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 <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/tests/validation/CPP/NormalizationLayer.cpp b/tests/validation/CPP/NormalizationLayer.cpp
new file mode 100644
index 0000000..3c6f5e1
--- /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