GitHub-Proxy/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2025-08-13 04:09:10 +08:00
Code Issues Packages Projects Releases Wiki Activity

Made the tensor fft test compile on tegra x1

Browse Source
This commit is contained in:
Benoit Steiner 2016-02-06 01:42:14 +00:00
parent 5b2d287878
commit fb00a4af2b
1 changed files with 23 additions and 23 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

46
unsupported/test/cxx11_tensor_fft.cpp
View File

@ -14,7 +14,7 @@ using Eigen::Tensor;
template <int DataLayout> template <int DataLayout>
static void test_fft_2D_golden() { static void test_fft_2D_golden() {
Tensor<float, 2, DataLayout, long> input(2, 3); Tensor<float, 2, DataLayout> input(2, 3);
input(0, 0) = 1; input(0, 0) = 1;
input(0, 1) = 2; input(0, 1) = 2;
input(0, 2) = 3; input(0, 2) = 3;
@ -22,11 +22,11 @@ static void test_fft_2D_golden() {
input(1, 1) = 5; input(1, 1) = 5;
input(1, 2) = 6; input(1, 2) = 6;
array<int, 2> fft; array<ptrdiff_t, 2> fft;
fft[0] = 0; fft[0] = 0;
fft[1] = 1; fft[1] = 1;
Tensor<std::complex<float>, 2, DataLayout, long> output = input.template fft<Eigen::BothParts, Eigen::FFT_FORWARD>(fft); Tensor<std::complex<float>, 2, DataLayout> output = input.template fft<Eigen::BothParts, Eigen::FFT_FORWARD>(fft);
std::complex<float> output_golden[6]; // in ColMajor order std::complex<float> output_golden[6]; // in ColMajor order
output_golden[0] = std::complex<float>(21, 0); output_golden[0] = std::complex<float>(21, 0);
@ -57,24 +57,24 @@ static void test_fft_2D_golden() {
} }
static void test_fft_complex_input_golden() { static void test_fft_complex_input_golden() {
Tensor<std::complex<float>, 1, ColMajor, long> input(5); Tensor<std::complex<float>, 1, ColMajor> input(5);
input(0) = std::complex<float>(1, 1); input(0) = std::complex<float>(1, 1);
input(1) = std::complex<float>(2, 2); input(1) = std::complex<float>(2, 2);
input(2) = std::complex<float>(3, 3); input(2) = std::complex<float>(3, 3);
input(3) = std::complex<float>(4, 4); input(3) = std::complex<float>(4, 4);
input(4) = std::complex<float>(5, 5); input(4) = std::complex<float>(5, 5);
array<int, 1> fft; array<ptrdiff_t, 1> fft;
fft[0] = 0; fft[0] = 0;
Tensor<std::complex<float>, 1, ColMajor, long> forward_output_both_parts = input.fft<BothParts, FFT_FORWARD>(fft); Tensor<std::complex<float>, 1, ColMajor> forward_output_both_parts = input.fft<BothParts, FFT_FORWARD>(fft);
Tensor<std::complex<float>, 1, ColMajor, long> reverse_output_both_parts = input.fft<BothParts, FFT_REVERSE>(fft); Tensor<std::complex<float>, 1, ColMajor> reverse_output_both_parts = input.fft<BothParts, FFT_REVERSE>(fft);
Tensor<float, 1, ColMajor, long> forward_output_real_part = input.fft<RealPart, FFT_FORWARD>(fft); Tensor<float, 1, ColMajor> forward_output_real_part = input.fft<RealPart, FFT_FORWARD>(fft);
Tensor<float, 1, ColMajor, long> reverse_output_real_part = input.fft<RealPart, FFT_REVERSE>(fft); Tensor<float, 1, ColMajor> reverse_output_real_part = input.fft<RealPart, FFT_REVERSE>(fft);
Tensor<float, 1, ColMajor, long> forward_output_imag_part = input.fft<ImagPart, FFT_FORWARD>(fft); Tensor<float, 1, ColMajor> forward_output_imag_part = input.fft<ImagPart, FFT_FORWARD>(fft);
Tensor<float, 1, ColMajor, long> reverse_output_imag_part = input.fft<ImagPart, FFT_REVERSE>(fft); Tensor<float, 1, ColMajor> reverse_output_imag_part = input.fft<ImagPart, FFT_REVERSE>(fft);
VERIFY_IS_EQUAL(forward_output_both_parts.dimension(0), input.dimension(0)); VERIFY_IS_EQUAL(forward_output_both_parts.dimension(0), input.dimension(0));
VERIFY_IS_EQUAL(reverse_output_both_parts.dimension(0), input.dimension(0)); VERIFY_IS_EQUAL(reverse_output_both_parts.dimension(0), input.dimension(0));
@ -114,24 +114,24 @@ static void test_fft_complex_input_golden() {
} }
static void test_fft_real_input_golden() { static void test_fft_real_input_golden() {
Tensor<float, 1, ColMajor, long> input(5); Tensor<float, 1, ColMajor> input(5);
input(0) = 1.0; input(0) = 1.0;
input(1) = 2.0; input(1) = 2.0;
input(2) = 3.0; input(2) = 3.0;
input(3) = 4.0; input(3) = 4.0;
input(4) = 5.0; input(4) = 5.0;
array<int, 1> fft; array<ptrdiff_t, 1> fft;
fft[0] = 0; fft[0] = 0;
Tensor<std::complex<float>, 1, ColMajor, long> forward_output_both_parts = input.fft<BothParts, FFT_FORWARD>(fft); Tensor<std::complex<float>, 1, ColMajor> forward_output_both_parts = input.fft<BothParts, FFT_FORWARD>(fft);
Tensor<std::complex<float>, 1, ColMajor, long> reverse_output_both_parts = input.fft<BothParts, FFT_REVERSE>(fft); Tensor<std::complex<float>, 1, ColMajor> reverse_output_both_parts = input.fft<BothParts, FFT_REVERSE>(fft);
Tensor<float, 1, ColMajor, long> forward_output_real_part = input.fft<RealPart, FFT_FORWARD>(fft); Tensor<float, 1, ColMajor> forward_output_real_part = input.fft<RealPart, FFT_FORWARD>(fft);
Tensor<float, 1, ColMajor, long> reverse_output_real_part = input.fft<RealPart, FFT_REVERSE>(fft); Tensor<float, 1, ColMajor> reverse_output_real_part = input.fft<RealPart, FFT_REVERSE>(fft);
Tensor<float, 1, ColMajor, long> forward_output_imag_part = input.fft<ImagPart, FFT_FORWARD>(fft); Tensor<float, 1, ColMajor> forward_output_imag_part = input.fft<ImagPart, FFT_FORWARD>(fft);
Tensor<float, 1, ColMajor, long> reverse_output_imag_part = input.fft<ImagPart, FFT_REVERSE>(fft); Tensor<float, 1, ColMajor> reverse_output_imag_part = input.fft<ImagPart, FFT_REVERSE>(fft);
VERIFY_IS_EQUAL(forward_output_both_parts.dimension(0), input.dimension(0)); VERIFY_IS_EQUAL(forward_output_both_parts.dimension(0), input.dimension(0));
VERIFY_IS_EQUAL(reverse_output_both_parts.dimension(0), input.dimension(0)); VERIFY_IS_EQUAL(reverse_output_both_parts.dimension(0), input.dimension(0));
@ -178,17 +178,17 @@ static void test_fft_real_input_golden() {
template <int DataLayout, typename RealScalar, bool isComplexInput, int FFTResultType, int FFTDirection, int TensorRank> template <int DataLayout, typename RealScalar, bool isComplexInput, int FFTResultType, int FFTDirection, int TensorRank>
static void test_fft_real_input_energy() { static void test_fft_real_input_energy() {
Eigen::DSizes<long, TensorRank> dimensions; Eigen::DSizes<ptrdiff_t, TensorRank> dimensions;
int total_size = 1; ptrdiff_t total_size = 1;
for (int i = 0; i < TensorRank; ++i) { for (int i = 0; i < TensorRank; ++i) {
dimensions[i] = rand() % 20 + 1; dimensions[i] = rand() % 20 + 1;
total_size *= dimensions[i]; total_size *= dimensions[i];
} }
const DSizes<long, TensorRank> arr = dimensions; const DSizes<ptrdiff_t, TensorRank> arr = dimensions;
typedef typename internal::conditional<isComplexInput == true, std::complex<RealScalar>, RealScalar>::type InputScalar; typedef typename internal::conditional<isComplexInput == true, std::complex<RealScalar>, RealScalar>::type InputScalar;
Tensor<InputScalar, TensorRank, DataLayout, long> input; Tensor<InputScalar, TensorRank, DataLayout> input;
input.resize(arr); input.resize(arr);
input.setRandom(); input.setRandom();