tests/validation/reference/PoolingLayer.cpp - platform/external/ARMComputeLibrary - Git at Google

 /*
  * Copyright (c) 2017-2021 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 "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "tests/validation/Helpers.h"

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 namespace reference
 {
 using namespace arm_compute::misc::shape_calculator;

 template <typename T, typename ACC_T, typename std::enable_if<is_floating_point<T>::value, int>::type>
 SimpleTensor<T> pooling_layer_internal(const SimpleTensor<T> &src, const PoolingLayerInfo &info, SimpleTensor<uint32_t> *indices, DataLayout data_layout)
 {
     ARM_COMPUTE_ERROR_ON(info.is_global_pooling && (src.shape().x() != src.shape().y()));
     // Create reference
     SimpleTensor<T> dst{ compute_pool_shape(TensorInfo(src.shape(), 1, src.data_type()), info), src.data_type(), 1 };
     auto            pooled_shape = compute_pool_shape(TensorInfo(src.shape(), 1, src.data_type()), info);
     if(indices)
     {
         *indices = SimpleTensor<uint32_t> { pooled_shape, DataType::U32, 1 };
     }
     const int   pool_size_x     = info.is_global_pooling ? src.shape().x() : info.pool_size.width;
     const int   pool_size_y     = info.is_global_pooling ? src.shape().y() : info.pool_size.height;
     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_left        = info.pad_stride_info.pad_left();
     int         pad_top         = info.pad_stride_info.pad_top();
     int         pad_right       = info.pad_stride_info.pad_right();
     int         pad_bottom      = info.pad_stride_info.pad_bottom();
     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 auto z_src = static_cast<int>(src.shape()[2]);
     const auto b_src = static_cast<int>(src.shape()[3]);

     const int upper_dims = src.shape().total_size() / (w_src * h_src);

     const auto w_dst = static_cast<int>(dst.shape()[0]);
     const auto h_dst = static_cast<int>(dst.shape()[1]);
     const auto z_dst = static_cast<int>(dst.shape()[2]);

     TensorShape shape_nhwc(src.shape());
     permute(shape_nhwc, PermutationVector(2U, 0U, 1U));
     if(type == PoolingType::MAX)
     {
         for(int b = 0; b < b_src; ++b)
         {
             for(int r = 0; r < z_src; ++r)
             {
                 for(int h = 0; h < h_dst; ++h)
                 {
                     for(int w = 0; w < w_dst; ++w)
                     {
                         int wstart   = w * pool_stride_x - pad_left;
                         int hstart   = h * pool_stride_y - pad_top;
                         int wend     = std::min(wstart + pool_size_x, w_src);
                         int hend     = std::min(hstart + pool_size_y, h_src);
                         wstart       = std::max(wstart, 0);
                         hstart       = std::max(hstart, 0);
                         auto max_val = -std::numeric_limits<ACC_T>::infinity();
                         int  max_index{ 0 };
                         for(int y = hstart; y < hend; ++y)
                         {
                             for(int x = wstart; x < wend; ++x)
                             {
                                 const auto val = static_cast<ACC_T>(src[b * z_src * h_src * w_src + r * h_src * w_src + y * w_src + x]);
                                 if(val > max_val)
                                 {
                                     max_val = val;
                                     if(data_layout == DataLayout::NCHW)
                                     {
                                         max_index = coord2index(src.shape(), Coordinates(x, y, r, 0));
                                     }
                                     else
                                     {
                                         max_index = coord2index(shape_nhwc, Coordinates(r, x, y, 0));
                                     }
                                 }
                             }
                         }

                         dst[b * z_dst * h_dst * w_dst + r * h_dst * w_dst + h * w_dst + w] = static_cast<T>(max_val);
                         if(indices)
                         {
                             (*indices)[b * z_dst * h_dst * w_dst + r * h_dst * w_dst + h * w_dst + w] = max_index;
                         }
                     }
                 }
             }
         }
     }
     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)
                 {
                     ACC_T avg_val(0);
                     int   wstart = w * pool_stride_x - pad_left;
                     int   hstart = h * pool_stride_y - pad_top;
                     int   wend   = std::min(wstart + pool_size_x, w_src + pad_right);
                     int   hend   = std::min(hstart + pool_size_y, h_src + pad_bottom);
                     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 += static_cast<ACC_T>(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 auto val = static_cast<ACC_T>(src[r * h_src * w_src + y * w_src + x]);
                                 avg_val += val * val;
                             }
                         }
                         dst[r * h_dst * w_dst + h * w_dst + w] = static_cast<T>(std::sqrt(avg_val / pool));
                     }
                 }
             }
         }
     }
     return dst;
 }

 template SimpleTensor<float> pooling_layer_internal<float>(const SimpleTensor<float> &src, const PoolingLayerInfo &info, SimpleTensor<uint32_t> *indices, DataLayout data_layout);

 template SimpleTensor<half> pooling_layer_internal<half>(const SimpleTensor<half> &src, const PoolingLayerInfo &info, SimpleTensor<uint32_t> *indices, DataLayout data_layout);

 template SimpleTensor<half> pooling_layer_internal<half, float>(const SimpleTensor<half> &src, const PoolingLayerInfo &info, SimpleTensor<uint32_t> *indices, DataLayout data_layout);

 template <typename T>
 SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, const PoolingLayerInfo &info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices, DataLayout data_layout)
 {
     ARM_COMPUTE_UNUSED(output_qinfo);
     return pooling_layer_internal<T, T>(src, info, indices, data_layout);
 }

 template <>
 SimpleTensor<uint8_t> pooling_layer<uint8_t>(const SimpleTensor<uint8_t> &src, const PoolingLayerInfo &info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices,
                                              DataLayout data_layout)
 {
     SimpleTensor<float>   src_tmp = convert_from_asymmetric(src);
     SimpleTensor<float>   dst_tmp = pooling_layer_internal<float>(src_tmp, info, indices, data_layout);
     SimpleTensor<uint8_t> dst     = convert_to_asymmetric<uint8_t>(dst_tmp, output_qinfo);
     return dst;
 }

 template <>
 SimpleTensor<int8_t> pooling_layer<int8_t>(const SimpleTensor<int8_t> &src, const PoolingLayerInfo &info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices, DataLayout data_layout)
 {
     SimpleTensor<float>  src_tmp = convert_from_asymmetric(src);
     SimpleTensor<float>  dst_tmp = pooling_layer_internal<float>(src_tmp, info, indices, data_layout);
     SimpleTensor<int8_t> dst     = convert_to_asymmetric<int8_t>(dst_tmp, output_qinfo);
     return dst;
 }

 template <>
 SimpleTensor<half> pooling_layer(const SimpleTensor<half> &src, const PoolingLayerInfo &info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices, DataLayout data_layout)
 {
     ARM_COMPUTE_UNUSED(output_qinfo);
     if(src.data_type() == DataType::F16 && info.fp_mixed_precision)
     {
         return pooling_layer_internal<half, float>(src, info, indices, data_layout);
     }

     return pooling_layer_internal<half>(src, info, indices, data_layout);
 }

 template SimpleTensor<float> pooling_layer(const SimpleTensor<float> &src, const PoolingLayerInfo &info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices, DataLayout data_layout);

 } // namespace reference
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2021 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 "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "tests/validation/Helpers.h"

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	namespace reference
	{
	using namespace arm_compute::misc::shape_calculator;

	template <typename T, typename ACC_T, typename std::enable_if<is_floating_point<T>::value, int>::type>
	SimpleTensor<T> pooling_layer_internal(const SimpleTensor<T> &src, const PoolingLayerInfo &info, SimpleTensor<uint32_t> *indices, DataLayout data_layout)
	{
	ARM_COMPUTE_ERROR_ON(info.is_global_pooling && (src.shape().x() != src.shape().y()));
	// Create reference
	SimpleTensor<T> dst{ compute_pool_shape(TensorInfo(src.shape(), 1, src.data_type()), info), src.data_type(), 1 };
	auto pooled_shape = compute_pool_shape(TensorInfo(src.shape(), 1, src.data_type()), info);
	if(indices)
	{
	*indices = SimpleTensor<uint32_t> { pooled_shape, DataType::U32, 1 };
	}
	const int pool_size_x = info.is_global_pooling ? src.shape().x() : info.pool_size.width;
	const int pool_size_y = info.is_global_pooling ? src.shape().y() : info.pool_size.height;
	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_left = info.pad_stride_info.pad_left();
	int pad_top = info.pad_stride_info.pad_top();
	int pad_right = info.pad_stride_info.pad_right();
	int pad_bottom = info.pad_stride_info.pad_bottom();
	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 auto z_src = static_cast<int>(src.shape()[2]);
	const auto b_src = static_cast<int>(src.shape()[3]);

	const int upper_dims = src.shape().total_size() / (w_src * h_src);

	const auto w_dst = static_cast<int>(dst.shape()[0]);
	const auto h_dst = static_cast<int>(dst.shape()[1]);
	const auto z_dst = static_cast<int>(dst.shape()[2]);

	TensorShape shape_nhwc(src.shape());
	permute(shape_nhwc, PermutationVector(2U, 0U, 1U));
	if(type == PoolingType::MAX)
	{
	for(int b = 0; b < b_src; ++b)
	{
	for(int r = 0; r < z_src; ++r)
	{
	for(int h = 0; h < h_dst; ++h)
	{
	for(int w = 0; w < w_dst; ++w)
	{
	int wstart = w * pool_stride_x - pad_left;
	int hstart = h * pool_stride_y - pad_top;
	int wend = std::min(wstart + pool_size_x, w_src);
	int hend = std::min(hstart + pool_size_y, h_src);
	wstart = std::max(wstart, 0);
	hstart = std::max(hstart, 0);
	auto max_val = -std::numeric_limits<ACC_T>::infinity();
	int max_index{ 0 };
	for(int y = hstart; y < hend; ++y)
	{
	for(int x = wstart; x < wend; ++x)
	{
	const auto val = static_cast<ACC_T>(src[b * z_src * h_src * w_src + r * h_src * w_src + y * w_src + x]);
	if(val > max_val)
	{
	max_val = val;
	if(data_layout == DataLayout::NCHW)
	{
	max_index = coord2index(src.shape(), Coordinates(x, y, r, 0));
	}
	else
	{
	max_index = coord2index(shape_nhwc, Coordinates(r, x, y, 0));
	}
	}
	}
	}

	dst[b * z_dst * h_dst * w_dst + r * h_dst * w_dst + h * w_dst + w] = static_cast<T>(max_val);
	if(indices)
	{
	(indices)[b z_dst * h_dst * w_dst + r * h_dst * w_dst + h * w_dst + w] = max_index;
	}
	}
	}
	}
	}
	}
	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)
	{
	ACC_T avg_val(0);
	int wstart = w * pool_stride_x - pad_left;
	int hstart = h * pool_stride_y - pad_top;
	int wend = std::min(wstart + pool_size_x, w_src + pad_right);
	int hend = std::min(hstart + pool_size_y, h_src + pad_bottom);
	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 += static_cast<ACC_T>(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 auto val = static_cast<ACC_T>(src[r * h_src * w_src + y * w_src + x]);
	avg_val += val * val;
	}
	}
	dst[r * h_dst * w_dst + h * w_dst + w] = static_cast<T>(std::sqrt(avg_val / pool));
	}
	}
	}
	}
	}
	return dst;
	}

	template SimpleTensor<float> pooling_layer_internal<float>(const SimpleTensor<float> &src, const PoolingLayerInfo &info, SimpleTensor<uint32_t> *indices, DataLayout data_layout);

	template SimpleTensor<half> pooling_layer_internal<half>(const SimpleTensor<half> &src, const PoolingLayerInfo &info, SimpleTensor<uint32_t> *indices, DataLayout data_layout);

	template SimpleTensor<half> pooling_layer_internal<half, float>(const SimpleTensor<half> &src, const PoolingLayerInfo &info, SimpleTensor<uint32_t> *indices, DataLayout data_layout);

	template <typename T>
	SimpleTensor<T> pooling_layer(const SimpleTensor<T> &src, const PoolingLayerInfo &info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices, DataLayout data_layout)
	{
	ARM_COMPUTE_UNUSED(output_qinfo);
	return pooling_layer_internal<T, T>(src, info, indices, data_layout);
	}

	template <>
	SimpleTensor<uint8_t> pooling_layer<uint8_t>(const SimpleTensor<uint8_t> &src, const PoolingLayerInfo &info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices,
	DataLayout data_layout)
	{
	SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
	SimpleTensor<float> dst_tmp = pooling_layer_internal<float>(src_tmp, info, indices, data_layout);
	SimpleTensor<uint8_t> dst = convert_to_asymmetric<uint8_t>(dst_tmp, output_qinfo);
	return dst;
	}

	template <>
	SimpleTensor<int8_t> pooling_layer<int8_t>(const SimpleTensor<int8_t> &src, const PoolingLayerInfo &info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices, DataLayout data_layout)
	{
	SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
	SimpleTensor<float> dst_tmp = pooling_layer_internal<float>(src_tmp, info, indices, data_layout);
	SimpleTensor<int8_t> dst = convert_to_asymmetric<int8_t>(dst_tmp, output_qinfo);
	return dst;
	}

	template <>
	SimpleTensor<half> pooling_layer(const SimpleTensor<half> &src, const PoolingLayerInfo &info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices, DataLayout data_layout)
	{
	ARM_COMPUTE_UNUSED(output_qinfo);
	if(src.data_type() == DataType::F16 && info.fp_mixed_precision)
	{
	return pooling_layer_internal<half, float>(src, info, indices, data_layout);
	}

	return pooling_layer_internal<half>(src, info, indices, data_layout);
	}

	template SimpleTensor<float> pooling_layer(const SimpleTensor<float> &src, const PoolingLayerInfo &info, const QuantizationInfo &output_qinfo, SimpleTensor<uint32_t> *indices, DataLayout data_layout);

	} // namespace reference
	} // namespace validation
	} // namespace test
	} // namespace arm_compute