Openblas加速二维矩阵卷积操作-增加pad计算

上个博客中（http://blog.youkuaiyun.com/samylee/article/details/73251042）提到了openblas加速二维矩阵的卷积计算，本博客增加了pad计算。

下个博客将介绍openblas在三维矩阵卷积计算中的加速。

代码如下（以下程序经博主测试准确无误）：

 

//cblas加速二维矩阵卷积操作
//注意：stride=1
//作者：samylee

#include <cblas.h>  
#include <iostream>

using namespace std;

//A加pad的计算
void comput_Apad(const int pad_w, const int Map, float *A_pad, const float *A)
{
	for (int i = 0; i < pad_w; i++)
	{
		for (int j = 0; j < pad_w; j++)
		{
			int col = i*pad_w + j;

			if (i == 0 || i == pad_w - 1)
			{
				A_pad[col] = 0;
			}
			else
			{
				if (j == 0 || j == pad_w - 1)
				{
					A_pad[col] = 0;
				}
				else
				{
					A_pad[col] = A[(i - 1)*Map + j - 1];
				}
			}
		}
	}
}

//pad_A的转换，以适用于Openblas
void convertA(float *A_convert, const int outM, const int convAw, const int pad_w, float *A_pad)
{
	for (int i = 0; i < outM; i++)
	{
		for (int j = 0; j < outM; j++)
		{
			int wh = i * outM * convAw + j * convAw;

			int col1 = i * pad_w + j;
			A_convert[wh] = A_pad[col1];
			A_convert[wh + 1] = A_pad[col1 + 1];
			A_convert[wh + 2] = A_pad[col1 + 2];

			int col2 = (i + 1) * pad_w + j;
			A_convert[wh + 3] = A_pad[col2];
			A_convert[wh + 4] = A_pad[col2 + 1];
			A_convert[wh + 5] = A_pad[col2 + 2];

			int col3 = (i + 2) * pad_w + j;
			A_convert[wh + 6] = A_pad[col3];
			A_convert[wh + 7] = A_pad[col3 + 1];
			A_convert[wh + 8] = A_pad[col3 + 2];
		}
	}
}

//Openblas矩阵乘积计算
void Matrixmul_blas(const int convAh, const int convAw, float *A_convert, float *B, float *C)
{
	const enum CBLAS_ORDER Order = CblasRowMajor;
	const enum CBLAS_TRANSPOSE TransA = CblasNoTrans;
	const enum CBLAS_TRANSPOSE TransB = CblasNoTrans;
	const int M = convAh;//A的行数，C的行数
	const int N = 1;//B的列数，C的列数
	const int K = convAw;//A的列数，B的行数
	const float alpha = 1;
	const float beta = 0;
	const int lda = K;//A的列
	const int ldb = N;//B的列
	const int ldc = N;//C的列

	cblas_sgemm(Order, TransA, TransB, M, N, K, alpha, A_convert, lda, B, ldb, beta, C, ldc);
}

int main() 
{
	//卷积参数初始化
	const int pad = 1;
	const int stride = 1;

	//定义被卷积矩阵
	const int Map = 6;
	const float A[Map * Map] = { 
		1,2,3,4,5,6,
		1,2,3,4,5,6,
		1,2,3,4,5,6,
		1,2,3,4,5,6,
		1,2,3,4,5,6,
		1,2,3,4,5,6 };
	
	//定义卷积核
	const int Kernel = 3;
	float B[Kernel * Kernel] = {
		1,1,1,
		1,1,1,
		1,1,1 };
	
	//计算卷积输出矩阵宽高
	const int outM = (Map - Kernel + 2 * pad) / stride + 1;

	//计算pad_A
	const int pad_w = Map + 2 * pad;
	float A_pad[pad_w*pad_w];
	comput_Apad(pad_w, Map, A_pad, A);

	//定义被卷积矩阵宽高
	const int convAw = Kernel*Kernel;
	const int convAh = outM*outM;

	//转换被卷积矩阵
	float A_convert[convAh*convAw];
	convertA(A_convert, outM, convAw, pad_w, A_pad);

	//定义卷积输出矩阵
	float C[convAh*1];
	//cblas计算输出矩阵
	Matrixmul_blas(convAh, convAw, A_convert, B, C);

	//输出验证
	cout << "A_pad is:" << endl;
	for (int i = 0; i < pad_w; i++)
	{
		for (int j = 0; j < pad_w; j++)
		{
			cout << A_pad[i*pad_w + j] << " ";
		}
		cout << endl;
	}
	cout << endl;

	cout << "B is:" << endl;
	for (int i = 0; i < Kernel; i++)
	{
		for (int j = 0; j < Kernel; j++)
		{
			cout << B[i*Kernel + j] << " ";
		}
		cout << endl;
	}
	cout << endl;

	cout << "C is:" << endl;
	for (int i = 0; i < outM; i++)
	{
		for (int j = 0; j < outM; j++)
		{
			cout << C[i*outM + j] << " ";
		}
		cout << endl;
	}
	cout << endl;

	system("pause");
	return EXIT_SUCCESS;
}

 

 

 

 

 

效果如下：

 

 

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