| /* |
| * 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 "arm_compute/core/Types.h" |
| #include "tests/validation/FixedPoint.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> fully_connected_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &weights, const SimpleTensor<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 |