src/runtime/CL/functions/CLPadLayer.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to
  * deal in the Software without restriction, including without limitation the
  * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
  * sell copies of the Software, and to permit persons to whom the Software is
  * furnished to do so, subject to the following conditions:
  *
  * The above copyright notice and this permission notice shall be included in all
  * copies or substantial portions of the Software.
  *
  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
 #include "arm_compute/runtime/CL/functions/CLPadLayer.h"

 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "support/ToolchainSupport.h"

 namespace arm_compute
 {
 CLPadLayer::CLPadLayer()
     : _copy_kernel(), _mode(), _padding(), _memset_kernel(), _num_dimensions(0), _slice_functions(nullptr), _concat_functions(nullptr), _slice_results(nullptr), _concat_results(nullptr)
 {
 }

 void CLPadLayer::configure_constant_mode(ICLTensor *input, ICLTensor *output, const PaddingList &padding, const PixelValue constant_value)
 {
     // Set the pages of the output to the constant_value.
     _memset_kernel.configure(output, constant_value);

     // Fill out padding list with zeroes.
     PaddingList padding_extended = padding;
     for(size_t i = padding.size(); i < TensorShape::num_max_dimensions; i++)
     {
         padding_extended.emplace_back(PaddingInfo{ 0, 0 });
     }

     // Create a window within the output tensor where the input will be copied.
     Window copy_window = Window();
     for(uint32_t i = 0; i < output->info()->num_dimensions(); ++i)
     {
         copy_window.set(i, Window::Dimension(padding_extended[i].first, padding_extended[i].first + input->info()->dimension(i), 1));
     }
     // Copy the input to the output, leaving the padding filled with the constant_value.
     _copy_kernel.configure(input, output, PaddingList(), &copy_window);
 }

 void CLPadLayer::configure_reflect_symmetric_mode(ICLTensor *input, ICLTensor *output)
 {
     int64_t last_padding_dimension = _padding.size() - 1;
     // Reflecting can be performed by effectively unfolding the input as follows:
     // For each dimension starting at DimX:
     //      Create a before and after slice, which values depend on the selected padding mode
     //      Concatenate the before and after padding with the tensor to be padded

     // Two strided slice functions will be required for each dimension padded as well as a
     // concatenate function and the tensors to hold the temporary results.
     _slice_functions  = arm_compute::support::cpp14::make_unique<CLStridedSlice[]>(2 * _num_dimensions);
     _slice_results    = arm_compute::support::cpp14::make_unique<CLTensor[]>(2 * _num_dimensions);
     _concat_functions = arm_compute::support::cpp14::make_unique<CLConcatenateLayer[]>(_num_dimensions);
     _concat_results   = arm_compute::support::cpp14::make_unique<CLTensor[]>(_num_dimensions - 1);
     Coordinates starts_before, ends_before, starts_after, ends_after, strides;
     ICLTensor *prev = input;
     for(uint32_t i = 0; i < _num_dimensions; ++i)
     {
         // Values in strides from the previous dimensions need to be set to 1 to avoid reversing again.
         if(i > 0)
         {
             strides.set(i - 1, 1);
         }

         if(_padding[i].first > 0 || _padding[i].second > 0)
         {
             // Set the starts, ends, and strides values for the current dimension.
             // Due to the bit masks passed to strided slice, the values below the current dimension in
             // starts and ends will be ignored so do not need to be modified.
             if(_mode == PaddingMode::REFLECT)
             {
                 starts_before.set(i, _padding[i].first);
                 ends_before.set(i, 0);
                 starts_after.set(i, input->info()->dimension(i) - 2);
                 ends_after.set(i, input->info()->dimension(i) - _padding[i].second - 2);
                 strides.set(i, -1);
             }
             else
             {
                 starts_before.set(i, _padding[i].first - 1);
                 ends_before.set(i, -1);
                 starts_after.set(i, input->info()->dimension(i) - 1);
                 ends_after.set(i, input->info()->dimension(i) - _padding[i].second - 1);
                 strides.set(i, -1);
             }

             // Strided slice wraps negative indexes around to the end of the range,
             // instead this should indicate use of the full range and so the bit mask will be modified.
             const int32_t begin_mask_before = starts_before[i] < 0 ? ~0 : ~(1u << i);
             const int32_t end_mask_before   = ends_before[i] < 0 ? ~0 : ~(1u << i);
             const int32_t begin_mask_after  = starts_after[i] < 0 ? ~0 : ~(1u << i);
             const int32_t end_mask_after    = ends_after[i] < 0 ? ~0 : ~(1u << i);

             // Reflect the input values for the padding before and after the input.
             std::vector<ICLTensor *> concat_vector;
             if(_padding[i].first > 0)
             {
                 if(i < prev->info()->num_dimensions())
                 {
                     _slice_functions[2 * i].configure(prev, &_slice_results[2 * i], starts_before, ends_before, strides, begin_mask_before, end_mask_before);
                     concat_vector.push_back(&_slice_results[2 * i]);
                 }
                 else
                 {
                     // Performing the slice is unnecessary if the result would simply be a copy of the tensor.
                     concat_vector.push_back(prev);
                 }
             }
             concat_vector.push_back(prev);
             if(_padding[i].second > 0)
             {
                 if(i < prev->info()->num_dimensions())
                 {
                     _slice_functions[2 * i + 1].configure(prev, &_slice_results[2 * i + 1], starts_after, ends_after, strides, begin_mask_after, end_mask_after);
                     concat_vector.push_back(&_slice_results[2 * i + 1]);
                 }
                 else
                 {
                     // Performing the slice is unnecessary if the result would simply be a copy of the tensor.
                     concat_vector.push_back(prev);
                 }
             }
             // Concatenate the padding before and after with the input.
             ICLTensor *out = (static_cast<int32_t>(i) == last_padding_dimension) ? output : &_concat_results[i];
             _concat_functions[i].configure(concat_vector, out, i);
             prev = out;
         }
     }
     for(uint32_t i = 0; i < _num_dimensions; ++i)
     {
         if((static_cast<int32_t>(i) != last_padding_dimension))
         {
             _concat_results[i].allocator()->allocate();
         }
         _slice_results[2 * i].allocator()->allocate();
         _slice_results[2 * i + 1].allocator()->allocate();
     }
 }

 void CLPadLayer::configure(ICLTensor *input, ICLTensor *output, const PaddingList &padding, PixelValue constant_value, PaddingMode mode)
 {
     ARM_COMPUTE_ERROR_THROW_ON(validate(input->info(), output->info(), padding, constant_value, mode));

     _padding = padding;
     _mode    = mode;

     TensorShape padded_shape = misc::shape_calculator::compute_padded_shape(input->info()->tensor_shape(), _padding);

     auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(padded_shape));

     // Find the last dimension requiring padding so that it is known when to write to output and whether any padding is applied.
     int64_t last_padding_dimension = _padding.size() - 1;
     for(; last_padding_dimension >= 0; --last_padding_dimension)
     {
         if(_padding[last_padding_dimension].first > 0 || _padding[last_padding_dimension].second > 0)
         {
             break;
         }
     }
     _num_dimensions = last_padding_dimension + 1;
     if(_num_dimensions > 0)
     {
         switch(_mode)
         {
             case PaddingMode::CONSTANT:
             {
                 configure_constant_mode(input, output, padding, constant_value);
                 break;
             }
             case PaddingMode::REFLECT:
             case PaddingMode::SYMMETRIC:
             {
                 configure_reflect_symmetric_mode(input, output);
                 break;
             }
             default:
                 ARM_COMPUTE_ERROR("Padding mode not supported.");
         }
     }
     else
     {
         // Copy the input to the whole output if no padding is applied
         _copy_kernel.configure(input, output);
     }
 }

 Status CLPadLayer::validate(const ITensorInfo *input, const ITensorInfo *output, const PaddingList &padding, PixelValue constant_value, PaddingMode mode)
 {
     ARM_COMPUTE_RETURN_ERROR_ON(padding.size() > input->num_dimensions());

     TensorShape padded_shape = misc::shape_calculator::compute_padded_shape(input->tensor_shape(), padding);

     // Use CLCopyKernel and CLMemsetKernel to validate all padding modes as this includes all of the shape and info validation.
     PaddingList padding_extended = padding;
     for(size_t i = padding.size(); i < TensorShape::num_max_dimensions; i++)
     {
         padding_extended.emplace_back(PaddingInfo{ 0, 0 });
     }

     Window copy_window = Window();
     for(uint32_t i = 0; i < padded_shape.num_dimensions(); ++i)
     {
         copy_window.set(i, Window::Dimension(padding_extended[i].first, padding_extended[i].first + input->dimension(i), 1));
     }
     if(output->total_size() > 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), padded_shape);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(output, input);
         ARM_COMPUTE_RETURN_ON_ERROR(CLCopyKernel::validate(input, output, PaddingList(), &copy_window));
         ARM_COMPUTE_RETURN_ON_ERROR(CLMemsetKernel::validate(output, constant_value));
     }
     else
     {
         ARM_COMPUTE_RETURN_ON_ERROR(CLCopyKernel::validate(input, &input->clone()->set_tensor_shape(padded_shape), PaddingList(), &copy_window));
         ARM_COMPUTE_RETURN_ON_ERROR(CLMemsetKernel::validate(&input->clone()->set_tensor_shape(padded_shape), constant_value));
     }

     switch(mode)
     {
         case PaddingMode::CONSTANT:
         {
             break;
         }
         case PaddingMode::REFLECT:
         case PaddingMode::SYMMETRIC:
         {
             for(uint32_t i = 0; i < padding.size(); ++i)
             {
                 if(mode == PaddingMode::REFLECT)
                 {
                     ARM_COMPUTE_RETURN_ERROR_ON(padding[i].first >= input->dimension(i));
                     ARM_COMPUTE_RETURN_ERROR_ON(padding[i].second >= input->dimension(i));
                 }
                 else
                 {
                     ARM_COMPUTE_RETURN_ERROR_ON(padding[i].first > input->dimension(i));
                     ARM_COMPUTE_RETURN_ERROR_ON(padding[i].second > input->dimension(i));
                 }
             }
             break;
         }
         default:
         {
             ARM_COMPUTE_ERROR("Invalid mode");
         }
     }
     return Status{};
 }

 void CLPadLayer::run()
 {
     if(_num_dimensions > 0)
     {
         switch(_mode)
         {
             case PaddingMode::CONSTANT:
             {
                 CLScheduler::get().enqueue(_memset_kernel, false);
                 CLScheduler::get().enqueue(_copy_kernel, true);
                 break;
             }
             case PaddingMode::REFLECT:
             case PaddingMode::SYMMETRIC:
             {
                 for(uint32_t i = 0; i < _num_dimensions; ++i)
                 {
                     if(_padding[i].first > 0 || _padding[i].second > 0)
                     {
                         if(_padding[i].first > 0 && _slice_results[2 * i].info()->total_size() > 0)
                         {
                             _slice_functions[2 * i].run();
                         }
                         if(_padding[i].second > 0 && _slice_results[2 * i + 1].info()->total_size() > 0)
                         {
                             _slice_functions[2 * i + 1].run();
                         }
                         CLScheduler::get().sync();
                         _concat_functions[i].run();
                         CLScheduler::get().sync();
                     }
                 }
                 break;
             }
             default:
                 ARM_COMPUTE_ERROR("Padding mode not supported.");
         }
     }
     else
     {
         CLScheduler::get().enqueue(_copy_kernel, true);
     }
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2018-2019 ARM Limited.
	*
	* SPDX-License-Identifier: MIT
	*
	* Permission is hereby granted, free of charge, to any person obtaining a copy
	* of this software and associated documentation files (the "Software"), to
	* deal in the Software without restriction, including without limitation the
	* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
	* sell copies of the Software, and to permit persons to whom the Software is
	* furnished to do so, subject to the following conditions:
	*
	* The above copyright notice and this permission notice shall be included in all
	* copies or substantial portions of the Software.
	*
	* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	* SOFTWARE.
	*/
	#include "arm_compute/runtime/CL/functions/CLPadLayer.h"

	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "support/ToolchainSupport.h"

	namespace arm_compute
	{
	CLPadLayer::CLPadLayer()
	: _copy_kernel(), _mode(), _padding(), _memset_kernel(), _num_dimensions(0), _slice_functions(nullptr), _concat_functions(nullptr), _slice_results(nullptr), _concat_results(nullptr)
	{
	}

	void CLPadLayer::configure_constant_mode(ICLTensor input, ICLTensor output, const PaddingList &padding, const PixelValue constant_value)
	{
	// Set the pages of the output to the constant_value.
	_memset_kernel.configure(output, constant_value);

	// Fill out padding list with zeroes.
	PaddingList padding_extended = padding;
	for(size_t i = padding.size(); i < TensorShape::num_max_dimensions; i++)
	{
	padding_extended.emplace_back(PaddingInfo{ 0, 0 });
	}

	// Create a window within the output tensor where the input will be copied.
	Window copy_window = Window();
	for(uint32_t i = 0; i < output->info()->num_dimensions(); ++i)
	{
	copy_window.set(i, Window::Dimension(padding_extended[i].first, padding_extended[i].first + input->info()->dimension(i), 1));
	}
	// Copy the input to the output, leaving the padding filled with the constant_value.
	_copy_kernel.configure(input, output, PaddingList(), &copy_window);
	}

	void CLPadLayer::configure_reflect_symmetric_mode(ICLTensor input, ICLTensor output)
	{
	int64_t last_padding_dimension = _padding.size() - 1;
	// Reflecting can be performed by effectively unfolding the input as follows:
	// For each dimension starting at DimX:
	// Create a before and after slice, which values depend on the selected padding mode
	// Concatenate the before and after padding with the tensor to be padded

	// Two strided slice functions will be required for each dimension padded as well as a
	// concatenate function and the tensors to hold the temporary results.
	_slice_functions = arm_compute::support::cpp14::make_unique<CLStridedSlice[]>(2 * _num_dimensions);
	_slice_results = arm_compute::support::cpp14::make_unique<CLTensor[]>(2 * _num_dimensions);
	_concat_functions = arm_compute::support::cpp14::make_unique<CLConcatenateLayer[]>(_num_dimensions);
	_concat_results = arm_compute::support::cpp14::make_unique<CLTensor[]>(_num_dimensions - 1);
	Coordinates starts_before, ends_before, starts_after, ends_after, strides;
	ICLTensor *prev = input;
	for(uint32_t i = 0; i < _num_dimensions; ++i)
	{
	// Values in strides from the previous dimensions need to be set to 1 to avoid reversing again.
	if(i > 0)
	{
	strides.set(i - 1, 1);
	}

	if(_padding[i].first > 0 \|\| _padding[i].second > 0)
	{
	// Set the starts, ends, and strides values for the current dimension.
	// Due to the bit masks passed to strided slice, the values below the current dimension in
	// starts and ends will be ignored so do not need to be modified.
	if(_mode == PaddingMode::REFLECT)
	{
	starts_before.set(i, _padding[i].first);
	ends_before.set(i, 0);
	starts_after.set(i, input->info()->dimension(i) - 2);
	ends_after.set(i, input->info()->dimension(i) - _padding[i].second - 2);
	strides.set(i, -1);
	}
	else
	{
	starts_before.set(i, _padding[i].first - 1);
	ends_before.set(i, -1);
	starts_after.set(i, input->info()->dimension(i) - 1);
	ends_after.set(i, input->info()->dimension(i) - _padding[i].second - 1);
	strides.set(i, -1);
	}

	// Strided slice wraps negative indexes around to the end of the range,
	// instead this should indicate use of the full range and so the bit mask will be modified.
	const int32_t begin_mask_before = starts_before[i] < 0 ? ~0 : ~(1u << i);
	const int32_t end_mask_before = ends_before[i] < 0 ? ~0 : ~(1u << i);
	const int32_t begin_mask_after = starts_after[i] < 0 ? ~0 : ~(1u << i);
	const int32_t end_mask_after = ends_after[i] < 0 ? ~0 : ~(1u << i);

	// Reflect the input values for the padding before and after the input.
	std::vector<ICLTensor *> concat_vector;
	if(_padding[i].first > 0)
	{
	if(i < prev->info()->num_dimensions())
	{
	_slice_functions[2 * i].configure(prev, &_slice_results[2 * i], starts_before, ends_before, strides, begin_mask_before, end_mask_before);
	concat_vector.push_back(&_slice_results[2 * i]);
	}
	else
	{
	// Performing the slice is unnecessary if the result would simply be a copy of the tensor.
	concat_vector.push_back(prev);
	}
	}
	concat_vector.push_back(prev);
	if(_padding[i].second > 0)
	{
	if(i < prev->info()->num_dimensions())
	{
	_slice_functions[2 * i + 1].configure(prev, &_slice_results[2 * i + 1], starts_after, ends_after, strides, begin_mask_after, end_mask_after);
	concat_vector.push_back(&_slice_results[2 * i + 1]);
	}
	else
	{
	// Performing the slice is unnecessary if the result would simply be a copy of the tensor.
	concat_vector.push_back(prev);
	}
	}
	// Concatenate the padding before and after with the input.
	ICLTensor *out = (static_cast<int32_t>(i) == last_padding_dimension) ? output : &_concat_results[i];
	_concat_functions[i].configure(concat_vector, out, i);
	prev = out;
	}
	}
	for(uint32_t i = 0; i < _num_dimensions; ++i)
	{
	if((static_cast<int32_t>(i) != last_padding_dimension))
	{
	_concat_results[i].allocator()->allocate();
	}
	_slice_results[2 * i].allocator()->allocate();
	_slice_results[2 * i + 1].allocator()->allocate();
	}
	}

	void CLPadLayer::configure(ICLTensor input, ICLTensor output, const PaddingList &padding, PixelValue constant_value, PaddingMode mode)
	{
	ARM_COMPUTE_ERROR_THROW_ON(validate(input->info(), output->info(), padding, constant_value, mode));

	_padding = padding;
	_mode = mode;

	TensorShape padded_shape = misc::shape_calculator::compute_padded_shape(input->info()->tensor_shape(), _padding);

	auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(padded_shape));

	// Find the last dimension requiring padding so that it is known when to write to output and whether any padding is applied.
	int64_t last_padding_dimension = _padding.size() - 1;
	for(; last_padding_dimension >= 0; --last_padding_dimension)
	{
	if(_padding[last_padding_dimension].first > 0 \|\| _padding[last_padding_dimension].second > 0)
	{
	break;
	}
	}
	_num_dimensions = last_padding_dimension + 1;
	if(_num_dimensions > 0)
	{
	switch(_mode)
	{
	case PaddingMode::CONSTANT:
	{
	configure_constant_mode(input, output, padding, constant_value);
	break;
	}
	case PaddingMode::REFLECT:
	case PaddingMode::SYMMETRIC:
	{
	configure_reflect_symmetric_mode(input, output);
	break;
	}
	default:
	ARM_COMPUTE_ERROR("Padding mode not supported.");
	}
	}
	else
	{
	// Copy the input to the whole output if no padding is applied
	_copy_kernel.configure(input, output);
	}
	}

	Status CLPadLayer::validate(const ITensorInfo input, const ITensorInfo output, const PaddingList &padding, PixelValue constant_value, PaddingMode mode)
	{
	ARM_COMPUTE_RETURN_ERROR_ON(padding.size() > input->num_dimensions());

	TensorShape padded_shape = misc::shape_calculator::compute_padded_shape(input->tensor_shape(), padding);

	// Use CLCopyKernel and CLMemsetKernel to validate all padding modes as this includes all of the shape and info validation.
	PaddingList padding_extended = padding;
	for(size_t i = padding.size(); i < TensorShape::num_max_dimensions; i++)
	{
	padding_extended.emplace_back(PaddingInfo{ 0, 0 });
	}

	Window copy_window = Window();
	for(uint32_t i = 0; i < padded_shape.num_dimensions(); ++i)
	{
	copy_window.set(i, Window::Dimension(padding_extended[i].first, padding_extended[i].first + input->dimension(i), 1));
	}
	if(output->total_size() > 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), padded_shape);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(output, input);
	ARM_COMPUTE_RETURN_ON_ERROR(CLCopyKernel::validate(input, output, PaddingList(), &copy_window));
	ARM_COMPUTE_RETURN_ON_ERROR(CLMemsetKernel::validate(output, constant_value));
	}
	else
	{
	ARM_COMPUTE_RETURN_ON_ERROR(CLCopyKernel::validate(input, &input->clone()->set_tensor_shape(padded_shape), PaddingList(), &copy_window));
	ARM_COMPUTE_RETURN_ON_ERROR(CLMemsetKernel::validate(&input->clone()->set_tensor_shape(padded_shape), constant_value));
	}

	switch(mode)
	{
	case PaddingMode::CONSTANT:
	{
	break;
	}
	case PaddingMode::REFLECT:
	case PaddingMode::SYMMETRIC:
	{
	for(uint32_t i = 0; i < padding.size(); ++i)
	{
	if(mode == PaddingMode::REFLECT)
	{
	ARM_COMPUTE_RETURN_ERROR_ON(padding[i].first >= input->dimension(i));
	ARM_COMPUTE_RETURN_ERROR_ON(padding[i].second >= input->dimension(i));
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON(padding[i].first > input->dimension(i));
	ARM_COMPUTE_RETURN_ERROR_ON(padding[i].second > input->dimension(i));
	}
	}
	break;
	}
	default:
	{
	ARM_COMPUTE_ERROR("Invalid mode");
	}
	}
	return Status{};
	}

	void CLPadLayer::run()
	{
	if(_num_dimensions > 0)
	{
	switch(_mode)
	{
	case PaddingMode::CONSTANT:
	{
	CLScheduler::get().enqueue(_memset_kernel, false);
	CLScheduler::get().enqueue(_copy_kernel, true);
	break;
	}
	case PaddingMode::REFLECT:
	case PaddingMode::SYMMETRIC:
	{
	for(uint32_t i = 0; i < _num_dimensions; ++i)
	{
	if(_padding[i].first > 0 \|\| _padding[i].second > 0)
	{
	if(_padding[i].first > 0 && _slice_results[2 * i].info()->total_size() > 0)
	{
	_slice_functions[2 * i].run();
	}
	if(_padding[i].second > 0 && _slice_results[2 * i + 1].info()->total_size() > 0)
	{
	_slice_functions[2 * i + 1].run();
	}
	CLScheduler::get().sync();
	_concat_functions[i].run();
	CLScheduler::get().sync();
	}
	}
	break;
	}
	default:
	ARM_COMPUTE_ERROR("Padding mode not supported.");
	}
	}
	else
	{
	CLScheduler::get().enqueue(_copy_kernel, true);
	}
	}
	} // namespace arm_compute