tests/validation/reference/PoolingLayer.cpp

/*
 * Copyright (c) 2017-2021, 2023 Arm Limited.
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 * SPDX-License-Identifier: MIT
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 * The above copyright notice and this permission notice shall be included in all
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 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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#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)
{
    // 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;

                        // Used to calculate kernel indices
                        int  kh_start = std::max(0, -hstart);
                        int  kw_start = std::max(0, -wstart);
                        int  max_ker_index{ 0 };

                        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 = info.use_inf_as_limit ? -std::numeric_limits<ACC_T>::infinity() : std::numeric_limits<ACC_T>::lowest();
                        int  max_index{ 0 };

                        for(int y = hstart, kh = kh_start; y < hend; ++y, ++kh)
                        {
                            for(int x = wstart, kw = kw_start; x < wend; ++x, ++kw)
                            {
                                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;
                                    max_ker_index = pool_size_x * (kh) + (kw);
                                    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] = (info.use_kernel_indices) ? max_ker_index : 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