caffe中各种cblas的函数使用总结
转来的,来自:http://www.cnblogs.com/huashiyiqike/p/3886670.html
总结的很赞,转到这里,留一下笔记。感觉cblas的函数名字很好记的,试着去找过源代码,但是是fortran的,我当时写过的那些fortran程序早忘记了。
Y=alpha * X +beta*Y
Y=alpha * X +beta*Y template <> void caffe_cpu_axpby<float>(const int N, const float alpha, const float* X, const float beta, float* Y) { cblas_saxpby(N, alpha, X, 1, beta, Y, 1); } template <> void caffe_cpu_axpby<double>(const int N, const double alpha, const double* X, const double beta, double* Y) { cblas_daxpby(N, alpha, X, 1, beta, Y, 1); } cblas_dscal(N, beta, Y, incY); Y=Y*beta cblas_daxpy(N, alpha, X, incX, Y, incY); Y= (alpha * X) + Y)
Y=alpha * X + Y
template <> void caffe_axpy<float>(const int N, const float alpha, const float* X, float* Y) { cblas_saxpy(N, alpha, X, 1, Y, 1); } template <> void caffe_axpy<double>(const int N, const double alpha, const double* X, double* Y) { cblas_daxpy(N, alpha, X, 1, Y, 1); }
DEFINE_VSL_BINARY_FUNC(Add, y[i] = a[i] + b[i]); DEFINE_VSL_BINARY_FUNC(Sub, y[i] = a[i] - b[i]); DEFINE_VSL_BINARY_FUNC(Mul, y[i] = a[i] * b[i]); DEFINE_VSL_BINARY_FUNC(Div, y[i] = a[i] / b[i]); template <> void caffe_add<float>(const int n, const float* a, const float* b, float* y) { vsAdd(n, a, b, y); } template <> void caffe_add<double>(const int n, const double* a, const double* b, double* y) { vdAdd(n, a, b, y); }
y=x;
template <> void caffe_copy<float>(const int N, const float* X, float* Y) { cblas_scopy(N, X, 1, Y, 1); } template <> void caffe_copy<double>(const int N, const double* X, double* Y) { cblas_dcopy(N, X, 1, Y, 1); } template <> void caffe_gpu_copy<float>(const int N, const float* X, float* Y) { CUBLAS_CHECK(cublasScopy(Caffe::cublas_handle(), N, X, 1, Y, 1)); } template <> void caffe_gpu_copy<double>(const int N, const double* X, double* Y) { CUBLAS_CHECK(cublasDcopy(Caffe::cublas_handle(), N, X, 1, Y, 1)); }
Computes alpha*x*y' + A.
cblas_sger Multiplies vector X by the transform of vector Y, then adds matrix A (single precison). Multiplies vector X by the transform of vector Y, then adds matrix A (single precison). void cblas_sger ( const enum CBLAS_ORDER Order, const int M, const int N, const float alpha, const float *X, const int incX, const float *Y, const int incY, float *A, const int lda );
Y(vetor)←αAX + βY This function multiplies A * X (after transposing A, if needed) and multiplies the resulting matrix by alpha. It then multiplies vector Y by beta. It stores the sum of these two products in vector Y. template <> void caffe_cpu_gemv<float>(const CBLAS_TRANSPOSE TransA, const int M, const int N, const float alpha, const float* A, const float* x, const float beta, float* y) { cblas_sgemv(CblasRowMajor, TransA, M, N, alpha, A, N, x, 1, beta, y, 1); }
C(matrix)←αAB + βC
template<typename T> void gpu_multmat(T* A, T* B, T* C, int M,int K,int N){ const T alpha = 1,beta=0; caffe_gpu_gemm(CblasNoTrans,CblasNoTrans,M,N,K,alpha,A,B,beta,C); } template<> void caffe_cpu_gemm<float>(const CBLAS_TRANSPOSE TransA, const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K, const float alpha, const float* A, const float* B, const float beta, float* C) { int lda = (TransA == CblasNoTrans) ? K : M; int ldb = (TransB == CblasNoTrans) ? N : K; cblas_sgemm(CblasRowMajor, TransA, TransB, M, N, K, alpha, A, lda, B, ldb, beta, C, N); }
A=M*N B=M*K C=A'*B N M K template<typename T> void cpu_multTmat(T* A, T* B, T* C, int M,int K,int N){ const T alpha = 1,beta=0; caffe_cpu_gemm(CblasTrans,CblasNoTrans,M,N,K,alpha,A,B,beta,C); // cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, M, N, K, alpha, A, M, B, K, beta, C, M); } A=M*N B=N*K C=A*B M N K template<typename T> void cpu_multmat(T* A, T* B, T* C, int M,int K,int N){ const T alpha = 1,beta=0; caffe_cpu_gemm(CblasNoTrans,CblasNoTrans,M,N,K,alpha,A,B,beta,C); // cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, M, N, K, alpha, A, M, B, K, beta, C, M); }
Sophie的世界,转载请注明出处,谢谢。