devtools/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2026-04-10 11:34:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

Clang-format tests, examples, libraries, benchmarks, etc.

Browse Source
This commit is contained in:
Antonio Sánchez
2023-12-05 21:22:55 +00:00
committed by Rasmus Munk Larsen
parent 3252ecc7a4
commit 46e9cdb7fe
876 changed files with 33453 additions and 37795 deletions
Download Patch File Download Diff File Expand all files Collapse all files

196
unsupported/test/cxx11_tensor_expr.cpp
View File

@@ -13,20 +13,25 @@
#include <Eigen/CXX11/Tensor>
using Eigen::Tensor;
using Eigen::RowMajor;
using Eigen::Tensor;
static void test_1d()
{
static void test_1d() {
Tensor<float, 1> vec1(6);
Tensor<float, 1, RowMajor> vec2(6);
vec1(0) = 4.0; vec2(0) = 0.0;
vec1(1) = 8.0; vec2(1) = 1.0;
vec1(2) = 15.0; vec2(2) = 2.0;
vec1(3) = 16.0; vec2(3) = 3.0;
vec1(4) = 23.0; vec2(4) = 4.0;
vec1(5) = 42.0; vec2(5) = 5.0;
vec1(0) = 4.0;
vec2(0) = 0.0;
vec1(1) = 8.0;
vec2(1) = 1.0;
vec1(2) = 15.0;
vec2(2) = 2.0;
vec1(3) = 16.0;
vec2(3) = 3.0;
vec1(4) = 23.0;
vec2(4) = 4.0;
vec1(5) = 42.0;
vec2(5) = 5.0;
float data3[6];
TensorMap<Tensor<float, 1>> vec3(data3, 6);
@@ -68,112 +73,109 @@ static void test_1d()
VERIFY_IS_APPROX(vec3(5), 42.0f + 5.0f);
}
static void test_2d()
{
static void test_2d() {
float data1[6];
TensorMap<Tensor<float, 2>> mat1(data1, 2, 3);
float data2[6];
TensorMap<Tensor<float, 2, RowMajor>> mat2(data2, 2, 3);
mat1(0,0) = 0.0;
mat1(0,1) = 1.0;
mat1(0,2) = 2.0;
mat1(1,0) = 3.0;
mat1(1,1) = 4.0;
mat1(1,2) = 5.0;
mat1(0, 0) = 0.0;
mat1(0, 1) = 1.0;
mat1(0, 2) = 2.0;
mat1(1, 0) = 3.0;
mat1(1, 1) = 4.0;
mat1(1, 2) = 5.0;
mat2(0,0) = -0.0;
mat2(0,1) = -1.0;
mat2(0,2) = -2.0;
mat2(1,0) = -3.0;
mat2(1,1) = -4.0;
mat2(1,2) = -5.0;
mat2(0, 0) = -0.0;
mat2(0, 1) = -1.0;
mat2(0, 2) = -2.0;
mat2(1, 0) = -3.0;
mat2(1, 1) = -4.0;
mat2(1, 2) = -5.0;
Tensor<float, 2> mat3(2,3);
Tensor<float, 2, RowMajor> mat4(2,3);
Tensor<float, 2> mat3(2, 3);
Tensor<float, 2, RowMajor> mat4(2, 3);
mat3 = mat1.abs();
mat4 = mat2.abs();
VERIFY_IS_APPROX(mat3(0,0), 0.0f);
VERIFY_IS_APPROX(mat3(0,1), 1.0f);
VERIFY_IS_APPROX(mat3(0,2), 2.0f);
VERIFY_IS_APPROX(mat3(1,0), 3.0f);
VERIFY_IS_APPROX(mat3(1,1), 4.0f);
VERIFY_IS_APPROX(mat3(1,2), 5.0f);
VERIFY_IS_APPROX(mat3(0, 0), 0.0f);
VERIFY_IS_APPROX(mat3(0, 1), 1.0f);
VERIFY_IS_APPROX(mat3(0, 2), 2.0f);
VERIFY_IS_APPROX(mat3(1, 0), 3.0f);
VERIFY_IS_APPROX(mat3(1, 1), 4.0f);
VERIFY_IS_APPROX(mat3(1, 2), 5.0f);
VERIFY_IS_APPROX(mat4(0,0), 0.0f);
VERIFY_IS_APPROX(mat4(0,1), 1.0f);
VERIFY_IS_APPROX(mat4(0,2), 2.0f);
VERIFY_IS_APPROX(mat4(1,0), 3.0f);
VERIFY_IS_APPROX(mat4(1,1), 4.0f);
VERIFY_IS_APPROX(mat4(1,2), 5.0f);
VERIFY_IS_APPROX(mat4(0, 0), 0.0f);
VERIFY_IS_APPROX(mat4(0, 1), 1.0f);
VERIFY_IS_APPROX(mat4(0, 2), 2.0f);
VERIFY_IS_APPROX(mat4(1, 0), 3.0f);
VERIFY_IS_APPROX(mat4(1, 1), 4.0f);
VERIFY_IS_APPROX(mat4(1, 2), 5.0f);
}
static void test_3d()
{
Tensor<float, 3> mat1(2,3,7);
Tensor<float, 3, RowMajor> mat2(2,3,7);
static void test_3d() {
Tensor<float, 3> mat1(2, 3, 7);
Tensor<float, 3, RowMajor> mat2(2, 3, 7);
float val = 1.0f;
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
for (int k = 0; k < 7; ++k) {
mat1(i,j,k) = val;
mat2(i,j,k) = val;
mat1(i, j, k) = val;
mat2(i, j, k) = val;
val += 1.0f;
}
}
}
Tensor<float, 3> mat3(2,3,7);
Tensor<float, 3> mat3(2, 3, 7);
mat3 = mat1 + mat1;
Tensor<float, 3, RowMajor> mat4(2,3,7);
Tensor<float, 3, RowMajor> mat4(2, 3, 7);
mat4 = mat2 * 3.14f;
Tensor<float, 3> mat5(2,3,7);
Tensor<float, 3> mat5(2, 3, 7);
mat5 = (mat1 + mat1.constant(1)).inverse().log();
Tensor<float, 3, RowMajor> mat6(2,3,7);
Tensor<float, 3, RowMajor> mat6(2, 3, 7);
mat6 = mat2.pow(0.5f) * 3.14f;
Tensor<float, 3> mat7(2,3,7);
Tensor<float, 3> mat7(2, 3, 7);
mat7 = mat1.cwiseMax(mat5 * 2.0f).exp();
Tensor<float, 3, RowMajor> mat8(2,3,7);
Tensor<float, 3, RowMajor> mat8(2, 3, 7);
mat8 = (-mat2).exp() * 3.14f;
Tensor<float, 3, RowMajor> mat9(2,3,7);
Tensor<float, 3, RowMajor> mat9(2, 3, 7);
mat9 = mat2 + 3.14f;
Tensor<float, 3, RowMajor> mat10(2,3,7);
Tensor<float, 3, RowMajor> mat10(2, 3, 7);
mat10 = mat2 - 3.14f;
Tensor<float, 3, RowMajor> mat11(2,3,7);
Tensor<float, 3, RowMajor> mat11(2, 3, 7);
mat11 = mat2 / 3.14f;
val = 1.0f;
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
for (int k = 0; k < 7; ++k) {
VERIFY_IS_APPROX(mat3(i,j,k), val + val);
VERIFY_IS_APPROX(mat4(i,j,k), val * 3.14f);
VERIFY_IS_APPROX(mat5(i,j,k), logf(1.0f/(val + 1)));
VERIFY_IS_APPROX(mat6(i,j,k), sqrtf(val) * 3.14f);
VERIFY_IS_APPROX(mat7(i,j,k), expf((std::max)(val, mat5(i,j,k) * 2.0f)));
VERIFY_IS_APPROX(mat8(i,j,k), expf(-val) * 3.14f);
VERIFY_IS_APPROX(mat9(i,j,k), val + 3.14f);
VERIFY_IS_APPROX(mat10(i,j,k), val - 3.14f);
VERIFY_IS_APPROX(mat11(i,j,k), val / 3.14f);
VERIFY_IS_APPROX(mat3(i, j, k), val + val);
VERIFY_IS_APPROX(mat4(i, j, k), val * 3.14f);
VERIFY_IS_APPROX(mat5(i, j, k), logf(1.0f / (val + 1)));
VERIFY_IS_APPROX(mat6(i, j, k), sqrtf(val) * 3.14f);
VERIFY_IS_APPROX(mat7(i, j, k), expf((std::max)(val, mat5(i, j, k) * 2.0f)));
VERIFY_IS_APPROX(mat8(i, j, k), expf(-val) * 3.14f);
VERIFY_IS_APPROX(mat9(i, j, k), val + 3.14f);
VERIFY_IS_APPROX(mat10(i, j, k), val - 3.14f);
VERIFY_IS_APPROX(mat11(i, j, k), val / 3.14f);
val += 1.0f;
}
}
}
}
static void test_constants()
{
Tensor<float, 3> mat1(2,3,7);
Tensor<float, 3> mat2(2,3,7);
Tensor<float, 3> mat3(2,3,7);
static void test_constants() {
Tensor<float, 3> mat1(2, 3, 7);
Tensor<float, 3> mat2(2, 3, 7);
Tensor<float, 3> mat3(2, 3, 7);
float val = 1.0f;
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
for (int k = 0; k < 7; ++k) {
mat1(i,j,k) = val;
mat1(i, j, k) = val;
val += 1.0f;
}
}
@@ -185,16 +187,15 @@ static void test_constants()
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
for (int k = 0; k < 7; ++k) {
VERIFY_IS_APPROX(mat2(i,j,k), 3.14f);
VERIFY_IS_APPROX(mat3(i,j,k), expf((std::max)(val, 7.3f)));
VERIFY_IS_APPROX(mat2(i, j, k), 3.14f);
VERIFY_IS_APPROX(mat3(i, j, k), expf((std::max)(val, 7.3f)));
val += 1.0f;
}
}
}
}
static void test_boolean()
{
static void test_boolean() {
const int kSize = 31;
Tensor<int, 1> vec(kSize);
std::iota(vec.data(), vec.data() + kSize, 0);
@@ -221,17 +222,16 @@ static void test_boolean()
bool3 = (vec < vec.constant(4)).cast<bool>() && bool2;
}
static void test_functors()
{
Tensor<float, 3> mat1(2,3,7);
Tensor<float, 3> mat2(2,3,7);
Tensor<float, 3> mat3(2,3,7);
static void test_functors() {
Tensor<float, 3> mat1(2, 3, 7);
Tensor<float, 3> mat2(2, 3, 7);
Tensor<float, 3> mat3(2, 3, 7);
float val = 1.0f;
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
for (int k = 0; k < 7; ++k) {
mat1(i,j,k) = val;
mat1(i, j, k) = val;
val += 1.0f;
}
}
@@ -243,19 +243,18 @@ static void test_functors()
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
for (int k = 0; k < 7; ++k) {
VERIFY_IS_APPROX(mat2(i,j,k), asinf(1.0f / mat1(i,j,k)));
VERIFY_IS_APPROX(mat3(i,j,k), tanhf(mat1(i,j,k)));
VERIFY_IS_APPROX(mat2(i, j, k), asinf(1.0f / mat1(i, j, k)));
VERIFY_IS_APPROX(mat3(i, j, k), tanhf(mat1(i, j, k)));
val += 1.0f;
}
}
}
}
static void test_type_casting()
{
Tensor<bool, 3> mat1(2,3,7);
Tensor<float, 3> mat2(2,3,7);
Tensor<double, 3> mat3(2,3,7);
static void test_type_casting() {
Tensor<bool, 3> mat1(2, 3, 7);
Tensor<float, 3> mat2(2, 3, 7);
Tensor<double, 3> mat3(2, 3, 7);
mat1.setRandom();
mat2.setRandom();
@@ -263,7 +262,7 @@ static void test_type_casting()
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
for (int k = 0; k < 7; ++k) {
VERIFY_IS_APPROX(mat3(i,j,k), mat1(i,j,k) ? 1.0 : 0.0);
VERIFY_IS_APPROX(mat3(i, j, k), mat1(i, j, k) ? 1.0 : 0.0);
}
}
}
@@ -272,20 +271,19 @@ static void test_type_casting()
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
for (int k = 0; k < 7; ++k) {
VERIFY_IS_APPROX(mat3(i,j,k), static_cast<double>(mat2(i,j,k)));
VERIFY_IS_APPROX(mat3(i, j, k), static_cast<double>(mat2(i, j, k)));
}
}
}
}
static void test_select()
{
static void test_select() {
using TypedGTOp = internal::scalar_cmp_op<float, float, internal::cmp_GT, true>;
Tensor<float, 3> selector(2,3,7);
Tensor<float, 3> mat1(2,3,7);
Tensor<float, 3> mat2(2,3,7);
Tensor<float, 3> result(2,3,7);
Tensor<float, 3> selector(2, 3, 7);
Tensor<float, 3> mat1(2, 3, 7);
Tensor<float, 3> mat2(2, 3, 7);
Tensor<float, 3> result(2, 3, 7);
selector.setRandom();
mat1.setRandom();
@@ -297,7 +295,7 @@ static void test_select()
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
for (int k = 0; k < 7; ++k) {
VERIFY_IS_APPROX(result(i,j,k), (selector(i,j,k) > 0.5f) ? mat1(i,j,k) : mat2(i,j,k));
VERIFY_IS_APPROX(result(i, j, k), (selector(i, j, k) > 0.5f) ? mat1(i, j, k) : mat2(i, j, k));
}
}
}
@@ -312,7 +310,6 @@ static void test_select()
}
}
}
}
template <typename Scalar>
@@ -327,7 +324,7 @@ void test_minmax_nan_propagation_templ() {
vec_full_nan.setConstant(kNaN);
vec_zero.setZero();
vec_one_nan.setZero();
vec_one_nan(size/2) = kNaN;
vec_one_nan(size / 2) = kNaN;
auto verify_all_nan = [&](const Tensor<Scalar, 1>& v) {
for (int i = 0; i < size; ++i) {
@@ -434,8 +431,7 @@ void test_minmax_nan_propagation_templ() {
}
}
static void test_clip()
{
static void test_clip() {
Tensor<float, 1> vec(6);
vec(0) = 4.0;
vec(1) = 8.0;
@@ -454,14 +450,12 @@ static void test_clip()
}
}
static void test_minmax_nan_propagation()
{
static void test_minmax_nan_propagation() {
test_minmax_nan_propagation_templ<float>();
test_minmax_nan_propagation_templ<double>();
}
EIGEN_DECLARE_TEST(cxx11_tensor_expr)
{
EIGEN_DECLARE_TEST(cxx11_tensor_expr) {
CALL_SUBTEST(test_1d());
CALL_SUBTEST(test_2d());
CALL_SUBTEST(test_3d());