src/core/TensorInfo.cpp - platform/external/ComputeLibrary - Git at Google

 /*
  * Copyright (c) 2016-2019 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/core/TensorInfo.h"

 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/HOGInfo.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Validate.h"
 #include "support/ToolchainSupport.h"

 using namespace arm_compute;

 TensorInfo::TensorInfo()
     : _total_size(0), _offset_first_element_in_bytes(0), _strides_in_bytes(), _num_channels(0), _tensor_shape(), _data_type(DataType::UNKNOWN), _format(Format::UNKNOWN), _is_resizable{ true },
       _valid_region{ Coordinates(), _tensor_shape }, _padding{ 0 }, _quantization_info(), _data_layout(DataLayout::NCHW)
 {
 }

 TensorInfo::TensorInfo(const ITensorInfo &info)
     : TensorInfo()
 {
     _total_size                    = info.total_size();
     _offset_first_element_in_bytes = info.offset_first_element_in_bytes();
     _strides_in_bytes              = info.strides_in_bytes();
     _num_channels                  = info.num_channels();
     _tensor_shape                  = info.tensor_shape();
     _data_type                     = info.data_type();
     _format                        = info.format();
     _is_resizable                  = info.is_resizable();
     _valid_region                  = info.valid_region();
     _padding                       = info.padding();
     _quantization_info             = info.quantization_info();
     _data_layout                   = info.data_layout();
 }

 TensorInfo::TensorInfo(Format format)
     : TensorInfo(TensorShape(), format)
 {
 }

 TensorInfo::TensorInfo(unsigned int width, unsigned int height, Format format)
     : TensorInfo(TensorShape(width, height), format)
 {
 }

 TensorInfo::TensorInfo(const TensorShape &tensor_shape, Format format)
     : TensorInfo()
 {
     init(tensor_shape, format);
 }

 TensorInfo::TensorInfo(size_t num_channels, DataType data_type)
     : TensorInfo()
 {
     init(TensorShape(), num_channels, data_type);
 }

 TensorInfo::TensorInfo(const TensorShape &tensor_shape, size_t num_channels, DataType data_type)
     : TensorInfo()
 {
     init(tensor_shape, num_channels, data_type);
 }

 TensorInfo::TensorInfo(const TensorShape &tensor_shape, size_t num_channels, DataType data_type, QuantizationInfo quantization_info)
     : TensorInfo()
 {
     init(tensor_shape, num_channels, data_type);
     _quantization_info = std::move(quantization_info);
 }

 TensorInfo::TensorInfo(const HOGInfo &hog_info, unsigned int width, unsigned int height)
     : TensorInfo()
 {
     init(hog_info, width, height);
 }

 void TensorInfo::init(Format format)
 {
     init(TensorShape(), format);
 }

 void TensorInfo::init(const TensorShape &tensor_shape, Format format)
 {
     size_t         num_channels = num_channels_from_format(format);
     const DataType type         = data_type_from_format(format);

     init(tensor_shape, num_channels, type);

     _format = format;
 }

 void TensorInfo::init(const TensorShape &tensor_shape, Format format,
                       const Strides &strides_in_bytes, size_t offset_first_element_in_bytes,
                       size_t total_size_in_bytes)
 {
     size_t         num_channels = num_channels_from_format(format);
     const DataType type         = data_type_from_format(format);

     init(tensor_shape, num_channels, type, strides_in_bytes, offset_first_element_in_bytes, total_size_in_bytes);

     _format = format;
 }

 void TensorInfo::init(size_t num_channels, DataType data_type)
 {
     init(TensorShape(), num_channels, data_type);
 }

 void TensorInfo::init(const TensorShape &tensor_shape, size_t num_channels, DataType data_type)
 {
     ARM_COMPUTE_ERROR_ON(num_channels == 0);

     _data_type    = data_type;
     _num_channels = num_channels;
     _format       = Format::UNKNOWN;

     set_tensor_shape(tensor_shape);
 }

 void TensorInfo::init(const TensorShape &tensor_shape, size_t num_channels, DataType data_type,
                       const Strides &strides_in_bytes, size_t offset_first_element_in_bytes,
                       size_t total_size_in_bytes)
 {
     ARM_COMPUTE_ERROR_ON(num_channels == 0);

     _data_type                     = data_type;
     _num_channels                  = num_channels;
     _format                        = Format::UNKNOWN;
     _tensor_shape                  = tensor_shape;
     _offset_first_element_in_bytes = offset_first_element_in_bytes;
     _strides_in_bytes              = strides_in_bytes;
     _total_size                    = total_size_in_bytes;

     _valid_region = ValidRegion{ Coordinates(), _tensor_shape };
 }

 void TensorInfo::init(const HOGInfo &hog_info, unsigned int width, unsigned int height)
 {
     // Number of cells for each block
     const Size2D num_cells_per_block = hog_info.num_cells_per_block();

     // Tensor Size = (Number of horizontal block positions) * (Number of vertical block positions)
     const Size2D num_block_positions_per_img = hog_info.num_block_positions_per_image(Size2D(width, height));

     // Number of tensor channels = (Number of cells per block) * (Number of bins per cell)
     const size_t num_channels = num_cells_per_block.area() * hog_info.num_bins();

     init(TensorShape(num_block_positions_per_img.width, num_block_positions_per_img.height), num_channels, DataType::F32);
 }

 size_t TensorInfo::init_auto_padding(const TensorShape &tensor_shape, Format format)
 {
     const size_t   num_channels = num_channels_from_format(format);
     const DataType type         = data_type_from_format(format);
     size_t         total_size   = init_auto_padding(tensor_shape, num_channels, type);

     _format = format;

     return total_size;
 }

 size_t TensorInfo::init_auto_padding(const TensorShape &tensor_shape, size_t num_channels, DataType data_type)
 {
     ARM_COMPUTE_ERROR_ON(num_channels == 0);

     _data_type    = data_type;
     _num_channels = num_channels;
     _format       = Format::UNKNOWN;
     _tensor_shape = tensor_shape;

     _valid_region = ValidRegion{ Coordinates(), _tensor_shape };

     auto_padding();

     return _total_size;
 }

 size_t TensorInfo::init_auto_padding(const HOGInfo &hog_info, unsigned int width, unsigned int height)
 {
     // Number of cells for each block
     const Size2D num_cells_per_block = hog_info.num_cells_per_block();

     // Tensor Size = (Number of horizontal block positions) * (Number of vertical block positions)
     const Size2D num_block_positions_per_img = hog_info.num_block_positions_per_image(Size2D(width, height));

     // Number of tensor channels = (Number of cells per block) * (Number of bins per cell)
     const size_t num_channels = num_cells_per_block.area() * hog_info.num_bins();

     return init_auto_padding(TensorShape(num_block_positions_per_img.width, num_block_positions_per_img.height), num_channels, DataType::F32);
 }

 bool TensorInfo::auto_padding()
 {
     ARM_COMPUTE_ERROR_ON(!_is_resizable);

     // Some kernels compute 32 elements at the time, worst case scenario they
     // will read 32 values after the last element
     const size_t extra_pad_x = _tensor_shape.num_dimensions() < 1 ? 0 : 32;
     const size_t pad_x       = _tensor_shape.num_dimensions() < 1 ? 0 : 4;
     const size_t pad_y       = _tensor_shape.num_dimensions() < 2 ? 0 : 4;

     return extend_padding(PaddingSize(pad_y, pad_x + extra_pad_x, pad_y, pad_x));
 }

 std::tuple<Strides, size_t, size_t> TensorInfo::calculate_padding_requirements(const PaddingSize &padding)
 {
     // Calculate resulting stride for the X, Y and Z dimension
     const size_t stride_x = element_size();
     const size_t stride_y = (padding.left + _tensor_shape[0] + padding.right) * stride_x;
     const size_t stride_z = (padding.top + _tensor_shape[1] + padding.bottom) * stride_y;

     Strides      required_strides;
     size_t       required_total_size           = 0;
     const size_t required_offset_first_element = padding.left * stride_x + padding.top * stride_y;

     switch(_tensor_shape.num_dimensions())
     {
         case 0:
         {
             if(_tensor_shape.total_size() > 0)
             {
                 required_strides    = Strides(stride_x, stride_x);
                 required_total_size = stride_z;
             }
             break;
         }
         case 1:
             required_strides    = compute_strides(*this, stride_x, stride_y);
             required_total_size = stride_z;
             break;
         case 2:
             required_strides    = compute_strides(*this, stride_x, stride_y);
             required_total_size = stride_z;
             break;
         default:
         {
             required_strides = compute_strides(*this, stride_x, stride_y, stride_z);

             const unsigned int idx_last_dimension = _tensor_shape.num_dimensions() - 1;

             required_total_size = _tensor_shape[idx_last_dimension] * required_strides[idx_last_dimension];
             break;
         }
     }

     return std::make_tuple(required_strides, required_offset_first_element, required_total_size);
 }

 bool TensorInfo::extend_padding(const PaddingSize &padding)
 {
     ARM_COMPUTE_ERROR_ON(!_is_resizable);

     bool updated = false;

     if(padding.top > _padding.top)
     {
         _padding.top = padding.top;
         updated      = true;
     }

     if(padding.right > _padding.right)
     {
         _padding.right = padding.right;
         updated        = true;
     }

     if(padding.bottom > _padding.bottom)
     {
         _padding.bottom = padding.bottom;
         updated         = true;
     }

     if(padding.left > _padding.left)
     {
         _padding.left = padding.left;
         updated       = true;
     }

     std::tie(_strides_in_bytes, _offset_first_element_in_bytes, _total_size) = calculate_padding_requirements(_padding);

     return updated;
 }

 std::unique_ptr<ITensorInfo> TensorInfo::clone() const
 {
     return support::cpp14::make_unique<TensorInfo>(*this);
 }

 ITensorInfo &TensorInfo::set_data_type(DataType data_type)
 {
     _data_type = data_type;
     _format    = Format::UNKNOWN;
     return set_tensor_shape(tensor_shape()); // Force total size and strides to update
 }

 ITensorInfo &TensorInfo::set_num_channels(int num_channels)
 {
     _num_channels = num_channels;
     _format       = Format::UNKNOWN;
     return *this;
 }

 ITensorInfo &TensorInfo::set_format(Format format)
 {
     _format = format;

     if(_data_type == DataType::UNKNOWN)
     {
         _num_channels = num_channels_from_format(format);
         _data_type    = data_type_from_format(format);
     }
     else
     {
         ARM_COMPUTE_ERROR_ON(num_channels_from_format(format) != _num_channels);
         ARM_COMPUTE_ERROR_ON(data_type_from_format(format) != _data_type);
     }
     return *this;
 }

 ITensorInfo &TensorInfo::set_tensor_shape(const TensorShape &shape)
 {
     _tensor_shape                  = shape;
     _offset_first_element_in_bytes = 0;
     _strides_in_bytes              = compute_strides(*this);

     if(_tensor_shape.num_dimensions() == 0)
     {
         _total_size = _strides_in_bytes[0];
     }
     else
     {
         const unsigned int idx_last_dimension = _tensor_shape.num_dimensions() - 1;
         _total_size                           = _tensor_shape[idx_last_dimension] * _strides_in_bytes[idx_last_dimension];
     }

     std::tie(_strides_in_bytes, _offset_first_element_in_bytes, _total_size) = calculate_padding_requirements(_padding);

     _valid_region = ValidRegion{ Coordinates(), _tensor_shape };
     return *this;
 }

 ITensorInfo &TensorInfo::set_quantization_info(const QuantizationInfo &quantization_info)
 {
     _quantization_info = quantization_info;
     return *this;
 }

 ITensorInfo &TensorInfo::set_data_layout(const DataLayout &data_layout)
 {
     _data_layout = data_layout;
     return *this;
 }

 ITensorInfo &TensorInfo::reset_padding()
 {
     _padding = PaddingSize();
     if(((_format != Format::UNKNOWN) || (_data_type != DataType::UNKNOWN)) && _total_size != 0)
     {
         std::tie(_strides_in_bytes, _offset_first_element_in_bytes, _total_size) = calculate_padding_requirements(_padding);
     }
     return *this;
 }

 size_t TensorInfo::offset_element_in_bytes(const Coordinates &pos) const
 {
     ARM_COMPUTE_ERROR_ON_COORDINATES_DIMENSIONS_GTE(pos, _tensor_shape.num_dimensions());

     size_t offset = _offset_first_element_in_bytes;

     for(size_t i = 0; i < _tensor_shape.num_dimensions(); ++i)
     {
         offset += pos[i] * _strides_in_bytes[i];
     }

     return offset;
 }
	/*
	* Copyright (c) 2016-2019 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/core/TensorInfo.h"

	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/HOGInfo.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Validate.h"
	#include "support/ToolchainSupport.h"

	using namespace arm_compute;

	TensorInfo::TensorInfo()
	: _total_size(0), _offset_first_element_in_bytes(0), _strides_in_bytes(), _num_channels(0), _tensor_shape(), _data_type(DataType::UNKNOWN), _format(Format::UNKNOWN), _is_resizable{ true },
	_valid_region{ Coordinates(), _tensor_shape }, _padding{ 0 }, _quantization_info(), _data_layout(DataLayout::NCHW)
	{
	}

	TensorInfo::TensorInfo(const ITensorInfo &info)
	: TensorInfo()
	{
	_total_size = info.total_size();
	_offset_first_element_in_bytes = info.offset_first_element_in_bytes();
	_strides_in_bytes = info.strides_in_bytes();
	_num_channels = info.num_channels();
	_tensor_shape = info.tensor_shape();
	_data_type = info.data_type();
	_format = info.format();
	_is_resizable = info.is_resizable();
	_valid_region = info.valid_region();
	_padding = info.padding();
	_quantization_info = info.quantization_info();
	_data_layout = info.data_layout();
	}

	TensorInfo::TensorInfo(Format format)
	: TensorInfo(TensorShape(), format)
	{
	}

	TensorInfo::TensorInfo(unsigned int width, unsigned int height, Format format)
	: TensorInfo(TensorShape(width, height), format)
	{
	}

	TensorInfo::TensorInfo(const TensorShape &tensor_shape, Format format)
	: TensorInfo()
	{
	init(tensor_shape, format);
	}

	TensorInfo::TensorInfo(size_t num_channels, DataType data_type)
	: TensorInfo()
	{
	init(TensorShape(), num_channels, data_type);
	}

	TensorInfo::TensorInfo(const TensorShape &tensor_shape, size_t num_channels, DataType data_type)
	: TensorInfo()
	{
	init(tensor_shape, num_channels, data_type);
	}

	TensorInfo::TensorInfo(const TensorShape &tensor_shape, size_t num_channels, DataType data_type, QuantizationInfo quantization_info)
	: TensorInfo()
	{
	init(tensor_shape, num_channels, data_type);
	_quantization_info = std::move(quantization_info);
	}

	TensorInfo::TensorInfo(const HOGInfo &hog_info, unsigned int width, unsigned int height)
	: TensorInfo()
	{
	init(hog_info, width, height);
	}

	void TensorInfo::init(Format format)
	{
	init(TensorShape(), format);
	}

	void TensorInfo::init(const TensorShape &tensor_shape, Format format)
	{
	size_t num_channels = num_channels_from_format(format);
	const DataType type = data_type_from_format(format);

	init(tensor_shape, num_channels, type);

	_format = format;
	}

	void TensorInfo::init(const TensorShape &tensor_shape, Format format,
	const Strides &strides_in_bytes, size_t offset_first_element_in_bytes,
	size_t total_size_in_bytes)
	{
	size_t num_channels = num_channels_from_format(format);
	const DataType type = data_type_from_format(format);

	init(tensor_shape, num_channels, type, strides_in_bytes, offset_first_element_in_bytes, total_size_in_bytes);

	_format = format;
	}

	void TensorInfo::init(size_t num_channels, DataType data_type)
	{
	init(TensorShape(), num_channels, data_type);
	}

	void TensorInfo::init(const TensorShape &tensor_shape, size_t num_channels, DataType data_type)
	{
	ARM_COMPUTE_ERROR_ON(num_channels == 0);

	_data_type = data_type;
	_num_channels = num_channels;
	_format = Format::UNKNOWN;

	set_tensor_shape(tensor_shape);
	}

	void TensorInfo::init(const TensorShape &tensor_shape, size_t num_channels, DataType data_type,
	const Strides &strides_in_bytes, size_t offset_first_element_in_bytes,
	size_t total_size_in_bytes)
	{
	ARM_COMPUTE_ERROR_ON(num_channels == 0);

	_data_type = data_type;
	_num_channels = num_channels;
	_format = Format::UNKNOWN;
	_tensor_shape = tensor_shape;
	_offset_first_element_in_bytes = offset_first_element_in_bytes;
	_strides_in_bytes = strides_in_bytes;
	_total_size = total_size_in_bytes;

	_valid_region = ValidRegion{ Coordinates(), _tensor_shape };
	}

	void TensorInfo::init(const HOGInfo &hog_info, unsigned int width, unsigned int height)
	{
	// Number of cells for each block
	const Size2D num_cells_per_block = hog_info.num_cells_per_block();

	// Tensor Size = (Number of horizontal block positions) * (Number of vertical block positions)
	const Size2D num_block_positions_per_img = hog_info.num_block_positions_per_image(Size2D(width, height));

	// Number of tensor channels = (Number of cells per block) * (Number of bins per cell)
	const size_t num_channels = num_cells_per_block.area() * hog_info.num_bins();

	init(TensorShape(num_block_positions_per_img.width, num_block_positions_per_img.height), num_channels, DataType::F32);
	}

	size_t TensorInfo::init_auto_padding(const TensorShape &tensor_shape, Format format)
	{
	const size_t num_channels = num_channels_from_format(format);
	const DataType type = data_type_from_format(format);
	size_t total_size = init_auto_padding(tensor_shape, num_channels, type);

	_format = format;

	return total_size;
	}

	size_t TensorInfo::init_auto_padding(const TensorShape &tensor_shape, size_t num_channels, DataType data_type)
	{
	ARM_COMPUTE_ERROR_ON(num_channels == 0);

	_data_type = data_type;
	_num_channels = num_channels;
	_format = Format::UNKNOWN;
	_tensor_shape = tensor_shape;

	_valid_region = ValidRegion{ Coordinates(), _tensor_shape };

	auto_padding();

	return _total_size;
	}

	size_t TensorInfo::init_auto_padding(const HOGInfo &hog_info, unsigned int width, unsigned int height)
	{
	// Number of cells for each block
	const Size2D num_cells_per_block = hog_info.num_cells_per_block();

	// Tensor Size = (Number of horizontal block positions) * (Number of vertical block positions)
	const Size2D num_block_positions_per_img = hog_info.num_block_positions_per_image(Size2D(width, height));

	// Number of tensor channels = (Number of cells per block) * (Number of bins per cell)
	const size_t num_channels = num_cells_per_block.area() * hog_info.num_bins();

	return init_auto_padding(TensorShape(num_block_positions_per_img.width, num_block_positions_per_img.height), num_channels, DataType::F32);
	}

	bool TensorInfo::auto_padding()
	{
	ARM_COMPUTE_ERROR_ON(!_is_resizable);

	// Some kernels compute 32 elements at the time, worst case scenario they
	// will read 32 values after the last element
	const size_t extra_pad_x = _tensor_shape.num_dimensions() < 1 ? 0 : 32;
	const size_t pad_x = _tensor_shape.num_dimensions() < 1 ? 0 : 4;
	const size_t pad_y = _tensor_shape.num_dimensions() < 2 ? 0 : 4;

	return extend_padding(PaddingSize(pad_y, pad_x + extra_pad_x, pad_y, pad_x));
	}

	std::tuple<Strides, size_t, size_t> TensorInfo::calculate_padding_requirements(const PaddingSize &padding)
	{
	// Calculate resulting stride for the X, Y and Z dimension
	const size_t stride_x = element_size();
	const size_t stride_y = (padding.left + _tensor_shape[0] + padding.right) * stride_x;
	const size_t stride_z = (padding.top + _tensor_shape[1] + padding.bottom) * stride_y;

	Strides required_strides;
	size_t required_total_size = 0;
	const size_t required_offset_first_element = padding.left * stride_x + padding.top * stride_y;

	switch(_tensor_shape.num_dimensions())
	{
	case 0:
	{
	if(_tensor_shape.total_size() > 0)
	{
	required_strides = Strides(stride_x, stride_x);
	required_total_size = stride_z;
	}
	break;
	}
	case 1:
	required_strides = compute_strides(*this, stride_x, stride_y);
	required_total_size = stride_z;
	break;
	case 2:
	required_strides = compute_strides(*this, stride_x, stride_y);
	required_total_size = stride_z;
	break;
	default:
	{
	required_strides = compute_strides(*this, stride_x, stride_y, stride_z);

	const unsigned int idx_last_dimension = _tensor_shape.num_dimensions() - 1;

	required_total_size = _tensor_shape[idx_last_dimension] * required_strides[idx_last_dimension];
	break;
	}
	}

	return std::make_tuple(required_strides, required_offset_first_element, required_total_size);
	}

	bool TensorInfo::extend_padding(const PaddingSize &padding)
	{
	ARM_COMPUTE_ERROR_ON(!_is_resizable);

	bool updated = false;

	if(padding.top > _padding.top)
	{
	_padding.top = padding.top;
	updated = true;
	}

	if(padding.right > _padding.right)
	{
	_padding.right = padding.right;
	updated = true;
	}

	if(padding.bottom > _padding.bottom)
	{
	_padding.bottom = padding.bottom;
	updated = true;
	}

	if(padding.left > _padding.left)
	{
	_padding.left = padding.left;
	updated = true;
	}

	std::tie(_strides_in_bytes, _offset_first_element_in_bytes, _total_size) = calculate_padding_requirements(_padding);

	return updated;
	}

	std::unique_ptr<ITensorInfo> TensorInfo::clone() const
	{
	return support::cpp14::make_unique<TensorInfo>(*this);
	}

	ITensorInfo &TensorInfo::set_data_type(DataType data_type)
	{
	_data_type = data_type;
	_format = Format::UNKNOWN;
	return set_tensor_shape(tensor_shape()); // Force total size and strides to update
	}

	ITensorInfo &TensorInfo::set_num_channels(int num_channels)
	{
	_num_channels = num_channels;
	_format = Format::UNKNOWN;
	return *this;
	}

	ITensorInfo &TensorInfo::set_format(Format format)
	{
	_format = format;

	if(_data_type == DataType::UNKNOWN)
	{
	_num_channels = num_channels_from_format(format);
	_data_type = data_type_from_format(format);
	}
	else
	{
	ARM_COMPUTE_ERROR_ON(num_channels_from_format(format) != _num_channels);
	ARM_COMPUTE_ERROR_ON(data_type_from_format(format) != _data_type);
	}
	return *this;
	}

	ITensorInfo &TensorInfo::set_tensor_shape(const TensorShape &shape)
	{
	_tensor_shape = shape;
	_offset_first_element_in_bytes = 0;
	_strides_in_bytes = compute_strides(*this);

	if(_tensor_shape.num_dimensions() == 0)
	{
	_total_size = _strides_in_bytes[0];
	}
	else
	{
	const unsigned int idx_last_dimension = _tensor_shape.num_dimensions() - 1;
	_total_size = _tensor_shape[idx_last_dimension] * _strides_in_bytes[idx_last_dimension];
	}

	std::tie(_strides_in_bytes, _offset_first_element_in_bytes, _total_size) = calculate_padding_requirements(_padding);

	_valid_region = ValidRegion{ Coordinates(), _tensor_shape };
	return *this;
	}

	ITensorInfo &TensorInfo::set_quantization_info(const QuantizationInfo &quantization_info)
	{
	_quantization_info = quantization_info;
	return *this;
	}

	ITensorInfo &TensorInfo::set_data_layout(const DataLayout &data_layout)
	{
	_data_layout = data_layout;
	return *this;
	}

	ITensorInfo &TensorInfo::reset_padding()
	{
	_padding = PaddingSize();
	if(((_format != Format::UNKNOWN) \|\| (_data_type != DataType::UNKNOWN)) && _total_size != 0)
	{
	std::tie(_strides_in_bytes, _offset_first_element_in_bytes, _total_size) = calculate_padding_requirements(_padding);
	}
	return *this;
	}

	size_t TensorInfo::offset_element_in_bytes(const Coordinates &pos) const
	{
	ARM_COMPUTE_ERROR_ON_COORDINATES_DIMENSIONS_GTE(pos, _tensor_shape.num_dimensions());

	size_t offset = _offset_first_element_in_bytes;

	for(size_t i = 0; i < _tensor_shape.num_dimensions(); ++i)
	{
	offset += pos[i] * _strides_in_bytes[i];
	}

	return offset;
	}