tests/validation/CPP/PoolingLayer.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to
  * deal in the Software without restriction, including without limitation the
  * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
  * sell copies of the Software, and to permit persons to whom the Software is
  * furnished to do so, subject to the following conditions:
  *
  * The above copyright notice and this permission notice shall be included in all
  * copies or substantial portions of the Software.
  *
  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
 #include "PoolingLayer.h"

 #include "arm_compute/core/Types.h"
 #include "tests/validation/FixedPoint.h"
 #include "tests/validation/Helpers.h"

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 namespace reference
 {
 namespace
 {
 TensorShape calculate_output_shape(TensorShape shape, PoolingLayerInfo info)
 {
     TensorShape dst_shape = shape;
     const int   pool_size = info.is_global_pooling() ? shape.x() : info.pool_size();
     const std::pair<unsigned int, unsigned int> scaled_dims = arm_compute::scaled_dimensions(shape.x(),
                                                                                              shape.y(),
                                                                                              pool_size,
                                                                                              pool_size,
                                                                                              info.pad_stride_info());
     dst_shape.set(0, scaled_dims.first);
     dst_shape.set(1, scaled_dims.second);

     return dst_shape;
 }
 } // namespace

 template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type>
 SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, PoolingLayerInfo info)
 {
     ARM_COMPUTE_ERROR_ON(info.is_global_pooling() && (src.shape().x() != src.shape().y()));

     const int   pool_size       = info.is_global_pooling() ? src.shape().x() : info.pool_size();
     PoolingType type            = info.pool_type();
     int         pool_stride_x   = info.pad_stride_info().stride().first;
     int         pool_stride_y   = info.pad_stride_info().stride().second;
     int         pad_x           = info.pad_stride_info().pad().first;
     int         pad_y           = info.pad_stride_info().pad().second;
     bool        exclude_padding = info.exclude_padding();

     const auto w_src      = static_cast<int>(src.shape()[0]);
     const auto h_src      = static_cast<int>(src.shape()[1]);
     const int  upper_dims = src.shape().total_size() / (w_src * h_src);

     // Create reference
     SimpleTensor<T> dst{ calculate_output_shape(src.shape(), info), src.data_type(), 1, src.fixed_point_position() };

     const auto w_dst = static_cast<int>(dst.shape()[0]);
     const auto h_dst = static_cast<int>(dst.shape()[1]);

     if(type == PoolingType::MAX)
     {
         for(int r = 0; r < upper_dims; ++r)
         {
             for(int h = 0; h < h_dst; ++h)
             {
                 for(int w = 0; w < w_dst; ++w)
                 {
                     int wstart = w * pool_stride_x - pad_x;
                     int hstart = h * pool_stride_y - pad_y;
                     int wend   = std::min(wstart + pool_size, w_src);
                     int hend   = std::min(hstart + pool_size, h_src);
                     wstart     = std::max(wstart, 0);
                     hstart     = std::max(hstart, 0);

                     T max_val = std::numeric_limits<T>::lowest();
                     for(int y = hstart; y < hend; ++y)
                     {
                         for(int x = wstart; x < wend; ++x)
                         {
                             const T val = src[r * h_src * w_src + y * w_src + x];
                             if(val > max_val)
                             {
                                 max_val = val;
                             }
                         }
                     }

                     dst[r * h_dst * w_dst + h * w_dst + w] = max_val;
                 }
             }
         }
     }
     else // Average or l2 pooling
     {
         for(int r = 0; r < upper_dims; ++r)
         {
             for(int h = 0; h < h_dst; ++h)
             {
                 for(int w = 0; w < w_dst; ++w)
                 {
                     T   avg_val(0);
                     int wstart = w * pool_stride_x - pad_x;
                     int hstart = h * pool_stride_y - pad_y;
                     int wend   = std::min(wstart + pool_size, w_src + pad_x);
                     int hend   = std::min(hstart + pool_size, h_src + pad_y);
                     int pool   = (hend - hstart) * (wend - wstart);
                     wstart     = std::max(wstart, 0);
                     hstart     = std::max(hstart, 0);
                     wend       = std::min(wend, w_src);
                     hend       = std::min(hend, h_src);
                     // Exclude padding pixels from the average
                     if(exclude_padding)
                     {
                         pool = (hend - hstart) * (wend - wstart);
                     }

                     if(type == PoolingType::AVG)
                     {
                         for(int y = hstart; y < hend; ++y)
                         {
                             for(int x = wstart; x < wend; ++x)
                             {
                                 avg_val += src[r * h_src * w_src + y * w_src + x];
                             }
                         }
                         dst[r * h_dst * w_dst + h * w_dst + w] = avg_val / pool;
                     }
                     else
                     {
                         for(int y = hstart; y < hend; ++y)
                         {
                             for(int x = wstart; x < wend; ++x)
                             {
                                 const T val = src[r * h_src * w_src + y * w_src + x];
                                 avg_val += val * val;
                             }
                         }
                         dst[r * h_dst * w_dst + h * w_dst + w] = std::sqrt(avg_val / pool);
                     }
                 }
             }
         }
     }

     return dst;
 }

 template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type>
 SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, PoolingLayerInfo info)
 {
     ARM_COMPUTE_ERROR_ON(info.is_global_pooling() && (src.shape().x() != src.shape().y()));

     const int   pool_size       = info.is_global_pooling() ? src.shape().x() : info.pool_size();
     PoolingType type            = info.pool_type();
     int         pool_stride_x   = info.pad_stride_info().stride().first;
     int         pool_stride_y   = info.pad_stride_info().stride().second;
     int         pad_x           = info.pad_stride_info().pad().first;
     int         pad_y           = info.pad_stride_info().pad().second;
     bool        exclude_padding = info.exclude_padding();

     const auto w_src      = static_cast<int>(src.shape()[0]);
     const auto h_src      = static_cast<int>(src.shape()[1]);
     const int  upper_dims = src.shape().total_size() / (w_src * h_src);

     // Create reference
     SimpleTensor<T> dst{ calculate_output_shape(src.shape(), info), src.data_type(), 1, src.fixed_point_position() };

     const auto w_dst = static_cast<int>(dst.shape()[0]);
     const auto h_dst = static_cast<int>(dst.shape()[1]);

     if(type == PoolingType::MAX)
     {
         for(int r = 0; r < upper_dims; ++r)
         {
             for(int h = 0; h < h_dst; ++h)
             {
                 for(int w = 0; w < w_dst; ++w)
                 {
                     int wstart = w * pool_stride_x - pad_x;
                     int hstart = h * pool_stride_y - pad_y;
                     int wend   = std::min(wstart + pool_size, w_src);
                     int hend   = std::min(hstart + pool_size, h_src);
                     wstart     = std::max(wstart, 0);
                     hstart     = std::max(hstart, 0);

                     T max_val = std::numeric_limits<T>::lowest();
                     for(int y = hstart; y < hend; ++y)
                     {
                         for(int x = wstart; x < wend; ++x)
                         {
                             const T val = src[r * h_src * w_src + y * w_src + x];
                             if(val > max_val)
                             {
                                 max_val = val;
                             }
                         }
                     }

                     dst[r * h_dst * w_dst + h * w_dst + w] = max_val;
                 }
             }
         }
     }
     else // Average or l2 pooling
     {
         for(int r = 0; r < upper_dims; ++r)
         {
             for(int h = 0; h < h_dst; ++h)
             {
                 for(int w = 0; w < w_dst; ++w)
                 {
                     int wstart = w * pool_stride_x - pad_x;
                     int hstart = h * pool_stride_y - pad_y;
                     int wend   = std::min(wstart + pool_size, w_src + pad_x);
                     int hend   = std::min(hstart + pool_size, h_src + pad_y);
                     int pool   = (hend - hstart) * (wend - wstart);
                     wstart     = std::max(wstart, 0);
                     hstart     = std::max(hstart, 0);
                     wend       = std::min(wend, w_src);
                     hend       = std::min(hend, h_src);
                     // Exclude padding pixels from the average
                     if(exclude_padding)
                     {
                         pool = (hend - hstart) * (wend - wstart);
                     }

                     using namespace fixed_point_arithmetic;

                     const int            fixed_point_position = src.fixed_point_position();
                     const fixed_point<T> const_1(1, fixed_point_position);
                     const fixed_point<T> invpool_fp(1.f / static_cast<float>(pool), fixed_point_position);
                     fixed_point<T>       avg_val(0, fixed_point_position, true);

                     if(type == PoolingType::AVG)
                     {
                         for(int y = hstart; y < hend; ++y)
                         {
                             for(int x = wstart; x < wend; ++x)
                             {
                                 const fixed_point<T> in_fp(src[r * h_src * w_src + y * w_src + x], fixed_point_position, true);
                                 avg_val = add(avg_val, in_fp);
                             }
                         }
                         dst[r * h_dst * w_dst + h * w_dst + w] = mul(avg_val, invpool_fp).raw();
                     }
                     else
                     {
                         for(int y = hstart; y < hend; ++y)
                         {
                             for(int x = wstart; x < wend; ++x)
                             {
                                 const fixed_point<T> in_fp(src[r * h_src * w_src + y * w_src + x], fixed_point_position, true);
                                 avg_val = add(avg_val, mul(in_fp, in_fp));
                             }
                         }
                         auto res                               = div(const_1, (inv_sqrt(mul(avg_val, invpool_fp))));
                         dst[r * h_dst * w_dst + h * w_dst + w] = res.raw();
                     }
                 }
             }
         }
     }

     return dst;
 }

 template <>
 SimpleTensor<uint8_t> pooling_layer<uint8_t>(const SimpleTensor<uint8_t> &src, PoolingLayerInfo info)
 {
     SimpleTensor<float>   src_tmp = convert_from_asymmetric(src);
     SimpleTensor<float>   dst_tmp = pooling_layer<float>(src_tmp, info);
     SimpleTensor<uint8_t> dst     = convert_to_asymmetric(dst_tmp, src.quantization_info());
     return dst;
 }

 template SimpleTensor<float> pooling_layer(const SimpleTensor<float> &src, PoolingLayerInfo info);
 template SimpleTensor<half> pooling_layer(const SimpleTensor<half> &src, PoolingLayerInfo info);
 template SimpleTensor<qint8_t> pooling_layer(const SimpleTensor<qint8_t> &src, PoolingLayerInfo info);
 template SimpleTensor<qint16_t> pooling_layer(const SimpleTensor<qint16_t> &src, PoolingLayerInfo info);
 } // namespace reference
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
	/*
	* 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 "PoolingLayer.h"

	#include "arm_compute/core/Types.h"
	#include "tests/validation/FixedPoint.h"
	#include "tests/validation/Helpers.h"

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	namespace reference
	{
	namespace
	{
	TensorShape calculate_output_shape(TensorShape shape, PoolingLayerInfo info)
	{
	TensorShape dst_shape = shape;
	const int pool_size = info.is_global_pooling() ? shape.x() : info.pool_size();
	const std::pair<unsigned int, unsigned int> scaled_dims = arm_compute::scaled_dimensions(shape.x(),
	shape.y(),
	pool_size,
	pool_size,
	info.pad_stride_info());
	dst_shape.set(0, scaled_dims.first);
	dst_shape.set(1, scaled_dims.second);

	return dst_shape;
	}
	} // namespace

	template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type>
	SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, PoolingLayerInfo info)
	{
	ARM_COMPUTE_ERROR_ON(info.is_global_pooling() && (src.shape().x() != src.shape().y()));

	const int pool_size = info.is_global_pooling() ? src.shape().x() : info.pool_size();
	PoolingType type = info.pool_type();
	int pool_stride_x = info.pad_stride_info().stride().first;
	int pool_stride_y = info.pad_stride_info().stride().second;
	int pad_x = info.pad_stride_info().pad().first;
	int pad_y = info.pad_stride_info().pad().second;
	bool exclude_padding = info.exclude_padding();

	const auto w_src = static_cast<int>(src.shape()[0]);
	const auto h_src = static_cast<int>(src.shape()[1]);
	const int upper_dims = src.shape().total_size() / (w_src * h_src);

	// Create reference
	SimpleTensor<T> dst{ calculate_output_shape(src.shape(), info), src.data_type(), 1, src.fixed_point_position() };

	const auto w_dst = static_cast<int>(dst.shape()[0]);
	const auto h_dst = static_cast<int>(dst.shape()[1]);

	if(type == PoolingType::MAX)
	{
	for(int r = 0; r < upper_dims; ++r)
	{
	for(int h = 0; h < h_dst; ++h)
	{
	for(int w = 0; w < w_dst; ++w)
	{
	int wstart = w * pool_stride_x - pad_x;
	int hstart = h * pool_stride_y - pad_y;
	int wend = std::min(wstart + pool_size, w_src);
	int hend = std::min(hstart + pool_size, h_src);
	wstart = std::max(wstart, 0);
	hstart = std::max(hstart, 0);

	T max_val = std::numeric_limits<T>::lowest();
	for(int y = hstart; y < hend; ++y)
	{
	for(int x = wstart; x < wend; ++x)
	{
	const T val = src[r * h_src * w_src + y * w_src + x];
	if(val > max_val)
	{
	max_val = val;
	}
	}
	}

	dst[r * h_dst * w_dst + h * w_dst + w] = max_val;
	}
	}
	}
	}
	else // Average or l2 pooling
	{
	for(int r = 0; r < upper_dims; ++r)
	{
	for(int h = 0; h < h_dst; ++h)
	{
	for(int w = 0; w < w_dst; ++w)
	{
	T avg_val(0);
	int wstart = w * pool_stride_x - pad_x;
	int hstart = h * pool_stride_y - pad_y;
	int wend = std::min(wstart + pool_size, w_src + pad_x);
	int hend = std::min(hstart + pool_size, h_src + pad_y);
	int pool = (hend - hstart) * (wend - wstart);
	wstart = std::max(wstart, 0);
	hstart = std::max(hstart, 0);
	wend = std::min(wend, w_src);
	hend = std::min(hend, h_src);
	// Exclude padding pixels from the average
	if(exclude_padding)
	{
	pool = (hend - hstart) * (wend - wstart);
	}

	if(type == PoolingType::AVG)
	{
	for(int y = hstart; y < hend; ++y)
	{
	for(int x = wstart; x < wend; ++x)
	{
	avg_val += src[r * h_src * w_src + y * w_src + x];
	}
	}
	dst[r * h_dst * w_dst + h * w_dst + w] = avg_val / pool;
	}
	else
	{
	for(int y = hstart; y < hend; ++y)
	{
	for(int x = wstart; x < wend; ++x)
	{
	const T val = src[r * h_src * w_src + y * w_src + x];
	avg_val += val * val;
	}
	}
	dst[r * h_dst * w_dst + h * w_dst + w] = std::sqrt(avg_val / pool);
	}
	}
	}
	}
	}

	return dst;
	}

	template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type>
	SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, PoolingLayerInfo info)
	{
	ARM_COMPUTE_ERROR_ON(info.is_global_pooling() && (src.shape().x() != src.shape().y()));

	const int pool_size = info.is_global_pooling() ? src.shape().x() : info.pool_size();
	PoolingType type = info.pool_type();
	int pool_stride_x = info.pad_stride_info().stride().first;
	int pool_stride_y = info.pad_stride_info().stride().second;
	int pad_x = info.pad_stride_info().pad().first;
	int pad_y = info.pad_stride_info().pad().second;
	bool exclude_padding = info.exclude_padding();

	const auto w_src = static_cast<int>(src.shape()[0]);
	const auto h_src = static_cast<int>(src.shape()[1]);
	const int upper_dims = src.shape().total_size() / (w_src * h_src);

	// Create reference
	SimpleTensor<T> dst{ calculate_output_shape(src.shape(), info), src.data_type(), 1, src.fixed_point_position() };

	const auto w_dst = static_cast<int>(dst.shape()[0]);
	const auto h_dst = static_cast<int>(dst.shape()[1]);

	if(type == PoolingType::MAX)
	{
	for(int r = 0; r < upper_dims; ++r)
	{
	for(int h = 0; h < h_dst; ++h)
	{
	for(int w = 0; w < w_dst; ++w)
	{
	int wstart = w * pool_stride_x - pad_x;
	int hstart = h * pool_stride_y - pad_y;
	int wend = std::min(wstart + pool_size, w_src);
	int hend = std::min(hstart + pool_size, h_src);
	wstart = std::max(wstart, 0);
	hstart = std::max(hstart, 0);

	T max_val = std::numeric_limits<T>::lowest();
	for(int y = hstart; y < hend; ++y)
	{
	for(int x = wstart; x < wend; ++x)
	{
	const T val = src[r * h_src * w_src + y * w_src + x];
	if(val > max_val)
	{
	max_val = val;
	}
	}
	}

	dst[r * h_dst * w_dst + h * w_dst + w] = max_val;
	}
	}
	}
	}
	else // Average or l2 pooling
	{
	for(int r = 0; r < upper_dims; ++r)
	{
	for(int h = 0; h < h_dst; ++h)
	{
	for(int w = 0; w < w_dst; ++w)
	{
	int wstart = w * pool_stride_x - pad_x;
	int hstart = h * pool_stride_y - pad_y;
	int wend = std::min(wstart + pool_size, w_src + pad_x);
	int hend = std::min(hstart + pool_size, h_src + pad_y);
	int pool = (hend - hstart) * (wend - wstart);
	wstart = std::max(wstart, 0);
	hstart = std::max(hstart, 0);
	wend = std::min(wend, w_src);
	hend = std::min(hend, h_src);
	// Exclude padding pixels from the average
	if(exclude_padding)
	{
	pool = (hend - hstart) * (wend - wstart);
	}

	using namespace fixed_point_arithmetic;

	const int fixed_point_position = src.fixed_point_position();
	const fixed_point<T> const_1(1, fixed_point_position);
	const fixed_point<T> invpool_fp(1.f / static_cast<float>(pool), fixed_point_position);
	fixed_point<T> avg_val(0, fixed_point_position, true);

	if(type == PoolingType::AVG)
	{
	for(int y = hstart; y < hend; ++y)
	{
	for(int x = wstart; x < wend; ++x)
	{
	const fixed_point<T> in_fp(src[r * h_src * w_src + y * w_src + x], fixed_point_position, true);
	avg_val = add(avg_val, in_fp);
	}
	}
	dst[r * h_dst * w_dst + h * w_dst + w] = mul(avg_val, invpool_fp).raw();
	}
	else
	{
	for(int y = hstart; y < hend; ++y)
	{
	for(int x = wstart; x < wend; ++x)
	{
	const fixed_point<T> in_fp(src[r * h_src * w_src + y * w_src + x], fixed_point_position, true);
	avg_val = add(avg_val, mul(in_fp, in_fp));
	}
	}
	auto res = div(const_1, (inv_sqrt(mul(avg_val, invpool_fp))));
	dst[r * h_dst * w_dst + h * w_dst + w] = res.raw();
	}
	}
	}
	}
	}

	return dst;
	}

	template <>
	SimpleTensor<uint8_t> pooling_layer<uint8_t>(const SimpleTensor<uint8_t> &src, PoolingLayerInfo info)
	{
	SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
	SimpleTensor<float> dst_tmp = pooling_layer<float>(src_tmp, info);
	SimpleTensor<uint8_t> dst = convert_to_asymmetric(dst_tmp, src.quantization_info());
	return dst;
	}

	template SimpleTensor<float> pooling_layer(const SimpleTensor<float> &src, PoolingLayerInfo info);
	template SimpleTensor<half> pooling_layer(const SimpleTensor<half> &src, PoolingLayerInfo info);
	template SimpleTensor<qint8_t> pooling_layer(const SimpleTensor<qint8_t> &src, PoolingLayerInfo info);
	template SimpleTensor<qint16_t> pooling_layer(const SimpleTensor<qint16_t> &src, PoolingLayerInfo info);
	} // namespace reference
	} // namespace validation
	} // namespace test
	} // namespace arm_compute