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 }