src/runtime/NEON/functions/NEPadLayer.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2018-2020 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/NEON/functions/NEPadLayer.h"

 #include "arm_compute/runtime/NEON/NEScheduler.h"

 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "src/core/NEON/kernels/NECopyKernel.h"
 #include "src/core/NEON/kernels/NEPadLayerKernel.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "support/MemorySupport.h"

 namespace arm_compute
 {
 namespace
 {
 uint32_t last_padding_dimension(const PaddingList &padding)
 {
     int last_padding_dim = padding.size() - 1;
     for(; last_padding_dim >= 0; --last_padding_dim)
     {
         if(padding[last_padding_dim].first > 0 || padding[last_padding_dim].second > 0)
         {
             break;
         }
     }
     return static_cast<uint32_t>(last_padding_dim);
 }
 } // namespace

 NEPadLayer::~NEPadLayer() = default;

 NEPadLayer::NEPadLayer()
     : _copy_kernel(), _pad_kernel(), _mode(), _padding(), _num_dimensions(0), _slice_functions(), _concat_functions(), _slice_results(), _concat_results()
 {
 }

 void NEPadLayer::configure_constant_mode(ITensor *input, ITensor *output, const PaddingList &padding, const PixelValue constant_value)
 {
     _pad_kernel = arm_compute::support::cpp14::make_unique<NEPadLayerKernel>();
     _pad_kernel->configure(input, output, padding, constant_value, PaddingMode::CONSTANT);
 }

 void NEPadLayer::configure_reflect_symmetric_mode(ITensor *input, ITensor *output)
 {
     // Reflecting can be performed by effectively unfolding the input as follows:
     // For each dimension starting at DimX:
     //      For before and after:
     //          Use strided slice to extract and reverse the part of the
     //          input / previously produced tensor required for the padding.
     //      Concatenate the before and after padding with the input / previously
     //      produced tensor along the current dimension.

     // 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.resize(2 * _num_dimensions);
     _slice_results.resize(2 * _num_dimensions);
     _concat_functions.resize(_num_dimensions);
     _concat_results.resize(_num_dimensions - 1);

     Coordinates starts_before{};
     Coordinates ends_before{};
     Coordinates starts_after{};
     Coordinates ends_after{};
     Coordinates strides{};
     ITensor    *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<const ITensor *> 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.emplace_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.emplace_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.
             ITensor *out = (i == _num_dimensions - 1) ? output : &_concat_results[i];
             _concat_functions[i].configure(concat_vector, out, i);
             if(i != _num_dimensions - 1)
             {
                 _concat_results[i].allocator()->allocate();
             }
             prev = out;
         }
         _slice_results[2 * i].allocator()->allocate();
         _slice_results[2 * i + 1].allocator()->allocate();
     }
 }

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

     _padding = padding;
     _mode    = mode;

     const 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.
     _num_dimensions = last_padding_dimension(padding) + 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 = arm_compute::support::cpp14::make_unique<NECopyKernel>();
         _copy_kernel->configure(input, output);
     }
 }

 Status NEPadLayer::validate(const ITensorInfo *input, const ITensorInfo *output, const PaddingList &padding, const PixelValue constant_value, const PaddingMode mode)
 {
     ARM_COMPUTE_UNUSED(constant_value);

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

     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(input, output);
     }

     switch(mode)
     {
         case PaddingMode::CONSTANT:
         {
             return NEPadLayerKernel::validate(input, output, padding, constant_value, mode);
         }
         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 NEPadLayer::run()
 {
     if(_num_dimensions > 0)
     {
         switch(_mode)
         {
             case PaddingMode::CONSTANT:
             {
                 NEScheduler::get().schedule(_pad_kernel.get(), Window::DimZ);
                 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();
                         }
                         _concat_functions[i].run();
                     }
                 }
                 break;
             }
             default:
                 ARM_COMPUTE_ERROR("Padding mode not supported.");
         }
     }
     else
     {
         NEScheduler::get().schedule(_copy_kernel.get(), Window::DimY);
     }
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2018-2020 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/NEON/functions/NEPadLayer.h"

	#include "arm_compute/runtime/NEON/NEScheduler.h"

	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "src/core/NEON/kernels/NECopyKernel.h"
	#include "src/core/NEON/kernels/NEPadLayerKernel.h"
	#include "src/core/helpers/AutoConfiguration.h"
	#include "support/MemorySupport.h"

	namespace arm_compute
	{
	namespace
	{
	uint32_t last_padding_dimension(const PaddingList &padding)
	{
	int last_padding_dim = padding.size() - 1;
	for(; last_padding_dim >= 0; --last_padding_dim)
	{
	if(padding[last_padding_dim].first > 0 \|\| padding[last_padding_dim].second > 0)
	{
	break;
	}
	}
	return static_cast<uint32_t>(last_padding_dim);
	}
	} // namespace

	NEPadLayer::~NEPadLayer() = default;

	NEPadLayer::NEPadLayer()
	: _copy_kernel(), _pad_kernel(), _mode(), _padding(), _num_dimensions(0), _slice_functions(), _concat_functions(), _slice_results(), _concat_results()
	{
	}

	void NEPadLayer::configure_constant_mode(ITensor input, ITensor output, const PaddingList &padding, const PixelValue constant_value)
	{
	_pad_kernel = arm_compute::support::cpp14::make_unique<NEPadLayerKernel>();
	_pad_kernel->configure(input, output, padding, constant_value, PaddingMode::CONSTANT);
	}

	void NEPadLayer::configure_reflect_symmetric_mode(ITensor input, ITensor output)
	{
	// Reflecting can be performed by effectively unfolding the input as follows:
	// For each dimension starting at DimX:
	// For before and after:
	// Use strided slice to extract and reverse the part of the
	// input / previously produced tensor required for the padding.
	// Concatenate the before and after padding with the input / previously
	// produced tensor along the current dimension.

	// 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.resize(2 * _num_dimensions);
	_slice_results.resize(2 * _num_dimensions);
	_concat_functions.resize(_num_dimensions);
	_concat_results.resize(_num_dimensions - 1);

	Coordinates starts_before{};
	Coordinates ends_before{};
	Coordinates starts_after{};
	Coordinates ends_after{};
	Coordinates strides{};
	ITensor *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<const ITensor *> 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.emplace_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.emplace_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.
	ITensor *out = (i == _num_dimensions - 1) ? output : &_concat_results[i];
	_concat_functions[i].configure(concat_vector, out, i);
	if(i != _num_dimensions - 1)
	{
	_concat_results[i].allocator()->allocate();
	}
	prev = out;
	}
	_slice_results[2 * i].allocator()->allocate();
	_slice_results[2 * i + 1].allocator()->allocate();
	}
	}

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

	_padding = padding;
	_mode = mode;

	const 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.
	_num_dimensions = last_padding_dimension(padding) + 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 = arm_compute::support::cpp14::make_unique<NECopyKernel>();
	_copy_kernel->configure(input, output);
	}
	}

	Status NEPadLayer::validate(const ITensorInfo input, const ITensorInfo output, const PaddingList &padding, const PixelValue constant_value, const PaddingMode mode)
	{
	ARM_COMPUTE_UNUSED(constant_value);

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

	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(input, output);
	}

	switch(mode)
	{
	case PaddingMode::CONSTANT:
	{
	return NEPadLayerKernel::validate(input, output, padding, constant_value, mode);
	}
	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 NEPadLayer::run()
	{
	if(_num_dimensions > 0)
	{
	switch(_mode)
	{
	case PaddingMode::CONSTANT:
	{
	NEScheduler::get().schedule(_pad_kernel.get(), Window::DimZ);
	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();
	}
	_concat_functions[i].run();
	}
	}
	break;
	}
	default:
	ARM_COMPUTE_ERROR("Padding mode not supported.");
	}
	}
	else
	{
	NEScheduler::get().schedule(_copy_kernel.get(), Window::DimY);
	}
	}
	} // namespace arm_compute