如何使用cblas函数计算向量中元素的值之和? [英] How to compute the sum of the values of elements in a vector using cblas functions?

问题描述

我需要对caffe中矩阵的所有元素求和，

I need to sum all the elements of a matrix in caffe,

但是我注意到，cblas函数('math_functions.hpp'和'math_functions.cpp')的caffe包装器使用 cblas_sasum 函数作为 caffe_cpu_asum 计算向量中元素的绝对值之和.

But as I noticed, the caffe wrapper of the cblas functions ('math_functions.hpp' & 'math_functions.cpp') is using cblas_sasum function as caffe_cpu_asum that computes the sum of the absolute values of elements in a vector.

由于我是cblas的新手，所以我试图找到一个合适的函数来摆脱 absolute ，但是似乎cblas中没有该属性的函数.

Since I'm a newbie in cblas, I tried to find a suitable function to get rid of absolute there, but it seems that there is no function with that property in cblas.

有什么建议吗?

推荐答案

有一种使用cblas函数的方法，尽管有点尴尬.

There is a way to do so using cblas functions, though it is a bit of an awkward way.

您需要做的是定义一个全1"向量，然后在该向量和矩阵之间做一个点积，结果就是和.

What you need to do is to define an "all 1" vector, and then do a dot product between this vector and your matrix, the result is the sum.

让myBlob成为要汇总其元素的Caffe Blob:

Let myBlob be a caffe Blob whose elements you want to sum:

vector<Dtype> mult_data( myBlob.count(), Dtype(1) );
Dtype sum = caffe_cpu_dot( myBlob.count(), &mult_data[0], myBlob.cpu_data() );

"Reduction"层的实现.

要使此答案均符合GPU，必须为mult_data分配 Blob 而不是std::vector(因为您需要的是pgu_data()):

To make this answer both GPU compliant, one need to allocate a Blob for mult_data and not a std::vector (because you need it's pgu_data()):

vector<int> sum_mult_shape(1, diff_.count());
Blob<Dtype> sum_multiplier_(sum_mult_shape);
const Dtype* mult_data = sum_multiplier_.cpu_data();
Dtype sum = caffe_cpu_dot( myBlob.count(), &mult_data[0], myBlob.cpu_data() );

对于GPU，(在'.cu'源文件中):

For GPU, (in a '.cu' source file):

vector<int> sum_mult_shape(1, diff_.count());
Blob<Dtype> sum_multiplier_(sum_mult_shape);
const Dtype* mult_data = sum_multiplier_.gpu_data();
Dtype sum;
caffe_gpu_dot( myBlob.count(), &mult_data[0], myBlob.gpu_data(), &sum );

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