tests/validation/reference/ROIAlignLayer.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2018-2020 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 "ROIAlignLayer.h"

 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "tests/validation/Helpers.h"

 #include <algorithm>

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 namespace reference
 {
 namespace
 {
 /** Average pooling over an aligned window */
 inline float roi_align_1x1(const float *input, TensorShape input_shape,
                            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 0;
     }
     else
     {
         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;

                 const size_t idx1  = coord2index(input_shape, Coordinates(x_low, y_low, pz));
                 float        data1 = input[idx1];

                 const size_t idx2  = coord2index(input_shape, Coordinates(x_high, y_low, pz));
                 float        data2 = input[idx2];

                 const size_t idx3  = coord2index(input_shape, Coordinates(x_low, y_high, pz));
                 float        data3 = input[idx3];

                 const size_t idx4  = coord2index(input_shape, Coordinates(x_high, y_high, pz));
                 float        data4 = input[idx4];

                 avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
             }
         }

         avg /= grid_size_x * grid_size_y;

         return avg;
     }
 }

 template <typename TI, typename TO>
 SimpleTensor<TO> float_converter(const SimpleTensor<TI> &tensor, DataType dst_dt)
 {
     SimpleTensor<TO> dst{ tensor.shape(), dst_dt, 1, QuantizationInfo(), tensor.data_layout() };
 #if defined(_OPENMP)
     #pragma omp parallel for
 #endif /* _OPENMP */
     for(int i = 0; i < tensor.num_elements(); ++i)
     {
         dst[i] = tensor[i];
     }
     return dst;
 }

 SimpleTensor<float> convert_rois_from_asymmetric(SimpleTensor<uint16_t> rois)
 {
     const UniformQuantizationInfo &quantization_info = rois.quantization_info().uniform();
     SimpleTensor<float>            dst{ rois.shape(), DataType::F32, 1, QuantizationInfo(), rois.data_layout() };

     for(int i = 0; i < rois.num_elements(); i += 5)
     {
         dst[i]     = static_cast<float>(rois[i]); // batch idx
         dst[i + 1] = dequantize_qasymm16(rois[i + 1], quantization_info);
         dst[i + 2] = dequantize_qasymm16(rois[i + 2], quantization_info);
         dst[i + 3] = dequantize_qasymm16(rois[i + 3], quantization_info);
         dst[i + 4] = dequantize_qasymm16(rois[i + 4], quantization_info);
     }
     return dst;
 }
 } // namespace

 template <>
 SimpleTensor<float> roi_align_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo)
 {
     ARM_COMPUTE_UNUSED(output_qinfo);

     const size_t values_per_roi = rois.shape()[0];
     const size_t num_rois       = rois.shape()[1];
     DataType     dst_data_type  = src.data_type();

     const auto *rois_ptr = static_cast<const float *>(rois.data());

     TensorShape         input_shape = src.shape();
     TensorShape         output_shape(pool_info.pooled_width(), pool_info.pooled_height(), src.shape()[2], num_rois);
     SimpleTensor<float> dst(output_shape, dst_data_type);

     // Iterate over every pixel of the input image
     for(size_t px = 0; px < pool_info.pooled_width(); ++px)
     {
         for(size_t py = 0; py < pool_info.pooled_height(); ++py)
         {
             for(size_t pw = 0; pw < num_rois; ++pw)
             {
                 const unsigned int roi_batch = rois_ptr[values_per_roi * pw];
                 const auto         x1        = float(rois_ptr[values_per_roi * pw + 1]);
                 const auto         y1        = float(rois_ptr[values_per_roi * pw + 2]);
                 const auto         x2        = float(rois_ptr[values_per_roi * pw + 3]);
                 const auto         y2        = float(rois_ptr[values_per_roi * pw + 4]);

                 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();
                 float region_start_x = px * bin_size_x + roi_anchor_x;
                 float region_start_y = py * bin_size_y + roi_anchor_y;
                 float region_end_x   = (px + 1) * bin_size_x + roi_anchor_x;
                 float region_end_y   = (py + 1) * bin_size_y + roi_anchor_y;

                 region_start_x = utility::clamp(region_start_x, 0.0f, float(input_shape[0]));
                 region_start_y = utility::clamp(region_start_y, 0.0f, float(input_shape[1]));
                 region_end_x   = utility::clamp(region_end_x, 0.0f, float(input_shape[0]));
                 region_end_y   = utility::clamp(region_end_y, 0.0f, float(input_shape[1]));

                 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));

                 // Move input and output pointer across the fourth dimension
                 const size_t input_stride_w  = input_shape[0] * input_shape[1] * input_shape[2];
                 const size_t output_stride_w = output_shape[0] * output_shape[1] * output_shape[2];
                 const float *input_ptr       = src.data() + roi_batch * input_stride_w;
                 float       *output_ptr      = dst.data() + px + py * output_shape[0] + pw * output_stride_w;

                 for(int pz = 0; pz < int(input_shape[2]); ++pz)
                 {
                     // For every pixel pool over an aligned region
                     *(output_ptr + pz * output_shape[0] * output_shape[1]) = roi_align_1x1(input_ptr, input_shape,
                                                                                            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, pz);
                 }
             }
         }
     }
     return dst;
 }

 template <>
 SimpleTensor<half> roi_align_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo)
 {
     SimpleTensor<float> src_tmp  = float_converter<half, float>(src, DataType::F32);
     SimpleTensor<float> rois_tmp = float_converter<half, float>(rois, DataType::F32);
     SimpleTensor<float> dst_tmp  = roi_align_layer<float, float>(src_tmp, rois_tmp, pool_info, output_qinfo);
     SimpleTensor<half>  dst      = float_converter<float, half>(dst_tmp, DataType::F16);
     return dst;
 }

 template <>
 SimpleTensor<uint8_t> roi_align_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint16_t> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo)
 {
     SimpleTensor<float>   src_tmp  = convert_from_asymmetric(src);
     SimpleTensor<float>   rois_tmp = convert_rois_from_asymmetric(rois);
     SimpleTensor<float>   dst_tmp  = roi_align_layer<float, float>(src_tmp, rois_tmp, pool_info, output_qinfo);
     SimpleTensor<uint8_t> dst      = convert_to_asymmetric<uint8_t>(dst_tmp, output_qinfo);
     return dst;
 }
 template <>
 SimpleTensor<int8_t> roi_align_layer(const SimpleTensor<int8_t> &src, const SimpleTensor<uint16_t> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo)
 {
     SimpleTensor<float>  src_tmp  = convert_from_asymmetric(src);
     SimpleTensor<float>  rois_tmp = convert_rois_from_asymmetric(rois);
     SimpleTensor<float>  dst_tmp  = roi_align_layer<float, float>(src_tmp, rois_tmp, pool_info, output_qinfo);
     SimpleTensor<int8_t> dst      = convert_to_asymmetric<int8_t>(dst_tmp, output_qinfo);
     return dst;
 }
 } // namespace reference
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
	/*
	* Copyright (c) 2018-2020 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 "ROIAlignLayer.h"

	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "tests/validation/Helpers.h"

	#include <algorithm>

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	namespace reference
	{
	namespace
	{
	/** Average pooling over an aligned window */
	inline float roi_align_1x1(const float *input, TensorShape input_shape,
	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 0;
	}
	else
	{
	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;

	const size_t idx1 = coord2index(input_shape, Coordinates(x_low, y_low, pz));
	float data1 = input[idx1];

	const size_t idx2 = coord2index(input_shape, Coordinates(x_high, y_low, pz));
	float data2 = input[idx2];

	const size_t idx3 = coord2index(input_shape, Coordinates(x_low, y_high, pz));
	float data3 = input[idx3];

	const size_t idx4 = coord2index(input_shape, Coordinates(x_high, y_high, pz));
	float data4 = input[idx4];

	avg += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4;
	}
	}

	avg /= grid_size_x * grid_size_y;

	return avg;
	}
	}

	template <typename TI, typename TO>
	SimpleTensor<TO> float_converter(const SimpleTensor<TI> &tensor, DataType dst_dt)
	{
	SimpleTensor<TO> dst{ tensor.shape(), dst_dt, 1, QuantizationInfo(), tensor.data_layout() };
	#if defined(_OPENMP)
	#pragma omp parallel for
	#endif /* _OPENMP */
	for(int i = 0; i < tensor.num_elements(); ++i)
	{
	dst[i] = tensor[i];
	}
	return dst;
	}

	SimpleTensor<float> convert_rois_from_asymmetric(SimpleTensor<uint16_t> rois)
	{
	const UniformQuantizationInfo &quantization_info = rois.quantization_info().uniform();
	SimpleTensor<float> dst{ rois.shape(), DataType::F32, 1, QuantizationInfo(), rois.data_layout() };

	for(int i = 0; i < rois.num_elements(); i += 5)
	{
	dst[i] = static_cast<float>(rois[i]); // batch idx
	dst[i + 1] = dequantize_qasymm16(rois[i + 1], quantization_info);
	dst[i + 2] = dequantize_qasymm16(rois[i + 2], quantization_info);
	dst[i + 3] = dequantize_qasymm16(rois[i + 3], quantization_info);
	dst[i + 4] = dequantize_qasymm16(rois[i + 4], quantization_info);
	}
	return dst;
	}
	} // namespace

	template <>
	SimpleTensor<float> roi_align_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo)
	{
	ARM_COMPUTE_UNUSED(output_qinfo);

	const size_t values_per_roi = rois.shape()[0];
	const size_t num_rois = rois.shape()[1];
	DataType dst_data_type = src.data_type();

	const auto rois_ptr = static_cast<const float >(rois.data());

	TensorShape input_shape = src.shape();
	TensorShape output_shape(pool_info.pooled_width(), pool_info.pooled_height(), src.shape()[2], num_rois);
	SimpleTensor<float> dst(output_shape, dst_data_type);

	// Iterate over every pixel of the input image
	for(size_t px = 0; px < pool_info.pooled_width(); ++px)
	{
	for(size_t py = 0; py < pool_info.pooled_height(); ++py)
	{
	for(size_t pw = 0; pw < num_rois; ++pw)
	{
	const unsigned int roi_batch = rois_ptr[values_per_roi * pw];
	const auto x1 = float(rois_ptr[values_per_roi * pw + 1]);
	const auto y1 = float(rois_ptr[values_per_roi * pw + 2]);
	const auto x2 = float(rois_ptr[values_per_roi * pw + 3]);
	const auto y2 = float(rois_ptr[values_per_roi * pw + 4]);

	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();
	float region_start_x = px * bin_size_x + roi_anchor_x;
	float region_start_y = py * bin_size_y + roi_anchor_y;
	float region_end_x = (px + 1) * bin_size_x + roi_anchor_x;
	float region_end_y = (py + 1) * bin_size_y + roi_anchor_y;

	region_start_x = utility::clamp(region_start_x, 0.0f, float(input_shape[0]));
	region_start_y = utility::clamp(region_start_y, 0.0f, float(input_shape[1]));
	region_end_x = utility::clamp(region_end_x, 0.0f, float(input_shape[0]));
	region_end_y = utility::clamp(region_end_y, 0.0f, float(input_shape[1]));

	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));

	// Move input and output pointer across the fourth dimension
	const size_t input_stride_w = input_shape[0] * input_shape[1] * input_shape[2];
	const size_t output_stride_w = output_shape[0] * output_shape[1] * output_shape[2];
	const float input_ptr = src.data() + roi_batch input_stride_w;
	float output_ptr = dst.data() + px + py output_shape[0] + pw * output_stride_w;

	for(int pz = 0; pz < int(input_shape[2]); ++pz)
	{
	// For every pixel pool over an aligned region
	(output_ptr + pz output_shape[0] * output_shape[1]) = roi_align_1x1(input_ptr, input_shape,
	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, pz);
	}
	}
	}
	}
	return dst;
	}

	template <>
	SimpleTensor<half> roi_align_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo)
	{
	SimpleTensor<float> src_tmp = float_converter<half, float>(src, DataType::F32);
	SimpleTensor<float> rois_tmp = float_converter<half, float>(rois, DataType::F32);
	SimpleTensor<float> dst_tmp = roi_align_layer<float, float>(src_tmp, rois_tmp, pool_info, output_qinfo);
	SimpleTensor<half> dst = float_converter<float, half>(dst_tmp, DataType::F16);
	return dst;
	}

	template <>
	SimpleTensor<uint8_t> roi_align_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint16_t> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo)
	{
	SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
	SimpleTensor<float> rois_tmp = convert_rois_from_asymmetric(rois);
	SimpleTensor<float> dst_tmp = roi_align_layer<float, float>(src_tmp, rois_tmp, pool_info, output_qinfo);
	SimpleTensor<uint8_t> dst = convert_to_asymmetric<uint8_t>(dst_tmp, output_qinfo);
	return dst;
	}
	template <>
	SimpleTensor<int8_t> roi_align_layer(const SimpleTensor<int8_t> &src, const SimpleTensor<uint16_t> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo)
	{
	SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
	SimpleTensor<float> rois_tmp = convert_rois_from_asymmetric(rois);
	SimpleTensor<float> dst_tmp = roi_align_layer<float, float>(src_tmp, rois_tmp, pool_info, output_qinfo);
	SimpleTensor<int8_t> dst = convert_to_asymmetric<int8_t>(dst_tmp, output_qinfo);
	return dst;
	}
	} // namespace reference
	} // namespace validation
	} // namespace test
	} // namespace arm_compute