xref: /aosp_15_r20/external/ComputeLibrary/tests/validation/reference/BatchNormalizationLayer.cpp (revision c217d954acce2dbc11938adb493fc0abd69584f3)

/*
 * Copyright (c) 2017-2020 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 "BatchNormalizationLayer.h"

#include "ActivationLayer.h"

#include "tests/validation/Helpers.h"

namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
// Batch Normalization Layer for floating point type
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type *>
SimpleTensor<T> batch_normalization_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &mean, const SimpleTensor<T> &var, const SimpleTensor<T> &beta, const SimpleTensor<T> &gamma, float epsilon,
                                          ActivationLayerInfo act_info)
{
    SimpleTensor<T> result(src.shape(), src.data_type());

    const auto cols       = static_cast<int>(src.shape()[0]);
    const auto rows       = static_cast<int>(src.shape()[1]);
    const auto depth      = static_cast<int>(src.shape()[2]);
    const int  upper_dims = src.shape().total_size() / (cols * rows * depth);
#if defined(_OPENMP)
    #pragma omp parallel for schedule(dynamic, 1) collapse(4)
#endif /* _OPENMP */
    for(int r = 0; r < upper_dims; ++r)
    {
        for(int i = 0; i < depth; ++i)
        {
            for(int k = 0; k < rows; ++k)
            {
                for(int l = 0; l < cols; ++l)
                {
                    const int   pos         = l + k * cols + i * rows * cols + r * cols * rows * depth;
                    const float denominator = sqrt(var[i] + epsilon);
                    const float numerator   = src[pos] - mean[i];
                    const float x_bar       = numerator / denominator;
                    result[pos]             = beta[i] + x_bar * gamma[i];
                }
            }
        }
    }

    if(act_info.enabled())
    {
        result = activation_layer(result, act_info);
    }

    return result;
}
template SimpleTensor<float> batch_normalization_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &mean, const SimpleTensor<float> &var, const SimpleTensor<float> &beta,
                                                       const SimpleTensor<float> &gamma, float epsilon, ActivationLayerInfo act_info);
template SimpleTensor<half> batch_normalization_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &mean, const SimpleTensor<half> &var,
                                                      const SimpleTensor<half> &beta,
                                                      const SimpleTensor<half> &gamma, float epsilon, ActivationLayerInfo act_info);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute