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 );