void scale_rows (
tensor& out,
const tensor& m,
const tensor& v
)
{
DLIB_CASSERT(have_same_dimensions(out,m));
DLIB_CASSERT(is_vector(v));
if (m.size() == 0 && v.size() == 0)
return;
DLIB_CASSERT(m.size() != 0);
DLIB_CASSERT(m.num_samples() == v.size());
#ifdef DLIB_USE_CUDA
cuda::scale_rows(out, m, v);
#else
out = scale_rows(mat(m), mat(v));
#endif
}
void scale_columns (
tensor& out,
const tensor& m,
const tensor& v
)
{
DLIB_CASSERT(have_same_dimensions(out,m));
DLIB_CASSERT(is_vector(v));
if (m.size() == 0 && v.size() == 0)
return;
DLIB_CASSERT(m.size() != 0);
DLIB_CASSERT(m.size()/m.num_samples() == v.size());
#ifdef DLIB_USE_CUDA
cuda::scale_columns(out, m, v);
#else
DLIB_CASSERT(false, "shouldn't be called right now");
out = scale_columns(mat(m), mat(v));
#endif
}
void scale_rows2 (
float beta,
tensor& out,
const tensor& m1,
const tensor& m2,
const tensor& v1,
const tensor& v2
)
{
DLIB_CASSERT(have_same_dimensions(out,m1));
DLIB_CASSERT(have_same_dimensions(out,m2));
DLIB_CASSERT(have_same_dimensions(v1,v2));
DLIB_CASSERT(is_vector(mat(v1)));
DLIB_CASSERT(v1.size() == m1.num_samples());
#ifdef DLIB_USE_CUDA
cuda::scale_rows2(beta, out, m1, m2, v1, v2);
#else
if (beta == 0)
out = scale_rows(mat(m1) - scale_rows(mat(m2),mat(v1)), mat(v2));
else
out = beta*mat(out) + scale_rows(mat(m1) - scale_rows(mat(m2),mat(v1)), mat(v2));
#endif
}
