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/*
* Copyright (c) 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.
*/
#ifndef ACL_SRC_CPU_KERNELS_POOL2D_NEON_IMPL_H
#define ACL_SRC_CPU_KERNELS_POOL2D_NEON_IMPL_H
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/Traits.h"
#include "src/core/helpers/WindowHelpers.h"
#include "src/core/NEON/wrapper/intrinsics/intrinsics.h"
#include "src/cpu/kernels/pool2d/neon/list.h"
#include <limits>
#ifdef ENABLE_NCHW_KERNELS
namespace arm_compute
{
namespace cpu
{
namespace
{
template <typename T>
auto read_2_boundary_aware_as_f32(int srcw, int srch, int pad_l, int pad_t, int x, int y, const T *ptr, T fval)
{
T vec[2];
const bool row_in_bounds((y >= pad_t) && (y < (srch + pad_t)));
for (int i = 0; i < 2; i++)
{
if (row_in_bounds && (x + i >= pad_l) && (x + i < (srcw + pad_l)))
{
vec[i] = *(ptr + i);
}
else
{
vec[i] = fval;
}
}
float32_t vec_f32[2] = {vec[0], vec[1]};
return wrapper::vload(vec_f32);
}
} // namespace
template <typename T>
void pooling2_nchw_maxpool_indices(const ITensor *src,
ITensor *dst0,
ITensor *dst1,
PoolingLayerInfo &pool_info,
const Window &window_src,
const Window &window)
{
Iterator in(src, window_src);
Iterator out(dst0, window);
Iterator indices(dst1, window);
const int pool_pad_top = pool_info.pad_stride_info.pad_top();
const int pool_pad_left = pool_info.pad_stride_info.pad_left();
int pool_stride_x = 0;
int pool_stride_y = 0;
std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info.stride();
const int src_w = src->info()->dimension(0);
const int src_h = src->info()->dimension(1);
const uint8_t *const src_top_ptr =
src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)));
const uint8_t *const src_bottom_ptr =
src->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1));
const int pad_left = src->info()->padding().left;
const int pad_right = src->info()->padding().right;
const int in_stride_y = static_cast<int>(src->info()->strides_in_bytes().y());
const T float_min = get_initial_min<T>(pool_info.use_inf_as_limit);
const T fill_value = (pool_info.pool_type == PoolingType::MAX) ? float_min : 0.f;
execute_window_loop(
window,
[&](const Coordinates &id)
{
const auto x_val = id.x() * pool_stride_x;
const auto y_val_0 = id.y() * pool_stride_y;
const auto y_val_1 = (id.y() * pool_stride_y) + 1;
auto top_data =
read_2_boundary_aware_as_f32(src_w, src_h, pool_pad_left, pool_pad_top, x_val, y_val_0,
reinterpret_cast<const T *>(src_top_ptr + in.offset()), fill_value);
auto bottom_data =
read_2_boundary_aware_as_f32(src_w, src_h, pool_pad_left, pool_pad_top, x_val, y_val_1,
reinterpret_cast<const T *>(src_bottom_ptr + in.offset()), fill_value);
// Calculate max data, compare top first, then bottom, to make sue the first max is recorded.
const float32x2_t max_data_top = vpmax_f32(top_data, top_data);
const float32x2_t max_data_bottom = vpmax_f32(bottom_data, bottom_data);
const float32x2_t max_data = vmax_f32(max_data_top, max_data_bottom);
*(reinterpret_cast<T *>(out.ptr())) = static_cast<T>(vget_lane_f32(max_data, 0));
// Calculate max data indice, which will be used in max unpool.
const uint32_t offset_base =
offset_no_padding<T>(in.offset(), id, *src->info(), pool_stride_x, pool_stride_y, DataLayout::NCHW);
const uint32_t offset_top = (uint32_t)(offset_base / sizeof(T));
const uint32_t offset_bottom = offset_top + in_stride_y / sizeof(T) - pad_right - pad_left;
const uint32x2_t voffset_top = {offset_top, offset_top + 1u};
const uint32x2_t voffset_bottom = {offset_bottom, offset_bottom + 1u};
const uint32x2_t tmp_indices_top =
vbsl_u32(vcge_f32(top_data, vrev64_f32(top_data)), voffset_top, vrev64_u32(voffset_top));
const uint32x2_t tmp_indices_bottom =
vbsl_u32(vcge_f32(bottom_data, vrev64_f32(bottom_data)), voffset_bottom, vrev64_u32(voffset_bottom));
*(reinterpret_cast<int *>(indices.ptr())) = vget_lane_u32(
vbsl_u32(vcge_f32(max_data_top, max_data_bottom), tmp_indices_top, tmp_indices_bottom), 0);
},
in, out, indices);
}
} // namespace cpu
} // namespace arm_compute
#endif // ENABLE_NCHW_KERNELS
#endif // ACL_SRC_CPU_KERNELS_POOL2D_NEON_IMPL_H
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