std::vector<float> reluslope;
Ptr<ActivationLayer> activ;
- Ptr<FastConv2d> fastConv2dImpl;
+ Ptr<FastConv> fastConvImpl;
#ifdef HAVE_OPENCL
Ptr<OCL4DNNConvSpatial<float> > convolutionOp;
}
#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)
{
#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);
}
}
#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
\ No newline at end of file
+}} // namespace cv::dnn
#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
#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)
{
#endif
} // namespace opt_AVX2
+} // namespace dnn
} // namespace cv
\ No newline at end of file
namespace cv { namespace dnn {
enum { VEC_ALIGN = 32, DFT_TYPE = CV_32F }; // Memory alignment.
-Ptr<FastConv2d> initFastConv2d(
+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,
+ int conv_dim,
bool useWinograd)
{
- Ptr<FastConv2d> conv = makePtr<FastConv2d>();
+ Ptr<FastConv> conv = makePtr<FastConv>();
CV_Assert(ngroups > 0 && K > 0 && C > 0 && K % ngroups == 0);
- CV_Assert(Hk > 0 && Wk > 0);
- CV_Assert(stride_y > 0 && stride_x > 0);
- CV_Assert(dilation_y > 0 && dilation_x > 0);
- conv->K = K; conv->C = C; conv->Hk = Hk; conv->Wk = Wk; // [K, iC, kH, kW]
- conv->stride_y = stride_y;
- conv->stride_x = stride_x;
- conv->dilation_y = dilation_y;
- conv->dilation_x = dilation_x;
+ // Weight shape, [K, C, Dk, Hk, Wk] for Conv3D, [K, C, Hk, Wk] for Conv2D, [K, C, Wk] for Conv1D.
+ int Dk = conv_dim == CONV_3D ? (int)kernel_size[0] : 1;
+ int Hk = conv_dim == CONV_1D ? 1 : (int)kernel_size[kernel_size.size() - 2];
+ int Wk = (int)kernel_size.back();
+ int karea = Wk*Hk*Dk;
+
+ conv->pad_front = conv_dim == CONV_3D ? (int)pads_begin[0] : 0;
+ conv->pad_top = conv_dim == CONV_1D ? 0 : (int)pads_begin[pads_begin.size() - 2];
+ conv->pad_left = (int)pads_begin.back();
+
+ conv->pad_behind = conv_dim == CONV_3D ? (int)pads_end[0] : 0;
+ conv->pad_bottom = conv_dim == CONV_1D ? 0 : (int)pads_end[pads_end.size() - 2];
+ conv->pad_right = (int)pads_end.back();
+ int stride_d = conv_dim == CONV_3D ? (int)strides[0] : 0;
+ int stride_h = conv_dim == CONV_1D ? 0 : (int)strides[strides.size() - 2];
+ int stride_w = (int)strides.back();
+
+ int dilation_d = conv_dim == CONV_3D ? (int)dilations[0] : 1;
+ int dilation_h = conv_dim == CONV_1D ? 1 : (int)dilations[dilations.size() - 2];
+ int dilation_w = (int)dilations.back();
+
+ CV_Assert(Dk > 0 && Hk > 0 && Wk > 0);
+ CV_Assert(stride_d >= 0 && stride_h >= 0 && stride_w > 0);
+ CV_Assert(dilation_d > 0 && dilation_h > 0 && dilation_w > 0);
+
+ conv->K = K; conv->C = C; conv->Hk = Hk; conv->Wk = Wk, conv->Dk = Dk;
+
+ conv->stride_d = stride_d;
+ conv->stride_h = stride_h;
+ conv->stride_w = stride_w;
+
+ conv->dilation_d = dilation_d;
+ conv->dilation_h = dilation_h;
+ conv->dilation_w = dilation_w;
+ conv->conv_dim = conv_dim;
conv->ngroups = ngroups;
- conv->pad_top = pads_begin[0];
- conv->pad_bottom = pads_end[0];
- conv->pad_left = pads_begin[1];
- conv->pad_right = pads_end[1];
- conv->conv_type =
- (ngroups > 1 && ngroups == K && ngroups == C) ? _FX_CONV_TYPE_DEPTHWISE :
- useWinograd && ((conv->useSIMD128 || conv->useAVX2 || conv->useNEON) && Hk == 3 && Wk == 3 &&
- dilation_y == 1 && dilation_x == 1 && stride_y == 1 && stride_x == 1) ? _FX_CONV_TYPE_WINOGRAD3X3 :
- _FX_CONV_TYPE_GENERIC;
-
- int VEC_NLANES = 4;
-#if CV_TRY_AVX2
- if (!conv->useAVX2 && conv->conv_type == _FX_CONV_TYPE_WINOGRAD3X3) // convert Winograd to generic conv.
+
+ bool ifRunDepthWise = ngroups > 1 && ngroups == K && ngroups == C;
+ bool ifRunDepthWiseRemain = false; // It's for big padding or big kernel or Conv3D depth-wise convolution.
+
+ if (ifRunDepthWise)
+ {
+ if (conv_dim == CONV_1D)
+ {
+ ifRunDepthWise &= Hk == 1 && Wk == 3 && (stride_w == 1 || (stride_w == 2 && dilation_w == 1))
+ && max(stride_w, dilation_w) >= conv->pad_left && conv->pad_left <= 1;
+ }
+ else if (conv_dim == CONV_2D)
+ {
+ ifRunDepthWise &= Hk == 3 && Wk == 3 && ((stride_w == 1) || (stride_w == 2 && dilation_w == 1)) &&
+ max(stride_w, dilation_w) >= conv->pad_left && max(stride_h, dilation_h) >= conv->pad_top
+ && conv->pad_left <= 1 && conv->pad_top <= 1;
+ }
+
+ if (!ifRunDepthWise || conv_dim == CONV_3D)
+ {
+ ifRunDepthWise = false;
+ ifRunDepthWiseRemain = true;
+ }
+ }
+
+ conv->conv_type = ifRunDepthWise && conv_dim != CONV_3D ? _FX_CONV_TYPE_DEPTHWISE :
+ useWinograd && (conv_dim == CONV_2D && (conv->useSIMD128 || conv->useAVX2 || conv->useNEON) &&
+ Hk == 3 && Wk == 3 && dilation_h == 1 && dilation_w == 1 && stride_h == 1 && stride_w == 1) ?
+ _FX_CONV_TYPE_WINOGRAD3X3 :
+ (ifRunDepthWiseRemain ? _FX_CONV_TYPE_DEPTHWISE_REMAIN : _FX_CONV_TYPE_GENERIC);
+
+#if !(CV_NEON || CV_SIMD128 || CV_TRY_AVX2)
+ if (conv->conv_type == _FX_CONV_TYPE_WINOGRAD3X3) // Disabel Winograd when CV_NEON, CV_SIMD128 and CV_TRY_AVX2 are not available.
conv->conv_type = _FX_CONV_TYPE_GENERIC;
- if (conv->useAVX2)
- VEC_NLANES = 8;
#endif
Mat weightsMat = _weightsMat.getMat();
const size_t wstep = weightsMat.step1();
float *srcWeights = (float *)weightsMat.data;
- if (conv->conv_type == _FX_CONV_TYPE_DEPTHWISE)
+ if (conv->conv_type == _FX_CONV_TYPE_DEPTHWISE || conv->conv_type == _FX_CONV_TYPE_DEPTHWISE_REMAIN)
{
+ // Handle the Conv1D, Conv2D and Conv3D depth-wise.
// for depth-wise convolutions on NCHW data we just preserve the weights in KCHW layout,
// but add some padding to make the weights array layout more SIMD-friendly
- int ksize = Hk*Wk;
+ int ksize = karea;
+ // TODO: simplify the following code with std::copy.
// this code aims to let memory fit with vector size.
- int padded_ksize = ((ksize + VEC_NLANES-1) / VEC_NLANES) * VEC_NLANES;
+ int padded_ksize = ((ksize + VEC_ALIGN-1) / VEC_ALIGN) * VEC_ALIGN;
int nweights = C*padded_ksize;
conv->weightsBuf.reserve(nweights + VEC_ALIGN);
conv->weightsBufPtr = alignPtr(conv->weightsBuf.data(), VEC_ALIGN);
else if (conv->conv_type == _FX_CONV_TYPE_GENERIC)
{
// The weights are packed as
- // ngroups x (ceil((K/ngroups)/CONV_MR)*CONV_MR) x (Cg*Hk*Wk) x CONV_MR tensor
+ // ngroups x (ceil((K/ngroups)/CONV_MR)*CONV_MR) x (Cg*Hk*Wk*Dk) x CONV_MR tensor
int Kg = K/ngroups, Cg = max(C/ngroups, 1);
int numStripsMR = (Kg + CONV_MR - 1) / CONV_MR;
int Kg_aligned = numStripsMR * CONV_MR;
- int HkWkCg = Hk*Wk*Cg;
- size_t nweights = ngroups*Kg_aligned*HkWkCg;
+ int DkHkWkCg = Dk*Hk*Wk*Cg;
+ size_t nweights = ngroups*Kg_aligned*DkHkWkCg;
conv->weightsBuf.reserve(nweights + VEC_ALIGN);
conv->weightsBufPtr = alignPtr(conv->weightsBuf.data(), VEC_ALIGN);
float* weightsBufPtr = conv->weightsBufPtr;
int startK = si * CONV_MR;
CV_Assert(startK < Kg_aligned);
- float* packed_wptr = weightsBufPtr + HkWkCg * (startK + g * Kg_aligned);
+ float* packed_wptr = weightsBufPtr + DkHkWkCg * (startK + g * Kg_aligned);
int dk = Kg - startK < CONV_MR ? Kg - startK : CONV_MR; // check if we need zero padding.
int k_idx = g*Kg + startK;
- for(int yx = 0; yx < Hk*Wk; yx++) {
+ for(int hwd = 0; hwd < Hk*Wk*Dk; hwd++) {
for(int c = 0; c < Cg; c++, packed_wptr += CONV_MR)
{
- const float* wptr = srcWeights + wstep * k_idx + c*Hk*Wk + yx;
+ const float* wptr = srcWeights + wstep * k_idx + c*Hk*Wk*Dk + hwd;
int k = 0;
for(; k < dk; k++, wptr += wstep)
packed_wptr[k] = *wptr;
// store bias; append some zero's to make sure that
// we can always read MR elements starting from any valid index
{
- int k = 0, nbias = K + 32;
+ int k = 0, nbias = K + VEC_ALIGN;
conv->biasBuf.reserve(nbias);
float* biasBufPtr = conv->biasBuf.data();
for(; k < K; k++)
return conv;
}
-void runFastConv2d(InputArray _input, OutputArray _output, const Ptr<FastConv2d>& conv, int ntasks,
- const Ptr<ActivationLayer>& actLayer, bool fusedAdd)
+static inline void packData8(float*& inpbuf, float*& inptrIn, int& in_w, int& x0, int& s0, const int* ofstab,
+ const int stride_w, const int ksize)
+{
+ float* inpbufC = inpbuf + s0;
+ float* inptrInC = inptrIn;
+
+ if (stride_w == 1)
+ for (int k = 0; k < ksize; k++)
+ {
+ int k1 = ofstab[k];
+ float v0 = inptrInC[k1];
+ float v1 = inptrInC[k1 + 1];
+ float v2 = inptrInC[k1 + 2];
+ float v3 = inptrInC[k1 + 3];
+ float v4 = inptrInC[k1 + 4];
+ float v5 = inptrInC[k1 + 5];
+ float v6 = inptrInC[k1 + 6];
+ float v7 = inptrInC[k1 + 7];
+
+ inpbufC[k*CONV_NR] = v0;
+ inpbufC[k*CONV_NR+1] = v1;
+ inpbufC[k*CONV_NR+2] = v2;
+ inpbufC[k*CONV_NR+3] = v3;
+ inpbufC[k*CONV_NR+4] = v4;
+ inpbufC[k*CONV_NR+5] = v5;
+ inpbufC[k*CONV_NR+6] = v6;
+ inpbufC[k*CONV_NR+7] = v7;
+ }
+ else
+ for (int k = 0; k < ksize; k++)
+ {
+ int k1 = ofstab[k];
+ float v0 = inptrInC[k1];
+ float v1 = inptrInC[k1 + stride_w];
+ float v2 = inptrInC[k1 + 2*stride_w];
+ float v3 = inptrInC[k1 + 3*stride_w];
+ float v4 = inptrInC[k1 + 4*stride_w];
+ float v5 = inptrInC[k1 + 5*stride_w];
+ float v6 = inptrInC[k1 + 6*stride_w];
+ float v7 = inptrInC[k1 + 7*stride_w];
+
+ inpbufC[k*CONV_NR] = v0;
+ inpbufC[k*CONV_NR+1] = v1;
+ inpbufC[k*CONV_NR+2] = v2;
+ inpbufC[k*CONV_NR+3] = v3;
+ inpbufC[k*CONV_NR+4] = v4;
+ inpbufC[k*CONV_NR+5] = v5;
+ inpbufC[k*CONV_NR+6] = v6;
+ inpbufC[k*CONV_NR+7] = v7;
+ }
+ x0+=7;
+ s0+=7;
+ inptrIn += 7*stride_w;
+ in_w += 7*stride_w;
+}
+
+static inline void packData2(float*& inpbuf, float*& inptrIn, int& in_w, int& x0, int& s0, const int* ofstab,
+ const int stride_w, const int ksize)
+{
+ float* inpbufC = inpbuf + s0;
+ float* inptrInC = inptrIn;
+
+ for (int k = 0; k < ksize; k++)
+ {
+ int k1 = ofstab[k];
+ float v0 = inptrInC[k1];
+ float v1 = inptrInC[k1 + stride_w];
+ inpbufC[k*CONV_NR] = v0;
+ inpbufC[k*CONV_NR+1] = v1;
+ }
+
+ x0++;
+ s0++;
+ inptrIn += stride_w;
+ in_w += stride_w;
+}
+
+void runFastConv(InputArray _input, OutputArray _output, const Ptr<FastConv>& conv, int ntasks,
+ const Ptr<ActivationLayer>& actLayer, const std::vector<float>& reluslope, bool fusedAdd)
{
Mat input = _input.getMat();
Mat output = _output.getMat();
+ int conv_dim = conv->conv_dim;
+
+ CV_Assert_N(input.dims == output.dims,
+ input.size[0] == output.size[0],
+ conv->C == input.size[1],
+ conv->K == output.size[1],
+ input.type() == output.type(),
+ input.isContinuous(),
+ output.isContinuous());
Mat fusedAddMat;
if (fusedAdd)
+ {
+ CV_Assert(conv->conv_dim != CONV_3D && "Conv3D does not support Conv+Add fusion optimization!");
fusedAddMat = _output.getMat();
+ }
+
+ if (conv->conv_type == _FX_CONV_TYPE_DEPTHWISE)
+ {
+ // Depthwise-Convolution layer should not be followed by Add layer.
+ CV_Assert(fusedAddMat.empty() && (conv_dim == CONV_1D || conv_dim == CONV_2D));
+ return runDepthwise(input, output, conv,actLayer.get(), reluslope);
+ }
MatShape inputShape = shape(input);
MatShape outputShape = shape(output);
- CV_Assert(inputShape.size() == 4 && outputShape.size() == 4);
- ActivationLayer* activ = 0;
+ CV_Assert(inputShape.size() == outputShape.size());
+
+ ActivationLayer* activ = nullptr;
float minval = -FLT_MAX, maxval = FLT_MAX;
bool ifMinMaxAct = false;
+
if (actLayer)
{
Ptr<ReLULayer> activ_relu = actLayer.dynamicCast<ReLULayer>();
else
activ = nullptr;
- if (conv->conv_type == _FX_CONV_TYPE_DEPTHWISE)
+ if (conv->conv_type == _FX_CONV_TYPE_WINOGRAD3X3) // winograd
{
- CV_Assert(fusedAddMat.empty()); // Depthwise-Convolution layer should not be followed by Add layer.
- return runDepthwise(input, output, conv, minval, maxval, activ, ifMinMaxAct);
- }
- else if (conv->conv_type == _FX_CONV_TYPE_WINOGRAD3X3) // winograd
- {
- CV_Assert(conv->weightsWinoBufPtr);
+ CV_Assert(conv->weightsWinoBufPtr && input.dims == 4 && conv_dim == CONV_2D);
if (runWinograd63(input, fusedAddMat, output, conv, ntasks, minval, maxval, activ, ifMinMaxAct))
return;
}
- int N = inputShape[0], C = inputShape[1], Hi = inputShape[2], Wi = inputShape[3]; // [N, C, H, W]
- int K = conv->K, Hk = conv->Hk, Wk = conv->Wk;
- int H0 = outputShape[2], W0 = outputShape[3], ngroups = conv->ngroups;
+ int N = inputShape[0], C = inputShape[1];
+
+ // input shape: [N, C, D, H, W] for Conv3D, [N, C, H, W] for Conv2D, [N, C, W] for Conv1D.
+ int Di = conv_dim == CONV_3D ? inputShape[2] : 1;
+ int Hi = conv_dim == CONV_1D ? 1 : inputShape[inputShape.size() - 2];
+ int Wi = inputShape[inputShape.size() - 1];
+
+ int ngroups = conv->ngroups;
+ int K = conv->K, Dk = conv->Dk, Hk = conv->Hk, Wk = conv->Wk;
+
+ int D0 = conv_dim == CONV_3D ? outputShape[2] : 1;
+ int H0 = conv_dim == CONV_1D ? 1 : outputShape[outputShape.size() - 2];
+ int W0 = outputShape[outputShape.size() - 1];
+
int Cg = C/ngroups, Kg = K/ngroups;
- const size_t inp_planesize = (size_t)Hi*Wi;
- const size_t out_planesize = (size_t)H0*W0;
+ const size_t inp_planesize = (size_t)Di*Hi*Wi;
+ const size_t out_planesize = (size_t)D0*H0*W0;
+ int pad_front = conv->pad_front;
int pad_top = conv->pad_top;
int pad_left = conv->pad_left;
- int stride_y = conv->stride_y, stride_x = conv->stride_x;
- int dilation_y = conv->dilation_y, dilation_x = conv->dilation_x;
+ int stride_d = conv->stride_d, stride_h = conv->stride_h, stride_w = conv->stride_w;
+ int dilation_d = conv->dilation_d, dilation_h = conv->dilation_h, dilation_w = conv->dilation_w;
- int ksize = Hk * Wk;
- bool fast_1x1 = stride_x == 1 && stride_y == 1 && ksize == 1;
- int HkWkCg = Hk*Wk*Cg;
+ int ksize = Dk*Hk*Wk;
+ bool fast_1x1 = stride_d == 1 && stride_w == 1 && stride_h == 1 && ksize == 1;
+ int DkHkWkCg = Dk*Hk*Wk*Cg;
- int MAX_STRIPES = 2; // (56 + CONV_NR - 1)/CONV_NR;
+ std::vector<int> ofstab_(Hk*Wk*Dk*4, 0);
+ int* ofstab = ofstab_.data();
+ int* dhwTab = ofstab + Hk*Wk*Dk;
+ int padded_ksize = ((ksize + VEC_ALIGN-1) / VEC_ALIGN) * VEC_ALIGN;
+
+ if (conv_dim == CONV_1D)
+ {
+ for( int w = 0; w < Wk; w++)
+ {
+ int dw = w*dilation_w;
+ dhwTab[w*3+2] = dw;
+ ofstab[w] = dw;
+ }
+ }
+ else if (conv_dim == CONV_2D)
+ {
+ for (int h = 0; h < Hk; h++)
+ for( int w = 0; w < Wk; w++)
+ {
+ int k = h*Wk + w;
+ int dh = h*dilation_h, dw = w*dilation_w;
+ dhwTab[k*3+1] = dh;
+ dhwTab[k*3+2] = dw;
+ ofstab[k] = dh*Wi + dw;
+ }
+ }
+ else
+ {
+ for (int d = 0; d < Dk; d++)
+ for (int h = 0; h < Hk; h++)
+ {
+ for (int w = 0; w < Wk; w++)
+ {
+ int k = d*Hk*Wk + h*Wk + w;
+ int dd = d*dilation_d, dh = h*dilation_h, dw = w*dilation_w;
+ dhwTab[k*3] = dd;
+ dhwTab[k*3+1] = dh;
+ dhwTab[k*3+2] = dw;
+ ofstab[k] = dd*Hi*Wi + dh*Wi + dw;
+ }
+ }
+ }
+
+ int MAX_STRIPES = (56 + CONV_NR - 1)/CONV_NR;
// Friendly to L1 cache
- const int K_BLOCK_SIZE = 32;
+ const int K_BLOCK_SIZE = conv->conv_type == _FX_CONV_TYPE_DEPTHWISE_REMAIN ? 1 : 32;
const int C_BLOCK_SIZE = 256;
int Kg_nblocks = (Kg + CONV_MR-1)/CONV_MR, Kg_aligned = Kg_nblocks * CONV_MR;
- int stripes_per_sample = (out_planesize + CONV_NR - 1) / CONV_NR;
+ int stripes_per_sample = ((int)out_planesize + CONV_NR - 1) / CONV_NR;
- if (stripes_per_sample < ntasks * 4)
+ if (stripes_per_sample < ntasks * 4 && conv->conv_type != _FX_CONV_TYPE_DEPTHWISE_REMAIN)
{
MAX_STRIPES = 1;
stripes_per_sample = 1;
inpbuf_all_.allocate(totalbufsize + VEC_ALIGN);
float* inpbuf_all = alignPtr(inpbuf_all_.data(), (int)(VEC_ALIGN*sizeof(inpbuf_all_[0])));
- std::vector<int> ofstab_(Hk*Wk*3, 0);
- int* ofstab = ofstab_.data();
- int* yxtab = ofstab + Hk*Wk;
-
- for (int y = 0; y < Hk; y++)
- for( int x = 0; x < Wk; x++)
- {
- int k = y*Wk + x;
- int dy = y*dilation_y, dx = x*dilation_x;
- yxtab[k*2] = dy;
- yxtab[k*2+1] = dx;
- ofstab[k] = dy*Wi + dx;
- }
-
float* inp = input.ptr<float>();
float* out = output.ptr<float>();
float* fusedAddPtr0 = fusedAddMat.empty() ? 0 : fusedAddMat.ptr<float>();
for (int subtask = ngs0; subtask < ngs1; )
{
int ng = subtask / Kstripes;
- int kyx0 = subtask - ng * Kstripes;
- int kyx1 = kyx0 + (ngs1 - subtask);
+ int kzyx0 = subtask - ng * Kstripes;
+ int kzyx1 = kzyx0 + (ngs1 - subtask);
int n = ng / ngroups, g = ng % ngroups; // ng - n * ngroups;
size_t inp_plane_ofs = (size_t)(n * ngroups + g) * Cg * inp_planesize;
- kyx1 = kyx1 <= Kstripes ? kyx1 : Kstripes;
- subtask += kyx1 - kyx0;
+ kzyx1 = kzyx1 <= Kstripes ? kzyx1 : Kstripes;
+ subtask += kzyx1 - kzyx0;
int k0, k1;
- int yx0, yx_limit, yx_block_limit = 0;
+ int zyx0, zyx_limit, zyx_block_limit = 0;
- if (stripes_per_sample == 1)
+ if (stripes_per_sample == 1 && conv->conv_type != _FX_CONV_TYPE_DEPTHWISE_REMAIN)
{
- k0 = kyx0 * CONV_MR;
- k1 = kyx1 * CONV_MR;
+ k0 = kzyx0 * CONV_MR;
+ k1 = kzyx1 * CONV_MR;
k1 = k1 <= Kg ? k1 : Kg;
- yx0 = 0;
- yx_limit = out_planesize;
+ zyx0 = 0;
+ zyx_limit = (int)out_planesize;
}
else
{
k0 = 0;
k1 = Kg;
- yx0 = kyx0 * CONV_NR;
- yx_limit = kyx1 * CONV_NR;
- yx_limit = yx_limit < out_planesize ? yx_limit : out_planesize;
+ zyx0 = kzyx0 * CONV_NR;
+ zyx_limit = kzyx1 * CONV_NR;
+ zyx_limit = zyx_limit < out_planesize ? zyx_limit : (int)out_planesize;
}
- for (; yx0 < yx_limit; yx0 = yx_block_limit)
+ for (; zyx0 < zyx_limit; zyx0 = zyx_block_limit)
{
// step 1. extract part of input tensor and represent it in zigzag form
- yx_block_limit = yx0 + CONV_NR * MAX_STRIPES;
- yx_block_limit = yx_block_limit < yx_limit ? yx_block_limit : yx_limit;
+ zyx_block_limit = zyx0 + CONV_NR * MAX_STRIPES;
+ zyx_block_limit = zyx_block_limit < zyx_limit ? zyx_block_limit : zyx_limit;
- int nstripes = (yx_block_limit - yx0 + CONV_NR - 1) / CONV_NR;
- int yx0_saved = yx0;
+ int nstripes = (zyx_block_limit - zyx0 + CONV_NR - 1) / CONV_NR;
+ int zyx0_saved = zyx0;
CV_Assert(nstripes <= MAX_STRIPES);
- for (int stripe = 0; yx0 < yx_block_limit; stripe++, yx0 += CONV_NR)
+ for (int stripe = 0; zyx0 < zyx_block_limit; stripe++, zyx0 += CONV_NR)
{
- float* inpbuf = inpbuf_task + stripe * stripesize;
- float* inptr = inp + inp_plane_ofs;
+ float *inpbuf = inpbuf_task + stripe * stripesize;
+ float *inptr = inp + inp_plane_ofs;
/*
1. pack the data. Copy the HkxWk CONV_NR-wide slices from
*/
if (fast_1x1)
{
- int slice_len = yx_block_limit - yx0;
+ int slice_len = zyx_block_limit - zyx0;
bool partial = slice_len < CONV_NR;
// Superfast branch for 1x1 convolutions with sy=sx=1.
// in this case each feature plane can be safely treated
// as 1D array, and we just extract next portion
// of CONV_NR elements from each feature plane and
// put it together.
- inptr += yx0;
+ inptr += zyx0;
if (!partial)
{
// Make special branch where memcpy() is called with a constant buffer size.
// Compilers will likely unroll this loop properly.
for (int c = 0; c < Cg; c++, inptr += inp_planesize, inpbuf += CONV_NR)
- memcpy(inpbuf, inptr, CONV_NR*sizeof(inpbuf[0]));
+ memcpy(inpbuf, inptr, CONV_NR * sizeof(inpbuf[0]));
}
else
{
}
}
}
+ else if (conv->conv_type == _FX_CONV_TYPE_DEPTHWISE_REMAIN)
+ {
+ CV_Assert(Cg == 1);
+ const int HW0 = H0 * W0;
+ const int HWi = Hi * Wi;
+ int slice_len = std::min(zyx_block_limit - zyx0, CONV_NR);
+
+ // 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(inpbuf, 0, stripesize*sizeof(inpbuf[0]));
+
+ int z0 = zyx0 / HW0, yx0 = zyx0 - z0 * HW0;
+ int y0 = yx0 / W0, x0 = yx0 - y0 * W0;
+
+ if (conv_dim == CONV_1D)
+ {
+ for (int slice_i = 0; slice_i < slice_len; y0++, x0=0)
+ {
+ int delta = std::min(slice_len - slice_i, W0 - x0);
+ int x1 = x0 + delta;
+
+ int in_w = x0 * stride_w - pad_left;
+ float* inptrIn = inptr + in_w;
+
+ int s0 = slice_i;
+
+ for (; x0 < x1; x0++, s0++, inptrIn += stride_w, in_w += stride_w)
+ {
+ // Pack 8
+ if (x0 + 8 <= x1 && 0 <= in_w &&
+ in_w + stride_w*8 <= Wi - (Wk-1)*dilation_w)
+ {
+ packData8(inpbuf, inptrIn, in_w, x0, s0, ofstab, stride_w, ksize);
+ }
+ else if (x0 + 2 <= x1 && 0 <= in_w &&
+ in_w + stride_w*2 <= Wi - (Wk-1)*dilation_w)
+ {
+ packData2(inpbuf, inptrIn, in_w, x0, s0, ofstab, stride_w, ksize);
+ }
+ else
+ {
+ int w0 = std::max(0, (-in_w + dilation_w-1)/dilation_w);
+ int w1 = std::min(Wk, (Wi - in_w + dilation_w-1)/dilation_w);
+
+ float* inpbufC = inpbuf + s0;
+ float* inptrInC = inptrIn;
+ for (int w = w0; w < w1; w++)
+ {
+ int imgofs = w*dilation_w;
+ inpbufC[w*CONV_NR] = inptrInC[imgofs];
+ }
+ }
+ }
+ slice_i += delta;
+ }
+ }
+ else if (conv_dim == CONV_2D)
+ {
+ for (int slice_i = 0; slice_i < slice_len; y0++, x0=0)
+ {
+ int delta = std::min(slice_len - slice_i, W0 - x0);
+ int x1 = x0 + delta;
+
+ int in_h = y0 * stride_h - pad_top;
+ int in_w = x0 * stride_w - pad_left;
+
+ float* inptrIn = inptr + in_h*Wi + in_w;
+
+ bool ok_i = 0 <= in_h && in_h < Hi - (Hk-1)*dilation_h;
+ int h0 = std::max(0, (-in_h + dilation_h-1)/dilation_h);
+ int h1 = std::min(Hk, (Hi - in_h + dilation_h-1)/dilation_h);
+
+ int s0 = slice_i;
+ for (; x0 < x1; x0++, s0++, inptrIn += stride_w, in_w += stride_w)
+ {
+ // Pack 8
+ if (ok_i && x0 + 8 <= x1 && 0 <= in_w &&
+ in_w + stride_w*8 <= Wi - (Wk-1)*dilation_w)
+ {
+ packData8(inpbuf, inptrIn, in_w, x0, s0, ofstab, stride_w, ksize);
+ }
+ else if (ok_i && x0 + 2 <= x1 && 0 <= in_w &&
+ in_w + stride_w*2 <= Wi - (Wk-1)*dilation_w)
+ {
+ packData2(inpbuf, inptrIn, in_w, x0, s0, ofstab, stride_w, ksize);
+ }
+ else
+ {
+ int w0 = std::max(0, (-in_w + dilation_w-1)/dilation_w);
+ int w1 = std::min(Wk, (Wi - in_w + dilation_w-1)/dilation_w);
+
+ float* inpbufC = inpbuf + s0;
+ float* inptrInC = inptrIn;
+
+ for (int h = h0; h < h1; h++)
+ {
+ for (int w = w0; w < w1; w++)
+ {
+ int imgofs = h*(dilation_h*Wi) + w*dilation_w;
+ inpbufC[(h*Wk + w)*CONV_NR] = inptrInC[imgofs];
+ }
+ }
+ }
+ }
+ slice_i += delta;
+ }
+ }
+ else if (conv_dim == CONV_3D)
+ {
+ for (int slice_i = 0; slice_i < slice_len; z0 += (y0+1)/H0, y0 = (y0+1)%H0, x0=0)
+ {
+ int delta = std::min(slice_len - slice_i, W0 - x0);
+ int x1 = x0 + delta;
+
+ int in_d = z0 * stride_d - pad_front;
+ int in_h = y0 * stride_h - pad_top;
+ int in_w = x0 * stride_w - pad_left;
+
+ float* inptrIn = inptr + in_d*HWi + in_h*Wi + in_w;
+
+ int d0 = std::max(0, (-in_d + dilation_d - 1) / dilation_d);
+ int d1 = std::min(Dk, (Di - in_d + dilation_d - 1) / dilation_d);
+
+ bool ok_i = 0 <= in_h && in_h < Hi - (Hk-1)*dilation_h;
+ int h0 = std::max(0, (-in_h + dilation_h-1)/dilation_h);
+ int h1 = std::min(Hk, (Hi - in_h + dilation_h-1)/dilation_h);
+
+ int s0 = slice_i;
+ for (; x0 < x1; x0++, s0++, inptrIn += stride_w, in_w += stride_w)
+ {
+ // Pack 8
+ if (ok_i && x0 + 8 <= x1 && 0 <= in_w &&
+ in_w + stride_w*8 <= Wi - (Wk-1)*dilation_w)
+ {
+ packData8(inpbuf, inptrIn, in_w, x0, s0, ofstab, stride_w, ksize);
+ }
+ else if (ok_i && x0 + 2 <= x1 && 0 <= in_w &&
+ in_w + stride_w*2 <= Wi - (Wk-1)*dilation_w)
+ {
+ packData2(inpbuf, inptrIn, in_w, x0, s0, ofstab, stride_w, ksize);
+ }
+ else
+ {
+ int w0 = std::max(0, (-in_w + dilation_w-1)/dilation_w);
+ int w1 = std::min(Wk, (Wi - in_w + dilation_w-1)/dilation_w);
+
+ float* inpbufC = inpbuf + s0;
+ float* inptrInC = inptrIn;
+
+ for ( int d = d0; d < d1; d++)
+ {
+ for (int h = h0; h < h1; h++)
+ {
+ for (int w = w0; w < w1; w++)
+ {
+ int imgofs = d*dilation_d*HWi + h*(dilation_h*Wi) + w*dilation_w;
+ inpbufC[((d*Hk + h)*Wk + w)*CONV_NR] = inptrInC[imgofs];
+ }
+ }
+ }
+ }
+ }
+ slice_i += delta;
+ }
+ }
+ }
else
{
+ const int HW0 = H0 * W0;
+ const int HWi = Hi * Wi;
+ int z0_ = zyx0 / HW0, yx0 = zyx0 - z0_ * HW0;
int y0_ = yx0 / W0, x0_ = yx0 - y0_ * W0;
for (int k = 0; k < ksize; k++)
{
- int dy = yxtab[k * 2], dx = yxtab[k * 2 + 1];
- int i = 0, y0 = y0_, x0 = x0_;
+ int dz = dhwTab[k * 3], dy = dhwTab[k * 3 + 1], dx = dhwTab[k * 3 + 2];
+ int i = 0, z0 = z0_, y0 = y0_, x0 = x0_;
for (; i < CONV_NR;)
{
float *inpbuf_ki = inpbuf + k * CONV_NR * Cg + i;
- int yi = y0 * stride_y + dy - pad_top;
- int xi = x0 * stride_x + dx - pad_left;
+ int zi = z0 * stride_d + dz - pad_front;
+ int yi = y0 * stride_h + dy - pad_top;
+ int xi = x0 * stride_w + dx - pad_left;
- if ((unsigned) yi < (unsigned) Hi && (unsigned) xi < (unsigned) Wi)
+ if ((unsigned) zi < (unsigned) Di && (unsigned) yi < (unsigned) Hi &&
+ (unsigned) xi < (unsigned) Wi)
{
- const float *inptr_ki = inptr + yi * Wi + xi;
- if (i + 8 <= CONV_NR && x0 + 8 <= W0 && xi + stride_x * 8 <= Wi)
+ const float *inptr_ki = inptr + zi * HWi + yi * Wi + xi;
+ if (i + 8 <= CONV_NR && x0 + 8 <= W0 && xi + stride_w * 8 <= Wi)
{
- if (stride_x == 1)
+ if (stride_w == 1)
{
for (int c = 0; c < Cg; c++, inpbuf_ki += CONV_NR, inptr_ki += inp_planesize)
{
float t2 = inptr_ki[2], t3 = inptr_ki[3];
float t4 = inptr_ki[4], t5 = inptr_ki[5];
float t6 = inptr_ki[6], t7 = inptr_ki[7];
- inpbuf_ki[0] = t0; inpbuf_ki[1] = t1;
- inpbuf_ki[2] = t2; inpbuf_ki[3] = t3;
- inpbuf_ki[4] = t4; inpbuf_ki[5] = t5;
- inpbuf_ki[6] = t6; inpbuf_ki[7] = t7;
+ inpbuf_ki[0] = t0;
+ inpbuf_ki[1] = t1;
+ inpbuf_ki[2] = t2;
+ inpbuf_ki[3] = t3;
+ inpbuf_ki[4] = t4;
+ inpbuf_ki[5] = t5;
+ inpbuf_ki[6] = t6;
+ inpbuf_ki[7] = t7;
+ }
+ }
+ else if (stride_w == 2)
+ {
+ for (int c = 0; c < Cg; c++, inpbuf_ki += CONV_NR, inptr_ki += inp_planesize)
+ {
+ float t0 = inptr_ki[0], t1 = inptr_ki[2];
+ float t2 = inptr_ki[4], t3 = inptr_ki[6];
+ float t4 = inptr_ki[8], t5 = inptr_ki[10];
+ float t6 = inptr_ki[12], t7 = inptr_ki[14];
+ inpbuf_ki[0] = t0;
+ inpbuf_ki[1] = t1;
+ inpbuf_ki[2] = t2;
+ inpbuf_ki[3] = t3;
+ inpbuf_ki[4] = t4;
+ inpbuf_ki[5] = t5;
+ inpbuf_ki[6] = t6;
+ inpbuf_ki[7] = t7;
}
}
else
{
for (int c = 0; c < Cg; c++, inpbuf_ki += CONV_NR, inptr_ki += inp_planesize)
{
- float t0 = inptr_ki[0], t1 = inptr_ki[stride_x];
- float t2 = inptr_ki[stride_x*2], t3 = inptr_ki[stride_x*3];
- float t4 = inptr_ki[stride_x*4], t5 = inptr_ki[stride_x*5];
- float t6 = inptr_ki[stride_x*6], t7 = inptr_ki[stride_x*7];
- inpbuf_ki[0] = t0; inpbuf_ki[1] = t1;
- inpbuf_ki[2] = t2; inpbuf_ki[3] = t3;
- inpbuf_ki[4] = t4; inpbuf_ki[5] = t5;
- inpbuf_ki[6] = t6; inpbuf_ki[7] = t7;
+ float t0 = inptr_ki[0], t1 = inptr_ki[stride_w];
+ float t2 = inptr_ki[stride_w * 2], t3 = inptr_ki[stride_w * 3];
+ float t4 = inptr_ki[stride_w * 4], t5 = inptr_ki[stride_w * 5];
+ float t6 = inptr_ki[stride_w * 6], t7 = inptr_ki[stride_w * 7];
+ inpbuf_ki[0] = t0;
+ inpbuf_ki[1] = t1;
+ inpbuf_ki[2] = t2;
+ inpbuf_ki[3] = t3;
+ inpbuf_ki[4] = t4;
+ inpbuf_ki[5] = t5;
+ inpbuf_ki[6] = t6;
+ inpbuf_ki[7] = t7;
}
}
i += 8;
x0 += 8;
}
- else if (i + 4 <= CONV_NR && x0 + 4 <= W0 && xi + stride_x * 4 <= Wi)
+ else if (i + 4 <= CONV_NR && x0 + 4 <= W0 && xi + stride_w * 4 <= Wi)
{
- if (stride_x == 1)
+ if (stride_w == 1)
{
for (int c = 0; c < Cg; c++, inpbuf_ki += CONV_NR, inptr_ki += inp_planesize)
{
float t0 = inptr_ki[0], t1 = inptr_ki[1];
float t2 = inptr_ki[2], t3 = inptr_ki[3];
- inpbuf_ki[0] = t0; inpbuf_ki[1] = t1;
- inpbuf_ki[2] = t2; inpbuf_ki[3] = t3;
+ inpbuf_ki[0] = t0;
+ inpbuf_ki[1] = t1;
+ inpbuf_ki[2] = t2;
+ inpbuf_ki[3] = t3;
}
}
else
{
for (int c = 0; c < Cg; c++, inpbuf_ki += CONV_NR, inptr_ki += inp_planesize)
{
- float t0 = inptr_ki[0], t1 = inptr_ki[stride_x];
- float t2 = inptr_ki[stride_x*2], t3 = inptr_ki[stride_x*3];
- inpbuf_ki[0] = t0; inpbuf_ki[1] = t1;
- inpbuf_ki[2] = t2; inpbuf_ki[3] = t3;
+ float t0 = inptr_ki[0], t1 = inptr_ki[stride_w];
+ float t2 = inptr_ki[stride_w * 2], t3 = inptr_ki[stride_w * 3];
+ inpbuf_ki[0] = t0;
+ inpbuf_ki[1] = t1;
+ inpbuf_ki[2] = t2;
+ inpbuf_ki[3] = t3;
}
}
i += 4;
i++;
x0++;
}
+
int mask = x0 >= W0;
y0 += mask;
x0 &= mask - 1;
+
+ mask = y0 >= H0;
+ z0 += mask;
+ y0 &= mask - 1;
}
}
}
}
- yx0 = yx0_saved;
- float* weights = conv->weightsBufPtr + g * Kg_aligned * HkWkCg;
- const float* biasptr = conv->biasBuf.data() + Kg * g;
+ zyx0 = zyx0_saved;
+
+ // spacial branch for depth-wise convolution implemented using generic convolution.
+ // In this case, CONV_MR is 1, and CONV_NR is the same.
+ if (conv->conv_type == _FX_CONV_TYPE_DEPTHWISE_REMAIN)
+ {
+ size_t outofs = (n * ngroups + g) * out_planesize + zyx0;
+ float *cptr0 = cbuf_task;
+ float *weights = conv->weightsBufPtr + g * padded_ksize;
+ int out_width = zyx_block_limit - zyx0;
+ float *outptr = out + outofs;
+ const float biasVal = *(conv->biasBuf.data() + g);
+ for (int stripe = 0; stripe < nstripes; stripe++)
+ {
+ const float *inptr = inpbuf_task + stripe * stripesize;
+ const int outLen = std::min(out_width - stripe * CONV_NR, CONV_NR);
+ bool ifBuffer = outLen < CONV_NR;
+ float *cptr = outptr + stripe * CONV_NR;
+ if (ifBuffer)
+ {
+ memcpy(cptr0, cptr, outLen * sizeof(cptr[0]));
+ cptr = cptr0;
+ }
+#if CV_TRY_AVX2
+ if (conv->useAVX2 && outLen > CONV_NR/3)
+ opt_AVX2::convBlockMR1(DkHkWkCg, weights, inptr, cptr, biasVal, fusedAdd, minval, maxval, ifMinMaxAct);
+ else
+#endif
+ convBlockMR1(DkHkWkCg, weights, inptr, cptr, biasVal, fusedAdd, minval, maxval, ifMinMaxAct, outLen);
+
+ if (ifBuffer)
+ {
+ memcpy(outptr + stripe * CONV_NR, cptr, outLen * sizeof(cptr[0]));
+ }
+ }
+ if (activ)
+ activ->forwardSlice(outptr, outptr, out_width, out_planesize, g, g + 1);
+ continue;
+ }
+
+ float *weights = conv->weightsBufPtr + g * Kg_aligned * DkHkWkCg;
+ const float *biasptr = conv->biasBuf.data() + Kg * g;
int ldc = nstripes * CONV_NR;
- // 2. do convolution, compute Kg x (yx_block_limit - yx0) part of the output tensor
+ // 2. do convolution, compute Kg x (zyx_block_limit - zyx0) part of the output tensor
+ int out_width = zyx_block_limit - zyx0;
for (int k0_block = k0; k0_block < k1; k0_block += K_BLOCK_SIZE)
{
int k1_block = k0_block + K_BLOCK_SIZE < k1 ? k0_block + K_BLOCK_SIZE : k1;
- for (int c0 = 0; c0 < HkWkCg; c0 += C_BLOCK_SIZE)
+ for (int c0 = 0; c0 < DkHkWkCg; c0 += C_BLOCK_SIZE)
{
- int c1 = c0 + C_BLOCK_SIZE < HkWkCg ? c0 + C_BLOCK_SIZE : HkWkCg;
+ int c1 = c0 + C_BLOCK_SIZE < DkHkWkCg ? c0 + C_BLOCK_SIZE : DkHkWkCg;
for (int stripe = 0; stripe < nstripes; stripe++)
{
- float* wptr = weights + k0_block*HkWkCg + c0*CONV_MR;
- const float* inptr = inpbuf_task + stripe*stripesize + c0 * CONV_NR;
- float* cptr = cbuf_task + stripe * CONV_NR;
+ const int outLen = std::min(out_width - stripe * CONV_NR, CONV_NR);
+
+#if CV_TRY_AVX2 || CV_TRY_NEON
+ // The possible CONV_NR is 28, 24, 12, so the possible CONV_NR/3 is 9, 8, 4.
+ bool runOpt = outLen > std::min(8, CONV_NR/3);
+#endif
+ float *wptr = weights + k0_block * DkHkWkCg + c0 * CONV_MR;
+ const float *inptr = inpbuf_task + stripe * stripesize + c0 * CONV_NR;
+ float *cptr = cbuf_task + stripe * CONV_NR;
for (int k = k0_block; k < k1_block; k += CONV_MR,
- wptr += HkWkCg * CONV_MR, cptr += CONV_MR * ldc)
+ wptr += DkHkWkCg * CONV_MR, cptr += CONV_MR * ldc)
{
#if CV_TRY_AVX2
- if (conv->useAVX2)
+ if (conv->useAVX2 && runOpt)
opt_AVX2::convBlock_AVX2(c1 - c0, wptr, inptr, cptr, ldc, c0 == 0);
else
#endif
#if CV_TRY_NEON
- if (conv->useNEON)
+ if (conv->useNEON && runOpt)
opt_NEON::convBlock_NEON(c1 - c0, wptr, inptr, cptr, ldc, c0 == 0);
else
#endif
- convBlock(c1 - c0, wptr, inptr, cptr, ldc, c0 == 0);
+ // The possible outLen range is 24 or 8~1.
+ convBlock(c1 - c0, wptr, inptr, cptr, ldc, c0 == 0, outLen);
}
}
}
- size_t outofs = ((n*ngroups + g) * Kg + k0_block) * out_planesize + yx0;
- int out_width = yx_block_limit - yx0;
- const float* cptr = cbuf_task;
+ size_t outofs = ((n * ngroups + g) * Kg + k0_block) * out_planesize + zyx0;
+ const float *cptr = cbuf_task;
- float* outptr = out + outofs;
- const float* pbptr = fusedAddPtr0 ? fusedAddPtr0 + outofs : 0;
+ float *outptr = out + outofs;
+ const float *pbptr = fusedAddPtr0 ? fusedAddPtr0 + outofs : 0;
for (int k = k0_block; k < k1_block; k++,
cptr += ldc, outptr += out_planesize,
- pbptr += (pbptr ? out_planesize : 0))
- {
+ pbptr += (pbptr ? out_planesize : 0)) {
float biasval = biasptr[k];
int j = 0;
#if CV_SIMD128
- v_float32x4 vbias = v_setall_f32(biasval), vmax = v_setall_f32(maxval), vmin = v_setall_f32(minval);
+ v_float32x4 vbias = v_setall_f32(biasval);
+ v_float32x4 vmax = v_setall_f32(maxval);
+ v_float32x4 vmin = v_setall_f32(minval);
+
if (pbptr)
{
for (; j + 7 < out_width; j += 8)
}
#endif
if (pbptr) {
- for (; j < out_width; j++)
- {
+ for (; j < out_width; j++) {
float v = cptr[j] + biasval;
v += pbptr[j];
if (ifMinMaxAct)
v = std::min(std::max(v, minval), maxval);
outptr[j] = v;
}
- }
- else
- {
- for (; j < out_width; j++)
- {
+ } else {
+ for (; j < out_width; j++) {
float v = cptr[j] + biasval;
if (ifMinMaxAct)
_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;
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);
#endif
} // namespace opt_AVX2
+} // namespace dnn
} // namespace cv
#endif //OPENCV_FAST_CONVOLUTION_HPP
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;
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
#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();
#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;
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,
#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);
}
}
-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}
#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);
projects / platform / upstream / opencv.git / commitdiff