aboutsummaryrefslogtreecommitdiff
path: root/tests/validation/CPP/NormalizationLayer.cpp
diff options
context:
space:
mode:
Diffstat (limited to 'tests/validation/CPP/NormalizationLayer.cpp')
-rw-r--r--tests/validation/CPP/NormalizationLayer.cpp275
1 files changed, 0 insertions, 275 deletions
diff --git a/tests/validation/CPP/NormalizationLayer.cpp b/tests/validation/CPP/NormalizationLayer.cpp
deleted file mode 100644
index 226af96fe3..0000000000
--- a/tests/validation/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 "arm_compute/core/Types.h"
-#include "tests/validation/FixedPoint.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> normalization_layer(const SimpleTensor<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
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-12 08:25:24 +0000