| /* |
| * Copyright (c) 2017-2020 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 "SoftmaxLayer.h" |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/Types.h" |
| #include "utils/TypePrinter.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace reference |
| { |
| template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type> |
| SimpleTensor<T> softmax_layer_generic(const SimpleTensor<T> &src, float beta, int32_t axis, bool is_log) |
| { |
| // Create reference |
| SimpleTensor<T> dst{ src.shape(), src.data_type(), 1 }; |
| |
| const int32_t n_dims = static_cast<int32_t>(src.shape().num_dimensions()); |
| ARM_COMPUTE_ERROR_ON(axis < -n_dims || axis >= n_dims); |
| |
| const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, n_dims)); |
| Window window; |
| window.use_tensor_dimensions(src.shape()); |
| const unsigned int axis_dimension = src.shape()[actual_axis]; |
| window.set(actual_axis, Window::Dimension(0, 1, 1)); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| // Find max along axis |
| Coordinates offset(id); |
| offset.set(actual_axis, 0); |
| T max = *reinterpret_cast<const T *>(src(offset)); |
| for(unsigned int axis_id = 1; axis_id < axis_dimension; ++axis_id) |
| { |
| offset.set(actual_axis, axis_id); |
| const T val = *reinterpret_cast<const T *>(src(offset)); |
| if(val > max) |
| { |
| max = val; |
| } |
| } |
| |
| // Regularize |
| T sum(0.f); |
| for(unsigned int axis_id = 0; axis_id < axis_dimension; ++axis_id) |
| { |
| offset.set(actual_axis, axis_id); |
| const T val = *reinterpret_cast<const T *>(src(offset)); |
| T res{ (val - max) *beta }; |
| if(is_log) |
| { |
| sum += std::exp(res); |
| } |
| else |
| { |
| res = std::exp(res); |
| sum += res; |
| } |
| *reinterpret_cast<T *>(dst(offset)) = res; |
| } |
| |
| // Normalize |
| for(unsigned int axis_id = 0; axis_id < axis_dimension; ++axis_id) |
| { |
| offset.set(actual_axis, axis_id); |
| const T val = *reinterpret_cast<const T *>(dst(offset)); |
| if(is_log) |
| { |
| *reinterpret_cast<T *>(dst(offset)) = val - static_cast<T>(std::log(sum)); |
| } |
| else |
| { |
| *reinterpret_cast<T *>(dst(offset)) = val / sum; |
| } |
| } |
| }); |
| return dst; |
| } |
| |
| template SimpleTensor<float> softmax_layer_generic(const SimpleTensor<float> &src, float beta, int32_t axis, bool is_log); |
| template SimpleTensor<half> softmax_layer_generic(const SimpleTensor<half> &src, float beta, int32_t axis, bool is_log); |
| |
| template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type> |
| SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta, int32_t axis, bool is_log) |
| { |
| return softmax_layer_generic<T>(src, beta, axis, is_log); |
| } |
| |
| template < typename T, typename std::enable_if < std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int >::type > |
| SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta, int32_t axis, bool is_log) |
| { |
| const QuantizationInfo output_quantization_info = arm_compute::get_softmax_output_quantization_info(src.data_type(), is_log); |
| |
| SimpleTensor<float> src_tmp = convert_from_asymmetric(src); |
| SimpleTensor<float> dst_tmp = softmax_layer<float>(src_tmp, beta, axis, is_log); |
| SimpleTensor<T> dst = convert_to_asymmetric<T>(dst_tmp, output_quantization_info); |
| return dst; |
| } |
| |
| template SimpleTensor<float> softmax_layer(const SimpleTensor<float> &src, float beta, int32_t axis, bool is_log); |
| template SimpleTensor<half> softmax_layer(const SimpleTensor<half> &src, float beta, int32_t axis, bool is_log); |
| template SimpleTensor<uint8_t> softmax_layer(const SimpleTensor<uint8_t> &src, float beta, int32_t axis, bool is_log); |
| template SimpleTensor<int8_t> softmax_layer(const SimpleTensor<int8_t> &src, float beta, int32_t axis, bool is_log); |
| |
| } // namespace reference |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |