// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> // // This Source Code Form is subject to the terms of the Mozilla // Public License v. 2.0. If a copy of the MPL was not distributed // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. #include "main.h" #include <Eigen/CXX11/Tensor> using Eigen::Tensor; template <int DataLayout> static void test_simple_broadcasting() { Tensor<float, 4, DataLayout> tensor(2,3,5,7); tensor.setRandom(); array<ptrdiff_t, 4> broadcasts; broadcasts[0] = 1; broadcasts[1] = 1; broadcasts[2] = 1; broadcasts[3] = 1; Tensor<float, 4, DataLayout> no_broadcast; no_broadcast = tensor.broadcast(broadcasts); VERIFY_IS_EQUAL(no_broadcast.dimension(0), 2); VERIFY_IS_EQUAL(no_broadcast.dimension(1), 3); VERIFY_IS_EQUAL(no_broadcast.dimension(2), 5); VERIFY_IS_EQUAL(no_broadcast.dimension(3), 7); for (int i = 0; i < 2; ++i) { for (int j = 0; j < 3; ++j) { for (int k = 0; k < 5; ++k) { for (int l = 0; l < 7; ++l) { VERIFY_IS_EQUAL(tensor(i,j,k,l), no_broadcast(i,j,k,l)); } } } } broadcasts[0] = 2; broadcasts[1] = 3; broadcasts[2] = 1; broadcasts[3] = 4; Tensor<float, 4, DataLayout> broadcast; broadcast = tensor.broadcast(broadcasts); VERIFY_IS_EQUAL(broadcast.dimension(0), 4); VERIFY_IS_EQUAL(broadcast.dimension(1), 9); VERIFY_IS_EQUAL(broadcast.dimension(2), 5); VERIFY_IS_EQUAL(broadcast.dimension(3), 28); for (int i = 0; i < 4; ++i) { for (int j = 0; j < 9; ++j) { for (int k = 0; k < 5; ++k) { for (int l = 0; l < 28; ++l) { VERIFY_IS_EQUAL(tensor(i%2,j%3,k%5,l%7), broadcast(i,j,k,l)); } } } } } template <int DataLayout> static void test_vectorized_broadcasting() { Tensor<float, 3, DataLayout> tensor(8,3,5); tensor.setRandom(); array<ptrdiff_t, 3> broadcasts; broadcasts[0] = 2; broadcasts[1] = 3; broadcasts[2] = 4; Tensor<float, 3, DataLayout> broadcast; broadcast = tensor.broadcast(broadcasts); VERIFY_IS_EQUAL(broadcast.dimension(0), 16); VERIFY_IS_EQUAL(broadcast.dimension(1), 9); VERIFY_IS_EQUAL(broadcast.dimension(2), 20); for (int i = 0; i < 16; ++i) { for (int j = 0; j < 9; ++j) { for (int k = 0; k < 20; ++k) { VERIFY_IS_EQUAL(tensor(i%8,j%3,k%5), broadcast(i,j,k)); } } } tensor.resize(11,3,5); tensor.setRandom(); broadcast = tensor.broadcast(broadcasts); VERIFY_IS_EQUAL(broadcast.dimension(0), 22); VERIFY_IS_EQUAL(broadcast.dimension(1), 9); VERIFY_IS_EQUAL(broadcast.dimension(2), 20); for (int i = 0; i < 22; ++i) { for (int j = 0; j < 9; ++j) { for (int k = 0; k < 20; ++k) { VERIFY_IS_EQUAL(tensor(i%11,j%3,k%5), broadcast(i,j,k)); } } } } template <int DataLayout> static void test_static_broadcasting() { Tensor<float, 3, DataLayout> tensor(8,3,5); tensor.setRandom(); #if EIGEN_HAS_CONSTEXPR Eigen::IndexList<Eigen::type2index<2>, Eigen::type2index<3>, Eigen::type2index<4>> broadcasts; #else Eigen::array<int, 3> broadcasts; broadcasts[0] = 2; broadcasts[1] = 3; broadcasts[2] = 4; #endif Tensor<float, 3, DataLayout> broadcast; broadcast = tensor.broadcast(broadcasts); VERIFY_IS_EQUAL(broadcast.dimension(0), 16); VERIFY_IS_EQUAL(broadcast.dimension(1), 9); VERIFY_IS_EQUAL(broadcast.dimension(2), 20); for (int i = 0; i < 16; ++i) { for (int j = 0; j < 9; ++j) { for (int k = 0; k < 20; ++k) { VERIFY_IS_EQUAL(tensor(i%8,j%3,k%5), broadcast(i,j,k)); } } } tensor.resize(11,3,5); tensor.setRandom(); broadcast = tensor.broadcast(broadcasts); VERIFY_IS_EQUAL(broadcast.dimension(0), 22); VERIFY_IS_EQUAL(broadcast.dimension(1), 9); VERIFY_IS_EQUAL(broadcast.dimension(2), 20); for (int i = 0; i < 22; ++i) { for (int j = 0; j < 9; ++j) { for (int k = 0; k < 20; ++k) { VERIFY_IS_EQUAL(tensor(i%11,j%3,k%5), broadcast(i,j,k)); } } } } template <int DataLayout> static void test_fixed_size_broadcasting() { // Need to add a [] operator to the Size class for this to work #if 0 Tensor<float, 1, DataLayout> t1(10); t1.setRandom(); TensorFixedSize<float, Sizes<1>, DataLayout> t2; t2 = t2.constant(20.0f); Tensor<float, 1, DataLayout> t3 = t1 + t2.broadcast(Eigen::array<int, 1>{{10}}); for (int i = 0; i < 10; ++i) { VERIFY_IS_APPROX(t3(i), t1(i) + t2(0)); } TensorMap<TensorFixedSize<float, Sizes<1>, DataLayout> > t4(t2.data(), {{1}}); Tensor<float, 1, DataLayout> t5 = t1 + t4.broadcast(Eigen::array<int, 1>{{10}}); for (int i = 0; i < 10; ++i) { VERIFY_IS_APPROX(t5(i), t1(i) + t2(0)); } #endif } void test_cxx11_tensor_broadcasting() { CALL_SUBTEST(test_simple_broadcasting<ColMajor>()); CALL_SUBTEST(test_simple_broadcasting<RowMajor>()); CALL_SUBTEST(test_vectorized_broadcasting<ColMajor>()); CALL_SUBTEST(test_vectorized_broadcasting<RowMajor>()); CALL_SUBTEST(test_static_broadcasting<ColMajor>()); CALL_SUBTEST(test_static_broadcasting<RowMajor>()); CALL_SUBTEST(test_fixed_size_broadcasting<ColMajor>()); CALL_SUBTEST(test_fixed_size_broadcasting<RowMajor>()); }