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/*
* Copyright (c) 2016-2019 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 "arm_compute/core/NEON/kernels/NEMeanStdDevKernel.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/IAccessWindow.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
#include <arm_neon.h>
#include <cmath>
#include <tuple>
#include <utility>
using namespace arm_compute;
namespace arm_compute
{
class Coordinates;
} // namespace arm_compute
namespace
{
template <bool calc_sum_squared>
std::pair<uint64x1_t, uint64x1_t> accumulate(const Window &window, Iterator &iterator)
{
uint64x1_t sum = vdup_n_u64(0);
uint64x1_t sum_squared = vdup_n_u64(0);
// Calculate sum
execute_window_loop(window, [&](const Coordinates &)
{
const uint8x16_t in_data = vld1q_u8(iterator.ptr());
// Sum of the low and high elements of data
const uint16x8_t tmp0 = vaddl_u8(vget_low_u8(in_data), vget_high_u8(in_data));
const uint32x4_t tmp1 = vaddl_u16(vget_low_u16(tmp0), vget_high_u16(tmp0));
const uint32x2_t tmp2 = vadd_u32(vget_low_u32(tmp1), vget_high_u32(tmp1));
// Update sum
sum = vpadal_u32(sum, tmp2);
if(calc_sum_squared)
{
const uint16x8_t square_data_low = vmull_u8(vget_low_u8(in_data), vget_low_u8(in_data));
const uint16x8_t square_data_high = vmull_u8(vget_high_u8(in_data), vget_high_u8(in_data));
// Sum of the low and high elements of data
const uint32x4_t tmp0_low = vaddl_u16(vget_low_u16(square_data_low), vget_high_u16(square_data_low));
const uint32x4_t tmp0_high = vaddl_u16(vget_low_u16(square_data_high), vget_high_u16(square_data_high));
const uint32x4_t tmp1 = vaddq_u32(tmp0_low, tmp0_high);
const uint32x2_t tmp2 = vadd_u32(vget_low_u32(tmp1), vget_high_u32(tmp1));
// Update sum
sum_squared = vpadal_u32(sum_squared, tmp2);
}
},
iterator);
return std::make_pair(sum, sum_squared);
}
} // namespace
NEMeanStdDevKernel::NEMeanStdDevKernel()
: _input(nullptr), _mean(nullptr), _stddev(nullptr), _global_sum(nullptr), _global_sum_squared(nullptr), _mtx(), _border_size(0)
{
}
BorderSize NEMeanStdDevKernel::border_size() const
{
return _border_size;
}
void NEMeanStdDevKernel::configure(const IImage *input, float *mean, uint64_t *global_sum, float *stddev, uint64_t *global_sum_squared)
{
ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
ARM_COMPUTE_ERROR_ON(nullptr == mean);
ARM_COMPUTE_ERROR_ON(nullptr == global_sum);
ARM_COMPUTE_ERROR_ON(stddev && nullptr == global_sum_squared);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
_input = input;
_mean = mean;
_stddev = stddev;
_global_sum = global_sum;
_global_sum_squared = global_sum_squared;
constexpr unsigned int num_elems_processed_per_iteration = 16;
_border_size = BorderSize(ceil_to_multiple(input->info()->dimension(0), num_elems_processed_per_iteration) - input->info()->dimension(0));
// Configure kernel window
Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
update_window_and_padding(win, AccessWindowHorizontal(input->info(), 0, num_elems_processed_per_iteration));
INEKernel::configure(win);
}
void NEMeanStdDevKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
Iterator input(_input, window);
uint64x1_t local_sum = vdup_n_u64(0);
uint64x1_t local_sum_squared = vdup_n_u64(0);
if(_stddev != nullptr)
{
std::tie(local_sum, local_sum_squared) = accumulate<true>(window, input);
}
else
{
std::tie(local_sum, local_sum_squared) = accumulate<false>(window, input);
}
const float num_pixels = _input->info()->dimension(0) * _input->info()->dimension(1);
// Merge sum and calculate mean and stddev
std::unique_lock<arm_compute::Mutex> lock(_mtx);
*_global_sum += vget_lane_u64(local_sum, 0);
const float mean = *_global_sum / num_pixels;
*_mean = mean;
if(_stddev != nullptr)
{
const uint64_t tmp_sum_squared = vget_lane_u64(local_sum_squared, 0);
*_global_sum_squared += tmp_sum_squared;
*_stddev = std::sqrt((*_global_sum_squared / num_pixels) - (mean * mean));
}
lock.unlock();
}
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