arm_compute v17.12
diff --git a/tests/validation/reference/BatchNormalizationLayer.cpp b/tests/validation/reference/BatchNormalizationLayer.cpp
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+++ b/tests/validation/reference/BatchNormalizationLayer.cpp
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+/*
+ * 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 "BatchNormalizationLayer.h"
+
+#include "tests/validation/FixedPoint.h"
+#include "tests/validation/Helpers.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+// Batch Normalization Layer for fixed point type
+template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type *>
+SimpleTensor<T> batch_normalization_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &mean, const SimpleTensor<T> &var, const SimpleTensor<T> &beta, const SimpleTensor<T> &gamma, float epsilon,
+ int fixed_point_position)
+{
+ SimpleTensor<T> result(src.shape(), src.data_type());
+
+ const auto cols = static_cast<int>(src.shape()[0]);
+ const auto rows = static_cast<int>(src.shape()[1]);
+ const auto depth = static_cast<int>(src.shape()[2]);
+ const int upper_dims = src.shape().total_size() / (cols * rows * depth);
+
+ for(int r = 0; r < upper_dims; ++r)
+ {
+ for(int i = 0; i < depth; ++i)
+ {
+ for(int k = 0; k < rows; ++k)
+ {
+ for(int l = 0; l < cols; ++l)
+ {
+ const int pos = l + k * cols + i * rows * cols + r * cols * rows * depth;
+
+ fixed_point_arithmetic::fixed_point<T> src_qs(src[pos], fixed_point_position, true);
+ fixed_point_arithmetic::fixed_point<T> var_qs(var[i], fixed_point_position, true);
+ fixed_point_arithmetic::fixed_point<T> mean_qs(mean[i], fixed_point_position, true);
+ fixed_point_arithmetic::fixed_point<T> beta_qs(beta[i], fixed_point_position, true);
+ fixed_point_arithmetic::fixed_point<T> gamma_qs(gamma[i], fixed_point_position, true);
+ fixed_point_arithmetic::fixed_point<T> epsilon_qs(epsilon, fixed_point_position);
+
+ auto denominator = fixed_point_arithmetic::inv_sqrt(var_qs + epsilon_qs);
+ auto numerator = src_qs - mean_qs;
+ auto x_bar = numerator * denominator;
+ x_bar = beta_qs + x_bar * gamma_qs;
+ result[pos] = x_bar.raw();
+ }
+ }
+ }
+ }
+
+ return result;
+}
+
+// Batch Normalization Layer for floating point type
+template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type *>
+SimpleTensor<T> batch_normalization_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &mean, const SimpleTensor<T> &var, const SimpleTensor<T> &beta, const SimpleTensor<T> &gamma, float epsilon,
+ int fixed_point_position)
+{
+ ARM_COMPUTE_UNUSED(fixed_point_position);
+
+ SimpleTensor<T> result(src.shape(), src.data_type());
+
+ const auto cols = static_cast<int>(src.shape()[0]);
+ const auto rows = static_cast<int>(src.shape()[1]);
+ const auto depth = static_cast<int>(src.shape()[2]);
+ const int upper_dims = src.shape().total_size() / (cols * rows * depth);
+
+ for(int r = 0; r < upper_dims; ++r)
+ {
+ for(int i = 0; i < depth; ++i)
+ {
+ for(int k = 0; k < rows; ++k)
+ {
+ for(int l = 0; l < cols; ++l)
+ {
+ const int pos = l + k * cols + i * rows * cols + r * cols * rows * depth;
+ const float denominator = sqrt(var[i] + epsilon);
+ const float numerator = src[pos] - mean[i];
+ const float x_bar = numerator / denominator;
+ result[pos] = beta[i] + x_bar * gamma[i];
+ }
+ }
+ }
+ }
+ return result;
+}
+template SimpleTensor<float> batch_normalization_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &mean, const SimpleTensor<float> &var, const SimpleTensor<float> &beta,
+ const SimpleTensor<float> &gamma, float epsilon, int fixed_point_position);
+template SimpleTensor<int8_t> batch_normalization_layer(const SimpleTensor<int8_t> &src, const SimpleTensor<int8_t> &mean, const SimpleTensor<int8_t> &var, const SimpleTensor<int8_t> &beta,
+ const SimpleTensor<int8_t> &gamma, float epsilon, int fixed_point_position);
+template SimpleTensor<int16_t> batch_normalization_layer(const SimpleTensor<int16_t> &src, const SimpleTensor<int16_t> &mean, const SimpleTensor<int16_t> &var, const SimpleTensor<int16_t> &beta,
+ const SimpleTensor<int16_t> &gamma, float epsilon, int fixed_point_position);
+template SimpleTensor<half> batch_normalization_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &mean, const SimpleTensor<half> &var,
+ const SimpleTensor<half> &beta,
+ const SimpleTensor<half> &gamma, float epsilon, int fixed_point_position);
+
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute