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/arm_compute/core/Window.inl b/arm_compute/core/Window.inl
index 5ee4b57..d935507 100644
--- a/arm_compute/core/Window.inl
+++ b/arm_compute/core/Window.inl
@@ -26,7 +26,7 @@
inline Window::Window(const Window &src)
: _dims(), _is_broadcasted(utility::generate_array<bool, Coordinates::num_max_dimensions, false>::value)
{
- for(size_t i = 0; i < Coordinates::num_max_dimensions; ++i)
+ for (size_t i = 0; i < Coordinates::num_max_dimensions; ++i)
{
set(i, src[i]);
_is_broadcasted[i] = src.is_broadcasted(i);
@@ -65,32 +65,34 @@
return _is_broadcasted[dimension];
}
-inline Window Window::collapse_if_possible(const Window &full_window, const size_t first,
- const size_t last, bool *has_collapsed) const
+inline Window Window::collapse_if_possible(const Window &full_window,
+ const size_t first,
+ const size_t last,
+ bool *has_collapsed) const
{
Window collapsed(*this);
bool is_collapsable = true;
int collapsed_end = _dims[first].end();
- for(size_t d = first + 1; is_collapsable && (d < last); ++d)
+ for (size_t d = first + 1; is_collapsable && (d < last); ++d)
{
// The _dims's dimension must match the full _dims dimension to be collapsable:
- is_collapsable = (_dims[d].start() == 0) && (full_window[d].start() == 0) && (_dims[d].step() <= 1)
- && (full_window[d].end() == _dims[d].end());
+ is_collapsable = (_dims[d].start() == 0) && (full_window[d].start() == 0) && (_dims[d].step() <= 1) &&
+ (full_window[d].end() == _dims[d].end());
collapsed_end *= _dims[d].end();
}
- if(is_collapsable)
+ if (is_collapsable)
{
collapsed._dims.at(first).set_end(collapsed_end);
- for(size_t d = first + 1; is_collapsable && (d < last); ++d)
+ for (size_t d = first + 1; is_collapsable && (d < last); ++d)
{
collapsed.set(d, Dimension());
}
}
- if(has_collapsed != nullptr)
+ if (has_collapsed != nullptr)
{
*has_collapsed = is_collapsable;
}
@@ -101,7 +103,7 @@
inline Window Window::shift_dimensions(unsigned int shift_value) const
{
Window shifted_window;
- for(size_t n = 0; n < (Coordinates::num_max_dimensions - shift_value); n++)
+ for (size_t n = 0; n < (Coordinates::num_max_dimensions - shift_value); n++)
{
shifted_window.set(n, _dims[n + shift_value]);
}
@@ -120,9 +122,9 @@
inline Window Window::broadcast_if_dimension_le_one(const TensorShape &shape) const
{
Window broadcastWin(*this);
- for(size_t d = 0; d < TensorShape::num_max_dimensions; ++d)
+ for (size_t d = 0; d < TensorShape::num_max_dimensions; ++d)
{
- if(shape[d] <= 1)
+ if (shape[d] <= 1)
{
broadcastWin.set_broadcasted(d);
}
@@ -142,7 +144,7 @@
ARM_COMPUTE_ERROR_ON(dimension >= Coordinates::num_max_dimensions);
Window::Dimension &d = _dims[dimension];
- if(is_at_start)
+ if (is_at_start)
{
d = Window::Dimension(d.start() + adjust_value, d.end(), d.step());
}
@@ -172,7 +174,7 @@
inline void Window::validate() const
{
- for(size_t i = 0; i < Coordinates::num_max_dimensions; ++i)
+ for (size_t i = 0; i < Coordinates::num_max_dimensions; ++i)
{
ARM_COMPUTE_ERROR_ON(_dims[i].end() < _dims[i].start());
ARM_COMPUTE_ERROR_ON((_dims[i].step() != 0) && (((_dims[i].end() - _dims[i].start()) % _dims[i].step()) != 0));
@@ -193,9 +195,9 @@
Window out;
- for(size_t d = 0; d < Coordinates::num_max_dimensions; ++d)
+ for (size_t d = 0; d < Coordinates::num_max_dimensions; ++d)
{
- if(d == dimension)
+ if (d == dimension)
{
int start = _dims[d].start();
int end = _dims[d].end();
@@ -207,7 +209,7 @@
int it_start = work * id;
- if(int(id) < rem)
+ if (int(id) < rem)
{
++work;
it_start += id;
@@ -234,18 +236,18 @@
template <unsigned int window_dimension>
inline bool Window::slide_window_slice(Window &slice) const
{
- for(unsigned int n = window_dimension; n < Coordinates::num_max_dimensions; ++n)
+ for (unsigned int n = window_dimension; n < Coordinates::num_max_dimensions; ++n)
{
// Did we reach the end of this dimension?
const int v = slice._dims[n].start() + 1;
- if(v < _dims[n].end())
+ if (v < _dims[n].end())
{
// No: increment
slice._dims[n] = Dimension(v, v + 1, 1);
// Reset lower dimensions:
- for(unsigned int lower = window_dimension; lower < n; ++lower)
+ for (unsigned int lower = window_dimension; lower < n; ++lower)
{
slice._dims[lower] = Dimension(_dims[lower].start(), _dims[lower].start() + 1, 1);
}
@@ -258,14 +260,14 @@
}
template <unsigned int window_dimension>
-inline Window Window::first_slice_window() const
+inline Window Window::first_slice_window() const
{
Window slice;
std::copy_n(_dims.begin(), window_dimension, slice._dims.begin());
//Initialise higher dimensions to be the first slice.
- for(unsigned int n = window_dimension; n < Coordinates::num_max_dimensions; ++n)
+ for (unsigned int n = window_dimension; n < Coordinates::num_max_dimensions; ++n)
{
slice._dims[n] = Dimension(_dims[n].start(), _dims[n].start() + 1, 1);
}
@@ -275,7 +277,7 @@
inline void Window::use_tensor_dimensions(const TensorShape &shape, size_t first_dimension)
{
- for(unsigned int n = first_dimension; n < shape.num_dimensions(); ++n)
+ for (unsigned int n = first_dimension; n < shape.num_dimensions(); ++n)
{
set(n, Window::Dimension(0, std::max(shape[n], static_cast<size_t>(1))));
}
@@ -284,7 +286,7 @@
inline TensorShape Window::shape() const
{
TensorShape shape;
- for(size_t d = 0; d < TensorShape::num_max_dimensions; ++d)
+ for (size_t d = 0; d < TensorShape::num_max_dimensions; ++d)
{
shape.set(d, (_dims[d].end() - _dims[d].start()) / _dims[d].step());
}
@@ -294,7 +296,7 @@
inline size_t Window::num_iterations_total() const
{
size_t total = 1;
- for(size_t d = 0; d < Coordinates::num_max_dimensions; ++d)
+ for (size_t d = 0; d < Coordinates::num_max_dimensions; ++d)
{
total *= num_iterations(d);
}