mirror/eigen
2
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2024-12-21 07:19:46 +08:00
Code Issues Packages Projects Releases Wiki Activity

Avoid using the auto keyword to make the tensor block access test more portable

Browse Source
This commit is contained in:
Benoit Steiner 2018-08-16 10:49:47 -07:00
parent e23c8c294e
commit ede580ccda
1 changed files with 28 additions and 28 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

56
unsupported/test/cxx11_tensor_block_access.cpp
View File

@ -104,7 +104,7 @@ static void test_block_mapper_sanity()
VERIFY_IS_EQUAL(uniform_block_mapper.block_dims_total_size(), 100);
// 10x10 blocks
auto uniform_b0 = uniform_block_mapper.GetBlockForIndex(0, NULL);
typename TensorBlockMapper::Block uniform_b0 = uniform_block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(uniform_b0.block_sizes().at(0), 10);
VERIFY_IS_EQUAL(uniform_b0.block_sizes().at(1), 10);
// Depending on a layout we stride by cols rows.
@ -122,7 +122,7 @@ static void test_block_mapper_sanity()
VERIFY_IS_EQUAL(skewed_block_mapper.block_dims_total_size(), 100);
// 1x100 (100x1) rows/cols depending on a tensor layout.
auto skewed_b0 = skewed_block_mapper.GetBlockForIndex(0, NULL);
typename TensorBlockMapper::Block skewed_b0 = skewed_block_mapper.GetBlockForIndex(0, NULL);
VERIFY_IS_EQUAL(skewed_b0.block_sizes().at(0), choose(Layout, 100, 1));
VERIFY_IS_EQUAL(skewed_b0.block_sizes().at(1), choose(Layout, 1, 100));
// Depending on a layout we stride by cols rows.
@ -203,7 +203,7 @@ static void test_slice_block_mapper_maps_every_element() {
// Keep track of elements indices available via block access.
std::set<Index> coeff_set;
auto total_coeffs = static_cast<int>(tensor_slice_extents.TotalSize());
int total_coeffs = static_cast<int>(tensor_slice_extents.TotalSize());
// Pick a random dimension sizes for the tensor blocks.
DSizes<Index, NumDims> block_sizes;
@ -237,7 +237,7 @@ static void test_block_io_copy_data_from_source_to_target() {
TensorBlockWriter;
DSizes<Index, NumDims> input_tensor_dims = RandomDims<NumDims>();
const auto input_tensor_size = input_tensor_dims.TotalSize();
const Index input_tensor_size = input_tensor_dims.TotalSize();
T* input_data = GenerateRandomData<T>(input_tensor_size);
T* output_data = new T[input_tensor_size];
@ -316,7 +316,7 @@ static void test_block_io_copy_using_reordered_dimensions() {
TensorBlockWriter;
DSizes<Index, NumDims> input_tensor_dims = RandomDims<NumDims>();
const auto input_tensor_size = input_tensor_dims.TotalSize();
const Index input_tensor_size = input_tensor_dims.TotalSize();
// Create a random input tensor.
T* input_data = GenerateRandomData<T>(input_tensor_size);
@ -339,8 +339,8 @@ static void test_block_io_copy_using_reordered_dimensions() {
TensorBlockMapper block_mapper(output_tensor_dims, RandomShape(),
RandomTargetSize(input_tensor_dims));
auto* block_data = new T[block_mapper.block_dims_total_size()];
auto* output_data = new T[input_tensor_size];
T* block_data = new T[block_mapper.block_dims_total_size()];
T* output_data = new T[input_tensor_size];
array<Index, NumDims> input_tensor_strides =
ComputeStrides<Layout, NumDims>(input_tensor_dims);
@ -382,8 +382,8 @@ static void test_block_io_zero_stride()
input_tensor_dims[0] = 1;
input_tensor_dims[2] = 1;
input_tensor_dims[4] = 1;
const auto input_tensor_size = input_tensor_dims.TotalSize();
auto* input_data = GenerateRandomData<float>(input_tensor_size);
const Index input_tensor_size = input_tensor_dims.TotalSize();
float* input_data = GenerateRandomData<float>(input_tensor_size);
DSizes<Index, 5> output_tensor_dims = rnd_dims;
@ -424,7 +424,7 @@ static void test_block_io_zero_stride()
};
{
auto* output_data = new float[output_tensor_dims.TotalSize()];
float* output_data = new float[output_tensor_dims.TotalSize()];
TensorBlock read_block(0, output_tensor_dims, output_tensor_strides,
input_tensor_strides_with_zeros, output_data);
TensorBlockReader::Run(&read_block, input_data);
@ -433,7 +433,7 @@ static void test_block_io_zero_stride()
}
{
auto* output_data = new float[output_tensor_dims.TotalSize()];
float* output_data = new float[output_tensor_dims.TotalSize()];
TensorBlock write_block(0, output_tensor_dims,
input_tensor_strides_with_zeros,
output_tensor_strides, input_data);
@ -456,14 +456,14 @@ static void test_block_io_squeeze_ones() {
// Total size > 1.
{
DSizes<Index, 5> block_sizes(1, 2, 1, 2, 1);
const auto total_size = block_sizes.TotalSize();
const Index total_size = block_sizes.TotalSize();
// Create a random input tensor.
auto* input_data = GenerateRandomData<float>(total_size);
float* input_data = GenerateRandomData<float>(total_size);
DSizes<Index, 5> strides(ComputeStrides<Layout, 5>(block_sizes));
{
auto* output_data = new float[block_sizes.TotalSize()];
float* output_data = new float[block_sizes.TotalSize()];
TensorBlock read_block(0, block_sizes, strides, strides, output_data);
TensorBlockReader::Run(&read_block, input_data);
for (int i = 0; i < total_size; ++i) {
@ -473,7 +473,7 @@ static void test_block_io_squeeze_ones() {
}
{
auto* output_data = new float[block_sizes.TotalSize()];
float* output_data = new float[block_sizes.TotalSize()];
TensorBlock write_block(0, block_sizes, strides, strides, input_data);
TensorBlockWriter::Run(write_block, output_data);
for (int i = 0; i < total_size; ++i) {
@ -486,14 +486,14 @@ static void test_block_io_squeeze_ones() {
// Total size == 1.
{
DSizes<Index, 5> block_sizes(1, 1, 1, 1, 1);
const auto total_size = block_sizes.TotalSize();
const Index total_size = block_sizes.TotalSize();
// Create a random input tensor.
auto* input_data = GenerateRandomData<float>(total_size);
float* input_data = GenerateRandomData<float>(total_size);
DSizes<Index, 5> strides(ComputeStrides<Layout, 5>(block_sizes));
{
auto* output_data = new float[block_sizes.TotalSize()];
float* output_data = new float[block_sizes.TotalSize()];
TensorBlock read_block(0, block_sizes, strides, strides, output_data);
TensorBlockReader::Run(&read_block, input_data);
for (int i = 0; i < total_size; ++i) {
@ -503,7 +503,7 @@ static void test_block_io_squeeze_ones() {
}
{
auto* output_data = new float[block_sizes.TotalSize()];
float* output_data = new float[block_sizes.TotalSize()];
TensorBlock write_block(0, block_sizes, strides, strides, input_data);
TensorBlockWriter::Run(write_block, output_data);
for (int i = 0; i < total_size; ++i) {
@ -524,7 +524,7 @@ static void test_block_cwise_binary_io_basic() {
DSizes<Index, NumDims> block_sizes = RandomDims<NumDims>();
DSizes<Index, NumDims> strides(ComputeStrides<Layout, NumDims>(block_sizes));
const auto total_size = block_sizes.TotalSize();
const Index total_size = block_sizes.TotalSize();
// Create a random input tensors.
T* left_data = GenerateRandomData<T>(total_size);
@ -553,13 +553,13 @@ static void test_block_cwise_binary_io_squeeze_ones() {
DSizes<Index, 5> block_sizes(1, 2, 1, 3, 1);
DSizes<Index, 5> strides(ComputeStrides<Layout, 5>(block_sizes));
const auto total_size = block_sizes.TotalSize();
const Index total_size = block_sizes.TotalSize();
// Create a random input tensors.
auto* left_data = GenerateRandomData<float>(total_size);
auto* right_data = GenerateRandomData<float>(total_size);
float* left_data = GenerateRandomData<float>(total_size);
float* right_data = GenerateRandomData<float>(total_size);
auto* output_data = new float[total_size];
float* output_data = new float[total_size];
BinaryFunctor functor;
TensorBlockCwiseBinaryIO::Run(functor, block_sizes, strides, output_data,
strides, left_data, strides, right_data);
@ -600,14 +600,14 @@ static void test_block_cwise_binary_io_zero_strides() {
right_strides[3] = 0;
// Generate random data.
auto* left_data = GenerateRandomData<float>(left_sizes.TotalSize());
auto* right_data = GenerateRandomData<float>(right_sizes.TotalSize());
float* left_data = GenerateRandomData<float>(left_sizes.TotalSize());
float* right_data = GenerateRandomData<float>(right_sizes.TotalSize());
DSizes<Index, 5> output_sizes = rnd_dims;
DSizes<Index, 5> output_strides(ComputeStrides<Layout, 5>(output_sizes));
const auto output_total_size = output_sizes.TotalSize();
auto* output_data = new float[output_total_size];
const Index output_total_size = output_sizes.TotalSize();
float* output_data = new float[output_total_size];
BinaryFunctor functor;
TensorBlockCwiseBinaryIO::Run(functor, output_sizes, output_strides,