vs02_perm = (__m256)__lasx_xvpermi_d(vs02, (2<<6) + (3<<4) + (0<<2) + 1);
__m256 vs02_add_2w = __lasx_xvfadd_s(vs02, vs02_perm);
__m256 tmp02_srl = (__m256)__lasx_xvsrli_d(vs02_add_2w, 32);
__m256 vs02_add_4w = __lasx_xvfadd_s(vs02_add_2w, tmp02_srl);
__m256 vs03_perm = (__m256)__lasx_xvpermi_d(vs03, (2<<6) + (3<<4) + (0<<2) + 1);
__m256 vs03_add_2w = __lasx_xvfadd_s(vs03, vs03_perm);
__m256 tmp03_srl = (__m256)__lasx_xvsrli_d(vs03_add_2w, 32);
__m256 vs03_add_4w = __lasx_xvfadd_s(vs03_add_2w, tmp03_srl);
__m256i vs01_vs00 = __lasx_xvpackev_w((__m256i)vs01_add_4w, (__m256i)vs00_add_4w);
__m256i vs03_vs02 = __lasx_xvpackev_w((__m256i)vs03_add_4w, (__m256i)vs02_add_4w);
__m256 t0 = (__m256)__lasx_xvpackev_d(vs03_vs02, vs01_vs00);
/*t1*/
__m256 vs10_perm = (__m256)__lasx_xvpermi_d(vs10, (2<<6) + (3<<4) + (0<<2) + 1);
__m256 vs10_add_2w = __lasx_xvfadd_s(vs10, vs10_perm);
__m256 tmp10_srl = (__m256)__lasx_xvsrli_d(vs10_add_2w, 32);
__m256 vs10_add_4w = __lasx_xvfadd_s(vs10_add_2w, tmp10_srl);
__m256 vs11_perm = (__m256)__lasx_xvpermi_d(vs11, (2<<6) + (3<<4) + (0<<2) + 1);
__m256 vs11_add_2w = __lasx_xvfadd_s(vs11, vs11_perm);
__m256 tmp11_srl = (__m256)__lasx_xvsrli_d(vs11_add_2w, 32);
__m256 vs11_add_4w = __lasx_xvfadd_s(vs11_add_2w, tmp11_srl);
__m256 vs12_perm = (__m256)__lasx_xvpermi_d(vs12, (2<<6) + (3<<4) + (0<<2) + 1);
__m256 vs12_add_2w = __lasx_xvfadd_s(vs12, vs12_perm);
__m256 tmp12_srl = (__m256)__lasx_xvsrli_d(vs12_add_2w, 32);
__m256 vs12_add_4w = __lasx_xvfadd_s(vs12_add_2w, tmp12_srl);
__m256 vs13_perm = (__m256)__lasx_xvpermi_d(vs13, (2<<6) + (3<<4) + (0<<2) + 1);
__m256 vs13_add_2w = __lasx_xvfadd_s(vs13, vs13_perm);
__m256 tmp13_srl = (__m256)__lasx_xvsrli_d(vs13_add_2w, 32);
__m256 vs13_add_4w = __lasx_xvfadd_s(vs13_add_2w, tmp13_srl);
__m256i vs11_vs10 = __lasx_xvpackev_w((__m256i)vs11_add_4w, (__m256i)vs10_add_4w);
__m256i vs13_vs12 = __lasx_xvpackev_w((__m256i)vs13_add_4w, (__m256i)vs12_add_4w);
__m256 t1 = (__m256)__lasx_xvpackev_d(vs13_vs12, vs11_vs10);
/*t2*/
__m256 vs20_perm = (__m256)__lasx_xvpermi_d(vs20, (2<<6) + (3<<4) + (0<<2) + 1);
__m256 vs20_add_2w = __lasx_xvfadd_s(vs20, vs20_perm);
__m256 tmp20_srl = (__m256)__lasx_xvsrli_d(vs20_add_2w, 32);
__m256 vs20_add_4w = __lasx_xvfadd_s(vs20_add_2w, tmp20_srl);
__m256 vs21_perm = (__m256)__lasx_xvpermi_d(vs21, (2<<6) + (3<<4) + (0<<2) + 1);
__m256 vs21_add_2w = __lasx_xvfadd_s(vs21, vs21_perm);
__m256 tmp21_srl = (__m256)__lasx_xvsrli_d(vs21_add_2w, 32);
__m256 vs21_add_4w = __lasx_xvfadd_s(vs21_add_2w, tmp21_srl);
__m256 vs22_perm = (__m256)__lasx_xvpermi_d(vs22, (2<<6) + (3<<4) + (0<<2) + 1);
__m256 vs22_add_2w = __lasx_xvfadd_s(vs22, vs22_perm);
__m256 tmp22_srl = (__m256)__lasx_xvsrli_d(vs22_add_2w, 32);
__m256 vs22_add_4w = __lasx_xvfadd_s(vs22_add_2w, tmp22_srl);
__m256 vs23_perm = (__m256)__lasx_xvpermi_d(vs23, (2<<6) + (3<<4) + (0<<2) + 1);
__m256 vs23_add_2w = __lasx_xvfadd_s(vs23, vs23_perm);
__m256 tmp23_srl = (__m256)__lasx_xvsrli_d(vs23_add_2w, 32);
__m256 vs23_add_4w = __lasx_xvfadd_s(vs23_add_2w, tmp23_srl);
__m256i vs21_vs20 = __lasx_xvpackev_w((__m256i)vs21_add_4w, (__m256i)vs20_add_4w);
__m256i vs23_vs22 = __lasx_xvpackev_w((__m256i)vs23_add_4w, (__m256i)vs22_add_4w);
__m256 t2 = (__m256)__lasx_xvpackev_d(vs23_vs22, vs21_vs20);
t0 = __lasx_xvfadd_s(t0, (__m256)__lasx_xvpermi_q(t0, t0, 1));
t1 = __lasx_xvfadd_s(t1, (__m256)__lasx_xvpermi_q(t1, t1, 1));
t2 = __lasx_xvfadd_s(t2, (__m256)__lasx_xvpermi_q(t2, t2, 1));
__m128 s0, s1, s2;
if( initOutput )
{
s0 = _v256_extract_low(_v256_setall_ps(bias0));
s1 = _v256_extract_low(_v256_setall_ps(bias1));
s2 = _v256_extract_low(_v256_setall_ps(bias2));
}
else
{
s0 = (__m128)__lsx_vld(outptr0 + j, 0);
s1 = (__m128)__lsx_vld(outptr1 + j, 0);
s2 = (__m128)__lsx_vld(outptr2 + j, 0);
}
s0 = __lsx_vfadd_s(s0, *(__m128*)&t0);
s1 = __lsx_vfadd_s(s1, *(__m128*)&t1);
s2 = __lsx_vfadd_s(s2, *(__m128*)&t2);
if( relu )
{
__m128i m0 = __lsx_vfcmp_clt_s(z, s0);
__m128i m1 = __lsx_vfcmp_clt_s(z, s1);
__m128i m2 = __lsx_vfcmp_clt_s(z, s2);
s0 = (__m128)__lsx_vbitsel_v((__m128i)__lsx_vfmul_s(s0, vr0), (__m128i)s0, m0);
s1 = (__m128)__lsx_vbitsel_v((__m128i)__lsx_vfmul_s(s1, vr1), (__m128i)s1, m1);
s2 = (__m128)__lsx_vbitsel_v((__m128i)__lsx_vfmul_s(s2, vr2), (__m128i)s2, m2);
}
if( tail )
{
s0 = (__m128)__lsx_vbitsel_v(__lsx_vld(outptr0 + j, 0), (__m128i)s0, mask);
s1 = (__m128)__lsx_vbitsel_v(__lsx_vld(outptr1 + j, 0), (__m128i)s1, mask);
s2 = (__m128)__lsx_vbitsel_v(__lsx_vld(outptr2 + j, 0), (__m128i)s2, mask);
}
__lsx_vst(s0, outptr0 + j, 0);
__lsx_vst(s1, outptr1 + j, 0);
__lsx_vst(s2, outptr2 + j, 0);
}
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* rptr0 = 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 w0 = wptr0[k], w1 = wptr1[k], w2 = wptr2[k];
float r = rptr0[k];
s00 += w0*r; s10 += w1*r; s20 += w2*r;
}
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;
}
}
}
static inline void _v256_load_deinterleave(const float* ptr, __m256& a, __m256& b)
{
__m256 t0 = (__m256)__lasx_xvld(ptr, 0);

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