diff options
Diffstat (limited to 'tests/validation/CPP/NormalizationLayer.cpp')
-rw-r--r-- | tests/validation/CPP/NormalizationLayer.cpp | 275 |
1 files changed, 275 insertions, 0 deletions
diff --git a/tests/validation/CPP/NormalizationLayer.cpp b/tests/validation/CPP/NormalizationLayer.cpp new file mode 100644 index 0000000000..3c6f5e1a54 --- /dev/null +++ b/tests/validation/CPP/NormalizationLayer.cpp @@ -0,0 +1,275 @@ +/* + * Copyright (c) 2017 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 "NormalizationLayer.h" + +#include "tests/validation/FixedPoint.h" +#include "tests/validation/half.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace reference +{ +template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type> +SimpleTensor<T> normalization_layer(const SimpleTensor<T> &src, NormalizationLayerInfo info) +{ + // Create reference + SimpleTensor<T> dst{ src.shape(), src.data_type(), 1, src.fixed_point_position() }; + + // Compute reference + const uint32_t norm_size = info.norm_size(); + NormType type = info.type(); + float beta = info.beta(); + uint32_t kappa = info.kappa(); + + const int cols = src.shape()[0]; + const int rows = src.shape()[1]; + const int depth = src.shape()[2]; + int upper_dims = src.shape().total_size() / (cols * rows); + + float coeff = info.scale_coeff(); + int radius_cols = norm_size / 2; + + // IN_MAP_1D and CROSS_MAP normalize over a single axis only + int radius_rows = (NormType::IN_MAP_2D == type) ? norm_size / 2 : 0; + + if(type == NormType::CROSS_MAP) + { + // Remove also depth from upper dimensions since it is the dimension we + // want to use for normalization + upper_dims /= depth; + + for(int r = 0; r < upper_dims; ++r) + { + for(int i = 0; i < rows; ++i) + { + for(int k = 0; k < cols; ++k) + { + for(int l = 0; l < depth; ++l) + { + float accumulated_scale = 0.f; + + for(int j = -radius_cols; j <= radius_cols; ++j) + { + const int z = l + j; + + if(z >= 0 && z < depth) + { + const T value = src[k + i * cols + z * rows * cols + r * cols * rows * depth]; + accumulated_scale += value * value; + } + } + + dst[k + i * cols + l * rows * cols + r * cols * rows * depth] = kappa + accumulated_scale * coeff; + } + } + } + } + } + else + { + for(int r = 0; r < upper_dims; ++r) + { + for(int i = 0; i < rows; ++i) + { + for(int k = 0; k < cols; ++k) + { + float accumulated_scale = 0.f; + + for(int j = -radius_rows; j <= radius_rows; ++j) + { + const int y = i + j; + for(int l = -radius_cols; l <= radius_cols; ++l) + { + const int x = k + l; + + if((x >= 0 && y >= 0) && (x < cols && y < rows)) + { + const T value = src[x + y * cols + r * cols * rows]; + accumulated_scale += value * value; + } + } + } + + dst[k + i * cols + r * cols * rows] = kappa + accumulated_scale * coeff; + } + } + } + } + + if(beta == 1.f) + { + for(int i = 0; i < dst.num_elements(); ++i) + { + dst[i] = src[i] / dst[i]; + } + } + else if(beta == 0.5f) + { + for(int i = 0; i < dst.num_elements(); ++i) + { + dst[i] = src[i] / std::sqrt(dst[i]); + } + } + else + { + for(int i = 0; i < dst.num_elements(); ++i) + { + dst[i] = src[i] * std::exp(std::log(dst[i]) * -beta); + } + } + + return dst; +} + +template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type> +SimpleTensor<T> normalization_layer(const SimpleTensor<T> &src, NormalizationLayerInfo info) +{ + using namespace fixed_point_arithmetic; + + // Create reference + SimpleTensor<T> dst{ src.shape(), src.data_type(), 1, src.fixed_point_position() }; + + // Compute reference + const int fixed_point_position = src.fixed_point_position(); + + const uint32_t norm_size = info.norm_size(); + NormType type = info.type(); + fixed_point<T> beta(info.beta(), fixed_point_position); + fixed_point<T> kappa(info.kappa(), fixed_point_position); + + const int cols = src.shape()[0]; + const int rows = src.shape()[1]; + const int depth = src.shape()[2]; + int upper_dims = src.shape().total_size() / (cols * rows); + + fixed_point<T> coeff(info.scale_coeff(), fixed_point_position); + int radius_cols = norm_size / 2; + + // IN_MAP_1D and CROSS_MAP normalize over a single axis only + int radius_rows = (NormType::IN_MAP_2D == type) ? norm_size / 2 : 0; + + if(type == NormType::CROSS_MAP) + { + // Remove also depth from upper dimensions since it is the dimension we + // want to use for normalization + upper_dims /= depth; + + for(int r = 0; r < upper_dims; ++r) + { + for(int i = 0; i < rows; ++i) + { + for(int k = 0; k < cols; ++k) + { + for(int l = 0; l < depth; ++l) + { + fixed_point<T> accumulated_scale(0.f, fixed_point_position); + + for(int j = -radius_cols; j <= radius_cols; ++j) + { + const int z = l + j; + + if(z >= 0 && z < depth) + { + const T value = src[k + i * cols + z * rows * cols + r * cols * rows * depth]; + const fixed_point<T> fp_value(value, fixed_point_position, true); + accumulated_scale = add(accumulated_scale, mul(fp_value, fp_value)); + } + } + + accumulated_scale = add(kappa, mul(accumulated_scale, coeff)); + dst[k + i * cols + l * rows * cols + r * cols * rows * depth] = accumulated_scale.raw(); + } + } + } + } + } + else + { + for(int r = 0; r < upper_dims; ++r) + { + for(int i = 0; i < rows; ++i) + { + for(int k = 0; k < cols; ++k) + { + fixed_point<T> accumulated_scale(0.f, fixed_point_position); + + for(int j = -radius_rows; j <= radius_rows; ++j) + { + const int y = i + j; + + for(int l = -radius_cols; l <= radius_cols; ++l) + { + const int x = k + l; + + if((x >= 0 && y >= 0) && (x < cols && y < rows)) + { + const T value = src[x + y * cols + r * cols * rows]; + const fixed_point<T> fp_value(value, fixed_point_position, true); + accumulated_scale = add(accumulated_scale, mul(fp_value, fp_value)); + } + } + } + + accumulated_scale = add(kappa, mul(accumulated_scale, coeff)); + dst[k + i * cols + r * cols * rows] = accumulated_scale.raw(); + } + } + } + } + + if(info.beta() == 1.f) + { + for(int i = 0; i < dst.num_elements(); ++i) + { + fixed_point<T> res = div(fixed_point<T>(src[i], fixed_point_position, true), fixed_point<T>(dst[i], fixed_point_position, true)); + dst[i] = res.raw(); + } + } + else + { + const fixed_point<T> beta(info.beta(), fixed_point_position); + + for(int i = 0; i < dst.num_elements(); ++i) + { + fixed_point<T> res = pow(fixed_point<T>(dst[i], fixed_point_position, true), beta); + res = div(fixed_point<T>(src[i], fixed_point_position, true), res); + dst[i] = res.raw(); + } + } + + return dst; +} + +template SimpleTensor<float> normalization_layer(const SimpleTensor<float> &src, NormalizationLayerInfo info); +template SimpleTensor<half_float::half> normalization_layer(const SimpleTensor<half_float::half> &src, NormalizationLayerInfo info); +template SimpleTensor<qint8_t> normalization_layer(const SimpleTensor<qint8_t> &src, NormalizationLayerInfo info); +template SimpleTensor<qint16_t> normalization_layer(const SimpleTensor<qint16_t> &src, NormalizationLayerInfo info); +} // namespace reference +} // namespace validation +} // namespace test +} // namespace arm_compute |