From a09de0c8b2ed0f1481502d3b023375609362d9e3 Mon Sep 17 00:00:00 2001 From: Moritz Pflanzer Date: Fri, 1 Sep 2017 20:41:12 +0100 Subject: COMPMID-415: Rename and move tests The boost validation is now "standalone" in validation_old and builds as arm_compute_validation_old. The new validation builds now as arm_compute_validation. Change-Id: Ib93ba848a25680ac60afb92b461d574a0757150d Reviewed-on: http://mpd-gerrit.cambridge.arm.com/86187 Tested-by: Kaizen Reviewed-by: Anthony Barbier --- tests/validation_new/CPP/NormalizationLayer.cpp | 275 ------------------------ 1 file changed, 275 deletions(-) delete mode 100644 tests/validation_new/CPP/NormalizationLayer.cpp (limited to 'tests/validation_new/CPP/NormalizationLayer.cpp') diff --git a/tests/validation_new/CPP/NormalizationLayer.cpp b/tests/validation_new/CPP/NormalizationLayer.cpp deleted file mode 100644 index a8818d8b5c..0000000000 --- a/tests/validation_new/CPP/NormalizationLayer.cpp +++ /dev/null @@ -1,275 +0,0 @@ -/* - * 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_new/FixedPoint.h" -#include "tests/validation_new/half.h" - -namespace arm_compute -{ -namespace test -{ -namespace validation -{ -namespace reference -{ -template ::value, int>::type> -SimpleTensor normalization_layer(const SimpleTensor &src, NormalizationLayerInfo info) -{ - // Create reference - SimpleTensor 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 ::value, int>::type> -SimpleTensor normalization_layer(const SimpleTensor &src, NormalizationLayerInfo info) -{ - using namespace fixed_point_arithmetic; - - // Create reference - SimpleTensor 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 beta(info.beta(), fixed_point_position); - fixed_point 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 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 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 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 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 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 res = div(fixed_point(src[i], fixed_point_position, true), fixed_point(dst[i], fixed_point_position, true)); - dst[i] = res.raw(); - } - } - else - { - const fixed_point beta(info.beta(), fixed_point_position); - - for(int i = 0; i < dst.num_elements(); ++i) - { - fixed_point res = pow(fixed_point(dst[i], fixed_point_position, true), beta); - res = div(fixed_point(src[i], fixed_point_position, true), res); - dst[i] = res.raw(); - } - } - - return dst; -} - -template SimpleTensor normalization_layer(const SimpleTensor &src, NormalizationLayerInfo info); -template SimpleTensor normalization_layer(const SimpleTensor &src, NormalizationLayerInfo info); -template SimpleTensor normalization_layer(const SimpleTensor &src, NormalizationLayerInfo info); -template SimpleTensor normalization_layer(const SimpleTensor &src, NormalizationLayerInfo info); -} // namespace reference -} // namespace validation -} // namespace test -} // namespace arm_compute -- cgit v1.2.1