| // 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)); |
| } |
| } |
| } |
| |
| #if EIGEN_HAS_VARIADIC_TEMPLATES |
| tensor.resize(11,3,5); |
| #else |
| array<Index, 3> new_dims; |
| new_dims[0] = 11; |
| new_dims[1] = 3; |
| new_dims[2] = 5; |
| tensor.resize(new_dims); |
| #endif |
| |
| 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 defined(EIGEN_HAS_INDEX_LIST) |
| 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)); |
| } |
| } |
| } |
| |
| #if EIGEN_HAS_VARIADIC_TEMPLATES |
| tensor.resize(11,3,5); |
| #else |
| array<Index, 3> new_dims; |
| new_dims[0] = 11; |
| new_dims[1] = 3; |
| new_dims[2] = 5; |
| tensor.resize(new_dims); |
| #endif |
| |
| 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 |
| } |
| |
| template <int DataLayout> |
| static void test_simple_broadcasting_one_by_n() |
| { |
| Tensor<float, 4, DataLayout> tensor(1,13,5,7); |
| tensor.setRandom(); |
| array<ptrdiff_t, 4> broadcasts; |
| broadcasts[0] = 9; |
| broadcasts[1] = 1; |
| broadcasts[2] = 1; |
| broadcasts[3] = 1; |
| Tensor<float, 4, DataLayout> broadcast; |
| broadcast = tensor.broadcast(broadcasts); |
| |
| VERIFY_IS_EQUAL(broadcast.dimension(0), 9); |
| VERIFY_IS_EQUAL(broadcast.dimension(1), 13); |
| VERIFY_IS_EQUAL(broadcast.dimension(2), 5); |
| VERIFY_IS_EQUAL(broadcast.dimension(3), 7); |
| |
| for (int i = 0; i < 9; ++i) { |
| for (int j = 0; j < 13; ++j) { |
| for (int k = 0; k < 5; ++k) { |
| for (int l = 0; l < 7; ++l) { |
| VERIFY_IS_EQUAL(tensor(i%1,j%13,k%5,l%7), broadcast(i,j,k,l)); |
| } |
| } |
| } |
| } |
| } |
| |
| template <int DataLayout> |
| static void test_simple_broadcasting_n_by_one() |
| { |
| Tensor<float, 4, DataLayout> tensor(7,3,5,1); |
| tensor.setRandom(); |
| array<ptrdiff_t, 4> broadcasts; |
| broadcasts[0] = 1; |
| broadcasts[1] = 1; |
| broadcasts[2] = 1; |
| broadcasts[3] = 19; |
| Tensor<float, 4, DataLayout> broadcast; |
| broadcast = tensor.broadcast(broadcasts); |
| |
| VERIFY_IS_EQUAL(broadcast.dimension(0), 7); |
| VERIFY_IS_EQUAL(broadcast.dimension(1), 3); |
| VERIFY_IS_EQUAL(broadcast.dimension(2), 5); |
| VERIFY_IS_EQUAL(broadcast.dimension(3), 19); |
| |
| for (int i = 0; i < 7; ++i) { |
| for (int j = 0; j < 3; ++j) { |
| for (int k = 0; k < 5; ++k) { |
| for (int l = 0; l < 19; ++l) { |
| VERIFY_IS_EQUAL(tensor(i%7,j%3,k%5,l%1), broadcast(i,j,k,l)); |
| } |
| } |
| } |
| } |
| } |
| |
| template <int DataLayout> |
| static void test_simple_broadcasting_one_by_n_by_one_1d() |
| { |
| Tensor<float, 3, DataLayout> tensor(1,7,1); |
| tensor.setRandom(); |
| array<ptrdiff_t, 3> broadcasts; |
| broadcasts[0] = 5; |
| broadcasts[1] = 1; |
| broadcasts[2] = 13; |
| Tensor<float, 3, DataLayout> broadcasted; |
| broadcasted = tensor.broadcast(broadcasts); |
| |
| VERIFY_IS_EQUAL(broadcasted.dimension(0), 5); |
| VERIFY_IS_EQUAL(broadcasted.dimension(1), 7); |
| VERIFY_IS_EQUAL(broadcasted.dimension(2), 13); |
| |
| for (int i = 0; i < 5; ++i) { |
| for (int j = 0; j < 7; ++j) { |
| for (int k = 0; k < 13; ++k) { |
| VERIFY_IS_EQUAL(tensor(0,j%7,0), broadcasted(i,j,k)); |
| } |
| } |
| } |
| } |
| |
| template <int DataLayout> |
| static void test_simple_broadcasting_one_by_n_by_one_2d() |
| { |
| Tensor<float, 4, DataLayout> tensor(1,7,13,1); |
| tensor.setRandom(); |
| array<ptrdiff_t, 4> broadcasts; |
| broadcasts[0] = 5; |
| broadcasts[1] = 1; |
| broadcasts[2] = 1; |
| broadcasts[3] = 19; |
| Tensor<float, 4, DataLayout> broadcast; |
| broadcast = tensor.broadcast(broadcasts); |
| |
| VERIFY_IS_EQUAL(broadcast.dimension(0), 5); |
| VERIFY_IS_EQUAL(broadcast.dimension(1), 7); |
| VERIFY_IS_EQUAL(broadcast.dimension(2), 13); |
| VERIFY_IS_EQUAL(broadcast.dimension(3), 19); |
| |
| for (int i = 0; i < 5; ++i) { |
| for (int j = 0; j < 7; ++j) { |
| for (int k = 0; k < 13; ++k) { |
| for (int l = 0; l < 19; ++l) { |
| VERIFY_IS_EQUAL(tensor(0,j%7,k%13,0), broadcast(i,j,k,l)); |
| } |
| } |
| } |
| } |
| } |
| |
| EIGEN_DECLARE_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>()); |
| CALL_SUBTEST(test_simple_broadcasting_one_by_n<RowMajor>()); |
| CALL_SUBTEST(test_simple_broadcasting_n_by_one<RowMajor>()); |
| CALL_SUBTEST(test_simple_broadcasting_one_by_n<ColMajor>()); |
| CALL_SUBTEST(test_simple_broadcasting_n_by_one<ColMajor>()); |
| CALL_SUBTEST(test_simple_broadcasting_one_by_n_by_one_1d<ColMajor>()); |
| CALL_SUBTEST(test_simple_broadcasting_one_by_n_by_one_2d<ColMajor>()); |
| CALL_SUBTEST(test_simple_broadcasting_one_by_n_by_one_1d<RowMajor>()); |
| CALL_SUBTEST(test_simple_broadcasting_one_by_n_by_one_2d<RowMajor>()); |
| } |