src/cpu/kernels/roialign/generic/neon/impl.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2019-2023 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 "src/cpu/kernels/roialign/generic/neon/impl.h"

 #include "src/core/NEON/INEKernel.h"
 #include "src/cpu/CpuTypes.h"

 namespace arm_compute
 {
 namespace cpu
 {
 /** Average pooling over an aligned window */
 template <typename input_data_type>
 inline input_data_type roi_align_1x1(const ITensor *input,
                                      unsigned int   roi_batch,
                                      float          region_start_x,
                                      float          bin_size_x,
                                      int            grid_size_x,
                                      float          region_end_x,
                                      float          region_start_y,
                                      float          bin_size_y,
                                      int            grid_size_y,
                                      float          region_end_y,
                                      int            pz)
 {
     if((region_end_x <= region_start_x) || (region_end_y <= region_start_y))
     {
         return input_data_type(0);
     }
     else
     {
         const DataLayout data_layout = input->info()->data_layout();
         float            avg         = 0;
         // Iterate through the aligned pooling region
         for(int iy = 0; iy < grid_size_y; ++iy)
         {
             for(int ix = 0; ix < grid_size_x; ++ix)
             {
                 // Align the window in the middle of every bin
                 float y = region_start_y + (iy + 0.5) * bin_size_y / float(grid_size_y);
                 float x = region_start_x + (ix + 0.5) * bin_size_x / float(grid_size_x);

                 // Interpolation in the [0,0] [0,1] [1,0] [1,1] square
                 const int y_low  = y;
                 const int x_low  = x;
                 const int y_high = y_low + 1;
                 const int x_high = x_low + 1;

                 const float ly = y - y_low;
                 const float lx = x - x_low;
                 const float hy = 1. - ly;
                 const float hx = 1. - lx;

                 const float w1 = hy * hx;
                 const float w2 = hy * lx;
                 const float w3 = ly * hx;
                 const float w4 = ly * lx;
                 if(data_layout == DataLayout::NCHW)
                 {
                     const auto data1 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_low, y_low, pz, roi_batch)));
                     const auto data2 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_high, y_low, pz, roi_batch)));
                     const auto data3 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_low, y_high, pz, roi_batch)));
                     const auto data4 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_high, y_high, pz, roi_batch)));
                     avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
                 }
                 else
                 {
                     const auto data1 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_low, y_low, roi_batch)));
                     const auto data2 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_high, y_low, roi_batch)));
                     const auto data3 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_low, y_high, roi_batch)));
                     const auto data4 = *reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_high, y_high, roi_batch)));
                     avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
                 }
             }
         }

         avg /= grid_size_x * grid_size_y;
         return input_data_type(avg);
     }
 }

 /** Average pooling over an aligned window */
 template <typename input_data_type>
 inline input_data_type roi_align_1x1_qasymm8(const ITensor          *input,
                                              unsigned int            roi_batch,
                                              float                   region_start_x,
                                              float                   bin_size_x,
                                              int                     grid_size_x,
                                              float                   region_end_x,
                                              float                   region_start_y,
                                              float                   bin_size_y,
                                              int                     grid_size_y,
                                              float                   region_end_y,
                                              int                     pz,
                                              const QuantizationInfo &out_qinfo)
 {
     if((region_end_x <= region_start_x) || (region_end_y <= region_start_y))
     {
         return input_data_type(out_qinfo.uniform().offset);
     }
     else
     {
         float                         avg              = 0;
         const UniformQuantizationInfo input_qinfo      = input->info()->quantization_info().uniform();
         const bool                    is_qasymm_signed = is_data_type_quantized_asymmetric_signed(input->info()->data_type());
         const DataLayout              data_layout      = input->info()->data_layout();

         // Iterate through the aligned pooling region
         for(int iy = 0; iy < grid_size_y; ++iy)
         {
             for(int ix = 0; ix < grid_size_x; ++ix)
             {
                 // Align the window in the middle of every bin
                 float y = region_start_y + (iy + 0.5) * bin_size_y / float(grid_size_y);
                 float x = region_start_x + (ix + 0.5) * bin_size_x / float(grid_size_x);

                 // Interpolation in the [0,0] [0,1] [1,0] [1,1] square
                 const int y_low  = y;
                 const int x_low  = x;
                 const int y_high = y_low + 1;
                 const int x_high = x_low + 1;

                 const float ly = y - y_low;
                 const float lx = x - x_low;
                 const float hy = 1. - ly;
                 const float hx = 1. - lx;

                 const float w1 = hy * hx;
                 const float w2 = hy * lx;
                 const float w3 = ly * hx;
                 const float w4 = ly * lx;

                 if(data_layout == DataLayout::NCHW)
                 {
                     if(is_qasymm_signed)
                     {
                         float data1 = dequantize_qasymm8_signed(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_low, y_low, pz, roi_batch))), input_qinfo);
                         float data2 = dequantize_qasymm8_signed(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_high, y_low, pz, roi_batch))), input_qinfo);
                         float data3 = dequantize_qasymm8_signed(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_low, y_high, pz, roi_batch))), input_qinfo);
                         float data4 = dequantize_qasymm8_signed(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_high, y_high, pz, roi_batch))), input_qinfo);
                         avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
                     }
                     else
                     {
                         float data1 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_low, y_low, pz, roi_batch))), input_qinfo);
                         float data2 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_high, y_low, pz, roi_batch))), input_qinfo);
                         float data3 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_low, y_high, pz, roi_batch))), input_qinfo);
                         float data4 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(x_high, y_high, pz, roi_batch))), input_qinfo);
                         avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
                     }
                 }
                 else
                 {
                     if(is_qasymm_signed)
                     {
                         const auto data1 = dequantize_qasymm8_signed(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_low, y_low, roi_batch))), input_qinfo);
                         const auto data2 = dequantize_qasymm8_signed(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_high, y_low, roi_batch))), input_qinfo);
                         const auto data3 = dequantize_qasymm8_signed(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_low, y_high, roi_batch))), input_qinfo);
                         const auto data4 = dequantize_qasymm8_signed(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_high, y_high, roi_batch))), input_qinfo);
                         avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
                     }
                     else
                     {
                         const auto data1 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_low, y_low, roi_batch))), input_qinfo);
                         const auto data2 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_high, y_low, roi_batch))), input_qinfo);
                         const auto data3 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_low, y_high, roi_batch))), input_qinfo);
                         const auto data4 = dequantize_qasymm8(*reinterpret_cast<const input_data_type *>(input->ptr_to_element(Coordinates(pz, x_high, y_high, roi_batch))), input_qinfo);
                         avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
                     }
                 }
             }
         }

         avg /= grid_size_x * grid_size_y;

         input_data_type res = 0;
         if(is_qasymm_signed)
         {
             res = quantize_qasymm8_signed(avg, out_qinfo);
         }
         else
         {
             res = quantize_qasymm8(avg, out_qinfo);
         }
         return res;
     }
 }
 inline float compute_region_coordinate(int p, float bin_size, float roi_anchor, float max_value)
 {
     const float region_start = p * bin_size + roi_anchor;
     return utility::clamp(region_start, 0.0f, max_value);
 }

 template <typename input_data_type, typename roi_data_type>
 void roi_align(const ITensor *input, ITensor *output, const ITensor *rois, ROIPoolingLayerInfo pool_info, const Window &window, const ThreadInfo &info)
 {
     ARM_COMPUTE_UNUSED(info);

     const DataLayout data_layout    = input->info()->data_layout();
     const size_t     values_per_roi = rois->info()->dimension(0);

     const int roi_list_start = window.x().start();
     const int roi_list_end   = window.x().end();

     const unsigned int idx_width  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
     const unsigned int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
     const unsigned int idx_depth  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);

     const int input_width   = input->info()->dimension(idx_width);
     const int input_height  = input->info()->dimension(idx_height);
     const int input_chanels = input->info()->dimension(idx_depth);
     const int pooled_w      = pool_info.pooled_width();
     const int pooled_h      = pool_info.pooled_height();

     const DataType data_type = input->info()->data_type();
     const bool     is_qasymm = is_data_type_quantized_asymmetric(data_type);

     const auto             *rois_ptr   = reinterpret_cast<const roi_data_type *>(rois->buffer());
     const QuantizationInfo &rois_qinfo = rois->info()->quantization_info();
     for(int roi_indx = roi_list_start; roi_indx < roi_list_end; ++roi_indx)
     {
         const unsigned int roi_batch = rois_ptr[values_per_roi * roi_indx];

         roi_data_type qx1 = rois_ptr[values_per_roi * roi_indx + 1];
         roi_data_type qy1 = rois_ptr[values_per_roi * roi_indx + 2];
         roi_data_type qx2 = rois_ptr[values_per_roi * roi_indx + 3];
         roi_data_type qy2 = rois_ptr[values_per_roi * roi_indx + 4];
         float         x1(qx1);
         float         x2(qx2);
         float         y1(qy1);
         float         y2(qy2);
         if(is_qasymm)
         {
             x1 = dequantize_qasymm16(qx1, rois_qinfo);
             x2 = dequantize_qasymm16(qx2, rois_qinfo);
             y1 = dequantize_qasymm16(qy1, rois_qinfo);
             y2 = dequantize_qasymm16(qy2, rois_qinfo);
         }
         const float roi_anchor_x = x1 * pool_info.spatial_scale();
         const float roi_anchor_y = y1 * pool_info.spatial_scale();
         const float roi_dims_x   = std::max((x2 - x1) * pool_info.spatial_scale(), 1.0f);
         const float roi_dims_y   = std::max((y2 - y1) * pool_info.spatial_scale(), 1.0f);
         float       bin_size_x   = roi_dims_x / pool_info.pooled_width();
         float       bin_size_y   = roi_dims_y / pool_info.pooled_height();

         // Iterate through all feature maps
         for(int ch = 0; ch < input_chanels; ++ch)
         {
             // Iterate through all output pixels
             for(int py = 0; py < pooled_h; ++py)
             {
                 for(int px = 0; px < pooled_w; ++px)
                 {
                     const float     region_start_x = compute_region_coordinate(px, bin_size_x, roi_anchor_x, input_width);
                     const float     region_start_y = compute_region_coordinate(py, bin_size_y, roi_anchor_y, input_height);
                     const float     region_end_x   = compute_region_coordinate(px + 1, bin_size_x, roi_anchor_x, input_width);
                     const float     region_end_y   = compute_region_coordinate(py + 1, bin_size_y, roi_anchor_y, input_height);
                     const int       roi_bin_grid_x = (pool_info.sampling_ratio() > 0) ? pool_info.sampling_ratio() : int(ceil(bin_size_x));
                     const int       roi_bin_grid_y = (pool_info.sampling_ratio() > 0) ? pool_info.sampling_ratio() : int(ceil(bin_size_y));
                     input_data_type out_val(0);
                     if(is_qasymm)
                     {
                         out_val = roi_align_1x1_qasymm8<input_data_type>(
                                       input, roi_batch, region_start_x, bin_size_x,
                                       roi_bin_grid_x, region_end_x, region_start_y, bin_size_y,
                                       roi_bin_grid_y, region_end_y, ch, output->info()->quantization_info());
                     }
                     else
                     {
                         out_val = roi_align_1x1<input_data_type>(
                                       input, roi_batch, region_start_x, bin_size_x,
                                       roi_bin_grid_x, region_end_x, region_start_y, bin_size_y,
                                       roi_bin_grid_y, region_end_y, ch);
                     }

                     if(data_layout == DataLayout::NCHW)
                     {
                         auto out_ptr = reinterpret_cast<input_data_type *>(output->ptr_to_element(Coordinates(px, py, ch, roi_indx)));
                         *out_ptr     = out_val;
                     }
                     else
                     {
                         auto out_ptr = reinterpret_cast<input_data_type *>(output->ptr_to_element(Coordinates(ch, px, py, roi_indx)));
                         *out_ptr     = out_val;
                     }
                 }
             }
         }
     }
 }
 template void roi_align<float, float>(const ITensor *input, ITensor *output, const ITensor *rois, ROIPoolingLayerInfo pool_info, const Window &window, const ThreadInfo &info);
 template void roi_align<uint8_t, uint16_t>(const ITensor *input, ITensor *output, const ITensor *rois, ROIPoolingLayerInfo pool_info, const Window &window, const ThreadInfo &info);
 template void roi_align<int8_t, uint16_t>(const ITensor *input, ITensor *output, const ITensor *rois, ROIPoolingLayerInfo pool_info, const Window &window, const ThreadInfo &info);
 #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
 template void roi_align<float16_t, float16_t>(const ITensor *input, ITensor *output, const ITensor *rois, ROIPoolingLayerInfo pool_info, const Window &window, const ThreadInfo &info);
 #endif //defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
 } // namespace cpu
 } // namespace arm_compute
	/*
	* Copyright (c) 2019-2023 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 "src/cpu/kernels/roialign/generic/neon/impl.h"

	#include "src/core/NEON/INEKernel.h"
	#include "src/cpu/CpuTypes.h"

	namespace arm_compute
	{
	namespace cpu
	{
	/** Average pooling over an aligned window */
	template <typename input_data_type>
	inline input_data_type roi_align_1x1(const ITensor *input,
	unsigned int roi_batch,
	float region_start_x,
	float bin_size_x,
	int grid_size_x,
	float region_end_x,
	float region_start_y,
	float bin_size_y,
	int grid_size_y,
	float region_end_y,
	int pz)
	{
	if((region_end_x <= region_start_x) \|\| (region_end_y <= region_start_y))
	{
	return input_data_type(0);
	}
	else
	{
	const DataLayout data_layout = input->info()->data_layout();
	float avg = 0;
	// Iterate through the aligned pooling region
	for(int iy = 0; iy < grid_size_y; ++iy)
	{
	for(int ix = 0; ix < grid_size_x; ++ix)
	{
	// Align the window in the middle of every bin
	float y = region_start_y + (iy + 0.5) * bin_size_y / float(grid_size_y);
	float x = region_start_x + (ix + 0.5) * bin_size_x / float(grid_size_x);

	// Interpolation in the [0,0] [0,1] [1,0] [1,1] square
	const int y_low = y;
	const int x_low = x;
	const int y_high = y_low + 1;
	const int x_high = x_low + 1;

	const float ly = y - y_low;
	const float lx = x - x_low;
	const float hy = 1. - ly;
	const float hx = 1. - lx;

	const float w1 = hy * hx;
	const float w2 = hy * lx;
	const float w3 = ly * hx;
	const float w4 = ly * lx;
	if(data_layout == DataLayout::NCHW)
	{
	const auto data1 = reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(x_low, y_low, pz, roi_batch)));
	const auto data2 = reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(x_high, y_low, pz, roi_batch)));
	const auto data3 = reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(x_low, y_high, pz, roi_batch)));
	const auto data4 = reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(x_high, y_high, pz, roi_batch)));
	avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
	}
	else
	{
	const auto data1 = reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(pz, x_low, y_low, roi_batch)));
	const auto data2 = reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(pz, x_high, y_low, roi_batch)));
	const auto data3 = reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(pz, x_low, y_high, roi_batch)));
	const auto data4 = reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(pz, x_high, y_high, roi_batch)));
	avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
	}
	}
	}

	avg /= grid_size_x * grid_size_y;
	return input_data_type(avg);
	}
	}

	/** Average pooling over an aligned window */
	template <typename input_data_type>
	inline input_data_type roi_align_1x1_qasymm8(const ITensor *input,
	unsigned int roi_batch,
	float region_start_x,
	float bin_size_x,
	int grid_size_x,
	float region_end_x,
	float region_start_y,
	float bin_size_y,
	int grid_size_y,
	float region_end_y,
	int pz,
	const QuantizationInfo &out_qinfo)
	{
	if((region_end_x <= region_start_x) \|\| (region_end_y <= region_start_y))
	{
	return input_data_type(out_qinfo.uniform().offset);
	}
	else
	{
	float avg = 0;
	const UniformQuantizationInfo input_qinfo = input->info()->quantization_info().uniform();
	const bool is_qasymm_signed = is_data_type_quantized_asymmetric_signed(input->info()->data_type());
	const DataLayout data_layout = input->info()->data_layout();

	// Iterate through the aligned pooling region
	for(int iy = 0; iy < grid_size_y; ++iy)
	{
	for(int ix = 0; ix < grid_size_x; ++ix)
	{
	// Align the window in the middle of every bin
	float y = region_start_y + (iy + 0.5) * bin_size_y / float(grid_size_y);
	float x = region_start_x + (ix + 0.5) * bin_size_x / float(grid_size_x);

	// Interpolation in the [0,0] [0,1] [1,0] [1,1] square
	const int y_low = y;
	const int x_low = x;
	const int y_high = y_low + 1;
	const int x_high = x_low + 1;

	const float ly = y - y_low;
	const float lx = x - x_low;
	const float hy = 1. - ly;
	const float hx = 1. - lx;

	const float w1 = hy * hx;
	const float w2 = hy * lx;
	const float w3 = ly * hx;
	const float w4 = ly * lx;

	if(data_layout == DataLayout::NCHW)
	{
	if(is_qasymm_signed)
	{
	float data1 = dequantize_qasymm8_signed(reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(x_low, y_low, pz, roi_batch))), input_qinfo);
	float data2 = dequantize_qasymm8_signed(reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(x_high, y_low, pz, roi_batch))), input_qinfo);
	float data3 = dequantize_qasymm8_signed(reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(x_low, y_high, pz, roi_batch))), input_qinfo);
	float data4 = dequantize_qasymm8_signed(reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(x_high, y_high, pz, roi_batch))), input_qinfo);
	avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
	}
	else
	{
	float data1 = dequantize_qasymm8(reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(x_low, y_low, pz, roi_batch))), input_qinfo);
	float data2 = dequantize_qasymm8(reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(x_high, y_low, pz, roi_batch))), input_qinfo);
	float data3 = dequantize_qasymm8(reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(x_low, y_high, pz, roi_batch))), input_qinfo);
	float data4 = dequantize_qasymm8(reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(x_high, y_high, pz, roi_batch))), input_qinfo);
	avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
	}
	}
	else
	{
	if(is_qasymm_signed)
	{
	const auto data1 = dequantize_qasymm8_signed(reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(pz, x_low, y_low, roi_batch))), input_qinfo);
	const auto data2 = dequantize_qasymm8_signed(reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(pz, x_high, y_low, roi_batch))), input_qinfo);
	const auto data3 = dequantize_qasymm8_signed(reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(pz, x_low, y_high, roi_batch))), input_qinfo);
	const auto data4 = dequantize_qasymm8_signed(reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(pz, x_high, y_high, roi_batch))), input_qinfo);
	avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
	}
	else
	{
	const auto data1 = dequantize_qasymm8(reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(pz, x_low, y_low, roi_batch))), input_qinfo);
	const auto data2 = dequantize_qasymm8(reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(pz, x_high, y_low, roi_batch))), input_qinfo);
	const auto data3 = dequantize_qasymm8(reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(pz, x_low, y_high, roi_batch))), input_qinfo);
	const auto data4 = dequantize_qasymm8(reinterpret_cast<const input_data_type >(input->ptr_to_element(Coordinates(pz, x_high, y_high, roi_batch))), input_qinfo);
	avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
	}
	}
	}
	}

	avg /= grid_size_x * grid_size_y;

	input_data_type res = 0;
	if(is_qasymm_signed)
	{
	res = quantize_qasymm8_signed(avg, out_qinfo);
	}
	else
	{
	res = quantize_qasymm8(avg, out_qinfo);
	}
	return res;
	}
	}
	inline float compute_region_coordinate(int p, float bin_size, float roi_anchor, float max_value)
	{
	const float region_start = p * bin_size + roi_anchor;
	return utility::clamp(region_start, 0.0f, max_value);
	}

	template <typename input_data_type, typename roi_data_type>
	void roi_align(const ITensor input, ITensor output, const ITensor *rois, ROIPoolingLayerInfo pool_info, const Window &window, const ThreadInfo &info)
	{
	ARM_COMPUTE_UNUSED(info);

	const DataLayout data_layout = input->info()->data_layout();
	const size_t values_per_roi = rois->info()->dimension(0);

	const int roi_list_start = window.x().start();
	const int roi_list_end = window.x().end();

	const unsigned int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
	const unsigned int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
	const unsigned int idx_depth = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);

	const int input_width = input->info()->dimension(idx_width);
	const int input_height = input->info()->dimension(idx_height);
	const int input_chanels = input->info()->dimension(idx_depth);
	const int pooled_w = pool_info.pooled_width();
	const int pooled_h = pool_info.pooled_height();

	const DataType data_type = input->info()->data_type();
	const bool is_qasymm = is_data_type_quantized_asymmetric(data_type);

	const auto rois_ptr = reinterpret_cast<const roi_data_type >(rois->buffer());
	const QuantizationInfo &rois_qinfo = rois->info()->quantization_info();
	for(int roi_indx = roi_list_start; roi_indx < roi_list_end; ++roi_indx)
	{
	const unsigned int roi_batch = rois_ptr[values_per_roi * roi_indx];

	roi_data_type qx1 = rois_ptr[values_per_roi * roi_indx + 1];
	roi_data_type qy1 = rois_ptr[values_per_roi * roi_indx + 2];
	roi_data_type qx2 = rois_ptr[values_per_roi * roi_indx + 3];
	roi_data_type qy2 = rois_ptr[values_per_roi * roi_indx + 4];
	float x1(qx1);
	float x2(qx2);
	float y1(qy1);
	float y2(qy2);
	if(is_qasymm)
	{
	x1 = dequantize_qasymm16(qx1, rois_qinfo);
	x2 = dequantize_qasymm16(qx2, rois_qinfo);
	y1 = dequantize_qasymm16(qy1, rois_qinfo);
	y2 = dequantize_qasymm16(qy2, rois_qinfo);
	}
	const float roi_anchor_x = x1 * pool_info.spatial_scale();
	const float roi_anchor_y = y1 * pool_info.spatial_scale();
	const float roi_dims_x = std::max((x2 - x1) * pool_info.spatial_scale(), 1.0f);
	const float roi_dims_y = std::max((y2 - y1) * pool_info.spatial_scale(), 1.0f);
	float bin_size_x = roi_dims_x / pool_info.pooled_width();
	float bin_size_y = roi_dims_y / pool_info.pooled_height();

	// Iterate through all feature maps
	for(int ch = 0; ch < input_chanels; ++ch)
	{
	// Iterate through all output pixels
	for(int py = 0; py < pooled_h; ++py)
	{
	for(int px = 0; px < pooled_w; ++px)
	{
	const float region_start_x = compute_region_coordinate(px, bin_size_x, roi_anchor_x, input_width);
	const float region_start_y = compute_region_coordinate(py, bin_size_y, roi_anchor_y, input_height);
	const float region_end_x = compute_region_coordinate(px + 1, bin_size_x, roi_anchor_x, input_width);
	const float region_end_y = compute_region_coordinate(py + 1, bin_size_y, roi_anchor_y, input_height);
	const int roi_bin_grid_x = (pool_info.sampling_ratio() > 0) ? pool_info.sampling_ratio() : int(ceil(bin_size_x));
	const int roi_bin_grid_y = (pool_info.sampling_ratio() > 0) ? pool_info.sampling_ratio() : int(ceil(bin_size_y));
	input_data_type out_val(0);
	if(is_qasymm)
	{
	out_val = roi_align_1x1_qasymm8<input_data_type>(
	input, roi_batch, region_start_x, bin_size_x,
	roi_bin_grid_x, region_end_x, region_start_y, bin_size_y,
	roi_bin_grid_y, region_end_y, ch, output->info()->quantization_info());
	}
	else
	{
	out_val = roi_align_1x1<input_data_type>(
	input, roi_batch, region_start_x, bin_size_x,
	roi_bin_grid_x, region_end_x, region_start_y, bin_size_y,
	roi_bin_grid_y, region_end_y, ch);
	}

	if(data_layout == DataLayout::NCHW)
	{
	auto out_ptr = reinterpret_cast<input_data_type *>(output->ptr_to_element(Coordinates(px, py, ch, roi_indx)));
	*out_ptr = out_val;
	}
	else
	{
	auto out_ptr = reinterpret_cast<input_data_type *>(output->ptr_to_element(Coordinates(ch, px, py, roi_indx)));
	*out_ptr = out_val;
	}
	}
	}
	}
	}
	}
	template void roi_align<float, float>(const ITensor input, ITensor output, const ITensor *rois, ROIPoolingLayerInfo pool_info, const Window &window, const ThreadInfo &info);
	template void roi_align<uint8_t, uint16_t>(const ITensor input, ITensor output, const ITensor *rois, ROIPoolingLayerInfo pool_info, const Window &window, const ThreadInfo &info);
	template void roi_align<int8_t, uint16_t>(const ITensor input, ITensor output, const ITensor *rois, ROIPoolingLayerInfo pool_info, const Window &window, const ThreadInfo &info);
	#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
	template void roi_align<float16_t, float16_t>(const ITensor input, ITensor output, const ITensor *rois, ROIPoolingLayerInfo pool_info, const Window &window, const ThreadInfo &info);
	#endif //defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
	} // namespace cpu
	} // namespace arm_compute