[NCNN学习笔记]-4

1、前言

继续学习NCNN。本次学习binaryop和eltwise。

2、学习内容

2.1、binaryop

binaryop是用来二元计算的op，先来看binaryop.h的中关于二元计算的定义，其中二元计算定义了如下操作。

enum OperationType
{
    Operation_ADD = 0,
    Operation_SUB = 1,
    Operation_MUL = 2,
    Operation_DIV = 3,
    Operation_MAX = 4,
    Operation_MIN = 5,
    Operation_POW = 6,
    Operation_RSUB = 7,
    Operation_RDIV = 8,
    Operation_RPOW = 9,
    Operation_ATAN2 = 10,
    Operation_RATAN2 = 11
};

在binaryop.cpp中，实际调用了二元计算函数。

template<typename Op>
static void binary_op_broadcast(const Mat& a, const Mat& b, Mat& c, const Option& opt){
    ...
}

static void binary_op_broadcast(const Mat& a, const Mat& b, Mat& c, int op_type, const Option& opt)
{
    if (op_type == BinaryOp::Operation_ADD) return binary_op_broadcast<binary_op_add>(a, b, c, opt);
    if (op_type == BinaryOp::Operation_SUB) return binary_op_broadcast<binary_op_sub>(a, b, c, opt);
    if (op_type == BinaryOp::Operation_MUL) return binary_op_broadcast<binary_op_mul>(a, b, c, opt);
    if (op_type == BinaryOp::Operation_DIV) return binary_op_broadcast<binary_op_div>(a, b, c, opt);
    if (op_type == BinaryOp::Operation_MAX) return binary_op_broadcast<binary_op_max>(a, b, c, opt);
    if (op_type == BinaryOp::Operation_MIN) return binary_op_broadcast<binary_op_min>(a, b, c, opt);
    if (op_type == BinaryOp::Operation_POW) return binary_op_broadcast<binary_op_pow>(a, b, c, opt);
    if (op_type == BinaryOp::Operation_RSUB) return binary_op_broadcast<binary_op_sub>(b, a, c, opt);
    if (op_type == BinaryOp::Operation_RDIV) return binary_op_broadcast<binary_op_div>(b, a, c, opt);
    if (op_type == BinaryOp::Operation_RPOW) return binary_op_broadcast<binary_op_pow>(b, a, c, opt);
    if (op_type == BinaryOp::Operation_ATAN2) return binary_op_broadcast<binary_op_atan2>(a, b, c, opt);
    if (op_type == BinaryOp::Operation_RATAN2) return binary_op_broadcast<binary_op_atan2>(b, a, c, opt);
}

可以看出，二元操作是通过函数模板实现的。在binaryop_arm.cpp中实现了具体的二元操作的例子。

template<typename Op>
static void binary_op_vector_no_broadcast(const float* ptr, const float* ptr1, float* outptr, int size)
{
    const Op op;

    int i = 0;
#if __ARM_NEON
    for (; i + 3 < size; i += 4)
    {
        float32x4_t _p = vld1q_f32(ptr);
        float32x4_t _b = vld1q_f32(ptr1);
        float32x4_t _outp = op(_p, _b);    // 通过op来确定最终的操作
        vst1q_f32(outptr, _outp);
        ptr += 4;
        ptr1 += 4;
        outptr += 4;
    }
#endif // __ARM_NEON
    for (; i < size; i++)
    {
        *outptr = op(*ptr, *ptr1);
        ptr += 1;
        ptr1 += 1;
        outptr += 1;
    }
}

2.2、eltwise

elwise 主要用于两个Mat类型的数据逐元素之间的操作。在eltwise.h 中定义了三种三种计算方式

enum OperationType
{
    Operation_PROD = 0,
    Operation_SUM = 1,
    Operation_MAX = 2
};

eltwise_arm.h 继承于 eltwise.h，在eltwise_arm.cpp中实现了三种操作在arm上的实现方式。

int Eltwise_arm::forward(const std::vector<Mat>& bottom_blobs, std::vector<Mat>& top_blobs, const Option& opt) const
{
    // first blob
    const Mat& bottom_blob = bottom_blobs[0];  
    int w = bottom_blob.w;
    int h = bottom_blob.h;
    int d = bottom_blob.d;
    int channels = bottom_blob.c;
    int elempack = bottom_blob.elempack;
    int size = w * h * d * elempack;
    // 逐元素相乘
    if (op_type == Operation_PROD)
    { 
		// second blob
        const Mat& bottom_blob1 = bottom_blobs[1];    // out = bottom_blobs[0] .* bottom_blobs[1];
        for (int q = 0; q < channels; q++){  // 每个channel计算
            const float* ptr = bottom_blob.channel(q); // 0
            const float* ptr1 = bottom_blob1.channel(q); // 1
            float* outptr = top_blob.channel(q);   // 0
            // 计算8的整数倍的数据
            for (; i + 7 < size; i += 8){}
            // 计算4的整数倍的数据
            for (; i + 3 < size; i += 4){}
        }   
        
        // out  = out .* bottom_blobs[i]
        for (size_t b = 2; b < bottom_blobs.size(); b++){
            
        }

    }
    
    // 逐元素相加
    if (op_type == Operation_SUM)
    {
        
    }
    
    // 逐元素计算最大值
    if (op_type == Operation_SUM)
    {
        
    }

}