// 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 <numeric> #include "main.h" #include <Eigen/CXX11/Tensor> using Eigen::RowMajor; using Eigen::Tensor; 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; float data3[6]; TensorMap<Tensor<float, 1>> vec3(data3, 6); vec3 = vec1.sqrt(); float data4[6]; TensorMap<Tensor<float, 1, RowMajor>> vec4(data4, 6); vec4 = vec2.square(); float data5[6]; TensorMap<Tensor<float, 1, RowMajor>> vec5(data5, 6); vec5 = vec2.cube(); VERIFY_IS_APPROX(vec3(0), sqrtf(4.0)); VERIFY_IS_APPROX(vec3(1), sqrtf(8.0)); VERIFY_IS_APPROX(vec3(2), sqrtf(15.0)); VERIFY_IS_APPROX(vec3(3), sqrtf(16.0)); VERIFY_IS_APPROX(vec3(4), sqrtf(23.0)); VERIFY_IS_APPROX(vec3(5), sqrtf(42.0)); VERIFY_IS_APPROX(vec4(0), 0.0f); VERIFY_IS_APPROX(vec4(1), 1.0f); VERIFY_IS_APPROX(vec4(2), 2.0f * 2.0f); VERIFY_IS_APPROX(vec4(3), 3.0f * 3.0f); VERIFY_IS_APPROX(vec4(4), 4.0f * 4.0f); VERIFY_IS_APPROX(vec4(5), 5.0f * 5.0f); VERIFY_IS_APPROX(vec5(0), 0.0f); VERIFY_IS_APPROX(vec5(1), 1.0f); VERIFY_IS_APPROX(vec5(2), 2.0f * 2.0f * 2.0f); VERIFY_IS_APPROX(vec5(3), 3.0f * 3.0f * 3.0f); VERIFY_IS_APPROX(vec5(4), 4.0f * 4.0f * 4.0f); VERIFY_IS_APPROX(vec5(5), 5.0f * 5.0f * 5.0f); vec3 = vec1 + vec2; VERIFY_IS_APPROX(vec3(0), 4.0f + 0.0f); VERIFY_IS_APPROX(vec3(1), 8.0f + 1.0f); VERIFY_IS_APPROX(vec3(2), 15.0f + 2.0f); VERIFY_IS_APPROX(vec3(3), 16.0f + 3.0f); VERIFY_IS_APPROX(vec3(4), 23.0f + 4.0f); VERIFY_IS_APPROX(vec3(5), 42.0f + 5.0f); } 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; 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); 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(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); 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; val += 1.0f; } } } Tensor<float, 3> mat3(2, 3, 7); mat3 = mat1 + mat1; Tensor<float, 3, RowMajor> mat4(2, 3, 7); mat4 = mat2 * 3.14f; Tensor<float, 3> mat5(2, 3, 7); mat5 = (mat1 + mat1.constant(1)).inverse().log(); Tensor<float, 3, RowMajor> mat6(2, 3, 7); mat6 = mat2.pow(0.5f) * 3.14f; Tensor<float, 3> mat7(2, 3, 7); mat7 = mat1.cwiseMax(mat5 * 2.0f).exp(); Tensor<float, 3, RowMajor> mat8(2, 3, 7); mat8 = (-mat2).exp() * 3.14f; Tensor<float, 3, RowMajor> mat9(2, 3, 7); mat9 = mat2 + 3.14f; Tensor<float, 3, RowMajor> mat10(2, 3, 7); mat10 = mat2 - 3.14f; 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); 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); 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; val += 1.0f; } } } mat2 = mat1.constant(3.14f); mat3 = mat1.cwiseMax(7.3f).exp(); 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(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() { const int kSize = 31; Tensor<int, 1> vec(kSize); std::iota(vec.data(), vec.data() + kSize, 0); // Test ||. Tensor<bool, 1> bool1 = (vec < vec.constant(1) || vec > vec.constant(4)).cast<bool>(); for (int i = 0; i < kSize; ++i) { bool expected = i < 1 || i > 4; VERIFY_IS_EQUAL(bool1[i], expected); } // Test &&, including cast of operand vec. Tensor<bool, 1> bool2 = vec.cast<bool>() && (vec < vec.constant(4)).cast<bool>(); for (int i = 0; i < kSize; ++i) { bool expected = bool(i) && i < 4; VERIFY_IS_EQUAL(bool2[i], expected); } // Compilation tests: // Test Tensor<bool> against results of cast or comparison; verifies that // CoeffReturnType is set to match Op return type of bool for Unary and Binary // Ops. Tensor<bool, 1> bool3 = vec.cast<bool>() && bool2; 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); 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; val += 1.0f; } } } mat2 = mat1.inverse().unaryExpr(&asinf); mat3 = mat1.unaryExpr(&tanhf); 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(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); mat1.setRandom(); mat2.setRandom(); mat3 = mat1.cast<double>(); 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); } } } mat3 = mat2.cast<double>(); 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))); } } } } 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); selector.setRandom(); mat1.setRandom(); mat2.setRandom(); // test select with a boolean condition result = (selector > selector.constant(0.5f)).select(mat1, mat2); 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)); } } } // test select with a typed condition result = selector.binaryExpr(selector.constant(0.5f), TypedGTOp()).select(mat1, mat2); 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)); } } } } template <typename Scalar> void test_minmax_nan_propagation_templ() { for (int size = 1; size < 17; ++size) { const Scalar kNaN = std::numeric_limits<Scalar>::quiet_NaN(); const Scalar kInf = std::numeric_limits<Scalar>::infinity(); const Scalar kZero(0); Tensor<Scalar, 1> vec_full_nan(size); Tensor<Scalar, 1> vec_one_nan(size); Tensor<Scalar, 1> vec_zero(size); vec_full_nan.setConstant(kNaN); vec_zero.setZero(); vec_one_nan.setZero(); vec_one_nan(size / 2) = kNaN; auto verify_all_nan = [&](const Tensor<Scalar, 1>& v) { for (int i = 0; i < size; ++i) { VERIFY((numext::isnan)(v(i))); } }; auto verify_all_zero = [&](const Tensor<Scalar, 1>& v) { for (int i = 0; i < size; ++i) { VERIFY_IS_EQUAL(v(i), Scalar(0)); } }; // Test NaN propagating max. // max(nan, nan) = nan // max(nan, 0) = nan // max(0, nan) = nan // max(0, 0) = 0 verify_all_nan(vec_full_nan.template cwiseMax<PropagateNaN>(kNaN)); verify_all_nan(vec_full_nan.template cwiseMax<PropagateNaN>(vec_full_nan)); verify_all_nan(vec_full_nan.template cwiseMax<PropagateNaN>(kZero)); verify_all_nan(vec_full_nan.template cwiseMax<PropagateNaN>(vec_zero)); verify_all_nan(vec_zero.template cwiseMax<PropagateNaN>(kNaN)); verify_all_nan(vec_zero.template cwiseMax<PropagateNaN>(vec_full_nan)); verify_all_zero(vec_zero.template cwiseMax<PropagateNaN>(kZero)); verify_all_zero(vec_zero.template cwiseMax<PropagateNaN>(vec_zero)); // Test number propagating max. // max(nan, nan) = nan // max(nan, 0) = 0 // max(0, nan) = 0 // max(0, 0) = 0 verify_all_nan(vec_full_nan.template cwiseMax<PropagateNumbers>(kNaN)); verify_all_nan(vec_full_nan.template cwiseMax<PropagateNumbers>(vec_full_nan)); verify_all_zero(vec_full_nan.template cwiseMax<PropagateNumbers>(kZero)); verify_all_zero(vec_full_nan.template cwiseMax<PropagateNumbers>(vec_zero)); verify_all_zero(vec_zero.template cwiseMax<PropagateNumbers>(kNaN)); verify_all_zero(vec_zero.template cwiseMax<PropagateNumbers>(vec_full_nan)); verify_all_zero(vec_zero.template cwiseMax<PropagateNumbers>(kZero)); verify_all_zero(vec_zero.template cwiseMax<PropagateNumbers>(vec_zero)); // Test NaN propagating min. // min(nan, nan) = nan // min(nan, 0) = nan // min(0, nan) = nan // min(0, 0) = 0 verify_all_nan(vec_full_nan.template cwiseMin<PropagateNaN>(kNaN)); verify_all_nan(vec_full_nan.template cwiseMin<PropagateNaN>(vec_full_nan)); verify_all_nan(vec_full_nan.template cwiseMin<PropagateNaN>(kZero)); verify_all_nan(vec_full_nan.template cwiseMin<PropagateNaN>(vec_zero)); verify_all_nan(vec_zero.template cwiseMin<PropagateNaN>(kNaN)); verify_all_nan(vec_zero.template cwiseMin<PropagateNaN>(vec_full_nan)); verify_all_zero(vec_zero.template cwiseMin<PropagateNaN>(kZero)); verify_all_zero(vec_zero.template cwiseMin<PropagateNaN>(vec_zero)); // Test number propagating min. // min(nan, nan) = nan // min(nan, 0) = 0 // min(0, nan) = 0 // min(0, 0) = 0 verify_all_nan(vec_full_nan.template cwiseMin<PropagateNumbers>(kNaN)); verify_all_nan(vec_full_nan.template cwiseMin<PropagateNumbers>(vec_full_nan)); verify_all_zero(vec_full_nan.template cwiseMin<PropagateNumbers>(kZero)); verify_all_zero(vec_full_nan.template cwiseMin<PropagateNumbers>(vec_zero)); verify_all_zero(vec_zero.template cwiseMin<PropagateNumbers>(kNaN)); verify_all_zero(vec_zero.template cwiseMin<PropagateNumbers>(vec_full_nan)); verify_all_zero(vec_zero.template cwiseMin<PropagateNumbers>(kZero)); verify_all_zero(vec_zero.template cwiseMin<PropagateNumbers>(vec_zero)); // Test min and max reduction Tensor<Scalar, 0> val; val = vec_zero.minimum(); VERIFY_IS_EQUAL(val(), kZero); val = vec_zero.template minimum<PropagateNaN>(); VERIFY_IS_EQUAL(val(), kZero); val = vec_zero.template minimum<PropagateNumbers>(); VERIFY_IS_EQUAL(val(), kZero); val = vec_zero.maximum(); VERIFY_IS_EQUAL(val(), kZero); val = vec_zero.template maximum<PropagateNaN>(); VERIFY_IS_EQUAL(val(), kZero); val = vec_zero.template maximum<PropagateNumbers>(); VERIFY_IS_EQUAL(val(), kZero); // Test NaN propagation for tensor of all NaNs. val = vec_full_nan.template minimum<PropagateNaN>(); VERIFY((numext::isnan)(val())); val = vec_full_nan.template minimum<PropagateNumbers>(); VERIFY_IS_EQUAL(val(), kInf); val = vec_full_nan.template maximum<PropagateNaN>(); VERIFY((numext::isnan)(val())); val = vec_full_nan.template maximum<PropagateNumbers>(); VERIFY_IS_EQUAL(val(), -kInf); // Test NaN propagation for tensor with a single NaN. val = vec_one_nan.template minimum<PropagateNaN>(); VERIFY((numext::isnan)(val())); val = vec_one_nan.template minimum<PropagateNumbers>(); VERIFY_IS_EQUAL(val(), (size == 1 ? kInf : kZero)); val = vec_one_nan.template maximum<PropagateNaN>(); VERIFY((numext::isnan)(val())); val = vec_one_nan.template maximum<PropagateNumbers>(); VERIFY_IS_EQUAL(val(), (size == 1 ? -kInf : kZero)); } } static void test_clip() { Tensor<float, 1> vec(6); vec(0) = 4.0; vec(1) = 8.0; vec(2) = 15.0; vec(3) = 16.0; vec(4) = 23.0; vec(5) = 42.0; float kMin = 20; float kMax = 30; Tensor<float, 1> vec_clipped(6); vec_clipped = vec.clip(kMin, kMax); for (int i = 0; i < 6; ++i) { VERIFY_IS_EQUAL(vec_clipped(i), numext::mini(numext::maxi(vec(i), kMin), kMax)); } } static void test_minmax_nan_propagation() { test_minmax_nan_propagation_templ<float>(); test_minmax_nan_propagation_templ<double>(); } EIGEN_DECLARE_TEST(cxx11_tensor_expr) { CALL_SUBTEST(test_1d()); CALL_SUBTEST(test_2d()); CALL_SUBTEST(test_3d()); CALL_SUBTEST(test_constants()); CALL_SUBTEST(test_boolean()); CALL_SUBTEST(test_functors()); CALL_SUBTEST(test_type_casting()); CALL_SUBTEST(test_select()); CALL_SUBTEST(test_clip()); // Nan propagation does currently not work like one would expect from std::max/std::min, // so we disable it for now #if !EIGEN_ARCH_ARM_OR_ARM64 CALL_SUBTEST(test_minmax_nan_propagation()); #endif }