86 lines
3.7 KiB
C++
86 lines
3.7 KiB
C++
/*
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* Copyright (c) 2017-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "BatchNormalizationLayer.h"
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#include "ActivationLayer.h"
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#include "tests/validation/Helpers.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace reference
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{
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// Batch Normalization Layer for floating point type
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template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type *>
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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,
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ActivationLayerInfo act_info)
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{
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SimpleTensor<T> result(src.shape(), src.data_type());
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const auto cols = static_cast<int>(src.shape()[0]);
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const auto rows = static_cast<int>(src.shape()[1]);
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const auto depth = static_cast<int>(src.shape()[2]);
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const int upper_dims = src.shape().total_size() / (cols * rows * depth);
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#if defined(_OPENMP)
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#pragma omp parallel for schedule(dynamic, 1) collapse(4)
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#endif /* _OPENMP */
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for(int r = 0; r < upper_dims; ++r)
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{
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for(int i = 0; i < depth; ++i)
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{
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for(int k = 0; k < rows; ++k)
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{
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for(int l = 0; l < cols; ++l)
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{
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const int pos = l + k * cols + i * rows * cols + r * cols * rows * depth;
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const float denominator = sqrt(var[i] + epsilon);
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const float numerator = src[pos] - mean[i];
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const float x_bar = numerator / denominator;
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result[pos] = beta[i] + x_bar * gamma[i];
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}
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}
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}
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}
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if(act_info.enabled())
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{
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result = activation_layer(result, act_info);
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}
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return result;
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}
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template SimpleTensor<float> batch_normalization_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &mean, const SimpleTensor<float> &var, const SimpleTensor<float> &beta,
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const SimpleTensor<float> &gamma, float epsilon, ActivationLayerInfo act_info);
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template SimpleTensor<half> batch_normalization_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &mean, const SimpleTensor<half> &var,
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const SimpleTensor<half> &beta,
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const SimpleTensor<half> &gamma, float epsilon, ActivationLayerInfo act_info);
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} // namespace reference
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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