COMPMID-415: Move FullyConnectedLayer to new validation
Change-Id: I7f60d6fb484d3962b88874e1531cec734c11e416
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/83556
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/tests/validation_new/CPP/FullyConnectedLayer.cpp b/tests/validation_new/CPP/FullyConnectedLayer.cpp
new file mode 100644
index 0000000..7852dab
--- /dev/null
+++ b/tests/validation_new/CPP/FullyConnectedLayer.cpp
@@ -0,0 +1,133 @@
+/*
+ * 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 "FullyConnectedLayer.h"
+
+#include "tests/validation_new/FixedPoint.h"
+#include "tests/validation_new/half.h"
+
+#include <numeric>
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+namespace
+{
+// Vector matrix multiply for floating point
+template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0>
+void vector_matrix_multiply(const T *src, const T *weights, const T *bias, T *dst, int cols_weights, int rows_weights, uint8_t fixed_point_position)
+{
+ ARM_COMPUTE_UNUSED(fixed_point_position);
+
+ for(int y = 0; y < rows_weights; ++y)
+ {
+ dst[y] = std::inner_product(src, src + cols_weights, weights, static_cast<T>(0)) + bias[y];
+ weights += cols_weights;
+ }
+}
+
+// Vector matrix multiply for fixed point type
+template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
+void vector_matrix_multiply(const T *src, const T *weights, const T *bias, T *dst, int cols_weights, int rows_weights, uint8_t fixed_point_position)
+{
+ using namespace fixed_point_arithmetic;
+ using promoted_type = fixed_point_arithmetic::traits::promote_t<T>;
+
+ for(int y = 0; y < rows_weights; ++y)
+ {
+ // Reset accumulator
+ fixed_point<promoted_type> acc(0, fixed_point_position);
+
+ for(int x = 0; x < cols_weights; ++x)
+ {
+ const fixed_point<promoted_type> i_value(src[x], fixed_point_position, true);
+ const fixed_point<promoted_type> w_value(weights[x], fixed_point_position, true);
+ acc = acc + i_value * w_value;
+ }
+
+ // Get the bias
+ const fixed_point<T> b(bias[y], fixed_point_position, true);
+
+ // Convert back and accumulate the bias
+ fixed_point<T> res(acc);
+ res = res + b;
+
+ // Store the result
+ dst[y] = res.raw();
+
+ weights += cols_weights;
+ }
+}
+} // namespace
+
+template <typename T>
+SimpleTensor<T> fully_connected_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, const TensorShape &dst_shape)
+{
+ // Create reference
+ SimpleTensor<T> dst{ TensorShape{ dst_shape }, src.data_type(), 1, src.fixed_point_position() };
+
+ // Sanity checks
+ const int num_batch_dimensions = std::max(0, static_cast<int>(dst_shape.num_dimensions()) - 1);
+ const int num_input_dimensions = src.shape().num_dimensions() - num_batch_dimensions;
+ const unsigned int linear_input_size = src.shape().total_size_lower(num_input_dimensions);
+
+ ARM_COMPUTE_UNUSED(num_batch_dimensions);
+ ARM_COMPUTE_UNUSED(num_input_dimensions);
+ ARM_COMPUTE_UNUSED(linear_input_size);
+ ARM_COMPUTE_ERROR_ON(weights.shape().x() != linear_input_size);
+ ARM_COMPUTE_ERROR_ON(weights.shape().y() != bias.shape().x());
+ ARM_COMPUTE_ERROR_ON(weights.shape().y() != dst.shape().x());
+
+ // Compute reference
+ const int cols_weights = weights.shape().x();
+ const int rows_weights = weights.shape().y();
+ const int num_batches = dst_shape.total_size_upper(1);
+
+ for(int k = 0; k < num_batches; ++k)
+ {
+ vector_matrix_multiply<T>(src.data() + k * cols_weights,
+ weights.data(),
+ bias.data(),
+ dst.data() + k * rows_weights,
+ cols_weights,
+ rows_weights,
+ src.fixed_point_position());
+ }
+
+ return dst;
+}
+
+template SimpleTensor<float> fully_connected_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &dst_shape);
+template SimpleTensor<half_float::half> fully_connected_layer(const SimpleTensor<half_float::half> &src, const SimpleTensor<half_float::half> &weights, const SimpleTensor<half_float::half> &bias,
+ const TensorShape &dst_shape);
+template SimpleTensor<qint8_t> fully_connected_layer(const SimpleTensor<qint8_t> &src, const SimpleTensor<qint8_t> &weights, const SimpleTensor<qint8_t> &bias, const TensorShape &dst_shape);
+template SimpleTensor<qint16_t> fully_connected_layer(const SimpleTensor<qint16_t> &src, const SimpleTensor<qint16_t> &weights, const SimpleTensor<qint16_t> &bias, const TensorShape &dst_shape);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute