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Diffstat (limited to 'tests/validation/CPP/FullyConnectedLayer.cpp')
-rw-r--r-- | tests/validation/CPP/FullyConnectedLayer.cpp | 201 |
1 files changed, 0 insertions, 201 deletions
diff --git a/tests/validation/CPP/FullyConnectedLayer.cpp b/tests/validation/CPP/FullyConnectedLayer.cpp deleted file mode 100644 index 6b618a955c..0000000000 --- a/tests/validation/CPP/FullyConnectedLayer.cpp +++ /dev/null @@ -1,201 +0,0 @@ -/* - * 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/CPP/UtilsQuantizedAsymm.h" -#include "tests/validation/FixedPoint.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, uint8_t fixed_point_position) -{ - ARM_COMPUTE_UNUSED(fixed_point_position); - - 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 fixed point type -template < typename T, typename TB, typename std::enable_if < std::is_integral<T>::value &&std::is_integral<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, uint8_t fixed_point_position) -{ - 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; - - 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_ptr[x], fixed_point_position, true); - const fixed_point<promoted_type> w_value(weights_ptr[x], fixed_point_position, true); - acc = acc + i_value * w_value; - } - - // Get the bias - const fixed_point<T> b(bias_ptr[y], fixed_point_position, true); - - // Convert back and accumulate the bias - fixed_point<T> res(acc); - res = res + b; - - // Store the result - dst_ptr[y] = res.raw(); - - weights_ptr += cols_weights; - } -} - -// Vector matrix multiply for quantized type -template <> -void vector_matrix_multiply(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, SimpleTensor<uint8_t> &dst, int offset_src, int offset_dst, - int cols_weights, int rows_weights, uint8_t fixed_point_position) -{ - ARM_COMPUTE_UNUSED(fixed_point_position); - - const uint8_t *src_ptr = src.data() + offset_src; - const uint8_t *weights_ptr = weights.data(); - const int32_t *bias_ptr = bias.data(); - uint8_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 = clamp<int32_t>(acc, 0, 255); - - // Store the result - dst_ptr[y] = static_cast<uint8_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.fixed_point_position(), 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, - 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); -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 |