mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-27 07:29:52 +08:00
480 lines
13 KiB
C++
480 lines
13 KiB
C++
// 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 <limits>
|
|
#include <numeric>
|
|
#include <Eigen/CXX11/Tensor>
|
|
|
|
using Eigen::Tensor;
|
|
|
|
template <int DataLayout>
|
|
static void test_trivial_reductions() {
|
|
{
|
|
Tensor<float, 0, DataLayout> tensor;
|
|
tensor.setRandom();
|
|
array<ptrdiff_t, 0> reduction_axis;
|
|
|
|
Tensor<float, 0, DataLayout> result = tensor.sum(reduction_axis);
|
|
VERIFY_IS_EQUAL(result(), tensor());
|
|
}
|
|
|
|
{
|
|
Tensor<float, 1, DataLayout> tensor(7);
|
|
tensor.setRandom();
|
|
array<ptrdiff_t, 0> reduction_axis;
|
|
|
|
Tensor<float, 1, DataLayout> result = tensor.sum(reduction_axis);
|
|
VERIFY_IS_EQUAL(result.dimension(0), 7);
|
|
for (int i = 0; i < 7; ++i) {
|
|
VERIFY_IS_EQUAL(result(i), tensor(i));
|
|
}
|
|
}
|
|
|
|
{
|
|
Tensor<float, 2, DataLayout> tensor(2, 3);
|
|
tensor.setRandom();
|
|
array<ptrdiff_t, 0> reduction_axis;
|
|
|
|
Tensor<float, 2, DataLayout> result = tensor.sum(reduction_axis);
|
|
VERIFY_IS_EQUAL(result.dimension(0), 2);
|
|
VERIFY_IS_EQUAL(result.dimension(1), 3);
|
|
for (int i = 0; i < 2; ++i) {
|
|
for (int j = 0; j < 3; ++j) {
|
|
VERIFY_IS_EQUAL(result(i, j), tensor(i, j));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
template <int DataLayout>
|
|
static void test_simple_reductions() {
|
|
Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
|
|
tensor.setRandom();
|
|
array<ptrdiff_t, 2> reduction_axis2;
|
|
reduction_axis2[0] = 1;
|
|
reduction_axis2[1] = 3;
|
|
|
|
Tensor<float, 2, DataLayout> result = tensor.sum(reduction_axis2);
|
|
VERIFY_IS_EQUAL(result.dimension(0), 2);
|
|
VERIFY_IS_EQUAL(result.dimension(1), 5);
|
|
for (int i = 0; i < 2; ++i) {
|
|
for (int j = 0; j < 5; ++j) {
|
|
float sum = 0.0f;
|
|
for (int k = 0; k < 3; ++k) {
|
|
for (int l = 0; l < 7; ++l) {
|
|
sum += tensor(i, k, j, l);
|
|
}
|
|
}
|
|
VERIFY_IS_APPROX(result(i, j), sum);
|
|
}
|
|
}
|
|
|
|
{
|
|
Tensor<float, 0, DataLayout> sum1 = tensor.sum();
|
|
VERIFY_IS_EQUAL(sum1.rank(), 0);
|
|
|
|
array<ptrdiff_t, 4> reduction_axis4;
|
|
reduction_axis4[0] = 0;
|
|
reduction_axis4[1] = 1;
|
|
reduction_axis4[2] = 2;
|
|
reduction_axis4[3] = 3;
|
|
Tensor<float, 0, DataLayout> sum2 = tensor.sum(reduction_axis4);
|
|
VERIFY_IS_EQUAL(sum2.rank(), 0);
|
|
|
|
VERIFY_IS_APPROX(sum1(), sum2());
|
|
}
|
|
|
|
reduction_axis2[0] = 0;
|
|
reduction_axis2[1] = 2;
|
|
result = tensor.prod(reduction_axis2);
|
|
VERIFY_IS_EQUAL(result.dimension(0), 3);
|
|
VERIFY_IS_EQUAL(result.dimension(1), 7);
|
|
for (int i = 0; i < 3; ++i) {
|
|
for (int j = 0; j < 7; ++j) {
|
|
float prod = 1.0f;
|
|
for (int k = 0; k < 2; ++k) {
|
|
for (int l = 0; l < 5; ++l) {
|
|
prod *= tensor(k, i, l, j);
|
|
}
|
|
}
|
|
VERIFY_IS_APPROX(result(i, j), prod);
|
|
}
|
|
}
|
|
|
|
{
|
|
Tensor<float, 0, DataLayout> prod1 = tensor.prod();
|
|
VERIFY_IS_EQUAL(prod1.rank(), 0);
|
|
|
|
array<ptrdiff_t, 4> reduction_axis4;
|
|
reduction_axis4[0] = 0;
|
|
reduction_axis4[1] = 1;
|
|
reduction_axis4[2] = 2;
|
|
reduction_axis4[3] = 3;
|
|
Tensor<float, 0, DataLayout> prod2 = tensor.prod(reduction_axis4);
|
|
VERIFY_IS_EQUAL(prod2.rank(), 0);
|
|
|
|
VERIFY_IS_APPROX(prod1(), prod2());
|
|
}
|
|
|
|
reduction_axis2[0] = 0;
|
|
reduction_axis2[1] = 2;
|
|
result = tensor.maximum(reduction_axis2);
|
|
VERIFY_IS_EQUAL(result.dimension(0), 3);
|
|
VERIFY_IS_EQUAL(result.dimension(1), 7);
|
|
for (int i = 0; i < 3; ++i) {
|
|
for (int j = 0; j < 7; ++j) {
|
|
float max_val = std::numeric_limits<float>::lowest();
|
|
for (int k = 0; k < 2; ++k) {
|
|
for (int l = 0; l < 5; ++l) {
|
|
max_val = (std::max)(max_val, tensor(k, i, l, j));
|
|
}
|
|
}
|
|
VERIFY_IS_APPROX(result(i, j), max_val);
|
|
}
|
|
}
|
|
|
|
{
|
|
Tensor<float, 0, DataLayout> max1 = tensor.maximum();
|
|
VERIFY_IS_EQUAL(max1.rank(), 0);
|
|
|
|
array<ptrdiff_t, 4> reduction_axis4;
|
|
reduction_axis4[0] = 0;
|
|
reduction_axis4[1] = 1;
|
|
reduction_axis4[2] = 2;
|
|
reduction_axis4[3] = 3;
|
|
Tensor<float, 0, DataLayout> max2 = tensor.maximum(reduction_axis4);
|
|
VERIFY_IS_EQUAL(max2.rank(), 0);
|
|
|
|
VERIFY_IS_APPROX(max1(), max2());
|
|
}
|
|
|
|
reduction_axis2[0] = 0;
|
|
reduction_axis2[1] = 1;
|
|
result = tensor.minimum(reduction_axis2);
|
|
VERIFY_IS_EQUAL(result.dimension(0), 5);
|
|
VERIFY_IS_EQUAL(result.dimension(1), 7);
|
|
for (int i = 0; i < 5; ++i) {
|
|
for (int j = 0; j < 7; ++j) {
|
|
float min_val = (std::numeric_limits<float>::max)();
|
|
for (int k = 0; k < 2; ++k) {
|
|
for (int l = 0; l < 3; ++l) {
|
|
min_val = (std::min)(min_val, tensor(k, l, i, j));
|
|
}
|
|
}
|
|
VERIFY_IS_APPROX(result(i, j), min_val);
|
|
}
|
|
}
|
|
|
|
{
|
|
Tensor<float, 0, DataLayout> min1 = tensor.minimum();
|
|
VERIFY_IS_EQUAL(min1.rank(), 0);
|
|
|
|
array<ptrdiff_t, 4> reduction_axis4;
|
|
reduction_axis4[0] = 0;
|
|
reduction_axis4[1] = 1;
|
|
reduction_axis4[2] = 2;
|
|
reduction_axis4[3] = 3;
|
|
Tensor<float, 0, DataLayout> min2 = tensor.minimum(reduction_axis4);
|
|
VERIFY_IS_EQUAL(min2.rank(), 0);
|
|
|
|
VERIFY_IS_APPROX(min1(), min2());
|
|
}
|
|
|
|
reduction_axis2[0] = 0;
|
|
reduction_axis2[1] = 1;
|
|
result = tensor.mean(reduction_axis2);
|
|
VERIFY_IS_EQUAL(result.dimension(0), 5);
|
|
VERIFY_IS_EQUAL(result.dimension(1), 7);
|
|
for (int i = 0; i < 5; ++i) {
|
|
for (int j = 0; j < 7; ++j) {
|
|
float sum = 0.0f;
|
|
int count = 0;
|
|
for (int k = 0; k < 2; ++k) {
|
|
for (int l = 0; l < 3; ++l) {
|
|
sum += tensor(k, l, i, j);
|
|
++count;
|
|
}
|
|
}
|
|
VERIFY_IS_APPROX(result(i, j), sum / count);
|
|
}
|
|
}
|
|
|
|
{
|
|
Tensor<float, 0, DataLayout> mean1 = tensor.mean();
|
|
VERIFY_IS_EQUAL(mean1.rank(), 0);
|
|
|
|
array<ptrdiff_t, 4> reduction_axis4;
|
|
reduction_axis4[0] = 0;
|
|
reduction_axis4[1] = 1;
|
|
reduction_axis4[2] = 2;
|
|
reduction_axis4[3] = 3;
|
|
Tensor<float, 0, DataLayout> mean2 = tensor.mean(reduction_axis4);
|
|
VERIFY_IS_EQUAL(mean2.rank(), 0);
|
|
|
|
VERIFY_IS_APPROX(mean1(), mean2());
|
|
}
|
|
|
|
{
|
|
Tensor<int, 1> ints(10);
|
|
std::iota(ints.data(), ints.data() + ints.dimension(0), 0);
|
|
|
|
TensorFixedSize<bool, Sizes<> > all;
|
|
all = ints.all();
|
|
VERIFY(!all());
|
|
all = (ints >= ints.constant(0)).all();
|
|
VERIFY(all());
|
|
|
|
TensorFixedSize<bool, Sizes<> > any;
|
|
any = (ints > ints.constant(10)).any();
|
|
VERIFY(!any());
|
|
any = (ints < ints.constant(1)).any();
|
|
VERIFY(any());
|
|
}
|
|
}
|
|
|
|
template <int DataLayout>
|
|
static void test_full_reductions() {
|
|
Tensor<float, 2, DataLayout> tensor(2, 3);
|
|
tensor.setRandom();
|
|
array<ptrdiff_t, 2> reduction_axis;
|
|
reduction_axis[0] = 0;
|
|
reduction_axis[1] = 1;
|
|
|
|
Tensor<float, 0, DataLayout> result = tensor.sum(reduction_axis);
|
|
VERIFY_IS_EQUAL(result.rank(), 0);
|
|
|
|
float sum = 0.0f;
|
|
for (int i = 0; i < 2; ++i) {
|
|
for (int j = 0; j < 3; ++j) {
|
|
sum += tensor(i, j);
|
|
}
|
|
}
|
|
VERIFY_IS_APPROX(result(0), sum);
|
|
|
|
result = tensor.square().sum(reduction_axis).sqrt();
|
|
VERIFY_IS_EQUAL(result.rank(), 0);
|
|
|
|
sum = 0.0f;
|
|
for (int i = 0; i < 2; ++i) {
|
|
for (int j = 0; j < 3; ++j) {
|
|
sum += tensor(i, j) * tensor(i, j);
|
|
}
|
|
}
|
|
VERIFY_IS_APPROX(result(), sqrtf(sum));
|
|
}
|
|
|
|
struct UserReducer {
|
|
static const bool PacketAccess = false;
|
|
UserReducer(float offset) : offset_(offset) {}
|
|
void reduce(const float val, float* accum) { *accum += val * val; }
|
|
float initialize() const { return 0; }
|
|
float finalize(const float accum) const { return 1.0f / (accum + offset_); }
|
|
|
|
private:
|
|
const float offset_;
|
|
};
|
|
|
|
template <int DataLayout>
|
|
static void test_user_defined_reductions() {
|
|
Tensor<float, 2, DataLayout> tensor(5, 7);
|
|
tensor.setRandom();
|
|
array<ptrdiff_t, 1> reduction_axis;
|
|
reduction_axis[0] = 1;
|
|
|
|
UserReducer reducer(10.0f);
|
|
Tensor<float, 1, DataLayout> result = tensor.reduce(reduction_axis, reducer);
|
|
VERIFY_IS_EQUAL(result.dimension(0), 5);
|
|
for (int i = 0; i < 5; ++i) {
|
|
float expected = 10.0f;
|
|
for (int j = 0; j < 7; ++j) {
|
|
expected += tensor(i, j) * tensor(i, j);
|
|
}
|
|
expected = 1.0f / expected;
|
|
VERIFY_IS_APPROX(result(i), expected);
|
|
}
|
|
}
|
|
|
|
template <int DataLayout>
|
|
static void test_tensor_maps() {
|
|
int inputs[2 * 3 * 5 * 7];
|
|
TensorMap<Tensor<int, 4, DataLayout> > tensor_map(inputs, 2, 3, 5, 7);
|
|
TensorMap<Tensor<const int, 4, DataLayout> > tensor_map_const(inputs, 2, 3, 5,
|
|
7);
|
|
const TensorMap<Tensor<const int, 4, DataLayout> > tensor_map_const_const(
|
|
inputs, 2, 3, 5, 7);
|
|
|
|
tensor_map.setRandom();
|
|
array<ptrdiff_t, 2> reduction_axis;
|
|
reduction_axis[0] = 1;
|
|
reduction_axis[1] = 3;
|
|
|
|
Tensor<int, 2, DataLayout> result = tensor_map.sum(reduction_axis);
|
|
Tensor<int, 2, DataLayout> result2 = tensor_map_const.sum(reduction_axis);
|
|
Tensor<int, 2, DataLayout> result3 =
|
|
tensor_map_const_const.sum(reduction_axis);
|
|
|
|
for (int i = 0; i < 2; ++i) {
|
|
for (int j = 0; j < 5; ++j) {
|
|
int sum = 0;
|
|
for (int k = 0; k < 3; ++k) {
|
|
for (int l = 0; l < 7; ++l) {
|
|
sum += tensor_map(i, k, j, l);
|
|
}
|
|
}
|
|
VERIFY_IS_EQUAL(result(i, j), sum);
|
|
VERIFY_IS_EQUAL(result2(i, j), sum);
|
|
VERIFY_IS_EQUAL(result3(i, j), sum);
|
|
}
|
|
}
|
|
}
|
|
|
|
template <int DataLayout>
|
|
static void test_static_dims() {
|
|
Tensor<float, 4, DataLayout> in(72, 53, 97, 113);
|
|
Tensor<float, 2, DataLayout> out(72, 97);
|
|
in.setRandom();
|
|
|
|
#if !EIGEN_HAS_CONSTEXPR
|
|
array<int, 2> reduction_axis;
|
|
reduction_axis[0] = 1;
|
|
reduction_axis[1] = 3;
|
|
#else
|
|
Eigen::IndexList<Eigen::type2index<1>, Eigen::type2index<3> > reduction_axis;
|
|
#endif
|
|
|
|
out = in.maximum(reduction_axis);
|
|
|
|
for (int i = 0; i < 72; ++i) {
|
|
for (int j = 0; j < 97; ++j) {
|
|
float expected = -1e10f;
|
|
for (int k = 0; k < 53; ++k) {
|
|
for (int l = 0; l < 113; ++l) {
|
|
expected = (std::max)(expected, in(i, k, j, l));
|
|
}
|
|
}
|
|
VERIFY_IS_APPROX(out(i, j), expected);
|
|
}
|
|
}
|
|
}
|
|
|
|
template <int DataLayout>
|
|
static void test_innermost_last_dims() {
|
|
Tensor<float, 4, DataLayout> in(72, 53, 97, 113);
|
|
Tensor<float, 2, DataLayout> out(97, 113);
|
|
in.setRandom();
|
|
|
|
// Reduce on the innermost dimensions.
|
|
#if !EIGEN_HAS_CONSTEXPR
|
|
array<int, 2> reduction_axis;
|
|
reduction_axis[0] = 0;
|
|
reduction_axis[1] = 1;
|
|
#else
|
|
// This triggers the use of packets for ColMajor.
|
|
Eigen::IndexList<Eigen::type2index<0>, Eigen::type2index<1> > reduction_axis;
|
|
#endif
|
|
|
|
out = in.maximum(reduction_axis);
|
|
|
|
for (int i = 0; i < 97; ++i) {
|
|
for (int j = 0; j < 113; ++j) {
|
|
float expected = -1e10f;
|
|
for (int k = 0; k < 53; ++k) {
|
|
for (int l = 0; l < 72; ++l) {
|
|
expected = (std::max)(expected, in(l, k, i, j));
|
|
}
|
|
}
|
|
VERIFY_IS_APPROX(out(i, j), expected);
|
|
}
|
|
}
|
|
}
|
|
|
|
template <int DataLayout>
|
|
static void test_innermost_first_dims() {
|
|
Tensor<float, 4, DataLayout> in(72, 53, 97, 113);
|
|
Tensor<float, 2, DataLayout> out(72, 53);
|
|
in.setRandom();
|
|
|
|
// Reduce on the innermost dimensions.
|
|
#if !EIGEN_HAS_CONSTEXPR
|
|
array<int, 2> reduction_axis;
|
|
reduction_axis[0] = 2;
|
|
reduction_axis[1] = 3;
|
|
#else
|
|
// This triggers the use of packets for RowMajor.
|
|
Eigen::IndexList<Eigen::type2index<2>, Eigen::type2index<3>> reduction_axis;
|
|
#endif
|
|
|
|
out = in.maximum(reduction_axis);
|
|
|
|
for (int i = 0; i < 72; ++i) {
|
|
for (int j = 0; j < 53; ++j) {
|
|
float expected = -1e10f;
|
|
for (int k = 0; k < 97; ++k) {
|
|
for (int l = 0; l < 113; ++l) {
|
|
expected = (std::max)(expected, in(i, j, k, l));
|
|
}
|
|
}
|
|
VERIFY_IS_APPROX(out(i, j), expected);
|
|
}
|
|
}
|
|
}
|
|
|
|
template <int DataLayout>
|
|
static void test_reduce_middle_dims() {
|
|
Tensor<float, 4, DataLayout> in(72, 53, 97, 113);
|
|
Tensor<float, 2, DataLayout> out(72, 53);
|
|
in.setRandom();
|
|
|
|
// Reduce on the innermost dimensions.
|
|
#if !EIGEN_HAS_CONSTEXPR
|
|
array<int, 2> reduction_axis;
|
|
reduction_axis[0] = 1;
|
|
reduction_axis[1] = 2;
|
|
#else
|
|
// This triggers the use of packets for RowMajor.
|
|
Eigen::IndexList<Eigen::type2index<1>, Eigen::type2index<2>> reduction_axis;
|
|
#endif
|
|
|
|
out = in.maximum(reduction_axis);
|
|
|
|
for (int i = 0; i < 72; ++i) {
|
|
for (int j = 0; j < 113; ++j) {
|
|
float expected = -1e10f;
|
|
for (int k = 0; k < 53; ++k) {
|
|
for (int l = 0; l < 97; ++l) {
|
|
expected = (std::max)(expected, in(i, k, l, j));
|
|
}
|
|
}
|
|
VERIFY_IS_APPROX(out(i, j), expected);
|
|
}
|
|
}
|
|
}
|
|
|
|
void test_cxx11_tensor_reduction() {
|
|
CALL_SUBTEST(test_trivial_reductions<ColMajor>());
|
|
CALL_SUBTEST(test_trivial_reductions<RowMajor>());
|
|
CALL_SUBTEST(test_simple_reductions<ColMajor>());
|
|
CALL_SUBTEST(test_simple_reductions<RowMajor>());
|
|
CALL_SUBTEST(test_full_reductions<ColMajor>());
|
|
CALL_SUBTEST(test_full_reductions<RowMajor>());
|
|
CALL_SUBTEST(test_user_defined_reductions<ColMajor>());
|
|
CALL_SUBTEST(test_user_defined_reductions<RowMajor>());
|
|
CALL_SUBTEST(test_tensor_maps<ColMajor>());
|
|
CALL_SUBTEST(test_tensor_maps<RowMajor>());
|
|
CALL_SUBTEST(test_static_dims<ColMajor>());
|
|
CALL_SUBTEST(test_static_dims<RowMajor>());
|
|
CALL_SUBTEST(test_innermost_last_dims<ColMajor>());
|
|
CALL_SUBTEST(test_innermost_last_dims<RowMajor>());
|
|
CALL_SUBTEST(test_innermost_first_dims<ColMajor>());
|
|
CALL_SUBTEST(test_innermost_first_dims<RowMajor>());
|
|
CALL_SUBTEST(test_reduce_middle_dims<ColMajor>());
|
|
CALL_SUBTEST(test_reduce_middle_dims<RowMajor>());
|
|
}
|