src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017 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/CLDirectConvolutionLayerKernel.h"

 #include "arm_compute/core/AccessWindowStatic.h"
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/IAccessWindow.h"
 #include "arm_compute/core/ITensor.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "support/ToolchainSupport.h"

 using namespace arm_compute;

 CLDirectConvolutionLayerKernel::CLDirectConvolutionLayerKernel()
     : _input(nullptr), _biases(nullptr), _weights(nullptr), _output(nullptr), _border_size(0), _conv_pad_x(0), _conv_pad_y(0), _conv_stride_x(0), _conv_stride_y(0)
 {
 }

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

 void CLDirectConvolutionLayerKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
     ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) != weights->info()->dimension(1),
                              "Weights should have same width as length");
     ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) != 1 && weights->info()->dimension(0) != 3 && weights->info()->dimension(0) != 5,
                              "Kernel sizes other than 1x1, 3x3 or 5x5 are not supported");
     ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2));
     ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1));
     ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
     ARM_COMPUTE_ERROR_ON_MSG((weights->info()->dimension(0) == 1) && std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported for 1x1 convolution.");
     ARM_COMPUTE_ERROR_ON_MSG((weights->info()->dimension(0) == 3 || weights->info()->dimension(0) == 5) && std::get<0>(conv_info.stride()) > 2, "Strides larger than 2 not supported for 3x3 convolution.");

     if(biases != nullptr)
     {
         ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
         ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
         ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
     }

     const unsigned int kernel_size = weights->info()->dimension(0);

     // Get convolved dimensions
     unsigned int output_width  = 0;
     unsigned int output_height = 0;
     std::tie(output_width, output_height) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_size, kernel_size, conv_info);

     TensorShape output_shape = input->info()->tensor_shape();
     output_shape.set(0, output_width);
     output_shape.set(1, output_height);
     output_shape.set(2, weights->info()->dimension(3));

     // Output auto inizialitation if not yet initialized
     auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());

     ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);

     _conv_stride_x = std::get<0>(conv_info.stride());
     _conv_stride_y = std::get<1>(conv_info.stride());
     _conv_pad_x    = std::min(std::get<0>(conv_info.pad()), kernel_size / 2);
     _conv_pad_y    = std::min(std::get<1>(conv_info.pad()), kernel_size / 2);

     _input       = input;
     _weights     = weights;
     _output      = output;
     _biases      = biases;
     _border_size = BorderSize(_conv_pad_y, _conv_pad_x);

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

     const GPUTarget gpu_target = get_arch_from_target(get_target());

     if(_biases != nullptr)
     {
         options.emplace("-DHAS_BIAS");
     }

     if((gpu_target == GPUTarget::BIFROST) && (kernel_size <= 5) && (_conv_stride_x == 1) && (_conv_stride_y == 1) && (input->info()->data_type() == DataType::F32))
     {
         options.emplace("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(2)));

         std::string kernel_name = "direct_convolution" + support::cpp11::to_string(kernel_size) + "x" + support::cpp11::to_string(kernel_size) + "_f32_bifrost";
         _kernel                 = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, options));

         // Configure kernel window
         Window win = calculate_max_window(*output->info());

         unsigned int num_elems_read_per_iteration_x    = 0;
         unsigned int num_elems_read_per_iteration_y    = 0;
         unsigned int num_elems_written_per_iteration_x = 0;
         unsigned int num_elems_written_per_iteration_y = 0;

         switch(kernel_size)
         {
             case 1:
             {
                 num_elems_read_per_iteration_x    = 4;
                 num_elems_read_per_iteration_y    = 4;
                 num_elems_written_per_iteration_x = 4;
                 num_elems_written_per_iteration_y = 4;
                 break;
             }
             case 3:
             {
                 num_elems_read_per_iteration_x    = 6;
                 num_elems_read_per_iteration_y    = 5;
                 num_elems_written_per_iteration_x = 4;
                 num_elems_written_per_iteration_y = 3;
                 break;
             }
             case 5:
             {
                 num_elems_read_per_iteration_x    = 8;
                 num_elems_read_per_iteration_y    = 6;
                 num_elems_written_per_iteration_x = 4;
                 num_elems_written_per_iteration_y = 2;
                 break;
             }
             default:
             {
                 ARM_COMPUTE_ERROR("Kernel size not optimized for Bifrost");
             }
         }

         // Calculate right and bottom border
         const int input_width  = input->info()->dimension(0) - kernel_size / 2 + _conv_pad_x;
         const int input_height = input->info()->dimension(1) - kernel_size / 2 + _conv_pad_y;

         // Create window and update padding
         win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y));

         AccessWindowStatic    input_access(input->info(), -_conv_pad_x, -_conv_pad_y, input_width + num_elems_read_per_iteration_x, input_height + num_elems_read_per_iteration_y);
         AccessWindowStatic    weights_access(weights->info(), 0, 0, kernel_size, kernel_size);
         AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);

         update_window_and_padding(win, input_access, weights_access, output_access);

         output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));

         ICLKernel::configure(win);
     }
     else
     {
         std::stringstream kernel_name;
         kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size;
         DataType promoted_type = input->info()->data_type();

         options.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
         options.emplace("-DDATA_SIZE=" + get_data_size_from_data_type(input->info()->data_type()));
         options.emplace("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(2)));
         options.emplace("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x));

         if(is_data_type_fixed_point(input->info()->data_type()))
         {
             options.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));

             switch(input->info()->data_type())
             {
                 case DataType::QS8:
                     promoted_type = DataType::QS16;
                     break;
                 case DataType::QS16:
                     promoted_type = DataType::QS32;
                     break;
                 default:
                     ARM_COMPUTE_ERROR("Datatype not supported");
             }
         }

         options.emplace("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(promoted_type));

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

         // Configure kernel window

         bool is_stride2 = ((kernel_size != 1) && (_conv_stride_x == 2));

         const unsigned int num_elems_read_per_iteration_x    = 8 + 2 * (kernel_size / 2) + (is_stride2 ? 6 + kernel_size / 2 : 0);
         const unsigned int num_elems_read_per_iteration_y    = kernel_size;
         const unsigned int num_elems_written_per_iteration_x = 8;
         const unsigned int num_elems_written_per_iteration_y = 1;

         // Calculate right and bottom border
         const int input_width  = input->info()->dimension(0) - kernel_size / 2 + _conv_pad_x;
         const int input_height = input->info()->dimension(1) - kernel_size / 2 + _conv_pad_y;

         // Create window and update padding
         Window win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y));

         AccessWindowStatic    input_access(input->info(), -_conv_pad_x, -_conv_pad_y, input_width + num_elems_read_per_iteration_x, input_height + num_elems_read_per_iteration_y);
         AccessWindowStatic    weights_access(weights->info(), 0, 0, kernel_size, kernel_size);
         AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);

         update_window_and_padding(win, input_access, weights_access, output_access);

         output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));

         ICLKernel::configure(win);
     }

     // Set config_id for enabling LWS tuning
     _config_id = "direct_convolution_";
     _config_id += lower_string(string_from_data_type(input->info()->data_type()));
     _config_id += "_";
     _config_id += support::cpp11::to_string(kernel_size);
     _config_id += "_";
     _config_id += support::cpp11::to_string(_conv_pad_x);
     _config_id += "_";
     _config_id += support::cpp11::to_string(_conv_pad_y);
     _config_id += "_";
     _config_id += support::cpp11::to_string(_conv_stride_x);
     _config_id += "_";
     _config_id += support::cpp11::to_string(_conv_stride_y);
     _config_id += "_";
     _config_id += support::cpp11::to_string(output->info()->dimension(0));
     _config_id += "_";
     _config_id += support::cpp11::to_string(output->info()->dimension(1));
 }

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

     // Get initial windows
     Window slice  = window.first_slice_window_3D();
     Window win_in = window;

     win_in.adjust(Window::DimX, -_conv_pad_x, true);
     win_in.adjust(Window::DimY, -_conv_pad_y, true);
     win_in.set_dimension_step(Window::DimX, window.x().step() * _conv_stride_x);
     win_in.set_dimension_step(Window::DimY, window.y().step() * _conv_stride_y);

     Window slice_in = win_in.first_slice_window_3D();

     unsigned int idx1 = 2 * num_arguments_per_3D_tensor();
     add_3D_tensor_argument(idx1, _weights, slice);

     if(_biases != nullptr)
     {
         Window slice_biases;
         slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape());
         add_1D_tensor_argument(idx1, _biases, slice_biases);
     }

     _kernel.setArg(idx1++, static_cast<unsigned int>(_weights->info()->strides_in_bytes()[3]));

     do
     {
         unsigned int idx = 0;
         add_3D_tensor_argument(idx, _input, slice_in);
         add_3D_tensor_argument(idx, _output, slice);

         enqueue(queue, *this, slice, _lws_hint);
     }
     while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in));
 }
	/*
	* Copyright (c) 2017 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/CLDirectConvolutionLayerKernel.h"

	#include "arm_compute/core/AccessWindowStatic.h"
	#include "arm_compute/core/CL/CLHelpers.h"
	#include "arm_compute/core/CL/CLKernelLibrary.h"
	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/IAccessWindow.h"
	#include "arm_compute/core/ITensor.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.h"
	#include "support/ToolchainSupport.h"

	using namespace arm_compute;

	CLDirectConvolutionLayerKernel::CLDirectConvolutionLayerKernel()
	: _input(nullptr), _biases(nullptr), _weights(nullptr), _output(nullptr), _border_size(0), _conv_pad_x(0), _conv_pad_y(0), _conv_stride_x(0), _conv_stride_y(0)
	{
	}

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

	void CLDirectConvolutionLayerKernel::configure(const ICLTensor input, const ICLTensor weights, const ICLTensor biases, ICLTensor output, const PadStrideInfo &conv_info)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
	ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) != weights->info()->dimension(1),
	"Weights should have same width as length");
	ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) != 1 && weights->info()->dimension(0) != 3 && weights->info()->dimension(0) != 5,
	"Kernel sizes other than 1x1, 3x3 or 5x5 are not supported");
	ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2));
	ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1));
	ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
	ARM_COMPUTE_ERROR_ON_MSG((weights->info()->dimension(0) == 1) && std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported for 1x1 convolution.");
	ARM_COMPUTE_ERROR_ON_MSG((weights->info()->dimension(0) == 3 \|\| weights->info()->dimension(0) == 5) && std::get<0>(conv_info.stride()) > 2, "Strides larger than 2 not supported for 3x3 convolution.");

	if(biases != nullptr)
	{
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
	ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
	ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
	}

	const unsigned int kernel_size = weights->info()->dimension(0);

	// Get convolved dimensions
	unsigned int output_width = 0;
	unsigned int output_height = 0;
	std::tie(output_width, output_height) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_size, kernel_size, conv_info);

	TensorShape output_shape = input->info()->tensor_shape();
	output_shape.set(0, output_width);
	output_shape.set(1, output_height);
	output_shape.set(2, weights->info()->dimension(3));

	// Output auto inizialitation if not yet initialized
	auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());

	ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);

	_conv_stride_x = std::get<0>(conv_info.stride());
	_conv_stride_y = std::get<1>(conv_info.stride());
	_conv_pad_x = std::min(std::get<0>(conv_info.pad()), kernel_size / 2);
	_conv_pad_y = std::min(std::get<1>(conv_info.pad()), kernel_size / 2);

	_input = input;
	_weights = weights;
	_output = output;
	_biases = biases;
	_border_size = BorderSize(_conv_pad_y, _conv_pad_x);

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

	const GPUTarget gpu_target = get_arch_from_target(get_target());

	if(_biases != nullptr)
	{
	options.emplace("-DHAS_BIAS");
	}

	if((gpu_target == GPUTarget::BIFROST) && (kernel_size <= 5) && (_conv_stride_x == 1) && (_conv_stride_y == 1) && (input->info()->data_type() == DataType::F32))
	{
	options.emplace("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(2)));

	std::string kernel_name = "direct_convolution" + support::cpp11::to_string(kernel_size) + "x" + support::cpp11::to_string(kernel_size) + "_f32_bifrost";
	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, options));

	// Configure kernel window
	Window win = calculate_max_window(*output->info());

	unsigned int num_elems_read_per_iteration_x = 0;
	unsigned int num_elems_read_per_iteration_y = 0;
	unsigned int num_elems_written_per_iteration_x = 0;
	unsigned int num_elems_written_per_iteration_y = 0;

	switch(kernel_size)
	{
	case 1:
	{
	num_elems_read_per_iteration_x = 4;
	num_elems_read_per_iteration_y = 4;
	num_elems_written_per_iteration_x = 4;
	num_elems_written_per_iteration_y = 4;
	break;
	}
	case 3:
	{
	num_elems_read_per_iteration_x = 6;
	num_elems_read_per_iteration_y = 5;
	num_elems_written_per_iteration_x = 4;
	num_elems_written_per_iteration_y = 3;
	break;
	}
	case 5:
	{
	num_elems_read_per_iteration_x = 8;
	num_elems_read_per_iteration_y = 6;
	num_elems_written_per_iteration_x = 4;
	num_elems_written_per_iteration_y = 2;
	break;
	}
	default:
	{
	ARM_COMPUTE_ERROR("Kernel size not optimized for Bifrost");
	}
	}

	// Calculate right and bottom border
	const int input_width = input->info()->dimension(0) - kernel_size / 2 + _conv_pad_x;
	const int input_height = input->info()->dimension(1) - kernel_size / 2 + _conv_pad_y;

	// Create window and update padding
	win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y));

	AccessWindowStatic input_access(input->info(), -_conv_pad_x, -_conv_pad_y, input_width + num_elems_read_per_iteration_x, input_height + num_elems_read_per_iteration_y);
	AccessWindowStatic weights_access(weights->info(), 0, 0, kernel_size, kernel_size);
	AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);

	update_window_and_padding(win, input_access, weights_access, output_access);

	output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));

	ICLKernel::configure(win);
	}
	else
	{
	std::stringstream kernel_name;
	kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size;
	DataType promoted_type = input->info()->data_type();

	options.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
	options.emplace("-DDATA_SIZE=" + get_data_size_from_data_type(input->info()->data_type()));
	options.emplace("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(2)));
	options.emplace("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x));

	if(is_data_type_fixed_point(input->info()->data_type()))
	{
	options.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));

	switch(input->info()->data_type())
	{
	case DataType::QS8:
	promoted_type = DataType::QS16;
	break;
	case DataType::QS16:
	promoted_type = DataType::QS32;
	break;
	default:
	ARM_COMPUTE_ERROR("Datatype not supported");
	}
	}

	options.emplace("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(promoted_type));

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

	// Configure kernel window

	bool is_stride2 = ((kernel_size != 1) && (_conv_stride_x == 2));

	const unsigned int num_elems_read_per_iteration_x = 8 + 2 * (kernel_size / 2) + (is_stride2 ? 6 + kernel_size / 2 : 0);
	const unsigned int num_elems_read_per_iteration_y = kernel_size;
	const unsigned int num_elems_written_per_iteration_x = 8;
	const unsigned int num_elems_written_per_iteration_y = 1;

	// Calculate right and bottom border
	const int input_width = input->info()->dimension(0) - kernel_size / 2 + _conv_pad_x;
	const int input_height = input->info()->dimension(1) - kernel_size / 2 + _conv_pad_y;

	// Create window and update padding
	Window win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y));

	AccessWindowStatic input_access(input->info(), -_conv_pad_x, -_conv_pad_y, input_width + num_elems_read_per_iteration_x, input_height + num_elems_read_per_iteration_y);
	AccessWindowStatic weights_access(weights->info(), 0, 0, kernel_size, kernel_size);
	AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);

	update_window_and_padding(win, input_access, weights_access, output_access);

	output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));

	ICLKernel::configure(win);
	}

	// Set config_id for enabling LWS tuning
	_config_id = "direct_convolution_";
	_config_id += lower_string(string_from_data_type(input->info()->data_type()));
	_config_id += "_";
	_config_id += support::cpp11::to_string(kernel_size);
	_config_id += "_";
	_config_id += support::cpp11::to_string(_conv_pad_x);
	_config_id += "_";
	_config_id += support::cpp11::to_string(_conv_pad_y);
	_config_id += "_";
	_config_id += support::cpp11::to_string(_conv_stride_x);
	_config_id += "_";
	_config_id += support::cpp11::to_string(_conv_stride_y);
	_config_id += "_";
	_config_id += support::cpp11::to_string(output->info()->dimension(0));
	_config_id += "_";
	_config_id += support::cpp11::to_string(output->info()->dimension(1));
	}

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

	// Get initial windows
	Window slice = window.first_slice_window_3D();
	Window win_in = window;

	win_in.adjust(Window::DimX, -_conv_pad_x, true);
	win_in.adjust(Window::DimY, -_conv_pad_y, true);
	win_in.set_dimension_step(Window::DimX, window.x().step() * _conv_stride_x);
	win_in.set_dimension_step(Window::DimY, window.y().step() * _conv_stride_y);

	Window slice_in = win_in.first_slice_window_3D();

	unsigned int idx1 = 2 * num_arguments_per_3D_tensor();
	add_3D_tensor_argument(idx1, _weights, slice);

	if(_biases != nullptr)
	{
	Window slice_biases;
	slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape());
	add_1D_tensor_argument(idx1, _biases, slice_biases);
	}

	_kernel.setArg(idx1++, static_cast<unsigned int>(_weights->info()->strides_in_bytes()[3]));

	do
	{
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, _input, slice_in);
	add_3D_tensor_argument(idx, _output, slice);

	enqueue(queue, *this, slice, _lws_hint);
	}
	while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in));
	}