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/*
* 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
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