src/core/CL/kernels/CLConvolutionKernel.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/CL/kernels/CLConvolutionKernel.h"

 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/CL/ICLKernel.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/CL/OpenCL.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"

 #include <set>
 #include <sstream>
 #include <string>

 namespace arm_compute
 {
 namespace
 {
 constexpr unsigned int max_matrix_size = 81;
 } // namespace

 /****************************************************************************************\
  *                                 Square Convolution                                *
 \****************************************************************************************/

 template <unsigned int matrix_size>
 BorderSize             CLConvolutionKernel<matrix_size>::border_size() const
 {
     return BorderSize(matrix_size / 2);
 }

 template <unsigned int matrix_size>
 void CLConvolutionKernel<matrix_size>::configure(const ICLTensor *input, ICLTensor *output, const int16_t *conv, uint32_t scale, bool border_undefined)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16);
     ARM_COMPUTE_ERROR_ON(conv == nullptr);

     _input  = input;
     _output = output;

     std::stringstream kernel_name;
     CLBuildOptions    build_opts;
     kernel_name << "convolution" << matrix_size << "x" << matrix_size << "_static";

     if(scale == 0)
     {
         scale = calculate_matrix_scale(conv, matrix_size);
     }

     for(unsigned int i = 0; i < matrix_size * matrix_size; i++)
     {
         std::stringstream mat_str;
         mat_str << "-DMAT" << i << "=" << conv[i];
         build_opts.add_option(mat_str.str());
     }

     build_opts.add_option("-DSCALE=" + support::cpp11::to_string(scale));

     DataType data_type = data_type_for_convolution_matrix(conv, matrix_size * matrix_size);
     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));

     std::stringstream out_type;
     out_type << "-DDATA_TYPE_OUT=" << get_cl_type_from_data_type(output->info()->data_type());
     build_opts.add_option(out_type.str());

     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name.str(), build_opts.options()));

     // Configure kernel window
     constexpr unsigned int num_elems_processed_per_iteration = 8;
     constexpr unsigned int num_elems_written_per_iteration   = 8;
     constexpr unsigned int num_elems_read_per_iteration      = 16;
     constexpr unsigned int num_rows_read_per_iteration       = matrix_size;

     Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());

     AccessWindowRectangle  input_access(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration);
     AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);

     update_window_and_padding(win, input_access, output_access);

     output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());

     ICLKernel::configure_internal(win);
 }

 /****************************************************************************************\
  *                                 Separable Convolution                                *
 \****************************************************************************************/
 template <unsigned int matrix_size>
 CLSeparableConvolutionHorKernel<matrix_size>::CLSeparableConvolutionHorKernel()
     : _border_size(0)
 {
 }

 template <unsigned int matrix_size>
 BorderSize             CLSeparableConvolutionHorKernel<matrix_size>::border_size() const
 {
     return _border_size;
 }

 template <unsigned int matrix_size>
 void CLSeparableConvolutionHorKernel<matrix_size>::configure(const ICLTensor *input, ICLTensor *output, const int16_t *conv, bool border_undefined)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U16, DataType::S16, DataType::S32);

     ARM_COMPUTE_ERROR_ON((matrix_size != 5) && (matrix_size != 7) && (matrix_size != 9));

     _input       = input;
     _output      = output;
     _border_size = BorderSize(border_undefined ? 0 : matrix_size / 2, matrix_size / 2);

     // Set build options
     std::set<std::string> build_opts;

     std::array<int16_t, matrix_size *matrix_size> mat = { 0 };
     memcpy(mat.data(), conv, matrix_size * sizeof(int16_t));

     for(unsigned int j = 0; j < matrix_size * matrix_size; j++)
     {
         build_opts.insert("-DMAT" + support::cpp11::to_string(j) + "=" + support::cpp11::to_string(mat[j]));
     }

     build_opts.insert("-DSCALE=0");

     build_opts.insert("-DDATA_TYPE=" + get_cl_type_from_data_type(output->info()->data_type()));

     // Create kernel
     const std::string kernel_name = "convolution_separable1x" + support::cpp11::to_string(matrix_size) + "_static";
     _kernel                       = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));

     // Configure kernel window
     constexpr unsigned int num_elems_processed_per_iteration = 8;
     constexpr unsigned int num_elems_read_per_iteration      = 16;
     constexpr unsigned int num_elems_written_per_iteration   = 8;

     Window win = calculate_max_window_horizontal(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());

     AccessWindowHorizontal input_access(input->info(), -border_size().left, num_elems_read_per_iteration);
     AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);

     update_window_and_padding(win, input_access, output_access);

     output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());

     ICLKernel::configure_internal(win);

     // Set config_id for enabling LWS tuning
     _config_id = kernel_name;
     _config_id += "_";
     _config_id += lower_string(string_from_data_type(input->info()->data_type()));
     _config_id += "_";
     _config_id += support::cpp11::to_string(input->info()->dimension(0));
     _config_id += "_";
     _config_id += support::cpp11::to_string(input->info()->dimension(1));
     _config_id += "_";
     _config_id += support::cpp11::to_string(output->info()->dimension(0));
     _config_id += "_";
     _config_id += support::cpp11::to_string(output->info()->dimension(1));
     _config_id += "_";
     _config_id += support::cpp11::to_string(border_undefined);
 }

 template <unsigned int matrix_size>
 BorderSize             CLSeparableConvolutionVertKernel<matrix_size>::border_size() const
 {
     return BorderSize{ matrix_size / 2, 0 };
 }

 template <unsigned int matrix_size>
 void CLSeparableConvolutionVertKernel<matrix_size>::configure(const ICLTensor *input, ICLTensor *output,
                                                               const int16_t *conv, uint32_t scale, bool border_undefined, DataType data_type)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U16, DataType::S16, DataType::S32);
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16);
     ARM_COMPUTE_ERROR_ON((matrix_size != 5) && (matrix_size != 7) && (matrix_size != 9));
     ARM_COMPUTE_ERROR_ON(scale == 0);

     _input  = input;
     _output = output;

     std::set<std::string> build_opts;

     std::array<int16_t, matrix_size *matrix_size> mat = { 0 };
     memcpy(mat.data() + matrix_size, conv, matrix_size * sizeof(int16_t));

     for(unsigned int j = 0; j < matrix_size * matrix_size; j++)
     {
         build_opts.insert("-DMAT" + support::cpp11::to_string(j) + "=" + support::cpp11::to_string(mat[j]));
     }

     build_opts.insert("-DSCALE=" + support::cpp11::to_string(scale));

     build_opts.insert("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));

     build_opts.insert("-DCOMPUTE_TYPE=" + get_cl_type_from_data_type(data_type));

     std::stringstream out_type;
     out_type << "-DDATA_TYPE_OUT=" << get_cl_type_from_data_type(output->info()->data_type());
     build_opts.insert(out_type.str());

     // Create kernel
     const std::string kernel_name = "convolution_separable" + support::cpp11::to_string(matrix_size) + "x1_static";
     _kernel                       = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));

     // Configure kernel window
     constexpr unsigned int num_elems_processed_per_iteration = 8;
     constexpr unsigned int num_elems_written_per_iteration   = 8;
     constexpr unsigned int num_elems_read_per_iteration      = 8;
     constexpr unsigned int num_rows_read_per_iteration       = matrix_size;

     Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());

     AccessWindowRectangle  input_access(input->info(), 0, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration);
     AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);

     update_window_and_padding(win, input_access, output_access);

     output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());

     ICLKernel::configure_internal(win);

     // Set config_id for enabling LWS tuning
     _config_id = kernel_name;
     _config_id += "_";
     _config_id += lower_string(string_from_data_type(data_type));
     _config_id += "_";
     _config_id += support::cpp11::to_string(input->info()->dimension(0));
     _config_id += "_";
     _config_id += support::cpp11::to_string(input->info()->dimension(1));
     _config_id += "_";
     _config_id += support::cpp11::to_string(output->info()->dimension(0));
     _config_id += "_";
     _config_id += support::cpp11::to_string(output->info()->dimension(1));
     _config_id += "_";
     _config_id += support::cpp11::to_string(border_undefined);
 }

 /****************************************************************************************\
  *                                 Rectangle Convolution                                *
 \****************************************************************************************/

 CLConvolutionRectangleKernel::CLConvolutionRectangleKernel()
     : _border_size(0), _input(nullptr), _output(nullptr)
 {
 }

 BorderSize CLConvolutionRectangleKernel::border_size() const
 {
     return _border_size;
 }

 void CLConvolutionRectangleKernel::configure(const ICLTensor *input, ICLTensor *output, const int16_t *conv, uint32_t width, uint32_t height, uint32_t scale, bool border_undefined)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16);
     ARM_COMPUTE_ERROR_ON(nullptr == conv);
     ARM_COMPUTE_ERROR_ON(3 != width && 5 != width && 7 != width && 9 != width);
     ARM_COMPUTE_ERROR_ON(3 != height && 5 != height && 7 != height && 9 != height);
     ARM_COMPUTE_ERROR_ON(0 == scale);

     _input       = input;
     _output      = output;
     _border_size = BorderSize(height / 2, width / 2);

     std::set<std::string> options;

     std::stringstream output_type;
     output_type << "-DDATA_TYPE_OUT=" << get_cl_type_from_data_type(output->info()->data_type());
     options.insert(output_type.str());

     uint32_t matrix_size = width * height;

     std::array<int16_t, max_matrix_size> mat = { 0 };

     memcpy(mat.data(), conv, matrix_size * sizeof(int16_t));

     for(unsigned int j = 0; j < max_matrix_size; j++)
     {
         options.insert("-DMAT" + support::cpp11::to_string(j) + "=" + support::cpp11::to_string(mat[j]));
     }

     options.insert("-DSCALE=" + support::cpp11::to_string(scale));

     DataType data_type = data_type_for_convolution_matrix(conv, matrix_size);
     options.insert("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));

     options.insert("-DMATRIX_WIDTH=" + support::cpp11::to_string(width));
     options.insert("-DMATRIX_HEIGHT=" + support::cpp11::to_string(height));

     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("convolution_rectangle", options));

     // Configure kernel window
     constexpr unsigned int num_elems_processed_per_iteration = 8;
     constexpr unsigned int num_elems_read_per_iteration      = 16;
     constexpr unsigned int num_elems_written_per_iteration   = 8;
     const unsigned int     num_rows_read_per_iteration       = height;

     Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());

     AccessWindowRectangle  input_access(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration);
     AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);

     update_window_and_padding(win, input_access, output_access);

     output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());

     ICLKernel::configure_internal(win);
 }

 void CLConvolutionRectangleKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

     Window slice = window.first_slice_window_2D();

     do
     {
         unsigned int idx = 0;
         add_2D_tensor_argument(idx, _input, slice);
         add_2D_tensor_argument(idx, _output, slice);
         enqueue(queue, *this, slice, lws_hint());
     }
     while(window.slide_window_slice_2D(slice));
 }

 template class arm_compute::CLConvolutionKernel<3>;
 template class arm_compute::CLConvolutionKernel<5>;
 template class arm_compute::CLConvolutionKernel<7>;
 template class arm_compute::CLConvolutionKernel<9>;
 template class arm_compute::CLSeparableConvolutionVertKernel<5>;
 template class arm_compute::CLSeparableConvolutionVertKernel<7>;
 template class arm_compute::CLSeparableConvolutionVertKernel<9>;
 template class arm_compute::CLSeparableConvolutionHorKernel<5>;
 template class arm_compute::CLSeparableConvolutionHorKernel<7>;
 template class arm_compute::CLSeparableConvolutionHorKernel<9>;
 } // namespace arm_compute
	/*
	* 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/CL/kernels/CLConvolutionKernel.h"

	#include "arm_compute/core/CL/CLHelpers.h"
	#include "arm_compute/core/CL/CLKernelLibrary.h"
	#include "arm_compute/core/CL/ICLKernel.h"
	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/CL/OpenCL.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.h"

	#include <set>
	#include <sstream>
	#include <string>

	namespace arm_compute
	{
	namespace
	{
	constexpr unsigned int max_matrix_size = 81;
	} // namespace

	/****************************************************************************************\
	* Square Convolution *
	\****************************************************************************************/

	template <unsigned int matrix_size>
	BorderSize CLConvolutionKernel<matrix_size>::border_size() const
	{
	return BorderSize(matrix_size / 2);
	}

	template <unsigned int matrix_size>
	void CLConvolutionKernel<matrix_size>::configure(const ICLTensor input, ICLTensor output, const int16_t *conv, uint32_t scale, bool border_undefined)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16);
	ARM_COMPUTE_ERROR_ON(conv == nullptr);

	_input = input;
	_output = output;

	std::stringstream kernel_name;
	CLBuildOptions build_opts;
	kernel_name << "convolution" << matrix_size << "x" << matrix_size << "_static";

	if(scale == 0)
	{
	scale = calculate_matrix_scale(conv, matrix_size);
	}

	for(unsigned int i = 0; i < matrix_size * matrix_size; i++)
	{
	std::stringstream mat_str;
	mat_str << "-DMAT" << i << "=" << conv[i];
	build_opts.add_option(mat_str.str());
	}

	build_opts.add_option("-DSCALE=" + support::cpp11::to_string(scale));

	DataType data_type = data_type_for_convolution_matrix(conv, matrix_size * matrix_size);
	build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));

	std::stringstream out_type;
	out_type << "-DDATA_TYPE_OUT=" << get_cl_type_from_data_type(output->info()->data_type());
	build_opts.add_option(out_type.str());

	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name.str(), build_opts.options()));

	// Configure kernel window
	constexpr unsigned int num_elems_processed_per_iteration = 8;
	constexpr unsigned int num_elems_written_per_iteration = 8;
	constexpr unsigned int num_elems_read_per_iteration = 16;
	constexpr unsigned int num_rows_read_per_iteration = matrix_size;

	Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());

	AccessWindowRectangle input_access(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration);
	AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);

	update_window_and_padding(win, input_access, output_access);

	output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());

	ICLKernel::configure_internal(win);
	}

	/****************************************************************************************\
	* Separable Convolution *
	\****************************************************************************************/
	template <unsigned int matrix_size>
	CLSeparableConvolutionHorKernel<matrix_size>::CLSeparableConvolutionHorKernel()
	: _border_size(0)
	{
	}

	template <unsigned int matrix_size>
	BorderSize CLSeparableConvolutionHorKernel<matrix_size>::border_size() const
	{
	return _border_size;
	}

	template <unsigned int matrix_size>
	void CLSeparableConvolutionHorKernel<matrix_size>::configure(const ICLTensor input, ICLTensor output, const int16_t *conv, bool border_undefined)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U16, DataType::S16, DataType::S32);

	ARM_COMPUTE_ERROR_ON((matrix_size != 5) && (matrix_size != 7) && (matrix_size != 9));

	_input = input;
	_output = output;
	_border_size = BorderSize(border_undefined ? 0 : matrix_size / 2, matrix_size / 2);

	// Set build options
	std::set<std::string> build_opts;

	std::array<int16_t, matrix_size *matrix_size> mat = { 0 };
	memcpy(mat.data(), conv, matrix_size * sizeof(int16_t));

	for(unsigned int j = 0; j < matrix_size * matrix_size; j++)
	{
	build_opts.insert("-DMAT" + support::cpp11::to_string(j) + "=" + support::cpp11::to_string(mat[j]));
	}

	build_opts.insert("-DSCALE=0");

	build_opts.insert("-DDATA_TYPE=" + get_cl_type_from_data_type(output->info()->data_type()));

	// Create kernel
	const std::string kernel_name = "convolution_separable1x" + support::cpp11::to_string(matrix_size) + "_static";
	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));

	// Configure kernel window
	constexpr unsigned int num_elems_processed_per_iteration = 8;
	constexpr unsigned int num_elems_read_per_iteration = 16;
	constexpr unsigned int num_elems_written_per_iteration = 8;

	Window win = calculate_max_window_horizontal(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());

	AccessWindowHorizontal input_access(input->info(), -border_size().left, num_elems_read_per_iteration);
	AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);

	update_window_and_padding(win, input_access, output_access);

	output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());

	ICLKernel::configure_internal(win);

	// Set config_id for enabling LWS tuning
	_config_id = kernel_name;
	_config_id += "_";
	_config_id += lower_string(string_from_data_type(input->info()->data_type()));
	_config_id += "_";
	_config_id += support::cpp11::to_string(input->info()->dimension(0));
	_config_id += "_";
	_config_id += support::cpp11::to_string(input->info()->dimension(1));
	_config_id += "_";
	_config_id += support::cpp11::to_string(output->info()->dimension(0));
	_config_id += "_";
	_config_id += support::cpp11::to_string(output->info()->dimension(1));
	_config_id += "_";
	_config_id += support::cpp11::to_string(border_undefined);
	}

	template <unsigned int matrix_size>
	BorderSize CLSeparableConvolutionVertKernel<matrix_size>::border_size() const
	{
	return BorderSize{ matrix_size / 2, 0 };
	}

	template <unsigned int matrix_size>
	void CLSeparableConvolutionVertKernel<matrix_size>::configure(const ICLTensor input, ICLTensor output,
	const int16_t *conv, uint32_t scale, bool border_undefined, DataType data_type)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U16, DataType::S16, DataType::S32);
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16);
	ARM_COMPUTE_ERROR_ON((matrix_size != 5) && (matrix_size != 7) && (matrix_size != 9));
	ARM_COMPUTE_ERROR_ON(scale == 0);

	_input = input;
	_output = output;

	std::set<std::string> build_opts;

	std::array<int16_t, matrix_size *matrix_size> mat = { 0 };
	memcpy(mat.data() + matrix_size, conv, matrix_size * sizeof(int16_t));

	for(unsigned int j = 0; j < matrix_size * matrix_size; j++)
	{
	build_opts.insert("-DMAT" + support::cpp11::to_string(j) + "=" + support::cpp11::to_string(mat[j]));
	}

	build_opts.insert("-DSCALE=" + support::cpp11::to_string(scale));

	build_opts.insert("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));

	build_opts.insert("-DCOMPUTE_TYPE=" + get_cl_type_from_data_type(data_type));

	std::stringstream out_type;
	out_type << "-DDATA_TYPE_OUT=" << get_cl_type_from_data_type(output->info()->data_type());
	build_opts.insert(out_type.str());

	// Create kernel
	const std::string kernel_name = "convolution_separable" + support::cpp11::to_string(matrix_size) + "x1_static";
	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));

	// Configure kernel window
	constexpr unsigned int num_elems_processed_per_iteration = 8;
	constexpr unsigned int num_elems_written_per_iteration = 8;
	constexpr unsigned int num_elems_read_per_iteration = 8;
	constexpr unsigned int num_rows_read_per_iteration = matrix_size;

	Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());

	AccessWindowRectangle input_access(input->info(), 0, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration);
	AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);

	update_window_and_padding(win, input_access, output_access);

	output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());

	ICLKernel::configure_internal(win);

	// Set config_id for enabling LWS tuning
	_config_id = kernel_name;
	_config_id += "_";
	_config_id += lower_string(string_from_data_type(data_type));
	_config_id += "_";
	_config_id += support::cpp11::to_string(input->info()->dimension(0));
	_config_id += "_";
	_config_id += support::cpp11::to_string(input->info()->dimension(1));
	_config_id += "_";
	_config_id += support::cpp11::to_string(output->info()->dimension(0));
	_config_id += "_";
	_config_id += support::cpp11::to_string(output->info()->dimension(1));
	_config_id += "_";
	_config_id += support::cpp11::to_string(border_undefined);
	}

	/****************************************************************************************\
	* Rectangle Convolution *
	\****************************************************************************************/

	CLConvolutionRectangleKernel::CLConvolutionRectangleKernel()
	: _border_size(0), _input(nullptr), _output(nullptr)
	{
	}

	BorderSize CLConvolutionRectangleKernel::border_size() const
	{
	return _border_size;
	}

	void CLConvolutionRectangleKernel::configure(const ICLTensor input, ICLTensor output, const int16_t *conv, uint32_t width, uint32_t height, uint32_t scale, bool border_undefined)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16);
	ARM_COMPUTE_ERROR_ON(nullptr == conv);
	ARM_COMPUTE_ERROR_ON(3 != width && 5 != width && 7 != width && 9 != width);
	ARM_COMPUTE_ERROR_ON(3 != height && 5 != height && 7 != height && 9 != height);
	ARM_COMPUTE_ERROR_ON(0 == scale);

	_input = input;
	_output = output;
	_border_size = BorderSize(height / 2, width / 2);

	std::set<std::string> options;

	std::stringstream output_type;
	output_type << "-DDATA_TYPE_OUT=" << get_cl_type_from_data_type(output->info()->data_type());
	options.insert(output_type.str());

	uint32_t matrix_size = width * height;

	std::array<int16_t, max_matrix_size> mat = { 0 };

	memcpy(mat.data(), conv, matrix_size * sizeof(int16_t));

	for(unsigned int j = 0; j < max_matrix_size; j++)
	{
	options.insert("-DMAT" + support::cpp11::to_string(j) + "=" + support::cpp11::to_string(mat[j]));
	}

	options.insert("-DSCALE=" + support::cpp11::to_string(scale));

	DataType data_type = data_type_for_convolution_matrix(conv, matrix_size);
	options.insert("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));

	options.insert("-DMATRIX_WIDTH=" + support::cpp11::to_string(width));
	options.insert("-DMATRIX_HEIGHT=" + support::cpp11::to_string(height));

	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("convolution_rectangle", options));

	// Configure kernel window
	constexpr unsigned int num_elems_processed_per_iteration = 8;
	constexpr unsigned int num_elems_read_per_iteration = 16;
	constexpr unsigned int num_elems_written_per_iteration = 8;
	const unsigned int num_rows_read_per_iteration = height;

	Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration), border_undefined, border_size());

	AccessWindowRectangle input_access(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration);
	AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);

	update_window_and_padding(win, input_access, output_access);

	output_access.set_valid_region(win, input->info()->valid_region(), border_undefined, border_size());

	ICLKernel::configure_internal(win);
	}

	void CLConvolutionRectangleKernel::run(const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

	Window slice = window.first_slice_window_2D();

	do
	{
	unsigned int idx = 0;
	add_2D_tensor_argument(idx, _input, slice);
	add_2D_tensor_argument(idx, _output, slice);
	enqueue(queue, *this, slice, lws_hint());
	}
	while(window.slide_window_slice_2D(slice));
	}

	template class arm_compute::CLConvolutionKernel<3>;
	template class arm_compute::CLConvolutionKernel<5>;
	template class arm_compute::CLConvolutionKernel<7>;
	template class arm_compute::CLConvolutionKernel<9>;
	template class arm_compute::CLSeparableConvolutionVertKernel<5>;
	template class arm_compute::CLSeparableConvolutionVertKernel<7>;
	template class arm_compute::CLSeparableConvolutionVertKernel<9>;
	template class arm_compute::CLSeparableConvolutionHorKernel<5>;
	template class arm_compute::CLSeparableConvolutionHorKernel<7>;
	template class arm_compute::CLSeparableConvolutionHorKernel<9>;
	} // namespace arm_compute