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/* Copyright (c) Chris Choy (chrischoy@ai.stanford.edu).
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
* IN THE SOFTWARE.
*
* Please cite "4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural
* Networks", CVPR'19 (https://arxiv.org/abs/1904.08755) if you use any part
* of the code.
*/
#include "math_functions.hpp"
namespace minkowski {
template <>
void cpu_gemm<float>(const CBLAS_ORDER Layout, 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, ldb, ldc;
if (Layout == CblasRowMajor) {
lda = (TransA == CblasNoTrans) ? K : M;
ldb = (TransB == CblasNoTrans) ? N : K;
ldc = N;
} else {
lda = (TransA == CblasNoTrans) ? M : K;
ldb = (TransB == CblasNoTrans) ? K : N;
ldc = M;
}
cblas_sgemm(Layout, TransA, TransB, M, N, K, alpha, A, lda, B, ldb, beta, C,
ldc);
}
template <>
void cpu_gemm<double>(const CBLAS_ORDER Layout, const CBLAS_TRANSPOSE TransA,
const CBLAS_TRANSPOSE TransB, const int M, const int N,
const int K, const double alpha, const double *A,
const double *B, const double beta, double *C) {
int lda, ldb, ldc;
if (Layout == CblasRowMajor) {
lda = (TransA == CblasNoTrans) ? K : M;
ldb = (TransB == CblasNoTrans) ? N : K;
ldc = N;
} else {
lda = (TransA == CblasNoTrans) ? M : K;
ldb = (TransB == CblasNoTrans) ? K : N;
ldc = M;
}
cblas_dgemm(Layout, TransA, TransB, M, N, K, alpha, A, lda, B, ldb, beta, C,
ldc);
}
template <>
void cpu_add<float>(const int n, const float *a, const float *b, float *y) {
vsAdd(n, a, b, y);
}
template <>
void cpu_add<double>(const int n, const double *a, const double *b, double *y) {
vdAdd(n, a, b, y);
}
template <>
void cpu_mul<float>(const int n, const float *a, const float *b, float *y) {
vsMul(n, a, b, y);
}
template <>
void cpu_mul<double>(const int n, const double *a, const double *b, double *y) {
vdMul(n, a, b, y);
}
template <>
void cpu_div<float>(const int n, const float *a, const float *b, float *y) {
vsDiv(n, a, b, y);
}
template <>
void cpu_div<double>(const int n, const double *a, const double *b, double *y) {
vdMul(n, a, b, y);
}
template <>
void cpu_axpy<float>(const int N, const float alpha, const float *X, float *Y) {
cblas_saxpy(N, alpha, X, 1, Y, 1);
}
template <>
void cpu_axpy<double>(const int N, const double alpha, const double *X,
double *Y) {
cblas_daxpy(N, alpha, X, 1, Y, 1);
}
} // end namespace minkowski
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