[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_NEONfor (; 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_NEONfor (; 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 blobconst 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 blobconst 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); // 0const float* ptr1 = bottom_blob1.channel(q); // 1float* 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){}}