| /* |
| * 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 "ReductionOperation.h" |
| |
| #include "tests/validation/Helpers.h" |
| |
| #include <algorithm> |
| #include <cmath> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace reference |
| { |
| namespace |
| { |
| template <typename T> |
| struct square |
| { |
| T operator()(const T &lhs, const T &rhs) const |
| { |
| return (lhs + rhs * rhs); |
| } |
| }; |
| |
| template <typename T> |
| struct sum |
| { |
| T operator()(const T &lhs, const T &rhs) const |
| { |
| return (lhs + rhs); |
| } |
| }; |
| |
| template <typename T> |
| T reduce_operation(T *ptr, int reduce_elements, ReductionOperation op) |
| { |
| switch(op) |
| { |
| case ReductionOperation::SUM_SQUARE: |
| return std::accumulate(ptr, ptr + reduce_elements, static_cast<T>(0), square<T>()); |
| case ReductionOperation::SUM: |
| case ReductionOperation::MEAN_SUM: |
| return std::accumulate(ptr, ptr + reduce_elements, static_cast<T>(0), sum<T>()); |
| default: |
| ARM_COMPUTE_ERROR("Unsupported reduction operation"); |
| } |
| } |
| } // namespace |
| |
| template <typename T> |
| SimpleTensor<T> reduction_operation(const SimpleTensor<T> &src, const TensorShape &dst_shape, unsigned int axis, ReductionOperation op) |
| { |
| // Create reference |
| SimpleTensor<T> dst{ dst_shape, src.data_type(), 1, src.quantization_info() }; |
| const unsigned int src_width = src.shape().x(); |
| const unsigned int src_height = src.shape().y(); |
| const unsigned int src_depth = src.shape().z(); |
| const unsigned int src_batch = src.shape()[3]; |
| const bool mean = op == ReductionOperation::MEAN_SUM; |
| |
| switch(axis) |
| { |
| case 0: |
| { |
| const int reduce_elems = src.shape()[axis]; |
| const unsigned int upper_dims = src.shape().total_size_upper(1); |
| for(unsigned int du = 0; du < upper_dims; ++du) |
| { |
| if(std::is_integral<T>::value) |
| { |
| uint32_t res = 0; |
| for(unsigned int x = 0; x < src_width; ++x) |
| { |
| res += static_cast<uint32_t>(src[du * src_width + x]); |
| } |
| if(mean && src_width > 0) |
| { |
| res /= src_width; |
| } |
| dst[du] = saturate_cast<uint8_t>(res); |
| } |
| else |
| { |
| const T *src_row_ptr = src.data() + du * reduce_elems; |
| |
| auto res = reduce_operation(src_row_ptr, reduce_elems, op); |
| if(mean && src_width > 0) |
| { |
| res /= src_width; |
| } |
| dst[du] = res; |
| } |
| } |
| } |
| break; |
| case 1: |
| { |
| const unsigned int upper_dims = src.shape().total_size_upper(2); |
| for(unsigned int du = 0; du < upper_dims; ++du) |
| { |
| for(unsigned int x = 0; x < src_width; ++x) |
| { |
| if(std::is_integral<T>::value) |
| { |
| uint32_t res = 0; |
| for(unsigned int y = 0; y < src_height; ++y) |
| { |
| res += static_cast<uint32_t>(src[du * src_height * src_width + y * src_width + x]); |
| } |
| if(mean && src_height > 0) |
| { |
| res /= src_height; |
| } |
| dst[du * src_width + x] = saturate_cast<uint8_t>(res); |
| } |
| else |
| { |
| auto res = T(0); |
| for(unsigned int y = 0; y < src_height; ++y) |
| { |
| res += src[du * src_height * src_width + y * src_width + x]; |
| } |
| if(mean && src_height > 0) |
| { |
| res /= src_height; |
| } |
| dst[du * src_width + x] = res; |
| } |
| } |
| } |
| } |
| break; |
| case 2: |
| { |
| const unsigned int upper_dims = src.shape().total_size_upper(3); |
| for(unsigned int du = 0; du < upper_dims; ++du) |
| { |
| for(unsigned int x = 0; x < src_width; ++x) |
| { |
| for(unsigned int y = 0; y < src_height; ++y) |
| { |
| if(std::is_integral<T>::value) |
| { |
| uint32_t res = T(0); |
| for(unsigned int z = 0; z < src_depth; ++z) |
| { |
| res += static_cast<uint32_t>(src[du * src_depth * src_height * src_width + z * src_height * src_width + y * src_width + x]); |
| } |
| if(mean && src_depth > 0) |
| { |
| res /= src_depth; |
| } |
| dst[du * src_width * src_height + y * src_width + x] = saturate_cast<uint8_t>(res); |
| } |
| else |
| { |
| auto res = T(0); |
| for(unsigned int z = 0; z < src_depth; ++z) |
| { |
| res += src[du * src_depth * src_height * src_width + z * src_height * src_width + y * src_width + x]; |
| } |
| if(mean && src_depth > 0) |
| { |
| res /= src_depth; |
| } |
| dst[du * src_width * src_height + y * src_width + x] = res; |
| } |
| } |
| } |
| } |
| } |
| break; |
| case 3: |
| { |
| const unsigned int upper_dims = src.shape().total_size_upper(4); |
| for(unsigned int du = 0; du < upper_dims; ++du) |
| { |
| for(unsigned int z = 0; z < src_depth; ++z) |
| { |
| for(unsigned int y = 0; y < src_height; ++y) |
| { |
| for(unsigned int x = 0; x < src_width; ++x) |
| { |
| if(std::is_integral<T>::value) |
| { |
| uint32_t res = 0; |
| for(unsigned int w = 0; w < src_batch; ++w) |
| { |
| res += static_cast<uint32_t>(src[du * src_batch * src_depth * src_height * src_width + w * src_width * src_height * src_depth + z * src_width * src_height + y * src_width + x]); |
| } |
| if(mean && src_batch > 0) |
| { |
| res /= src_batch; |
| } |
| |
| dst[du * src_depth * src_height * src_width + z * src_width * src_height + y * src_width + x] = saturate_cast<uint8_t>(res); |
| } |
| else |
| { |
| auto res = T(0); |
| for(unsigned int w = 0; w < src_batch; ++w) |
| { |
| res += src[du * src_batch * src_depth * src_height * src_width + w * src_width * src_height * src_depth + z * src_width * src_height + y * src_width + x]; |
| } |
| if(mean && src_batch > 0) |
| { |
| res /= src_batch; |
| } |
| |
| dst[du * src_depth * src_height * src_width + z * src_width * src_height + y * src_width + x] = res; |
| } |
| } |
| } |
| } |
| } |
| } |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Unsupported reduction axis"); |
| } |
| |
| return dst; |
| } |
| |
| template SimpleTensor<float> reduction_operation(const SimpleTensor<float> &src, const TensorShape &dst_shape, unsigned int axis, ReductionOperation op); |
| template SimpleTensor<half> reduction_operation(const SimpleTensor<half> &src, const TensorShape &dst_shape, unsigned int axis, ReductionOperation op); |
| template SimpleTensor<uint8_t> reduction_operation(const SimpleTensor<uint8_t> &src, const TensorShape &dst_shape, unsigned int axis, ReductionOperation op); |
| } // namespace reference |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |