| /* |
| * Copyright (c) 2017-2018 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/reference/UtilsQuantizedAsymm.h" |
| |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| |
| #include <numeric> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace reference |
| { |
| namespace |
| { |
| // Vector matrix multiply for floating point |
| template < typename T, typename TB, typename std::enable_if < is_floating_point<T>::value &&is_floating_point<TB>::value, int >::type = 0 > |
| void vector_matrix_multiply(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &dst, int offset_src, int offset_dst, int cols_weights, |
| int rows_weights) |
| { |
| const T *src_ptr = src.data() + offset_src; |
| const T *weights_ptr = weights.data(); |
| const TB *bias_ptr = bias.data(); |
| T *dst_ptr = dst.data() + offset_dst; |
| |
| for(int y = 0; y < rows_weights; ++y) |
| { |
| dst_ptr[y] = std::inner_product(src_ptr, src_ptr + cols_weights, weights_ptr, static_cast<T>(0)) + bias_ptr[y]; |
| weights_ptr += cols_weights; |
| } |
| } |
| |
| // Vector matrix multiply for quantized type |
| template < typename T, typename TB, typename std::enable_if < std::is_same<T, uint8_t>::value &&std::is_same<TB, int32_t>::value, int >::type = 0 > |
| void vector_matrix_multiply(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &dst, int offset_src, int offset_dst, |
| int cols_weights, int rows_weights) |
| { |
| const T *src_ptr = src.data() + offset_src; |
| const T *weights_ptr = weights.data(); |
| const TB *bias_ptr = bias.data(); |
| T *dst_ptr = dst.data() + offset_dst; |
| |
| const int input_offset = -src.quantization_info().offset; |
| const float input_scale = src.quantization_info().scale; |
| const int weights_offset = -weights.quantization_info().offset; |
| const float weights_scale = weights.quantization_info().scale; |
| const int output_offset = dst.quantization_info().offset; |
| const float output_scale = dst.quantization_info().scale; |
| |
| int output_multiplier = 0; |
| int output_shift = 0; |
| const float multiplier = input_scale * weights_scale / output_scale; |
| arm_compute::quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); |
| |
| for(int y = 0; y < rows_weights; ++y) |
| { |
| // Reset accumulator |
| int32_t acc = 0; |
| |
| for(int x = 0; x < cols_weights; ++x) |
| { |
| acc += (src_ptr[x] + input_offset) * (weights_ptr[x] + weights_offset); |
| } |
| |
| // Accumulate the bias |
| acc += bias_ptr[y]; |
| |
| acc = asymm_rounding_divide_by_pow2(asymm_int_mult(acc, output_multiplier), output_shift); |
| acc += output_offset; |
| acc = utility::clamp<int32_t>(acc, 0, 255); |
| |
| // Store the result |
| dst_ptr[y] = static_cast<T>(acc); |
| |
| weights_ptr += cols_weights; |
| } |
| } |
| } // namespace |
| |
| template <typename T, typename TB> |
| SimpleTensor<T> fully_connected_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const TensorShape &dst_shape) |
| { |
| // Create reference |
| SimpleTensor<T> dst{ TensorShape{ dst_shape }, src.data_type(), 1, src.quantization_info() }; |
| |
| // 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) |
| { |
| const int offset_in = k * cols_weights; |
| const int offset_out = k * rows_weights; |
| |
| vector_matrix_multiply<T>(src, |
| weights, |
| bias, |
| dst, |
| offset_in, |
| offset_out, |
| cols_weights, |
| rows_weights); |
| } |
| |
| 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<uint8_t> fully_connected_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, const TensorShape &dst_shape); |
| } // namespace reference |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |