I need to sum all the elements of a matrix in caffe,
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.
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.
Any suggestion?
There is a way to do so using cblas functions, though it is a bit of an awkward way.
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.
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() );
This trick is used in the implementation of "Reduction" layer.
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() );
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 );
Summation of all the elements of an array is simple enough to be implemented by a single for-loop. You only need to use proper compile options to vectorise it with SIMD instructions.
For Blob in caffe, you could use .cpu_data() to get the raw pointer of the array and then use for-loop.
