Apply clang-format on repository
Code is formatted as per a revised clang format configuration
file(not part of this delivery). Version 14.0.6 is used.
Exclusion List:
- files with .cl extension
- files that are not strictly C/C++ (e.g. Android.bp, Sconscript ...)
And the following directories
- compute_kernel_writer/validation/
- tests/
- include/
- src/core/NEON/kernels/convolution/
- src/core/NEON/kernels/arm_gemm/
- src/core/NEON/kernels/arm_conv/
- data/
There will be a follow up for formatting of .cl files and the
files under tests/ and compute_kernel_writer/validation/.
Signed-off-by: Felix Thomasmathibalan <felixjohnny.thomasmathibalan@arm.com>
Change-Id: Ib7eb1fcf4e7537b9feaefcfc15098a804a3fde0a
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10391
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
diff --git a/utils/Utils.h b/utils/Utils.h
index d181022..626cbcf 100644
--- a/utils/Utils.h
+++ b/utils/Utils.h
@@ -87,9 +87,9 @@
return true;
};
/** Run the example. */
- virtual void do_run() {};
+ virtual void do_run(){};
/** Teardown the example. */
- virtual void do_teardown() {};
+ virtual void do_teardown(){};
/** Default destructor. */
virtual ~Example() = default;
@@ -117,7 +117,8 @@
* @param[in] g Green colour to use
* @param[in] b Blue colour to use
*/
-void draw_detection_rectangle(arm_compute::ITensor *tensor, const arm_compute::DetectionWindow &rect, uint8_t r, uint8_t g, uint8_t b);
+void draw_detection_rectangle(
+ arm_compute::ITensor *tensor, const arm_compute::DetectionWindow &rect, uint8_t r, uint8_t g, uint8_t b);
/** Gets image type given a file
*
@@ -157,7 +158,7 @@
const unsigned int i = 1;
const char *c = reinterpret_cast<const char *>(&i);
std::string endianness;
- if(*c == 1)
+ if (*c == 1)
{
endianness = std::string("<");
}
@@ -167,7 +168,7 @@
}
const std::string no_endianness("|");
- switch(data_type)
+ switch (data_type)
{
case DataType::U8:
case DataType::QASYMM8:
@@ -253,7 +254,8 @@
template <typename T>
class uniform_real_distribution_16bit
{
- static_assert(std::is_same<T, half>::value || std::is_same<T, bfloat16>::value, "Only half and bfloat16 data types supported");
+ static_assert(std::is_same<T, half>::value || std::is_same<T, bfloat16>::value,
+ "Only half and bfloat16 data types supported");
public:
using result_type = T;
@@ -262,8 +264,7 @@
* @param[in] min Minimum value of the distribution
* @param[in] max Maximum value of the distribution
*/
- explicit uniform_real_distribution_16bit(float min = 0.f, float max = 1.0)
- : dist(min, max)
+ explicit uniform_real_distribution_16bit(float min = 0.f, float max = 1.0) : dist(min, max)
{
}
@@ -285,8 +286,7 @@
{
public:
/** Default constructor */
- NPYLoader()
- : _fs(), _shape(), _fortran_order(false), _typestring(), _file_layout(DataLayout::NCHW)
+ NPYLoader() : _fs(), _shape(), _fortran_order(false), _typestring(), _file_layout(DataLayout::NCHW)
{
}
@@ -310,7 +310,7 @@
_fortran_order = header.fortran_order;
_typestring = header.dtype.str();
}
- catch(const std::ifstream::failure &e)
+ catch (const std::ifstream::failure &e)
{
ARM_COMPUTE_ERROR_VAR("Accessing %s: %s", npy_filename.c_str(), e.what());
}
@@ -341,10 +341,10 @@
// Use the size of the input NPY tensor
TensorShape shape;
shape.set_num_dimensions(_shape.size());
- for(size_t i = 0; i < _shape.size(); ++i)
+ for (size_t i = 0; i < _shape.size(); ++i)
{
size_t src = i;
- if(_fortran_order)
+ if (_fortran_order)
{
src = _shape.size() - 1 - i;
}
@@ -365,7 +365,8 @@
void fill_tensor(T &tensor)
{
ARM_COMPUTE_ERROR_ON(!is_open());
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_NOT_IN(&tensor, arm_compute::DataType::QASYMM8, arm_compute::DataType::S32, arm_compute::DataType::F32, arm_compute::DataType::F16);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_NOT_IN(&tensor, arm_compute::DataType::QASYMM8, arm_compute::DataType::S32,
+ arm_compute::DataType::F32, arm_compute::DataType::F16);
try
{
// Map buffer if creating a CLTensor
@@ -377,7 +378,8 @@
const size_t end_position = _fs.tellg();
_fs.seekg(current_position, std::ios_base::beg);
- ARM_COMPUTE_ERROR_ON_MSG((end_position - current_position) < tensor.info()->tensor_shape().total_size() * tensor.info()->element_size(),
+ ARM_COMPUTE_ERROR_ON_MSG((end_position - current_position) <
+ tensor.info()->tensor_shape().total_size() * tensor.info()->element_size(),
"Not enough data in file");
ARM_COMPUTE_UNUSED(end_position);
@@ -385,12 +387,12 @@
std::string expect_typestr = get_typestring(tensor.info()->data_type());
bool enable_f32_to_f16_conversion = false;
- if(_typestring != expect_typestr)
+ if (_typestring != expect_typestr)
{
const std::string f32_typestring = "<f4";
const std::string f16_typestring = "<f2";
// if typestring does not match, check whether _typestring is F32 and can be downcasted to expect_typestr
- if(_typestring == f32_typestring && expect_typestr == f16_typestring)
+ if (_typestring == f32_typestring && expect_typestr == f16_typestring)
{
enable_f32_to_f16_conversion = true;
}
@@ -402,11 +404,11 @@
bool are_layouts_different = (_file_layout != tensor.info()->data_layout());
// Correct dimensions (Needs to match TensorShape dimension corrections)
- if(_shape.size() != tensor.info()->tensor_shape().num_dimensions())
+ if (_shape.size() != tensor.info()->tensor_shape().num_dimensions())
{
- for(int i = static_cast<int>(_shape.size()) - 1; i > 0; --i)
+ for (int i = static_cast<int>(_shape.size()) - 1; i > 0; --i)
{
- if(_shape[i] == 1)
+ if (_shape[i] == 1)
{
_shape.pop_back();
}
@@ -419,22 +421,28 @@
TensorShape permuted_shape = tensor.info()->tensor_shape();
arm_compute::PermutationVector perm;
- if(are_layouts_different && tensor.info()->tensor_shape().num_dimensions() > 2)
+ if (are_layouts_different && tensor.info()->tensor_shape().num_dimensions() > 2)
{
- perm = (tensor.info()->data_layout() == arm_compute::DataLayout::NHWC) ? arm_compute::PermutationVector(2U, 0U, 1U) : arm_compute::PermutationVector(1U, 2U, 0U);
- arm_compute::PermutationVector perm_vec = (tensor.info()->data_layout() == arm_compute::DataLayout::NCHW) ? arm_compute::PermutationVector(2U, 0U, 1U) : arm_compute::PermutationVector(1U, 2U, 0U);
+ perm = (tensor.info()->data_layout() == arm_compute::DataLayout::NHWC)
+ ? arm_compute::PermutationVector(2U, 0U, 1U)
+ : arm_compute::PermutationVector(1U, 2U, 0U);
+ arm_compute::PermutationVector perm_vec =
+ (tensor.info()->data_layout() == arm_compute::DataLayout::NCHW)
+ ? arm_compute::PermutationVector(2U, 0U, 1U)
+ : arm_compute::PermutationVector(1U, 2U, 0U);
arm_compute::permute(permuted_shape, perm_vec);
}
// Validate tensor shape
- ARM_COMPUTE_ERROR_ON_MSG(_shape.size() != tensor.info()->tensor_shape().num_dimensions(), "Tensor ranks mismatch");
- for(size_t i = 0; i < _shape.size(); ++i)
+ ARM_COMPUTE_ERROR_ON_MSG(_shape.size() != tensor.info()->tensor_shape().num_dimensions(),
+ "Tensor ranks mismatch");
+ for (size_t i = 0; i < _shape.size(); ++i)
{
ARM_COMPUTE_ERROR_ON_MSG(permuted_shape[i] != _shape[i], "Tensor dimensions mismatch");
}
- switch(tensor.info()->data_type())
+ switch (tensor.info()->data_type())
{
case arm_compute::DataType::QASYMM8:
case arm_compute::DataType::S32:
@@ -442,7 +450,8 @@
case arm_compute::DataType::F16:
{
// Read data
- if(!are_layouts_different && !_fortran_order && tensor.info()->padding().empty() && !enable_f32_to_f16_conversion)
+ if (!are_layouts_different && !_fortran_order && tensor.info()->padding().empty() &&
+ !enable_f32_to_f16_conversion)
{
// If tensor has no padding read directly from stream.
_fs.read(reinterpret_cast<char *>(tensor.buffer()), tensor.info()->total_size());
@@ -452,19 +461,19 @@
// If tensor has padding or is in fortran order accessing tensor elements through execution window.
Window window;
const unsigned int num_dims = _shape.size();
- if(_fortran_order)
+ if (_fortran_order)
{
- for(unsigned int dim = 0; dim < num_dims; dim++)
+ for (unsigned int dim = 0; dim < num_dims; dim++)
{
permuted_shape.set(dim, _shape[num_dims - dim - 1]);
perm.set(dim, num_dims - dim - 1);
}
- if(are_layouts_different)
+ if (are_layouts_different)
{
// Permute only if num_dimensions greater than 2
- if(num_dims > 2)
+ if (num_dims > 2)
{
- if(_file_layout == DataLayout::NHWC) // i.e destination is NCHW --> permute(1,2,0)
+ if (_file_layout == DataLayout::NHWC) // i.e destination is NCHW --> permute(1,2,0)
{
arm_compute::permute(perm, arm_compute::PermutationVector(1U, 2U, 0U));
}
@@ -477,22 +486,25 @@
}
window.use_tensor_dimensions(permuted_shape);
- execute_window_loop(window, [&](const Coordinates & id)
- {
- Coordinates dst(id);
- arm_compute::permute(dst, perm);
- if(enable_f32_to_f16_conversion)
- {
- float f32_val = 0;
- _fs.read(reinterpret_cast<char *>(&f32_val), 4u);
- half f16_val = half_float::half_cast<half, std::round_to_nearest>(f32_val);
- *(reinterpret_cast<half *>(tensor.ptr_to_element(dst))) = f16_val;
- }
- else
- {
- _fs.read(reinterpret_cast<char *>(tensor.ptr_to_element(dst)), tensor.info()->element_size());
- }
- });
+ execute_window_loop(window,
+ [&](const Coordinates &id)
+ {
+ Coordinates dst(id);
+ arm_compute::permute(dst, perm);
+ if (enable_f32_to_f16_conversion)
+ {
+ float f32_val = 0;
+ _fs.read(reinterpret_cast<char *>(&f32_val), 4u);
+ half f16_val =
+ half_float::half_cast<half, std::round_to_nearest>(f32_val);
+ *(reinterpret_cast<half *>(tensor.ptr_to_element(dst))) = f16_val;
+ }
+ else
+ {
+ _fs.read(reinterpret_cast<char *>(tensor.ptr_to_element(dst)),
+ tensor.info()->element_size());
+ }
+ });
}
break;
@@ -504,7 +516,7 @@
// Unmap buffer if creating a CLTensor
unmap(tensor);
}
- catch(const std::ifstream::failure &e)
+ catch (const std::ifstream::failure &e)
{
ARM_COMPUTE_ERROR_VAR("Loading NPY file: %s", e.what());
}
@@ -543,13 +555,12 @@
const unsigned int width = tensor.info()->tensor_shape()[0];
const unsigned int height = tensor.info()->tensor_shape()[1];
- fs << "P6\n"
- << width << " " << height << " 255\n";
+ fs << "P6\n" << width << " " << height << " 255\n";
// Map buffer if creating a CLTensor
map(tensor, true);
- switch(tensor.info()->format())
+ switch (tensor.info()->format())
{
case arm_compute::Format::U8:
{
@@ -559,13 +570,15 @@
arm_compute::Iterator in(&tensor, window);
- arm_compute::execute_window_loop(window, [&](const arm_compute::Coordinates &)
- {
- const unsigned char value = *in.ptr();
+ arm_compute::execute_window_loop(
+ window,
+ [&](const arm_compute::Coordinates &)
+ {
+ const unsigned char value = *in.ptr();
- fs << value << value << value;
- },
- in);
+ fs << value << value << value;
+ },
+ in);
break;
}
@@ -577,11 +590,13 @@
arm_compute::Iterator in(&tensor, window);
- arm_compute::execute_window_loop(window, [&](const arm_compute::Coordinates &)
- {
- fs.write(reinterpret_cast<std::fstream::char_type *>(in.ptr()), width * tensor.info()->element_size());
- },
- in);
+ arm_compute::execute_window_loop(
+ window,
+ [&](const arm_compute::Coordinates &) {
+ fs.write(reinterpret_cast<std::fstream::char_type *>(in.ptr()),
+ width * tensor.info()->element_size());
+ },
+ in);
break;
}
@@ -592,7 +607,7 @@
// Unmap buffer if creating a CLTensor
unmap(tensor);
}
- catch(const std::ofstream::failure &e)
+ catch (const std::ofstream::failure &e)
{
ARM_COMPUTE_ERROR_VAR("Writing %s: (%s)", ppm_filename.c_str(), e.what());
}
@@ -620,7 +635,7 @@
std::vector<npy::ndarray_len_t> shape(tensor.info()->num_dimensions());
- for(unsigned int i = 0, j = tensor.info()->num_dimensions() - 1; i < tensor.info()->num_dimensions(); ++i, --j)
+ for (unsigned int i = 0, j = tensor.info()->num_dimensions() - 1; i < tensor.info()->num_dimensions(); ++i, --j)
{
shape[i] = tensor.info()->tensor_shape()[!fortran_order ? j : i];
}
@@ -634,7 +649,7 @@
const npy::dtype_t dtype = npy::dtype_map.at(std::type_index(typeid(tmp)));
std::ofstream stream(npy_filename, std::ofstream::binary);
- npy::header_t header{ dtype, fortran_order, shape };
+ npy::header_t header{dtype, fortran_order, shape};
npy::write_header(stream, header);
arm_compute::Window window;
@@ -642,16 +657,16 @@
arm_compute::Iterator in(&tensor, window);
- arm_compute::execute_window_loop(window, [&](const arm_compute::Coordinates &)
- {
- stream.write(reinterpret_cast<const char *>(in.ptr()), sizeof(typestring_type));
- },
- in);
+ arm_compute::execute_window_loop(
+ window,
+ [&](const arm_compute::Coordinates &)
+ { stream.write(reinterpret_cast<const char *>(in.ptr()), sizeof(typestring_type)); },
+ in);
// Unmap buffer if creating a CLTensor
unmap(tensor);
}
- catch(const std::ofstream::failure &e)
+ catch (const std::ofstream::failure &e)
{
ARM_COMPUTE_ERROR_VAR("Writing %s: (%s)", npy_filename.c_str(), e.what());
}
@@ -675,7 +690,7 @@
// Open file
fs.open(filename, std::ios::in | std::ios::binary);
- if(!fs.good())
+ if (!fs.good())
{
throw std::runtime_error("Could not load binary data: " + filename);
}
@@ -687,23 +702,26 @@
window.set(arm_compute::Window::DimX, arm_compute::Window::Dimension(0, 1, 1));
- for(unsigned int d = 1; d < tensor.info()->num_dimensions(); ++d)
+ for (unsigned int d = 1; d < tensor.info()->num_dimensions(); ++d)
{
window.set(d, Window::Dimension(0, tensor.info()->tensor_shape()[d], 1));
}
arm_compute::Iterator in(&tensor, window);
- execute_window_loop(window, [&](const Coordinates &)
- {
- fs.read(reinterpret_cast<std::fstream::char_type *>(in.ptr()), tensor.info()->tensor_shape()[0] * tensor.info()->element_size());
- },
- in);
+ execute_window_loop(
+ window,
+ [&](const Coordinates &)
+ {
+ fs.read(reinterpret_cast<std::fstream::char_type *>(in.ptr()),
+ tensor.info()->tensor_shape()[0] * tensor.info()->element_size());
+ },
+ in);
// Unmap buffer if creating a CLTensor
unmap(tensor);
}
- catch(const std::ofstream::failure &e)
+ catch (const std::ofstream::failure &e)
{
ARM_COMPUTE_ERROR_VAR("Writing %s: (%s)", filename.c_str(), e.what());
}
@@ -718,11 +736,8 @@
window.use_tensor_dimensions(tensor.info()->tensor_shape());
Iterator it_tensor(&tensor, window);
- execute_window_loop(window, [&](const Coordinates &)
- {
- *reinterpret_cast<T *>(it_tensor.ptr()) = value;
- },
- it_tensor);
+ execute_window_loop(
+ window, [&](const Coordinates &) { *reinterpret_cast<T *>(it_tensor.ptr()) = value; }, it_tensor);
unmap(tensor);
}
@@ -745,22 +760,23 @@
int i = 0;
Iterator it_tensor(&tensor, window);
- execute_window_loop(window, [&](const Coordinates &)
- {
- *reinterpret_cast<T *>(it_tensor.ptr()) = vec.at(i++);
- },
- it_tensor);
+ execute_window_loop(
+ window, [&](const Coordinates &) { *reinterpret_cast<T *>(it_tensor.ptr()) = vec.at(i++); }, it_tensor);
unmap(tensor);
}
template <typename T, typename TensorType>
-void fill_random_tensor(TensorType &tensor, std::random_device::result_type seed, T lower_bound = std::numeric_limits<T>::lowest(), T upper_bound = std::numeric_limits<T>::max())
+void fill_random_tensor(TensorType &tensor,
+ std::random_device::result_type seed,
+ T lower_bound = std::numeric_limits<T>::lowest(),
+ T upper_bound = std::numeric_limits<T>::max())
{
constexpr bool is_fp_16bit = std::is_same<T, half>::value || std::is_same<T, bfloat16>::value;
constexpr bool is_integral = std::is_integral<T>::value && !is_fp_16bit;
- using fp_dist_type = typename std::conditional<is_fp_16bit, arm_compute::utils::uniform_real_distribution_16bit<T>, std::uniform_real_distribution<T>>::type;
+ using fp_dist_type = typename std::conditional<is_fp_16bit, arm_compute::utils::uniform_real_distribution_16bit<T>,
+ std::uniform_real_distribution<T>>::type;
using dist_type = typename std::conditional<is_integral, std::uniform_int_distribution<T>, fp_dist_type>::type;
std::mt19937 gen(seed);
@@ -772,17 +788,16 @@
window.use_tensor_dimensions(tensor.info()->tensor_shape());
Iterator it(&tensor, window);
- execute_window_loop(window, [&](const Coordinates &)
- {
- *reinterpret_cast<T *>(it.ptr()) = dist(gen);
- },
- it);
+ execute_window_loop(
+ window, [&](const Coordinates &) { *reinterpret_cast<T *>(it.ptr()) = dist(gen); }, it);
unmap(tensor);
}
template <typename T, typename TensorType>
-void fill_random_tensor(TensorType &tensor, T lower_bound = std::numeric_limits<T>::lowest(), T upper_bound = std::numeric_limits<T>::max())
+void fill_random_tensor(TensorType &tensor,
+ T lower_bound = std::numeric_limits<T>::lowest(),
+ T upper_bound = std::numeric_limits<T>::max())
{
std::random_device rd;
fill_random_tensor(tensor, rd(), lower_bound, upper_bound);
@@ -791,7 +806,8 @@
template <typename T>
void init_sgemm_output(T &dst, T &src0, T &src1, arm_compute::DataType dt)
{
- dst.allocator()->init(TensorInfo(TensorShape(src1.info()->dimension(0), src0.info()->dimension(1), src0.info()->dimension(2)), 1, dt));
+ dst.allocator()->init(TensorInfo(
+ TensorShape(src1.info()->dimension(0), src0.info()->dimension(1), src0.info()->dimension(2)), 1, dt));
}
/** This function returns the amount of memory free reading from /proc/meminfo
*
@@ -823,14 +839,16 @@
Iterator itensor1(&tensor1, window);
Iterator itensor2(&tensor2, window);
- execute_window_loop(window, [&](const Coordinates &)
- {
- if(std::abs(*reinterpret_cast<T *>(itensor1.ptr()) - *reinterpret_cast<T *>(itensor2.ptr())) > tolerance)
+ execute_window_loop(
+ window,
+ [&](const Coordinates &)
{
- ++num_mismatches;
- }
- },
- itensor1, itensor2);
+ if (std::abs(*reinterpret_cast<T *>(itensor1.ptr()) - *reinterpret_cast<T *>(itensor2.ptr())) > tolerance)
+ {
+ ++num_mismatches;
+ }
+ },
+ itensor1, itensor2);
unmap(itensor1);
unmap(itensor2);