arm_compute v19.11.1
diff --git a/src/runtime/NEON/functions/NEReduceMean.cpp b/src/runtime/NEON/functions/NEReduceMean.cpp
index 10437f5..72c63a8 100644
--- a/src/runtime/NEON/functions/NEReduceMean.cpp
+++ b/src/runtime/NEON/functions/NEReduceMean.cpp
@@ -26,40 +26,13 @@
#include "arm_compute/core/CPP/Validate.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
namespace arm_compute
{
namespace
{
-inline TensorShape calculate_reduce_mean_shape(ITensor *input, const Coordinates &reduction_axis, bool keep_dims)
-{
- const int reduction_ops = reduction_axis.num_dimensions();
- Coordinates axis_local = reduction_axis;
- const int input_dims = input->info()->num_dimensions();
- convert_negative_axis(axis_local, input_dims);
- TensorShape out_shape = input->info()->tensor_shape();
- // Configure reshape layer if we want to drop the dimensions
- if(!keep_dims)
- {
- // We have to sort the reduction axis vectors in order for remove_dimension
- // to work properly
- std::sort(axis_local.begin(), axis_local.begin() + reduction_ops);
- for(int i = 0; i < reduction_ops; ++i)
- {
- out_shape.remove_dimension(axis_local[i] - i);
- }
- return out_shape;
- }
- else
- {
- for(int i = 0; i < reduction_ops; ++i)
- {
- out_shape.set(axis_local[i], 1);
- }
- return out_shape;
- }
-}
} // namespace
NEReduceMean::NEReduceMean(std::shared_ptr<IMemoryManager> memory_manager)
@@ -130,7 +103,7 @@
// Perform validate step
ARM_COMPUTE_ERROR_THROW_ON(NEReduceMean::validate(input->info(), reduction_axis, keep_dims, output->info()));
// Output auto inizialitation if not yet initialized
- const TensorShape output_shape = calculate_reduce_mean_shape(input, reduction_axis, keep_dims);
+ const TensorShape output_shape = arm_compute::misc::shape_calculator::calculate_reduce_mean_shape(input, reduction_axis, keep_dims);
auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));
_reduction_ops = reduction_axis.num_dimensions();