src/core/NEON/kernels/NEPoolingLayerKernel.cpp - platform/external/ComputeLibrary - Git at Google

 /*
  * 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/NEON/kernels/NEPoolingLayerKernel.h"

 #include "arm_compute/core/AccessWindowStatic.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/FixedPoint.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/ITensor.h"
 #include "arm_compute/core/NEON/NEFixedPoint.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/Window.h"

 #include <algorithm>
 #include <arm_neon.h>
 #include <limits>
 #include <string>
 #include <tuple>

 using namespace arm_compute;

 namespace
 {
 inline float calculate_avg_scale(const Coordinates &id, const int pool_size, const int upper_bound_w, const int upper_bound_h,
                                  const int pad_x, const int pad_y, const int stride_x, const int stride_y)
 {
     int start_x = id.x() * stride_x - pad_x;
     int start_y = id.y() * stride_y - pad_y;
     int end_x   = std::min(start_x + pool_size, upper_bound_w);
     int end_y   = std::min(start_y + pool_size, upper_bound_h);
     return 1.f / ((end_y - start_y) * (end_x - start_x));
 }

 inline qint8_t calculate_avg_scale_q8(const Coordinates &id, int pool_size, int upper_bound_w, int upper_bound_h,
                                       int pad_x, int pad_y, int stride_x, int stride_y, int fixed_point_position)
 {
     static std::array<qint8_t, 10> scale_values_q8 =
     { { 0x0, 0x0, 0x40, 0x2A, 0x20, 0x19, 0x15, 0x12, 0x10, 0xE } };
     const int start_x = id.x() * stride_x - pad_x;
     const int start_y = id.y() * stride_y - pad_y;
     const int end_x   = std::min(start_x + pool_size, upper_bound_w);
     const int end_y   = std::min(start_y + pool_size, upper_bound_h);
     const int val     = ((end_y - start_y) * (end_x - start_x));
     return scale_values_q8[val] >> (7 - fixed_point_position);
 }
 } // namespace

 NEPoolingLayerKernel::NEPoolingLayerKernel()
     : _func(nullptr), _input(nullptr), _output(nullptr), _pool_info(), _num_elems_processed_per_iteration(0), _border_size(0)
 {
 }

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

 void NEPoolingLayerKernel::configure(const ITensor *input, ITensor *output, const PoolingLayerInfo &pool_info)
 {
     int                   pool_pad_x      = 0;
     int                   pool_pad_y      = 0;
     int                   pool_stride_x   = 0;
     int                   pool_stride_y   = 0;
     unsigned int          pooled_w        = 0;
     unsigned int          pooled_h        = 0;
     PoolingType           pool_type       = pool_info.pool_type();
     int                   pool_size       = pool_info.pool_size();
     const PadStrideInfo   pad_stride_info = pool_info.pad_stride_info();
     DimensionRoundingType pool_round      = pad_stride_info.round();
     std::tie(pool_pad_x, pool_pad_y)       = pad_stride_info.pad();
     std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();

     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32);
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
     ARM_COMPUTE_ERROR_ON(2 != pool_size && 3 != pool_size);
     ARM_COMPUTE_ERROR_ON(pool_pad_x >= pool_size || pool_pad_y >= pool_size);
     ARM_COMPUTE_ERROR_ON(input->info()->data_type() == DataType::QS8 && pool_type == PoolingType::AVG && input->info()->fixed_point_position() > 6);
     ARM_COMPUTE_ERROR_ON(input->info()->data_type() == DataType::QS8 && pool_stride_x > 2);

     // Check output dimensions
     std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1),
                                                      pool_size, pool_stride_x, pool_stride_y,
                                                      pool_pad_x, pool_pad_y, pool_round);
     ARM_COMPUTE_UNUSED(pooled_w);
     ARM_COMPUTE_UNUSED(pooled_h);
     ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pooled_w) || (output->info()->dimension(1) != pooled_h));

     unsigned int num_elems_read_per_iteration      = 0;
     unsigned int num_elems_processed_per_iteration = 0;
     unsigned int num_elems_horizontal_window       = 0;

     // Select element size
     switch(input->info()->data_type())
     {
         case DataType::QS8:
             num_elems_read_per_iteration      = 16;
             num_elems_processed_per_iteration = (pool_size == 2) ? 8 : 7;
             num_elems_horizontal_window       = 8;
             break;
         case DataType::F32:
             num_elems_read_per_iteration      = (pool_size == 2) ? 2 : 4; // We use vload4 for pooling3
             num_elems_processed_per_iteration = 1;
             num_elems_horizontal_window       = 1;
             break;
         default:
             ARM_COMPUTE_ERROR("Element size not supported");
             break;
     }

     _num_elems_processed_per_iteration = num_elems_processed_per_iteration;
     const int input_width              = input->info()->dimension(0);
     const int input_height             = input->info()->dimension(1);
     const int upper_bound_w            = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elems_read_per_iteration) - input_width;
     const int upper_bound_h            = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;

     // Set instance variables
     _input              = input;
     _output             = output;
     _pool_info          = pool_info;
     _border_size        = BorderSize(pool_pad_y, pool_pad_x);
     _border_size.right  = std::max(upper_bound_w, pool_pad_x);
     _border_size.bottom = std::max(upper_bound_h, pool_pad_y);

     // Select appropriate function
     switch(pool_size)
     {
         case 2:
             if(input->info()->data_type() == DataType::QS8)
             {
                 _func = (PoolingType::AVG == pool_type) ? &NEPoolingLayerKernel::pooling2_q8<PoolingType::AVG> : &NEPoolingLayerKernel::pooling2_q8<PoolingType::MAX>;
             }
             else if(input->info()->data_type() == DataType::F32)
             {
                 _func = (PoolingType::AVG == pool_type) ? &NEPoolingLayerKernel::pooling2_f32<PoolingType::AVG> : &NEPoolingLayerKernel::pooling2_f32<PoolingType::MAX>;
             }
             break;
         case 3:
             if(input->info()->data_type() == DataType::QS8)
             {
                 _func = (PoolingType::AVG == pool_type) ? &NEPoolingLayerKernel::pooling3_q8<PoolingType::AVG> : &NEPoolingLayerKernel::pooling3_q8<PoolingType::MAX>;
             }
             else if(input->info()->data_type() == DataType::F32)
             {
                 _func = (PoolingType::AVG == pool_type) ? &NEPoolingLayerKernel::pooling3_f32<PoolingType::AVG> : &NEPoolingLayerKernel::pooling3_f32<PoolingType::MAX>;
             }
             break;
         default:
             ARM_COMPUTE_ERROR("Unsupported pooling size");
             break;
     }

     // Configure kernel window
     Window                 win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
     AccessWindowStatic     input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right, input_height + _border_size.bottom);
     AccessWindowHorizontal output_access(output->info(), 0, num_elems_horizontal_window);
     update_window_and_padding(win, input_access, output_access);
     output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
     INEKernel::configure(win);
 }

 template <PoolingType pooling_type>
 void NEPoolingLayerKernel::pooling2_q8(const Window &window_input, const Window &window)
 {
     Iterator input(_input, window_input);
     Iterator output(_output, window);

     const int     fixed_point_position = _input->info()->fixed_point_position();
     constexpr int pool_size            = 2;
     int           pool_pad_x           = 0;
     int           pool_pad_y           = 0;
     int           pool_stride_x        = 0;
     int           pool_stride_y        = 0;
     std::tie(pool_pad_x, pool_pad_y)       = _pool_info.pad_stride_info().pad();
     std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
     const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x;
     const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y;

     const uint8_t *const input_top_ptr    = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y)));
     const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 1));

     execute_window_loop(window, [&](const Coordinates & id)
     {
         const auto top_data    = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_top_ptr + input.offset()));
         const auto bottom_data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_bottom_ptr + input.offset()));
         qint8x8_t  res         = {};
         if(pooling_type == PoolingType::AVG)
         {
             // Calculate scale
             const qint8_t   scale     = calculate_avg_scale_q8(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y, fixed_point_position);
             const qint8x8_t scale_vec = vdup_n_qs8(scale);

             // Perform pooling
             const qint8x16_t sum_data = vqaddq_qs8(top_data, bottom_data);
             res                       = vqmul_qs8(vpadd_s8(vget_low_s8(sum_data), vget_high_s8(sum_data)), scale_vec, fixed_point_position);
         }
         else
         {
             const qint8x16_t max_data = vmaxq_s8(top_data, bottom_data);
             res                       = vpmax_s8(vget_low_s8(max_data), vget_high_s8(max_data));
         }
         vst1_qs8(reinterpret_cast<qint8_t *>(output.ptr()), res);
     },
     input, output);
 }

 template <PoolingType pooling_type>
 void NEPoolingLayerKernel::pooling2_f32(const Window &window_input, const Window &window)
 {
     Iterator input(_input, window_input);
     Iterator output(_output, window);

     constexpr int pool_size = 2;
     int           pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0;
     std::tie(pool_pad_x, pool_pad_y)       = _pool_info.pad_stride_info().pad();
     std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
     const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x;
     const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y;

     const unsigned char *const input_top_ptr    = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y)));
     const unsigned char *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 1));

     execute_window_loop(window, [&](const Coordinates & id)
     {
         const float32x2_t top_data    = vld1_f32(reinterpret_cast<const float *>(input_top_ptr + input.offset()));
         const float32x2_t bottom_data = vld1_f32(reinterpret_cast<const float *>(input_bottom_ptr + input.offset()));
         float32x2_t       res         = {};
         if(pooling_type == PoolingType::AVG)
         {
             // Calculate scale
             float             scale   = calculate_avg_scale(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y);
             const float32x2_t scale_v = vdup_n_f32(scale);

             // Perform pooling
             const float32x2_t sum_data = vadd_f32(top_data, bottom_data);
             res                        = vmul_f32(vpadd_f32(sum_data, sum_data), scale_v);
         }
         else
         {
             const float32x2_t max_data = vmax_f32(top_data, bottom_data);
             res                        = vpmax_f32(max_data, max_data);
         }
         *(reinterpret_cast<float *>(output.ptr())) = vget_lane_f32(res, 0);
     },
     input, output);
 }

 template <PoolingType pooling_type>
 void NEPoolingLayerKernel::pooling3_q8(const Window &window_input, const Window &window)
 {
     Iterator input(_input, window_input);
     Iterator output(_output, window);

     const int     fixed_point_position = _input->info()->fixed_point_position();
     constexpr int pool_size            = 3;
     int           pool_pad_x           = 0;
     int           pool_pad_y           = 0;
     int           pool_stride_x        = 0;
     int           pool_stride_y        = 0;
     std::tie(pool_pad_x, pool_pad_y)       = _pool_info.pad_stride_info().pad();
     std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
     const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x;
     const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y;

     const uint8_t *const input_top_ptr    = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y)));
     const uint8_t *const input_middle_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 1));
     const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 2));

     execute_window_loop(window, [&](const Coordinates & id)
     {
         const auto top_data    = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_top_ptr + input.offset()));
         const auto middle_data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_middle_ptr + input.offset()));
         const auto bottom_data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_bottom_ptr + input.offset()));
         qint8x8_t  res         = {};
         if(pooling_type == PoolingType::AVG)
         {
             // Calculate scale
             const qint8_t   scale     = calculate_avg_scale_q8(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y, fixed_point_position);
             const qint8x8_t scale_vec = vdup_n_qs8(scale);

             // Perform pooling for stride 2
             const qint8x16_t sum_data  = vqaddq_qs8(vqaddq_qs8(top_data, bottom_data), middle_data);
             const qint8x16_t sum_data2 = vextq_s8(sum_data, sum_data, 1);
             const qint8x16_t sum_data3 = vextq_s8(sum_data, sum_data, 2);
             const qint8x16_t final_sum = vqaddq_qs8(vqaddq_qs8(sum_data, sum_data2), sum_data3);
             if(pool_stride_x == 2)
             {
                 const qint8x8x2_t      table      = { { vget_low_s8(final_sum), vget_high_s8(final_sum) } };
                 static const qint8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 };
                 res                               = vtbl2_s8(table, lookup_val);
             }
             else
             {
                 res = vget_low_s8(final_sum);
             }
             res = vqmul_qs8(res, scale_vec, fixed_point_position);
         }
         else
         {
             const qint8x16_t max_data  = vmaxq_s8(vmaxq_s8(top_data, bottom_data), middle_data);
             const qint8x16_t max_data2 = vextq_s8(max_data, max_data, 1);
             const qint8x16_t max_data3 = vextq_s8(max_data, max_data, 2);
             const qint8x16_t final_max = vmaxq_s8(vmaxq_s8(max_data, max_data2), max_data3);

             if(pool_stride_x == 2)
             {
                 const qint8x8x2_t      table      = { { vget_low_s8(final_max), vget_high_s8(final_max) } };
                 static const qint8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 };
                 res                               = vtbl2_s8(table, lookup_val);
             }
             else
             {
                 res = vget_low_s8(final_max);
             }
         }
         vst1_qs8(reinterpret_cast<qint8_t *>(output.ptr()), res);
     },
     input, output);
 }

 template <PoolingType pooling_type>
 void NEPoolingLayerKernel::pooling3_f32(const Window &window_input, const Window &window)
 {
     Iterator input(_input, window_input);
     Iterator output(_output, window);

     constexpr const int pool_size = 3;
     int                 pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0;
     std::tie(pool_pad_x, pool_pad_y)       = _pool_info.pad_stride_info().pad();
     std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
     const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x;
     const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y;

     const unsigned char *const input_top_ptr    = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y)));
     const unsigned char *const input_middle_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 1));
     const unsigned char *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 2));

     execute_window_loop(window, [&](const Coordinates & id)
     {
         const float32x4_t top_data    = vld1q_f32(reinterpret_cast<const float *>(input_top_ptr + input.offset()));
         const float32x4_t middle_data = vld1q_f32(reinterpret_cast<const float *>(input_middle_ptr + input.offset()));
         const float32x4_t bottom_data = vld1q_f32(reinterpret_cast<const float *>(input_bottom_ptr + input.offset()));
         float32x2_t       res         = {};
         if(pooling_type == PoolingType::AVG)
         {
             // Calculate scale
             float             scale   = calculate_avg_scale(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y);
             const float32x2_t scale_v = vdup_n_f32(scale);

             // Perform pooling
             const float32x4_t sum_data = vaddq_f32(vaddq_f32(top_data, bottom_data), middle_data);
             res                        = vpadd_f32(vget_high_f32(vsetq_lane_f32(0.f, sum_data, 3)), vget_low_f32(sum_data));
             res                        = vmul_f32(vpadd_f32(res, res), scale_v);
         }
         else
         {
             const float32x4_t max_data = vmaxq_f32(vmaxq_f32(top_data, bottom_data), middle_data);
             res                        = vpmax_f32(vget_high_f32(vsetq_lane_f32(-std::numeric_limits<float>::max(), max_data, 3)), vget_low_f32(max_data));
             res                        = vpmax_f32(res, res);
         }
         *(reinterpret_cast<float *>(output.ptr())) = vget_lane_f32(res, 0);
     },
     input, output);
 }

 void NEPoolingLayerKernel::run(const Window &window)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
     ARM_COMPUTE_ERROR_ON(_func == nullptr);

     unsigned int pool_stride_x, pool_stride_y = 0;
     std::tie(pool_stride_x, pool_stride_y)    = _pool_info.pad_stride_info().stride();

     // Set step for input in x and y direction for the input
     Window       window_input(window);
     unsigned int window_x_inc = 0;
     if(_input->info()->data_type() == DataType::QS8)
     {
         window_x_inc = (pool_stride_x == 2) ? _num_elems_processed_per_iteration * 2 : _num_elems_processed_per_iteration;
     }
     else
     {
         window_x_inc = pool_stride_x;
     }
     window_input.set(Window::DimX, Window::Dimension(window.x().start() * pool_stride_x, window.x().end() * pool_stride_x, window_x_inc));
     window_input.set(Window::DimY, Window::Dimension(window.y().start() * pool_stride_y, window.y().end() * pool_stride_y, pool_stride_y));

     // Run function
     (this->*_func)(window_input, window);
 }
	/*
	* 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/NEON/kernels/NEPoolingLayerKernel.h"

	#include "arm_compute/core/AccessWindowStatic.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/FixedPoint.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/ITensor.h"
	#include "arm_compute/core/NEON/NEFixedPoint.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/Window.h"

	#include <algorithm>
	#include <arm_neon.h>
	#include <limits>
	#include <string>
	#include <tuple>

	using namespace arm_compute;

	namespace
	{
	inline float calculate_avg_scale(const Coordinates &id, const int pool_size, const int upper_bound_w, const int upper_bound_h,
	const int pad_x, const int pad_y, const int stride_x, const int stride_y)
	{
	int start_x = id.x() * stride_x - pad_x;
	int start_y = id.y() * stride_y - pad_y;
	int end_x = std::min(start_x + pool_size, upper_bound_w);
	int end_y = std::min(start_y + pool_size, upper_bound_h);
	return 1.f / ((end_y - start_y) * (end_x - start_x));
	}

	inline qint8_t calculate_avg_scale_q8(const Coordinates &id, int pool_size, int upper_bound_w, int upper_bound_h,
	int pad_x, int pad_y, int stride_x, int stride_y, int fixed_point_position)
	{
	static std::array<qint8_t, 10> scale_values_q8 =
	{ { 0x0, 0x0, 0x40, 0x2A, 0x20, 0x19, 0x15, 0x12, 0x10, 0xE } };
	const int start_x = id.x() * stride_x - pad_x;
	const int start_y = id.y() * stride_y - pad_y;
	const int end_x = std::min(start_x + pool_size, upper_bound_w);
	const int end_y = std::min(start_y + pool_size, upper_bound_h);
	const int val = ((end_y - start_y) * (end_x - start_x));
	return scale_values_q8[val] >> (7 - fixed_point_position);
	}
	} // namespace

	NEPoolingLayerKernel::NEPoolingLayerKernel()
	: _func(nullptr), _input(nullptr), _output(nullptr), _pool_info(), _num_elems_processed_per_iteration(0), _border_size(0)
	{
	}

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

	void NEPoolingLayerKernel::configure(const ITensor input, ITensor output, const PoolingLayerInfo &pool_info)
	{
	int pool_pad_x = 0;
	int pool_pad_y = 0;
	int pool_stride_x = 0;
	int pool_stride_y = 0;
	unsigned int pooled_w = 0;
	unsigned int pooled_h = 0;
	PoolingType pool_type = pool_info.pool_type();
	int pool_size = pool_info.pool_size();
	const PadStrideInfo pad_stride_info = pool_info.pad_stride_info();
	DimensionRoundingType pool_round = pad_stride_info.round();
	std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad();
	std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();

	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32);
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F32);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
	ARM_COMPUTE_ERROR_ON(2 != pool_size && 3 != pool_size);
	ARM_COMPUTE_ERROR_ON(pool_pad_x >= pool_size \|\| pool_pad_y >= pool_size);
	ARM_COMPUTE_ERROR_ON(input->info()->data_type() == DataType::QS8 && pool_type == PoolingType::AVG && input->info()->fixed_point_position() > 6);
	ARM_COMPUTE_ERROR_ON(input->info()->data_type() == DataType::QS8 && pool_stride_x > 2);

	// Check output dimensions
	std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1),
	pool_size, pool_stride_x, pool_stride_y,
	pool_pad_x, pool_pad_y, pool_round);
	ARM_COMPUTE_UNUSED(pooled_w);
	ARM_COMPUTE_UNUSED(pooled_h);
	ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pooled_w) \|\| (output->info()->dimension(1) != pooled_h));

	unsigned int num_elems_read_per_iteration = 0;
	unsigned int num_elems_processed_per_iteration = 0;
	unsigned int num_elems_horizontal_window = 0;

	// Select element size
	switch(input->info()->data_type())
	{
	case DataType::QS8:
	num_elems_read_per_iteration = 16;
	num_elems_processed_per_iteration = (pool_size == 2) ? 8 : 7;
	num_elems_horizontal_window = 8;
	break;
	case DataType::F32:
	num_elems_read_per_iteration = (pool_size == 2) ? 2 : 4; // We use vload4 for pooling3
	num_elems_processed_per_iteration = 1;
	num_elems_horizontal_window = 1;
	break;
	default:
	ARM_COMPUTE_ERROR("Element size not supported");
	break;
	}

	_num_elems_processed_per_iteration = num_elems_processed_per_iteration;
	const int input_width = input->info()->dimension(0);
	const int input_height = input->info()->dimension(1);
	const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elems_read_per_iteration) - input_width;
	const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;

	// Set instance variables
	_input = input;
	_output = output;
	_pool_info = pool_info;
	_border_size = BorderSize(pool_pad_y, pool_pad_x);
	_border_size.right = std::max(upper_bound_w, pool_pad_x);
	_border_size.bottom = std::max(upper_bound_h, pool_pad_y);

	// Select appropriate function
	switch(pool_size)
	{
	case 2:
	if(input->info()->data_type() == DataType::QS8)
	{
	_func = (PoolingType::AVG == pool_type) ? &NEPoolingLayerKernel::pooling2_q8<PoolingType::AVG> : &NEPoolingLayerKernel::pooling2_q8<PoolingType::MAX>;
	}
	else if(input->info()->data_type() == DataType::F32)
	{
	_func = (PoolingType::AVG == pool_type) ? &NEPoolingLayerKernel::pooling2_f32<PoolingType::AVG> : &NEPoolingLayerKernel::pooling2_f32<PoolingType::MAX>;
	}
	break;
	case 3:
	if(input->info()->data_type() == DataType::QS8)
	{
	_func = (PoolingType::AVG == pool_type) ? &NEPoolingLayerKernel::pooling3_q8<PoolingType::AVG> : &NEPoolingLayerKernel::pooling3_q8<PoolingType::MAX>;
	}
	else if(input->info()->data_type() == DataType::F32)
	{
	_func = (PoolingType::AVG == pool_type) ? &NEPoolingLayerKernel::pooling3_f32<PoolingType::AVG> : &NEPoolingLayerKernel::pooling3_f32<PoolingType::MAX>;
	}
	break;
	default:
	ARM_COMPUTE_ERROR("Unsupported pooling size");
	break;
	}

	// Configure kernel window
	Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
	AccessWindowStatic input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right, input_height + _border_size.bottom);
	AccessWindowHorizontal output_access(output->info(), 0, num_elems_horizontal_window);
	update_window_and_padding(win, input_access, output_access);
	output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
	INEKernel::configure(win);
	}

	template <PoolingType pooling_type>
	void NEPoolingLayerKernel::pooling2_q8(const Window &window_input, const Window &window)
	{
	Iterator input(_input, window_input);
	Iterator output(_output, window);

	const int fixed_point_position = _input->info()->fixed_point_position();
	constexpr int pool_size = 2;
	int pool_pad_x = 0;
	int pool_pad_y = 0;
	int pool_stride_x = 0;
	int pool_stride_y = 0;
	std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad();
	std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
	const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x;
	const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y;

	const uint8_t *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y)));
	const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 1));

	execute_window_loop(window, [&](const Coordinates & id)
	{
	const auto top_data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_top_ptr + input.offset()));
	const auto bottom_data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_bottom_ptr + input.offset()));
	qint8x8_t res = {};
	if(pooling_type == PoolingType::AVG)
	{
	// Calculate scale
	const qint8_t scale = calculate_avg_scale_q8(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y, fixed_point_position);
	const qint8x8_t scale_vec = vdup_n_qs8(scale);

	// Perform pooling
	const qint8x16_t sum_data = vqaddq_qs8(top_data, bottom_data);
	res = vqmul_qs8(vpadd_s8(vget_low_s8(sum_data), vget_high_s8(sum_data)), scale_vec, fixed_point_position);
	}
	else
	{
	const qint8x16_t max_data = vmaxq_s8(top_data, bottom_data);
	res = vpmax_s8(vget_low_s8(max_data), vget_high_s8(max_data));
	}
	vst1_qs8(reinterpret_cast<qint8_t *>(output.ptr()), res);
	},
	input, output);
	}

	template <PoolingType pooling_type>
	void NEPoolingLayerKernel::pooling2_f32(const Window &window_input, const Window &window)
	{
	Iterator input(_input, window_input);
	Iterator output(_output, window);

	constexpr int pool_size = 2;
	int pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0;
	std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad();
	std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
	const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x;
	const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y;

	const unsigned char *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y)));
	const unsigned char *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 1));

	execute_window_loop(window, [&](const Coordinates & id)
	{
	const float32x2_t top_data = vld1_f32(reinterpret_cast<const float *>(input_top_ptr + input.offset()));
	const float32x2_t bottom_data = vld1_f32(reinterpret_cast<const float *>(input_bottom_ptr + input.offset()));
	float32x2_t res = {};
	if(pooling_type == PoolingType::AVG)
	{
	// Calculate scale
	float scale = calculate_avg_scale(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y);
	const float32x2_t scale_v = vdup_n_f32(scale);

	// Perform pooling
	const float32x2_t sum_data = vadd_f32(top_data, bottom_data);
	res = vmul_f32(vpadd_f32(sum_data, sum_data), scale_v);
	}
	else
	{
	const float32x2_t max_data = vmax_f32(top_data, bottom_data);
	res = vpmax_f32(max_data, max_data);
	}
	(reinterpret_cast<float >(output.ptr())) = vget_lane_f32(res, 0);
	},
	input, output);
	}

	template <PoolingType pooling_type>
	void NEPoolingLayerKernel::pooling3_q8(const Window &window_input, const Window &window)
	{
	Iterator input(_input, window_input);
	Iterator output(_output, window);

	const int fixed_point_position = _input->info()->fixed_point_position();
	constexpr int pool_size = 3;
	int pool_pad_x = 0;
	int pool_pad_y = 0;
	int pool_stride_x = 0;
	int pool_stride_y = 0;
	std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad();
	std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
	const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x;
	const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y;

	const uint8_t *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y)));
	const uint8_t *const input_middle_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 1));
	const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 2));

	execute_window_loop(window, [&](const Coordinates & id)
	{
	const auto top_data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_top_ptr + input.offset()));
	const auto middle_data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_middle_ptr + input.offset()));
	const auto bottom_data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_bottom_ptr + input.offset()));
	qint8x8_t res = {};
	if(pooling_type == PoolingType::AVG)
	{
	// Calculate scale
	const qint8_t scale = calculate_avg_scale_q8(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y, fixed_point_position);
	const qint8x8_t scale_vec = vdup_n_qs8(scale);

	// Perform pooling for stride 2
	const qint8x16_t sum_data = vqaddq_qs8(vqaddq_qs8(top_data, bottom_data), middle_data);
	const qint8x16_t sum_data2 = vextq_s8(sum_data, sum_data, 1);
	const qint8x16_t sum_data3 = vextq_s8(sum_data, sum_data, 2);
	const qint8x16_t final_sum = vqaddq_qs8(vqaddq_qs8(sum_data, sum_data2), sum_data3);
	if(pool_stride_x == 2)
	{
	const qint8x8x2_t table = { { vget_low_s8(final_sum), vget_high_s8(final_sum) } };
	static const qint8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 };
	res = vtbl2_s8(table, lookup_val);
	}
	else
	{
	res = vget_low_s8(final_sum);
	}
	res = vqmul_qs8(res, scale_vec, fixed_point_position);
	}
	else
	{
	const qint8x16_t max_data = vmaxq_s8(vmaxq_s8(top_data, bottom_data), middle_data);
	const qint8x16_t max_data2 = vextq_s8(max_data, max_data, 1);
	const qint8x16_t max_data3 = vextq_s8(max_data, max_data, 2);
	const qint8x16_t final_max = vmaxq_s8(vmaxq_s8(max_data, max_data2), max_data3);

	if(pool_stride_x == 2)
	{
	const qint8x8x2_t table = { { vget_low_s8(final_max), vget_high_s8(final_max) } };
	static const qint8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 };
	res = vtbl2_s8(table, lookup_val);
	}
	else
	{
	res = vget_low_s8(final_max);
	}
	}
	vst1_qs8(reinterpret_cast<qint8_t *>(output.ptr()), res);
	},
	input, output);
	}

	template <PoolingType pooling_type>
	void NEPoolingLayerKernel::pooling3_f32(const Window &window_input, const Window &window)
	{
	Iterator input(_input, window_input);
	Iterator output(_output, window);

	constexpr const int pool_size = 3;
	int pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0;
	std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad();
	std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
	const int upper_bound_w = _input->info()->dimension(0) + pool_pad_x;
	const int upper_bound_h = _input->info()->dimension(1) + pool_pad_y;

	const unsigned char *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y)));
	const unsigned char *const input_middle_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 1));
	const unsigned char *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_x), -static_cast<int>(pool_pad_y) + 2));

	execute_window_loop(window, [&](const Coordinates & id)
	{
	const float32x4_t top_data = vld1q_f32(reinterpret_cast<const float *>(input_top_ptr + input.offset()));
	const float32x4_t middle_data = vld1q_f32(reinterpret_cast<const float *>(input_middle_ptr + input.offset()));
	const float32x4_t bottom_data = vld1q_f32(reinterpret_cast<const float *>(input_bottom_ptr + input.offset()));
	float32x2_t res = {};
	if(pooling_type == PoolingType::AVG)
	{
	// Calculate scale
	float scale = calculate_avg_scale(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y);
	const float32x2_t scale_v = vdup_n_f32(scale);

	// Perform pooling
	const float32x4_t sum_data = vaddq_f32(vaddq_f32(top_data, bottom_data), middle_data);
	res = vpadd_f32(vget_high_f32(vsetq_lane_f32(0.f, sum_data, 3)), vget_low_f32(sum_data));
	res = vmul_f32(vpadd_f32(res, res), scale_v);
	}
	else
	{
	const float32x4_t max_data = vmaxq_f32(vmaxq_f32(top_data, bottom_data), middle_data);
	res = vpmax_f32(vget_high_f32(vsetq_lane_f32(-std::numeric_limits<float>::max(), max_data, 3)), vget_low_f32(max_data));
	res = vpmax_f32(res, res);
	}
	(reinterpret_cast<float >(output.ptr())) = vget_lane_f32(res, 0);
	},
	input, output);
	}

	void NEPoolingLayerKernel::run(const Window &window)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
	ARM_COMPUTE_ERROR_ON(_func == nullptr);

	unsigned int pool_stride_x, pool_stride_y = 0;
	std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();

	// Set step for input in x and y direction for the input
	Window window_input(window);
	unsigned int window_x_inc = 0;
	if(_input->info()->data_type() == DataType::QS8)
	{
	window_x_inc = (pool_stride_x == 2) ? _num_elems_processed_per_iteration * 2 : _num_elems_processed_per_iteration;
	}
	else
	{
	window_x_inc = pool_stride_x;
	}
	window_input.set(Window::DimX, Window::Dimension(window.x().start() * pool_stride_x, window.x().end() * pool_stride_x, window_x_inc));
	window_input.set(Window::DimY, Window::Dimension(window.y().start() * pool_stride_y, window.y().end() * pool_stride_y, pool_stride_y));

	// Run function
	(this->*_func)(window_input, window);
	}