src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017-2020 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/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.h"

 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/GLES_COMPUTE/GCHelpers.h"
 #include "arm_compute/core/GLES_COMPUTE/GCKernelLibrary.h"
 #include "arm_compute/core/GLES_COMPUTE/IGCTensor.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/Validate.h"
 #include "src/core/AccessWindowStatic.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "support/StringSupport.h"

 using namespace arm_compute;

 template <unsigned int kernel_size>
 GCDirectConvolutionLayerKernel<kernel_size>::GCDirectConvolutionLayerKernel()
     : _input(nullptr), _bias(nullptr), _weights(nullptr), _output(nullptr), _border_size(0), _conv_stride_x(0), _conv_stride_y(0), _conv_pad_x(0), _conv_pad_y(0), _lws(gles::NDRange(1U, 1U, 1U))
 {
 }

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

 template <unsigned int kernel_size>
 void GCDirectConvolutionLayerKernel<kernel_size>::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *bias, IGCTensor *output,
                                                             const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
     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((kernel_size == 3 && std::get<0>(conv_info.stride()) > 2), "Strides larger than 2 not supported in 3x3 direct convolution!");
     ARM_COMPUTE_ERROR_ON(kernel_size != weights->info()->dimension(0));
     ARM_COMPUTE_ERROR_ON(act_info.enabled() && act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU && act_info.activation() != ActivationLayerInfo::ActivationFunction::LOGISTIC);

     if(bias != nullptr)
     {
         ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, bias);
         // FIXME: Bug in framework, workaround it in tests currently.
         //ARM_COMPUTE_ERROR_ON(bias->info()->dimension(0) != weights->info()->dimension(3));
         ARM_COMPUTE_ERROR_ON(bias->info()->num_dimensions() > 1);
     }

     // Get convolved dimensions
     unsigned int owidth  = 0;
     unsigned int oheight = 0;
     std::tie(owidth, oheight) = 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, owidth);
     output_shape.set(1, oheight);
     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());

     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
     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(!conv_info.padding_is_symmetric());

     _conv_stride_x = std::get<0>(conv_info.stride());
     _conv_stride_y = std::get<1>(conv_info.stride());
     _conv_pad_x    = std::get<0>(conv_info.pad());
     _conv_pad_y    = std::get<1>(conv_info.pad());

     _input       = input;
     _weights     = weights;
     _output      = output;
     _bias        = bias;
     _border_size = BorderSize(_conv_pad_y, _conv_pad_x);

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

     options.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(_lws[0]));
     options.emplace("#define LOCAL_SIZE_Y " + support::cpp11::to_string(_lws[1]));
     options.emplace("#define LOCAL_SIZE_Z " + support::cpp11::to_string(_lws[2]));
     options.emplace("#define STRIDE_X " + support::cpp11::to_string(_conv_stride_x));
     options.emplace("#define STRIDE_Y " + support::cpp11::to_string(_conv_stride_y));

     std::string dt_name = (input->info()->data_type() == DataType::F32) ? "DATA_TYPE_FP32" : "DATA_TYPE_FP16";
     options.emplace(("#define " + dt_name));

     // Activation information in case of a fused activation
     if(act_info.enabled())
     {
         options.emplace("#define FUSED_ACTIVATION");
         options.emplace(("#define " + string_from_activation_func(act_info.activation())));
         options.emplace(("#define ACT_OP  " + lower_string(string_from_activation_func(act_info.activation())) + "_op"));
         options.emplace(("#define A_VAL " + float_to_string_with_full_precision(act_info.a())));
         options.emplace(("#define B_VAL " + float_to_string_with_full_precision(act_info.b())));
     }

     unsigned int num_elems_read_per_iteration_x    = kernel_size * _conv_stride_x;
     unsigned int num_elems_read_per_iteration_y    = 1;
     unsigned int num_elems_written_per_iteration_x = 1;
     unsigned int num_elems_written_per_iteration_y = 1;
     unsigned int num_elems_written_per_iteration_z = 1;

     if(kernel_size == 3)
     {
         if((_conv_stride_x == 1) && (_conv_stride_y == 1))
         {
             switch(input->info()->data_type())
             {
                 case DataType::F16:
                     // TODO(APPBROWSER-299): Choose the most optimal path and remove others.
 #define PROCESS_4X_3Y_1Z

 #if defined(PROCESS_8X_3Y_1Z)
                     options.emplace("#define PROCESS_8X_3Y_1Z");
                     num_elems_read_per_iteration_x    = 16;
                     num_elems_read_per_iteration_y    = 5;
                     num_elems_written_per_iteration_x = 8;
                     num_elems_written_per_iteration_y = 3;
 #elif defined(PROCESS_4X_3Y_1Z)
                     options.emplace("#define PROCESS_4X_3Y_1Z");
                     num_elems_read_per_iteration_x    = 8;
                     num_elems_read_per_iteration_y    = 5;
                     num_elems_written_per_iteration_x = 4;
                     num_elems_written_per_iteration_y = 3;
 #elif defined(PROCESS_4X_4Y_1Z)
                     options.emplace("#define PROCESS_4X_4Y_1Z");
                     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 = 4;
 #elif defined(PROCESS_4X_3Y_2Z)
                     options.emplace("#define PROCESS_4X_3Y_2Z");
                     num_elems_read_per_iteration_x    = 8;
                     num_elems_read_per_iteration_y    = 5;
                     num_elems_written_per_iteration_x = 4;
                     num_elems_written_per_iteration_y = 3;
                     num_elems_written_per_iteration_z = 2;
 #endif /* PROCESS_nX_nY_nZ */
 #undef PROCESS_8X_3Y_1Z
 #undef PROCESS_4X_3Y_1Z
 #undef PROCESS_4X_4Y_1Z
 #undef PROCESS_4X_3Y_2Z
                     break;

                 case DataType::F32:
                     options.emplace("#define PROCESS_4X_3Y_1Z");
                     num_elems_read_per_iteration_x    = 8;
                     num_elems_read_per_iteration_y    = 5;
                     num_elems_written_per_iteration_x = 4;
                     num_elems_written_per_iteration_y = 3;
                     break;

                 default:
                     ARM_COMPUTE_ERROR("Current data type is not supported");
                     break;
             }
         }
         // FIXME: Just keep one in release
         else
         {
             switch(input->info()->data_type())
             {
                 case DataType::F16:
                     options.emplace("#define PROCESS_4X_1Y_1Z");
                     num_elems_read_per_iteration_x    = 8;
                     num_elems_written_per_iteration_x = 4;
                     break;

                 case DataType::F32:
                     // TODO(APPBROWSER-299): Choose the most optimal path and remove others.
 #define PROCESS_4X_1Y_1Z

 #if defined(PROCESS_1X_1Y_1Z)
                     options.emplace("#define PROCESS_1X_1Y_1Z");
                     num_elems_read_per_iteration_x    = 3;
                     num_elems_written_per_iteration_x = 1;
 #elif defined(PROCESS_4X_1Y_1Z)
                     options.emplace("#define PROCESS_4X_1Y_1Z");
                     num_elems_read_per_iteration_x    = 8;
                     num_elems_written_per_iteration_x = 4;
 #elif defined(PROCESS_8X_1Y_1Z)
                     options.emplace("#define PROCESS_8X_1Y_1Z");
                     num_elems_read_per_iteration_x    = 12;
                     num_elems_written_per_iteration_x = 8;
 #else /* PROCESS_nX_nY_nZ */
 #error Have to declare how many elements to process in one thread.
 #endif /* PROCESS_nX_nY_nZ */
 #undef PROCESS_1X_1Y_1Z
 #undef PROCESS_4X_1Y_1Z
 #undef PROCESS_8X_1Y_1Z
                     break;

                 default:
                     ARM_COMPUTE_ERROR("Current data type is not supported");
                     break;
             }
         }
     }
     else if(kernel_size == 1)
     {
         if(weights->info()->dimension(2) % 2 == 0)
         {
             options.emplace("#define WEIGHTS_OPTIMIZATION");
         }
         switch(input->info()->data_type())
         {
             case DataType::F16:
 #define PROCESS_8X_2Y_1Z

 #if defined(PROCESS_4X_1Y_1Z)
                 options.emplace("#define PROCESS_4X_1Y_1Z");
                 num_elems_read_per_iteration_x    = 4;
                 num_elems_written_per_iteration_x = 4;
 #elif defined(PROCESS_4X_2Y_1Z)
                 options.emplace("#define PROCESS_4X_2Y_1Z");
                 num_elems_read_per_iteration_x    = 4;
                 num_elems_read_per_iteration_y    = 2;
                 num_elems_written_per_iteration_x = 4;
                 num_elems_written_per_iteration_y = 2;
 #elif defined(PROCESS_4X_3Y_1Z)
                 options.emplace("#define PROCESS_4X_3Y_1Z");
                 num_elems_read_per_iteration_x    = 4;
                 num_elems_read_per_iteration_y    = 3;
                 num_elems_written_per_iteration_x = 4;
                 num_elems_written_per_iteration_y = 3;
 #elif defined(PROCESS_4X_4Y_1Z)
                 options.emplace("#define PROCESS_4X_4Y_1Z");
                 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;
 #elif defined(PROCESS_4X_2Y_2Z)
                 ARM_COMPUTE_ERROR_ON_MSG((weights->info()->dimension(4) % 2) == 1, "Current 'weights->info()->dimension(4) % 2) == 1' is not supported");
                 options.emplace("#define PROCESS_4X_2Y_2Z");
                 num_elems_read_per_iteration_x    = 4;
                 num_elems_read_per_iteration_y    = 2;
                 num_elems_written_per_iteration_x = 4;
                 num_elems_written_per_iteration_y = 2;
                 num_elems_written_per_iteration_z = 2;
 #elif defined(PROCESS_8X_1Y_1Z)
                 options.emplace("#define PROCESS_8X_1Y_1Z");
                 num_elems_read_per_iteration_x    = 8;
                 num_elems_written_per_iteration_x = 8;
 #elif defined(PROCESS_8X_2Y_1Z)
                 options.emplace("#define PROCESS_8X_2Y_1Z");
                 num_elems_read_per_iteration_x    = 8;
                 num_elems_read_per_iteration_y    = 2;
                 num_elems_written_per_iteration_x = 8;
                 num_elems_written_per_iteration_y = 2;
 #else /* PROCESS_4X_1Y_1Z */
 #error Have to declare how many elements to process in one thread.
 #endif /* PROCESS_4X_1Y_1Z */
 #undef PROCESS_4X_1Y_1Z
 #undef PROCESS_4X_2Y_1Z
 #undef PROCESS_4X_3Y_1Z
 #undef PROCESS_4X_4Y_1Z
 #undef PROCESS_4X_2Y_2Z
 #undef PROCESS_8X_1Y_1Z
 #undef PROCESS_8X_2Y_1Z
                 break;

             case DataType::F32:
                 num_elems_read_per_iteration_x    = 1;
                 num_elems_written_per_iteration_x = 1;
                 break;

             default:
                 break;
         }
     }
     else if(kernel_size == 5)
     {
         switch(input->info()->data_type())
         {
             case DataType::F16:
                 options.emplace("#define PROCESS_4X_1Y_1Z");
                 num_elems_read_per_iteration_x    = 8;
                 num_elems_written_per_iteration_x = 4;

             default:
                 break;
         }
     }
     else
     {
     }

     if(_bias != nullptr)
     {
         options.emplace("#define BIAS");
     }

     std::stringstream kernel_name;
     kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size;

     _kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel(kernel_name.str(), options));

     unsigned int idx = (_bias == nullptr) ? 3 * num_arguments_per_3D_tensor() : (num_arguments_per_1D_tensor() + 3 * num_arguments_per_3D_tensor());

     // Calculate output right and bottom border
     const int output_width          = output->info()->dimension(0);
     const int output_height         = output->info()->dimension(1);
     const int output_padding_right  = ceil_to_multiple(output_width, num_elems_written_per_iteration_x * _lws[0]) - output_width;
     const int output_padding_bottom = ceil_to_multiple(output_height, num_elems_written_per_iteration_y * _lws[1]) - output_height;

     // Calculate input right and bottom border
     const int input_width        = input->info()->dimension(0);
     const int input_height       = input->info()->dimension(1);
     const int input_total_width  = std::max(int(input->info()->padding().left), int(_conv_pad_x)) + input_width + std::max(int(input->info()->padding().right), int(_conv_pad_x));
     const int input_total_height = std::max(int(input->info()->padding().top), int(_conv_pad_y)) + input_height + std::max(int(input->info()->padding().bottom), int(_conv_pad_y));
     const int padding_right1     = ceil_to_multiple(input_total_width, num_elems_read_per_iteration_x * _lws[0]) - input_width - _conv_pad_x;
     const int padding_bottom1    = ceil_to_multiple(input_total_height, num_elems_read_per_iteration_y * _lws[1]) - input_height - _conv_pad_y;

     const int upper_bound_w   = ceil_to_multiple(((output_width + output_padding_right) * _conv_stride_x + (kernel_size - 1)), num_elems_read_per_iteration_x * _lws[0]) - _conv_pad_x - input_width;
     const int upper_bound_h   = ceil_to_multiple(((output_height + output_padding_bottom) * _conv_stride_y + (kernel_size - 1)), num_elems_read_per_iteration_y * _lws[1]) - _conv_pad_y - input_height;
     const int padding_right2  = std::max(upper_bound_w, _conv_pad_x);
     const int padding_bottom2 = std::max(upper_bound_h, _conv_pad_y);

     const int padding_right  = std::max(padding_right1, padding_right2);
     const int padding_bottom = std::max(padding_bottom1, padding_bottom2);

     BorderSize border = BorderSize(0, output_padding_right, output_padding_bottom, 0);

     Window win = calculate_max_enlarged_window(*output->info(), Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y, num_elems_written_per_iteration_z), border);

     AccessWindowStatic input_access(input->info(), -_conv_pad_x, -_conv_pad_y, input_width + padding_right, input_height + padding_bottom);
     AccessWindowStatic weights_access = AccessWindowStatic(nullptr, 0, 0, 0, 0);
     AccessWindowStatic bias_access    = AccessWindowStatic(nullptr, 0, 0, 0, 1);

     switch(weights->info()->data_type())
     {
         case DataType::F16:
             if((weights->info()->dimension(2) % 2 != 0) || (kernel_size != 1))
             {
                 weights_access = AccessWindowStatic(weights->info(), 0, 0, kernel_size + 1, kernel_size);
             }
             if(_bias != nullptr)
             {
                 bias_access = AccessWindowStatic(_bias->info(), 0, 0, _bias->info()->dimension(0) + 1, 1);
             }
             break;

         case DataType::F32:
             weights_access = AccessWindowStatic(weights->info(), 0, 0, kernel_size, kernel_size);
             if(_bias != nullptr)
             {
                 bias_access = AccessWindowStatic(_bias->info(), 0, 0, _bias->info()->dimension(0), 1);
             }
             break;

         default:
             ARM_COMPUTE_ERROR("Current data type is not supported");
             break;
     }

     AccessWindowStatic output_access(output->info(), 0, 0, output_width + output_padding_right, output_height + output_padding_bottom);

     if(_bias != nullptr)
     {
         update_window_and_padding(win, input_access, weights_access, bias_access, output_access);
     }
     else
     {
         update_window_and_padding(win, input_access, weights_access, output_access);
     }

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

     _kernel.set_argument(idx++, _weights->info()->strides_in_bytes()[3]); // weights_stride_w
     _kernel.set_argument(idx++, _weights->info()->dimension(2));          // weights_depth

     IGCKernel::configure(win);
 }

 template <unsigned int kernel_size>
 void GCDirectConvolutionLayerKernel<kernel_size>::run(const Window &window)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

     _kernel.use();

     _output->set_needs_shifting(true);

     // 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, 3, slice);

     if(_bias != nullptr)
     {
         Window slice_bias;
         slice_bias.use_tensor_dimensions(_bias->info()->tensor_shape());
         add_1D_tensor_argument(idx1, _bias, 4, slice_bias);
     }

     slice.shift(Window::DimX, -(_output->info()->padding()).left);

     do
     {
         unsigned int idx = 0;

         add_3D_tensor_argument(idx, _input, 1, slice_in);
         add_3D_tensor_argument(idx, _output, 2, slice);

         _kernel.update_shader_params();
         enqueue(*this, slice, _lws);
     }
     while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in));
 }

 template class arm_compute::GCDirectConvolutionLayerKernel<1>;
 template class arm_compute::GCDirectConvolutionLayerKernel<3>;
 template class arm_compute::GCDirectConvolutionLayerKernel<5>;
	/*
	* Copyright (c) 2017-2020 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/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.h"

	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/GLES_COMPUTE/GCHelpers.h"
	#include "arm_compute/core/GLES_COMPUTE/GCKernelLibrary.h"
	#include "arm_compute/core/GLES_COMPUTE/IGCTensor.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/Validate.h"
	#include "src/core/AccessWindowStatic.h"
	#include "src/core/helpers/AutoConfiguration.h"
	#include "src/core/helpers/WindowHelpers.h"
	#include "support/StringSupport.h"

	using namespace arm_compute;

	template <unsigned int kernel_size>
	GCDirectConvolutionLayerKernel<kernel_size>::GCDirectConvolutionLayerKernel()
	: _input(nullptr), _bias(nullptr), _weights(nullptr), _output(nullptr), _border_size(0), _conv_stride_x(0), _conv_stride_y(0), _conv_pad_x(0), _conv_pad_y(0), _lws(gles::NDRange(1U, 1U, 1U))
	{
	}

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

	template <unsigned int kernel_size>
	void GCDirectConvolutionLayerKernel<kernel_size>::configure(const IGCTensor input, const IGCTensor weights, const IGCTensor bias, IGCTensor output,
	const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
	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((kernel_size == 3 && std::get<0>(conv_info.stride()) > 2), "Strides larger than 2 not supported in 3x3 direct convolution!");
	ARM_COMPUTE_ERROR_ON(kernel_size != weights->info()->dimension(0));
	ARM_COMPUTE_ERROR_ON(act_info.enabled() && act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU && act_info.activation() != ActivationLayerInfo::ActivationFunction::LOGISTIC);

	if(bias != nullptr)
	{
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, bias);
	// FIXME: Bug in framework, workaround it in tests currently.
	//ARM_COMPUTE_ERROR_ON(bias->info()->dimension(0) != weights->info()->dimension(3));
	ARM_COMPUTE_ERROR_ON(bias->info()->num_dimensions() > 1);
	}

	// Get convolved dimensions
	unsigned int owidth = 0;
	unsigned int oheight = 0;
	std::tie(owidth, oheight) = 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, owidth);
	output_shape.set(1, oheight);
	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());

	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
	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(!conv_info.padding_is_symmetric());

	_conv_stride_x = std::get<0>(conv_info.stride());
	_conv_stride_y = std::get<1>(conv_info.stride());
	_conv_pad_x = std::get<0>(conv_info.pad());
	_conv_pad_y = std::get<1>(conv_info.pad());

	_input = input;
	_weights = weights;
	_output = output;
	_bias = bias;
	_border_size = BorderSize(_conv_pad_y, _conv_pad_x);

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

	options.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(_lws[0]));
	options.emplace("#define LOCAL_SIZE_Y " + support::cpp11::to_string(_lws[1]));
	options.emplace("#define LOCAL_SIZE_Z " + support::cpp11::to_string(_lws[2]));
	options.emplace("#define STRIDE_X " + support::cpp11::to_string(_conv_stride_x));
	options.emplace("#define STRIDE_Y " + support::cpp11::to_string(_conv_stride_y));

	std::string dt_name = (input->info()->data_type() == DataType::F32) ? "DATA_TYPE_FP32" : "DATA_TYPE_FP16";
	options.emplace(("#define " + dt_name));

	// Activation information in case of a fused activation
	if(act_info.enabled())
	{
	options.emplace("#define FUSED_ACTIVATION");
	options.emplace(("#define " + string_from_activation_func(act_info.activation())));
	options.emplace(("#define ACT_OP " + lower_string(string_from_activation_func(act_info.activation())) + "_op"));
	options.emplace(("#define A_VAL " + float_to_string_with_full_precision(act_info.a())));
	options.emplace(("#define B_VAL " + float_to_string_with_full_precision(act_info.b())));
	}

	unsigned int num_elems_read_per_iteration_x = kernel_size * _conv_stride_x;
	unsigned int num_elems_read_per_iteration_y = 1;
	unsigned int num_elems_written_per_iteration_x = 1;
	unsigned int num_elems_written_per_iteration_y = 1;
	unsigned int num_elems_written_per_iteration_z = 1;

	if(kernel_size == 3)
	{
	if((_conv_stride_x == 1) && (_conv_stride_y == 1))
	{
	switch(input->info()->data_type())
	{
	case DataType::F16:
	// TODO(APPBROWSER-299): Choose the most optimal path and remove others.
	#define PROCESS_4X_3Y_1Z

	#if defined(PROCESS_8X_3Y_1Z)
	options.emplace("#define PROCESS_8X_3Y_1Z");
	num_elems_read_per_iteration_x = 16;
	num_elems_read_per_iteration_y = 5;
	num_elems_written_per_iteration_x = 8;
	num_elems_written_per_iteration_y = 3;
	#elif defined(PROCESS_4X_3Y_1Z)
	options.emplace("#define PROCESS_4X_3Y_1Z");
	num_elems_read_per_iteration_x = 8;
	num_elems_read_per_iteration_y = 5;
	num_elems_written_per_iteration_x = 4;
	num_elems_written_per_iteration_y = 3;
	#elif defined(PROCESS_4X_4Y_1Z)
	options.emplace("#define PROCESS_4X_4Y_1Z");
	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 = 4;
	#elif defined(PROCESS_4X_3Y_2Z)
	options.emplace("#define PROCESS_4X_3Y_2Z");
	num_elems_read_per_iteration_x = 8;
	num_elems_read_per_iteration_y = 5;
	num_elems_written_per_iteration_x = 4;
	num_elems_written_per_iteration_y = 3;
	num_elems_written_per_iteration_z = 2;
	#endif /* PROCESS_nX_nY_nZ */
	#undef PROCESS_8X_3Y_1Z
	#undef PROCESS_4X_3Y_1Z
	#undef PROCESS_4X_4Y_1Z
	#undef PROCESS_4X_3Y_2Z
	break;

	case DataType::F32:
	options.emplace("#define PROCESS_4X_3Y_1Z");
	num_elems_read_per_iteration_x = 8;
	num_elems_read_per_iteration_y = 5;
	num_elems_written_per_iteration_x = 4;
	num_elems_written_per_iteration_y = 3;
	break;

	default:
	ARM_COMPUTE_ERROR("Current data type is not supported");
	break;
	}
	}
	// FIXME: Just keep one in release
	else
	{
	switch(input->info()->data_type())
	{
	case DataType::F16:
	options.emplace("#define PROCESS_4X_1Y_1Z");
	num_elems_read_per_iteration_x = 8;
	num_elems_written_per_iteration_x = 4;
	break;

	case DataType::F32:
	// TODO(APPBROWSER-299): Choose the most optimal path and remove others.
	#define PROCESS_4X_1Y_1Z

	#if defined(PROCESS_1X_1Y_1Z)
	options.emplace("#define PROCESS_1X_1Y_1Z");
	num_elems_read_per_iteration_x = 3;
	num_elems_written_per_iteration_x = 1;
	#elif defined(PROCESS_4X_1Y_1Z)
	options.emplace("#define PROCESS_4X_1Y_1Z");
	num_elems_read_per_iteration_x = 8;
	num_elems_written_per_iteration_x = 4;
	#elif defined(PROCESS_8X_1Y_1Z)
	options.emplace("#define PROCESS_8X_1Y_1Z");
	num_elems_read_per_iteration_x = 12;
	num_elems_written_per_iteration_x = 8;
	#else /* PROCESS_nX_nY_nZ */
	#error Have to declare how many elements to process in one thread.
	#endif /* PROCESS_nX_nY_nZ */
	#undef PROCESS_1X_1Y_1Z
	#undef PROCESS_4X_1Y_1Z
	#undef PROCESS_8X_1Y_1Z
	break;

	default:
	ARM_COMPUTE_ERROR("Current data type is not supported");
	break;
	}
	}
	}
	else if(kernel_size == 1)
	{
	if(weights->info()->dimension(2) % 2 == 0)
	{
	options.emplace("#define WEIGHTS_OPTIMIZATION");
	}
	switch(input->info()->data_type())
	{
	case DataType::F16:
	#define PROCESS_8X_2Y_1Z

	#if defined(PROCESS_4X_1Y_1Z)
	options.emplace("#define PROCESS_4X_1Y_1Z");
	num_elems_read_per_iteration_x = 4;
	num_elems_written_per_iteration_x = 4;
	#elif defined(PROCESS_4X_2Y_1Z)
	options.emplace("#define PROCESS_4X_2Y_1Z");
	num_elems_read_per_iteration_x = 4;
	num_elems_read_per_iteration_y = 2;
	num_elems_written_per_iteration_x = 4;
	num_elems_written_per_iteration_y = 2;
	#elif defined(PROCESS_4X_3Y_1Z)
	options.emplace("#define PROCESS_4X_3Y_1Z");
	num_elems_read_per_iteration_x = 4;
	num_elems_read_per_iteration_y = 3;
	num_elems_written_per_iteration_x = 4;
	num_elems_written_per_iteration_y = 3;
	#elif defined(PROCESS_4X_4Y_1Z)
	options.emplace("#define PROCESS_4X_4Y_1Z");
	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;
	#elif defined(PROCESS_4X_2Y_2Z)
	ARM_COMPUTE_ERROR_ON_MSG((weights->info()->dimension(4) % 2) == 1, "Current 'weights->info()->dimension(4) % 2) == 1' is not supported");
	options.emplace("#define PROCESS_4X_2Y_2Z");
	num_elems_read_per_iteration_x = 4;
	num_elems_read_per_iteration_y = 2;
	num_elems_written_per_iteration_x = 4;
	num_elems_written_per_iteration_y = 2;
	num_elems_written_per_iteration_z = 2;
	#elif defined(PROCESS_8X_1Y_1Z)
	options.emplace("#define PROCESS_8X_1Y_1Z");
	num_elems_read_per_iteration_x = 8;
	num_elems_written_per_iteration_x = 8;
	#elif defined(PROCESS_8X_2Y_1Z)
	options.emplace("#define PROCESS_8X_2Y_1Z");
	num_elems_read_per_iteration_x = 8;
	num_elems_read_per_iteration_y = 2;
	num_elems_written_per_iteration_x = 8;
	num_elems_written_per_iteration_y = 2;
	#else /* PROCESS_4X_1Y_1Z */
	#error Have to declare how many elements to process in one thread.
	#endif /* PROCESS_4X_1Y_1Z */
	#undef PROCESS_4X_1Y_1Z
	#undef PROCESS_4X_2Y_1Z
	#undef PROCESS_4X_3Y_1Z
	#undef PROCESS_4X_4Y_1Z
	#undef PROCESS_4X_2Y_2Z
	#undef PROCESS_8X_1Y_1Z
	#undef PROCESS_8X_2Y_1Z
	break;

	case DataType::F32:
	num_elems_read_per_iteration_x = 1;
	num_elems_written_per_iteration_x = 1;
	break;

	default:
	break;
	}
	}
	else if(kernel_size == 5)
	{
	switch(input->info()->data_type())
	{
	case DataType::F16:
	options.emplace("#define PROCESS_4X_1Y_1Z");
	num_elems_read_per_iteration_x = 8;
	num_elems_written_per_iteration_x = 4;

	default:
	break;
	}
	}
	else
	{
	}

	if(_bias != nullptr)
	{
	options.emplace("#define BIAS");
	}

	std::stringstream kernel_name;
	kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size;

	_kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel(kernel_name.str(), options));

	unsigned int idx = (_bias == nullptr) ? 3 * num_arguments_per_3D_tensor() : (num_arguments_per_1D_tensor() + 3 * num_arguments_per_3D_tensor());

	// Calculate output right and bottom border
	const int output_width = output->info()->dimension(0);
	const int output_height = output->info()->dimension(1);
	const int output_padding_right = ceil_to_multiple(output_width, num_elems_written_per_iteration_x * _lws[0]) - output_width;
	const int output_padding_bottom = ceil_to_multiple(output_height, num_elems_written_per_iteration_y * _lws[1]) - output_height;

	// Calculate input right and bottom border
	const int input_width = input->info()->dimension(0);
	const int input_height = input->info()->dimension(1);
	const int input_total_width = std::max(int(input->info()->padding().left), int(_conv_pad_x)) + input_width + std::max(int(input->info()->padding().right), int(_conv_pad_x));
	const int input_total_height = std::max(int(input->info()->padding().top), int(_conv_pad_y)) + input_height + std::max(int(input->info()->padding().bottom), int(_conv_pad_y));
	const int padding_right1 = ceil_to_multiple(input_total_width, num_elems_read_per_iteration_x * _lws[0]) - input_width - _conv_pad_x;
	const int padding_bottom1 = ceil_to_multiple(input_total_height, num_elems_read_per_iteration_y * _lws[1]) - input_height - _conv_pad_y;

	const int upper_bound_w = ceil_to_multiple(((output_width + output_padding_right) * _conv_stride_x + (kernel_size - 1)), num_elems_read_per_iteration_x * _lws[0]) - _conv_pad_x - input_width;
	const int upper_bound_h = ceil_to_multiple(((output_height + output_padding_bottom) * _conv_stride_y + (kernel_size - 1)), num_elems_read_per_iteration_y * _lws[1]) - _conv_pad_y - input_height;
	const int padding_right2 = std::max(upper_bound_w, _conv_pad_x);
	const int padding_bottom2 = std::max(upper_bound_h, _conv_pad_y);

	const int padding_right = std::max(padding_right1, padding_right2);
	const int padding_bottom = std::max(padding_bottom1, padding_bottom2);

	BorderSize border = BorderSize(0, output_padding_right, output_padding_bottom, 0);

	Window win = calculate_max_enlarged_window(*output->info(), Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y, num_elems_written_per_iteration_z), border);

	AccessWindowStatic input_access(input->info(), -_conv_pad_x, -_conv_pad_y, input_width + padding_right, input_height + padding_bottom);
	AccessWindowStatic weights_access = AccessWindowStatic(nullptr, 0, 0, 0, 0);
	AccessWindowStatic bias_access = AccessWindowStatic(nullptr, 0, 0, 0, 1);

	switch(weights->info()->data_type())
	{
	case DataType::F16:
	if((weights->info()->dimension(2) % 2 != 0) \|\| (kernel_size != 1))
	{
	weights_access = AccessWindowStatic(weights->info(), 0, 0, kernel_size + 1, kernel_size);
	}
	if(_bias != nullptr)
	{
	bias_access = AccessWindowStatic(_bias->info(), 0, 0, _bias->info()->dimension(0) + 1, 1);
	}
	break;

	case DataType::F32:
	weights_access = AccessWindowStatic(weights->info(), 0, 0, kernel_size, kernel_size);
	if(_bias != nullptr)
	{
	bias_access = AccessWindowStatic(_bias->info(), 0, 0, _bias->info()->dimension(0), 1);
	}
	break;

	default:
	ARM_COMPUTE_ERROR("Current data type is not supported");
	break;
	}

	AccessWindowStatic output_access(output->info(), 0, 0, output_width + output_padding_right, output_height + output_padding_bottom);

	if(_bias != nullptr)
	{
	update_window_and_padding(win, input_access, weights_access, bias_access, output_access);
	}
	else
	{
	update_window_and_padding(win, input_access, weights_access, output_access);
	}

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

	_kernel.set_argument(idx++, _weights->info()->strides_in_bytes()[3]); // weights_stride_w
	_kernel.set_argument(idx++, _weights->info()->dimension(2)); // weights_depth

	IGCKernel::configure(win);
	}

	template <unsigned int kernel_size>
	void GCDirectConvolutionLayerKernel<kernel_size>::run(const Window &window)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

	_kernel.use();

	_output->set_needs_shifting(true);

	// 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, 3, slice);

	if(_bias != nullptr)
	{
	Window slice_bias;
	slice_bias.use_tensor_dimensions(_bias->info()->tensor_shape());
	add_1D_tensor_argument(idx1, _bias, 4, slice_bias);
	}

	slice.shift(Window::DimX, -(_output->info()->padding()).left);

	do
	{
	unsigned int idx = 0;

	add_3D_tensor_argument(idx, _input, 1, slice_in);
	add_3D_tensor_argument(idx, _output, 2, slice);

	_kernel.update_shader_params();
	enqueue(*this, slice, _lws);
	}
	while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in));
	}

	template class arm_compute::GCDirectConvolutionLayerKernel<1>;
	template class arm_compute::GCDirectConvolutionLayerKernel<3>;
	template class arm_compute::GCDirectConvolutionLayerKernel<5>;