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Diffstat (limited to 'tests/validation_new/CPP/FullyConnectedLayer.cpp')
-rw-r--r-- | tests/validation_new/CPP/FullyConnectedLayer.cpp | 133 |
1 files changed, 133 insertions, 0 deletions
diff --git a/tests/validation_new/CPP/FullyConnectedLayer.cpp b/tests/validation_new/CPP/FullyConnectedLayer.cpp new file mode 100644 index 0000000000..7852dab27b --- /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 |