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/*
* Copyright (c) 2019-2023 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.
*/
#include "src/cpu/kernels/meanstddevnorm/generic/neon/impl.h"
#include "src/core/NEON/wrapper/wrapper.h"
namespace arm_compute
{
namespace cpu
{
template <typename ScalarType, int size>
void mean_stddev_normalization(ITensor *input, ITensor *output, float epsilon, const Window &window)
{
using ExactTagType = typename wrapper::traits::neon_vector<ScalarType, size>::tag_type;
// Set build options
Window win = window;
win.set(Window::DimX, Window::Dimension(0, 1, 1));
const int window_step_x = size;
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
Iterator input_itr(input, win);
Iterator output_itr(output, win);
execute_window_loop(win, [&](const Coordinates &)
{
int x = window_start_x;
auto in_ptr = reinterpret_cast<const ScalarType *>(input_itr.ptr());
auto out_ptr = reinterpret_cast<ScalarType *>(output_itr.ptr());
auto sum_vec = wrapper::vdup_n(static_cast<ScalarType>(0.f), ExactTagType{});
auto sum_sq_vec = wrapper::vdup_n(static_cast<ScalarType>(0.f), ExactTagType{});
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
auto data = wrapper::vloadq(in_ptr + x);
sum_vec = wrapper::vadd(sum_vec, data);
sum_sq_vec = wrapper::vadd(sum_sq_vec, wrapper::vmul(data, data));
}
auto sum_carry_res = wrapper::vpadd(wrapper::vgethigh(sum_vec), wrapper::vgetlow(sum_vec));
auto sum_sq_carry_res = wrapper::vpadd(wrapper::vgethigh(sum_sq_vec), wrapper::vgetlow(sum_sq_vec));
for(int i = 0; i < size / 4; ++i)
{
sum_carry_res = wrapper::vpadd(sum_carry_res, sum_carry_res);
sum_sq_carry_res = wrapper::vpadd(sum_sq_carry_res, sum_sq_carry_res);
}
auto sum = wrapper::vgetlane(sum_carry_res, 0);
auto sum_sq = wrapper::vgetlane(sum_sq_carry_res, 0);
// Compute left-over elements
for(; x < window_end_x; ++x)
{
ScalarType data = *(in_ptr + x);
sum += data;
sum_sq += data * data;
}
ScalarType mean = sum / input->info()->dimension(0);
ScalarType var = (sum_sq / input->info()->dimension(0)) - (mean * mean);
ScalarType stddev_inv = 1.f / sqrt(var + epsilon);
auto mean_vec = wrapper::vdup_n(mean, ExactTagType{});
auto stddev_inv_vec = wrapper::vdup_n(stddev_inv, ExactTagType{});
for(x = window_start_x; x <= (window_end_x - window_step_x); x += window_step_x)
{
auto data = wrapper::vloadq(in_ptr + x);
auto res = wrapper::vmul(wrapper::vsub(data, mean_vec), stddev_inv_vec);
// Store results
wrapper::vstore(out_ptr + x, res);
}
for(; x < window_end_x; ++x)
{
*(out_ptr + x) = (*(in_ptr + x) - mean) * stddev_inv;
}
},
input_itr, output_itr);
}
template void mean_stddev_normalization<float, 4>(ITensor *input, ITensor *output, float epsilon, const Window &window);
} // namespace cpu
} // namespace arm_compute
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