opencv/sources/modules/dnn/src/layers/cpu_kernels/convolution.hpp

// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.

#ifndef OPENCV_FAST_CONVOLUTION_HPP
#define OPENCV_FAST_CONVOLUTION_HPP

#include "opencv2/core/hal/intrin.hpp"

#ifndef CONV_PRAM
#define CONV_PRAM
#if CV_NEON && CV_NEON_AARCH64  // 32 registers.
#define CONV_MR_FP32 4
#define CONV_NR_FP32 28

// The FP16 can only be supported by ARM64 and with FP16 FMA supported.
#if CV_FP16 && CV_TRY_NEON_FP16 // check FP16 FMA.
#define CONV_ARM_FP16 1
#endif

#ifdef CONV_ARM_FP16
// Currently, only ARM 64 support FP16.
#define CONV_MR_FP16 8
#define CONV_NR_FP16 24
#endif

#elif CV_NEON              // 16 registers.
#define CONV_MR_FP32 4
#define CONV_NR_FP32 12
#else // SIMD 128, AVX or AVX2
#define CONV_MR_FP32 4
#define CONV_NR_FP32 24
#endif

enum {
    CONV_WINO_STEP=6,
    CONV_WINO_KSIZE=3,
    CONV_WINO_SIZE=CONV_WINO_STEP+CONV_WINO_KSIZE - 1, // 8
    CONV_WINO_AREA=CONV_WINO_SIZE*CONV_WINO_SIZE,
};

// NOTE that: CONV_TYPE_DEPTHWISE is for 3x3 depthwise conv, and others depthwise will be set as CONV_TYPE_DEPTHWISE_REMAIN.
enum { CONV_TYPE_GENERIC=0, CONV_TYPE_DEPTHWISE=1, CONV_TYPE_WINOGRAD3X3=2, CONV_TYPE_DEPTHWISE_REMAIN=3 };
enum { CONV_1D = 0, CONV_2D = 1, CONV_3D = 2 };

#endif

namespace cv {
namespace dnn {

struct FastConv
{
    int ngroups;
    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
    std::vector<float> weightsWinoBuf; // For Winograd F(6x6, 3x3).
    std::vector<float> biasBuf;
    float* getWeights();
    float* getWeightsWino();

    std::vector<hfloat> weightsBuf_FP16;
    std::vector<hfloat> weightsWinoBuf_FP16;
    hfloat* getWeightsFP16();
    hfloat* getWeightsWinoFP16();

    int conv_type;
    int conv_dim;  // Flag for conv1d, conv2d, or conv3d.
    bool useFP16 = false; // Only ARMv8 is supported.
#if CV_SIMD128
    bool useSIMD128 = true;
#else
    bool useSIMD128 = 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 FastConv instance.
Ptr<FastConv> initFastConv(
        InputArray weightsMat,
        float* srcBias,
        int ngroups,
        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,
        int conv_dim,
        const bool useFP16,
        bool useWinograd);

// It contains different computing branches, like winograd, 1x1 conv.
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<FastConv>& conv, ActivationLayer* activ,
                  const std::vector<float>& reluslope, bool fusedAdd);

int runWinograd63(InputArray _input, InputArray _fusedAddMat, OutputArray _output, const Ptr<FastConv>& conv, int ntasks,
                  float minval, float maxval, ActivationLayer* activ, bool ifMinMaxAct);

// Work around of NEON, the following functions are only used internally.
namespace opt_NEON {
#if CV_NEON
void convBlock_F32(int np, const float* a, const float* b, float* c, int ldc, bool init_c, int width, const int convMR, const int convNR);

void convBlockMR1_F32(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 width, const int convNR);

#if CV_NEON_AARCH64
/* Accumulate */
void winofunc_accum_F32(const float* inwptr, const float* wptr, float* outbuf, int Cg, int iblock,
                    const int winoIblock, const int winoKblock, const int winoAtom, const int winoNatom);

/*Input transform*/
void winofunc_BtXB_8x8_F32(const float* inptr, int inpstep,
                       float* outptr, int Cg, const int winoIblock, const int winoAtom);

/*Output transform*/
void winofunc_AtXA_8x8_F32(const float* inptr, int inpstep,
                       float* bpptr, int bpstep, float* outptr, int outstep,
                       float bias, float minval, float maxval, bool ifMinMaxAct);
#endif // CV_NEON_AARCH64
#endif // CV_NEON
} // namespace opt_NEON.


} // namespace dnn
} // namespace cv

#endif //OPENCV_FAST_CONVOLUTION_HPP