154 lines
5.0 KiB
C++
154 lines
5.0 KiB
C++
/*
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* Copyright (c) 2017-2018 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 "NormalizationLayer.h"
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#include "arm_compute/core/Types.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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template <typename T>
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SimpleTensor<T> normalization_layer(const SimpleTensor<T> &src, NormalizationLayerInfo info)
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{
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// Create reference
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SimpleTensor<T> dst{ src.shape(), src.data_type(), 1 };
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// Compute reference
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const uint32_t norm_size = info.norm_size();
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NormType type = info.type();
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float beta = info.beta();
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uint32_t kappa = info.kappa();
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const int cols = src.shape()[0];
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const int rows = src.shape()[1];
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const int depth = src.shape()[2];
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int upper_dims = src.shape().total_size() / (cols * rows);
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float coeff = info.scale_coeff();
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int radius_cols = norm_size / 2;
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// IN_MAP_1D and CROSS_MAP normalize over a single axis only
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int radius_rows = (NormType::IN_MAP_2D == type) ? norm_size / 2 : 0;
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if(info.is_cross_map())
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{
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// Remove also depth from upper dimensions since it is the dimension we
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// want to use for normalization
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upper_dims /= depth;
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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 < rows; ++i)
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{
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for(int k = 0; k < cols; ++k)
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{
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for(int l = 0; l < depth; ++l)
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{
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float accumulated_scale = 0.f;
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for(int j = -radius_cols; j <= radius_cols; ++j)
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{
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const int z = l + j;
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if(z >= 0 && z < depth)
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{
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const T value = src[k + i * cols + z * rows * cols + r * cols * rows * depth];
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accumulated_scale += value * value;
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}
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}
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dst[k + i * cols + l * rows * cols + r * cols * rows * depth] = kappa + accumulated_scale * coeff;
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}
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}
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}
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}
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}
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else
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{
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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 < rows; ++i)
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{
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for(int k = 0; k < cols; ++k)
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{
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float accumulated_scale = 0.f;
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for(int j = -radius_rows; j <= radius_rows; ++j)
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{
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const int y = i + j;
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for(int l = -radius_cols; l <= radius_cols; ++l)
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{
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const int x = k + l;
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if((x >= 0 && y >= 0) && (x < cols && y < rows))
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{
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const T value = src[x + y * cols + r * cols * rows];
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accumulated_scale += value * value;
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}
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}
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}
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dst[k + i * cols + r * cols * rows] = kappa + accumulated_scale * coeff;
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}
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}
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}
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}
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if(beta == 1.f)
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{
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for(int i = 0; i < dst.num_elements(); ++i)
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{
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dst[i] = src[i] / dst[i];
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}
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}
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else if(beta == 0.5f)
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{
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for(int i = 0; i < dst.num_elements(); ++i)
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{
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dst[i] = src[i] / std::sqrt(dst[i]);
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}
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}
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else
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{
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for(int i = 0; i < dst.num_elements(); ++i)
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{
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dst[i] = src[i] * std::exp(std::log(dst[i]) * -beta);
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}
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}
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return dst;
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}
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template SimpleTensor<float> normalization_layer(const SimpleTensor<float> &src, NormalizationLayerInfo info);
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template SimpleTensor<half> normalization_layer(const SimpleTensor<half> &src, NormalizationLayerInfo 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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