426 lines
13 KiB
C++
426 lines
13 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#include "main.h"
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#include <Eigen/CXX11/Tensor>
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using Eigen::Tensor;
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template<int DataLayout>
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static void test_simple_chip()
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{
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Tensor<float, 5, DataLayout> tensor(2,3,5,7,11);
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tensor.setRandom();
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Tensor<float, 4, DataLayout> chip1;
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chip1 = tensor.template chip<0>(1);
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VERIFY_IS_EQUAL(chip1.dimension(0), 3);
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VERIFY_IS_EQUAL(chip1.dimension(1), 5);
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VERIFY_IS_EQUAL(chip1.dimension(2), 7);
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VERIFY_IS_EQUAL(chip1.dimension(3), 11);
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for (int i = 0; i < 3; ++i) {
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for (int j = 0; j < 5; ++j) {
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for (int k = 0; k < 7; ++k) {
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for (int l = 0; l < 11; ++l) {
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VERIFY_IS_EQUAL(chip1(i,j,k,l), tensor(1,i,j,k,l));
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}
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}
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}
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}
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Tensor<float, 4, DataLayout> chip2 = tensor.template chip<1>(1);
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VERIFY_IS_EQUAL(chip2.dimension(0), 2);
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VERIFY_IS_EQUAL(chip2.dimension(1), 5);
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VERIFY_IS_EQUAL(chip2.dimension(2), 7);
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VERIFY_IS_EQUAL(chip2.dimension(3), 11);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 5; ++j) {
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for (int k = 0; k < 7; ++k) {
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for (int l = 0; l < 11; ++l) {
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VERIFY_IS_EQUAL(chip2(i,j,k,l), tensor(i,1,j,k,l));
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}
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}
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}
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}
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Tensor<float, 4, DataLayout> chip3 = tensor.template chip<2>(2);
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VERIFY_IS_EQUAL(chip3.dimension(0), 2);
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VERIFY_IS_EQUAL(chip3.dimension(1), 3);
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VERIFY_IS_EQUAL(chip3.dimension(2), 7);
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VERIFY_IS_EQUAL(chip3.dimension(3), 11);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 7; ++k) {
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for (int l = 0; l < 11; ++l) {
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VERIFY_IS_EQUAL(chip3(i,j,k,l), tensor(i,j,2,k,l));
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}
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}
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}
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}
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Tensor<float, 4, DataLayout> chip4(tensor.template chip<3>(5));
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VERIFY_IS_EQUAL(chip4.dimension(0), 2);
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VERIFY_IS_EQUAL(chip4.dimension(1), 3);
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VERIFY_IS_EQUAL(chip4.dimension(2), 5);
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VERIFY_IS_EQUAL(chip4.dimension(3), 11);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 11; ++l) {
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VERIFY_IS_EQUAL(chip4(i,j,k,l), tensor(i,j,k,5,l));
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}
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}
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}
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}
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Tensor<float, 4, DataLayout> chip5(tensor.template chip<4>(7));
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VERIFY_IS_EQUAL(chip5.dimension(0), 2);
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VERIFY_IS_EQUAL(chip5.dimension(1), 3);
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VERIFY_IS_EQUAL(chip5.dimension(2), 5);
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VERIFY_IS_EQUAL(chip5.dimension(3), 7);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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VERIFY_IS_EQUAL(chip5(i,j,k,l), tensor(i,j,k,l,7));
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}
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}
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}
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}
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}
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template<int DataLayout>
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static void test_dynamic_chip()
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{
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Tensor<float, 5, DataLayout> tensor(2,3,5,7,11);
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tensor.setRandom();
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Tensor<float, 4, DataLayout> chip1;
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chip1 = tensor.chip(1, 0);
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VERIFY_IS_EQUAL(chip1.dimension(0), 3);
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VERIFY_IS_EQUAL(chip1.dimension(1), 5);
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VERIFY_IS_EQUAL(chip1.dimension(2), 7);
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VERIFY_IS_EQUAL(chip1.dimension(3), 11);
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for (int i = 0; i < 3; ++i) {
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for (int j = 0; j < 5; ++j) {
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for (int k = 0; k < 7; ++k) {
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for (int l = 0; l < 11; ++l) {
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VERIFY_IS_EQUAL(chip1(i,j,k,l), tensor(1,i,j,k,l));
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}
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}
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}
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}
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Tensor<float, 4, DataLayout> chip2 = tensor.chip(1, 1);
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VERIFY_IS_EQUAL(chip2.dimension(0), 2);
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VERIFY_IS_EQUAL(chip2.dimension(1), 5);
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VERIFY_IS_EQUAL(chip2.dimension(2), 7);
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VERIFY_IS_EQUAL(chip2.dimension(3), 11);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 5; ++j) {
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for (int k = 0; k < 7; ++k) {
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for (int l = 0; l < 11; ++l) {
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VERIFY_IS_EQUAL(chip2(i,j,k,l), tensor(i,1,j,k,l));
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}
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}
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}
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}
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Tensor<float, 4, DataLayout> chip3 = tensor.chip(2, 2);
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VERIFY_IS_EQUAL(chip3.dimension(0), 2);
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VERIFY_IS_EQUAL(chip3.dimension(1), 3);
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VERIFY_IS_EQUAL(chip3.dimension(2), 7);
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VERIFY_IS_EQUAL(chip3.dimension(3), 11);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 7; ++k) {
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for (int l = 0; l < 11; ++l) {
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VERIFY_IS_EQUAL(chip3(i,j,k,l), tensor(i,j,2,k,l));
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}
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}
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}
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}
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Tensor<float, 4, DataLayout> chip4(tensor.chip(5, 3));
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VERIFY_IS_EQUAL(chip4.dimension(0), 2);
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VERIFY_IS_EQUAL(chip4.dimension(1), 3);
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VERIFY_IS_EQUAL(chip4.dimension(2), 5);
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VERIFY_IS_EQUAL(chip4.dimension(3), 11);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 11; ++l) {
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VERIFY_IS_EQUAL(chip4(i,j,k,l), tensor(i,j,k,5,l));
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}
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}
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}
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}
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Tensor<float, 4, DataLayout> chip5(tensor.chip(7, 4));
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VERIFY_IS_EQUAL(chip5.dimension(0), 2);
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VERIFY_IS_EQUAL(chip5.dimension(1), 3);
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VERIFY_IS_EQUAL(chip5.dimension(2), 5);
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VERIFY_IS_EQUAL(chip5.dimension(3), 7);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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VERIFY_IS_EQUAL(chip5(i,j,k,l), tensor(i,j,k,l,7));
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}
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}
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}
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}
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}
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template<int DataLayout>
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static void test_chip_in_expr() {
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Tensor<float, 5, DataLayout> input1(2,3,5,7,11);
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input1.setRandom();
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Tensor<float, 4, DataLayout> input2(3,5,7,11);
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input2.setRandom();
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Tensor<float, 4, DataLayout> result = input1.template chip<0>(0) + input2;
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for (int i = 0; i < 3; ++i) {
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for (int j = 0; j < 5; ++j) {
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for (int k = 0; k < 7; ++k) {
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for (int l = 0; l < 11; ++l) {
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float expected = input1(0,i,j,k,l) + input2(i,j,k,l);
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VERIFY_IS_EQUAL(result(i,j,k,l), expected);
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}
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}
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}
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}
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Tensor<float, 3, DataLayout> input3(3,7,11);
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input3.setRandom();
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Tensor<float, 3, DataLayout> result2 = input1.template chip<0>(0).template chip<1>(2) + input3;
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for (int i = 0; i < 3; ++i) {
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for (int j = 0; j < 7; ++j) {
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for (int k = 0; k < 11; ++k) {
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float expected = input1(0,i,2,j,k) + input3(i,j,k);
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VERIFY_IS_EQUAL(result2(i,j,k), expected);
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}
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}
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}
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}
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template<int DataLayout>
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static void test_chip_as_lvalue()
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{
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Tensor<float, 5, DataLayout> input1(2,3,5,7,11);
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input1.setRandom();
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Tensor<float, 4, DataLayout> input2(3,5,7,11);
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input2.setRandom();
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Tensor<float, 5, DataLayout> tensor = input1;
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tensor.template chip<0>(1) = input2;
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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for (int m = 0; m < 11; ++m) {
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if (i != 1) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
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} else {
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VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input2(j,k,l,m));
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}
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}
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}
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}
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}
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}
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Tensor<float, 4, DataLayout> input3(2,5,7,11);
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input3.setRandom();
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tensor = input1;
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tensor.template chip<1>(1) = input3;
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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for (int m = 0; m < 11; ++m) {
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if (j != 1) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
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} else {
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VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input3(i,k,l,m));
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}
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}
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}
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}
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}
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}
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Tensor<float, 4, DataLayout> input4(2,3,7,11);
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input4.setRandom();
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tensor = input1;
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tensor.template chip<2>(3) = input4;
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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for (int m = 0; m < 11; ++m) {
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if (k != 3) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
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} else {
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VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input4(i,j,l,m));
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}
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}
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}
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}
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}
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}
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Tensor<float, 4, DataLayout> input5(2,3,5,11);
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input5.setRandom();
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tensor = input1;
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tensor.template chip<3>(4) = input5;
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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for (int m = 0; m < 11; ++m) {
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if (l != 4) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
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} else {
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VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input5(i,j,k,m));
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}
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}
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}
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}
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}
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}
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Tensor<float, 4, DataLayout> input6(2,3,5,7);
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input6.setRandom();
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tensor = input1;
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tensor.template chip<4>(5) = input6;
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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for (int m = 0; m < 11; ++m) {
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if (m != 5) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
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} else {
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VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input6(i,j,k,l));
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}
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}
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}
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}
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}
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}
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Tensor<float, 5, DataLayout> input7(2,3,5,7,11);
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input7.setRandom();
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tensor = input1;
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tensor.chip(0, 0) = input7.chip(0, 0);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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for (int m = 0; m < 11; ++m) {
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if (i != 0) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
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} else {
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VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input7(i,j,k,l,m));
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}
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}
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}
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}
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}
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}
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}
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static void test_chip_raw_data_col_major()
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{
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Tensor<float, 5, ColMajor> tensor(2,3,5,7,11);
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tensor.setRandom();
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typedef TensorEvaluator<decltype(tensor.chip<4>(3)), DefaultDevice> Evaluator4;
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auto chip = Evaluator4(tensor.chip<4>(3), DefaultDevice());
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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int chip_index = i + 2 * (j + 3 * (k + 5 * l));
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VERIFY_IS_EQUAL(chip.data()[chip_index], tensor(i,j,k,l,3));
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}
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}
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}
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}
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typedef TensorEvaluator<decltype(tensor.chip<0>(0)), DefaultDevice> Evaluator0;
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auto chip0 = Evaluator0(tensor.chip<0>(0), DefaultDevice());
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VERIFY_IS_EQUAL(chip0.data(), static_cast<float*>(0));
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typedef TensorEvaluator<decltype(tensor.chip<1>(0)), DefaultDevice> Evaluator1;
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auto chip1 = Evaluator1(tensor.chip<1>(0), DefaultDevice());
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VERIFY_IS_EQUAL(chip1.data(), static_cast<float*>(0));
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typedef TensorEvaluator<decltype(tensor.chip<2>(0)), DefaultDevice> Evaluator2;
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auto chip2 = Evaluator2(tensor.chip<2>(0), DefaultDevice());
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VERIFY_IS_EQUAL(chip2.data(), static_cast<float*>(0));
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typedef TensorEvaluator<decltype(tensor.chip<3>(0)), DefaultDevice> Evaluator3;
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auto chip3 = Evaluator3(tensor.chip<3>(0), DefaultDevice());
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VERIFY_IS_EQUAL(chip3.data(), static_cast<float*>(0));
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}
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static void test_chip_raw_data_row_major()
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{
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Tensor<float, 5, RowMajor> tensor(11,7,5,3,2);
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tensor.setRandom();
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typedef TensorEvaluator<decltype(tensor.chip<0>(3)), DefaultDevice> Evaluator0;
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auto chip = Evaluator0(tensor.chip<0>(3), DefaultDevice());
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for (int i = 0; i < 7; ++i) {
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for (int j = 0; j < 5; ++j) {
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for (int k = 0; k < 3; ++k) {
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for (int l = 0; l < 2; ++l) {
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int chip_index = l + 2 * (k + 3 * (j + 5 * i));
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VERIFY_IS_EQUAL(chip.data()[chip_index], tensor(3,i,j,k,l));
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}
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}
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}
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}
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typedef TensorEvaluator<decltype(tensor.chip<1>(0)), DefaultDevice> Evaluator1;
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auto chip1 = Evaluator1(tensor.chip<1>(0), DefaultDevice());
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VERIFY_IS_EQUAL(chip1.data(), static_cast<float*>(0));
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typedef TensorEvaluator<decltype(tensor.chip<2>(0)), DefaultDevice> Evaluator2;
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auto chip2 = Evaluator2(tensor.chip<2>(0), DefaultDevice());
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VERIFY_IS_EQUAL(chip2.data(), static_cast<float*>(0));
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typedef TensorEvaluator<decltype(tensor.chip<3>(0)), DefaultDevice> Evaluator3;
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auto chip3 = Evaluator3(tensor.chip<3>(0), DefaultDevice());
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VERIFY_IS_EQUAL(chip3.data(), static_cast<float*>(0));
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typedef TensorEvaluator<decltype(tensor.chip<4>(0)), DefaultDevice> Evaluator4;
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auto chip4 = Evaluator4(tensor.chip<4>(0), DefaultDevice());
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VERIFY_IS_EQUAL(chip4.data(), static_cast<float*>(0));
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}
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EIGEN_DECLARE_TEST(cxx11_tensor_chipping)
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{
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CALL_SUBTEST(test_simple_chip<ColMajor>());
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CALL_SUBTEST(test_simple_chip<RowMajor>());
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CALL_SUBTEST(test_dynamic_chip<ColMajor>());
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CALL_SUBTEST(test_dynamic_chip<RowMajor>());
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CALL_SUBTEST(test_chip_in_expr<ColMajor>());
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CALL_SUBTEST(test_chip_in_expr<RowMajor>());
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CALL_SUBTEST(test_chip_as_lvalue<ColMajor>());
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CALL_SUBTEST(test_chip_as_lvalue<RowMajor>());
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CALL_SUBTEST(test_chip_raw_data_col_major());
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CALL_SUBTEST(test_chip_raw_data_row_major());
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}
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