344 lines
14 KiB
C++
344 lines
14 KiB
C++
/*
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* Copyright (c) 2017-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "ReductionOperation.h"
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#include "tests/validation/Helpers.h"
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#include <algorithm>
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#include <cmath>
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace reference
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{
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namespace
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{
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template <typename T, typename OT>
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OT reduce_operation(const T *ptr, int reduce_elements, ReductionOperation op, int stride)
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{
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using type = typename std::remove_cv<OT>::type;
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T res;
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switch(op)
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{
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case ReductionOperation::PROD:
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{
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res = type(1);
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}
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break;
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case ReductionOperation::MIN:
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case ReductionOperation::MAX:
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{
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res = *ptr;
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}
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break;
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default:
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{
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res = type(0);
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}
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}
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if(std::is_integral<type>::value)
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{
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auto int_res = static_cast<int32_t>(res);
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for(int i = 0; i < reduce_elements; ++i)
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{
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auto elem = *(ptr + stride * i);
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switch(op)
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{
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case ReductionOperation::MIN:
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if(static_cast<T>(int_res) > elem)
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{
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int_res = elem;
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}
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break;
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case ReductionOperation::MAX:
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if(static_cast<T>(int_res) < elem)
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{
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int_res = elem;
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}
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break;
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case ReductionOperation::SUM_SQUARE:
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int_res += elem * elem;
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break;
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case ReductionOperation::MEAN_SUM:
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case ReductionOperation::SUM:
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int_res += elem;
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break;
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case ReductionOperation::PROD:
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int_res *= elem;
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break;
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default:
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ARM_COMPUTE_ERROR("Operation not supported");
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}
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}
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if(op == ReductionOperation::MEAN_SUM && reduce_elements > 0)
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{
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int_res /= reduce_elements;
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}
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res = static_cast<type>(int_res);
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}
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else
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{
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for(int i = 0; i < reduce_elements; ++i)
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{
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auto elem = *(ptr + stride * i);
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switch(op)
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{
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case ReductionOperation::MIN:
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if(res > elem)
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{
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res = elem;
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}
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break;
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case ReductionOperation::MAX:
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if(res < elem)
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{
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res = elem;
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}
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break;
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case ReductionOperation::SUM_SQUARE:
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res += elem * elem;
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break;
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case ReductionOperation::MEAN_SUM:
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case ReductionOperation::SUM:
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res += elem;
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break;
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case ReductionOperation::PROD:
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res *= elem;
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break;
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default:
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ARM_COMPUTE_ERROR("Operation not supported");
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}
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}
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if(op == ReductionOperation::MEAN_SUM && reduce_elements > 0)
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{
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res /= reduce_elements;
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}
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}
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return res;
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}
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template <typename T, typename OT>
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OT reduce_operation_arg_min_max(const T *ptr, int reduce_elements, ReductionOperation op, int stride)
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{
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uint32_t res = 0;
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for(int i = 0; i < reduce_elements; ++i)
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{
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auto elem = *(ptr + stride * i);
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switch(op)
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{
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case ReductionOperation::ARG_IDX_MIN:
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if(*(ptr + stride * res) > elem)
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{
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res = static_cast<uint32_t>(i);
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}
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break;
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case ReductionOperation::ARG_IDX_MAX:
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if(*(ptr + stride * res) < elem)
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{
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res = static_cast<uint32_t>(i);
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}
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break;
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default:
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ARM_COMPUTE_ERROR("Operation not supported");
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}
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}
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return static_cast<OT>(res);
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}
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} // namespace
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template <typename T, typename OT>
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SimpleTensor<OT> compute_reduction_operation(const SimpleTensor<T> &src, const TensorShape &dst_shape, unsigned int axis, ReductionOperation op)
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{
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// Create reference
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const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::ARG_IDX_MAX);
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DataType output_data_type = is_arg_min_max ? DataType::S32 : src.data_type();
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SimpleTensor<OT> dst{ dst_shape, output_data_type, 1, src.quantization_info() };
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const unsigned int src_width = src.shape().x();
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const unsigned int src_height = src.shape().y();
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const unsigned int src_depth = src.shape().z();
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const unsigned int src_batch = src.shape()[3];
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const int reduce_elems = src.shape()[axis];
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switch(axis)
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{
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case 0:
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{
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const unsigned int upper_dims = src.shape().total_size_upper(1);
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for(unsigned int du = 0; du < upper_dims; ++du)
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{
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const T *src_row_ptr = src.data() + du * reduce_elems;
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dst[du] = is_arg_min_max ?
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reduce_operation_arg_min_max<T, OT>(src_row_ptr, reduce_elems, op, 1) :
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reduce_operation<T, OT>(src_row_ptr, reduce_elems, op, 1);
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}
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}
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break;
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case 1:
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{
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const unsigned int upper_dims = src.shape().total_size_upper(2);
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for(unsigned int du = 0; du < upper_dims; ++du)
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{
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for(unsigned int x = 0; x < src_width; ++x)
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{
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const int in_offset = du * src_height * src_width + x;
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const int out_offset = du * src_width + x;
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const T *src_row_ptr = src.data() + in_offset;
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dst[out_offset] = is_arg_min_max ?
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reduce_operation_arg_min_max<T, OT>(src_row_ptr, reduce_elems, op, src_width) :
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reduce_operation<T, OT>(src_row_ptr, reduce_elems, op, src_width);
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}
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}
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}
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break;
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case 2:
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{
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const unsigned int upper_dims = src.shape().total_size_upper(3);
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for(unsigned int du = 0; du < upper_dims; ++du)
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{
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for(unsigned int x = 0; x < src_width; ++x)
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{
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for(unsigned int y = 0; y < src_height; ++y)
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{
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const int in_offset = du * src_depth * src_height * src_width + y * src_width + x;
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const int out_offset = du * src_width * src_height + y * src_width + x;
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const T *src_row_ptr = src.data() + in_offset;
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dst[out_offset] = is_arg_min_max ?
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reduce_operation_arg_min_max<T, OT>(src_row_ptr, reduce_elems, op, src_width * src_height) :
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reduce_operation<T, OT>(src_row_ptr, reduce_elems, op, src_width * src_height);
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}
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}
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}
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}
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break;
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case 3:
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{
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const unsigned int upper_dims = src.shape().total_size_upper(4);
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for(unsigned int du = 0; du < upper_dims; ++du)
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{
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for(unsigned int z = 0; z < src_depth; ++z)
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{
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for(unsigned int y = 0; y < src_height; ++y)
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{
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for(unsigned int x = 0; x < src_width; ++x)
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{
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const int in_offset = du * src_batch * src_depth * src_height * src_width + z * src_width * src_height + y * src_width + x;
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const int out_offset = du * src_depth * src_height * src_width + z * src_width * src_height + y * src_width + x;
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const T *src_row_ptr = src.data() + in_offset;
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dst[out_offset] = is_arg_min_max ?
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reduce_operation_arg_min_max<T, OT>(src_row_ptr, reduce_elems, op, src_width * src_height * src_depth) :
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reduce_operation<T, OT>(src_row_ptr, reduce_elems, op, src_width * src_height * src_depth);
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}
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}
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}
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}
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}
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break;
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default:
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ARM_COMPUTE_ERROR("Unsupported reduction axis");
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}
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return dst;
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}
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template <typename T, typename OT>
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SimpleTensor<OT> reduction_operation(const SimpleTensor<T> &src, const TensorShape &dst_shape, unsigned int axis, ReductionOperation op, QuantizationInfo quantization_info_output)
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{
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ARM_COMPUTE_UNUSED(quantization_info_output);
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return compute_reduction_operation<T, OT>(src, dst_shape, axis, op);
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}
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template <>
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SimpleTensor<uint8_t> reduction_operation(const SimpleTensor<uint8_t> &src, const TensorShape &dst_shape, unsigned int axis, ReductionOperation op, QuantizationInfo quantization_info_output)
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{
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if(src.data_type() == DataType::QASYMM8)
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{
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// If the operation is MEAN_SUM, we can directly use the uint8 implementation without taking into account scale and offset
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if(op == ReductionOperation::MEAN_SUM && src.quantization_info() == quantization_info_output)
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{
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return compute_reduction_operation<uint8_t, uint8_t>(src, dst_shape, axis, op);
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}
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else
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{
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SimpleTensor<float> src_f = convert_from_asymmetric(src);
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SimpleTensor<float> dst_f = reference::reduction_operation<float, float>(src_f, dst_shape, axis, op);
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return convert_to_asymmetric<uint8_t>(dst_f, quantization_info_output);
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}
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}
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else
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{
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return compute_reduction_operation<uint8_t, uint8_t>(src, dst_shape, axis, op);
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}
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}
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template <>
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SimpleTensor<int8_t> reduction_operation(const SimpleTensor<int8_t> &src, const TensorShape &dst_shape, unsigned int axis, ReductionOperation op, QuantizationInfo quantization_info_output)
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{
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if(src.data_type() == DataType::QASYMM8_SIGNED)
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{
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// If the operation is MEAN_SUM, we can directly use the int8 implementation without taking into account scale and offset
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if(op == ReductionOperation::MEAN_SUM && src.quantization_info() == quantization_info_output)
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{
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return compute_reduction_operation<int8_t, int8_t>(src, dst_shape, axis, op);
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}
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else
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{
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SimpleTensor<float> src_f = convert_from_asymmetric(src);
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SimpleTensor<float> dst_f = reference::reduction_operation<float, float>(src_f, dst_shape, axis, op);
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return convert_to_asymmetric<int8_t>(dst_f, quantization_info_output);
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}
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}
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else
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{
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return compute_reduction_operation<int8_t, int8_t>(src, dst_shape, axis, op);
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}
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}
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template SimpleTensor<float> reduction_operation(const SimpleTensor<float> &src, const TensorShape &dst_shape, unsigned int axis, ReductionOperation op,
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QuantizationInfo quantization_info_output = QuantizationInfo());
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template SimpleTensor<half> reduction_operation(const SimpleTensor<half> &src, const TensorShape &dst_shape, unsigned int axis, ReductionOperation op,
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QuantizationInfo quantization_info_output = QuantizationInfo());
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template SimpleTensor<int32_t> reduction_operation(const SimpleTensor<float> &src, const TensorShape &dst_shape, unsigned int axis, ReductionOperation op,
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QuantizationInfo quantization_info_output = QuantizationInfo());
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template SimpleTensor<int32_t> reduction_operation(const SimpleTensor<int32_t> &src, const TensorShape &dst_shape, unsigned int axis, ReductionOperation op,
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QuantizationInfo quantization_info_output = QuantizationInfo());
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template SimpleTensor<int32_t> reduction_operation(const SimpleTensor<half> &src, const TensorShape &dst_shape, unsigned int axis, ReductionOperation op,
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QuantizationInfo quantization_info_output = QuantizationInfo());
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template SimpleTensor<int32_t> reduction_operation(const SimpleTensor<uint8_t> &src, const TensorShape &dst_shape, unsigned int axis, ReductionOperation op,
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QuantizationInfo quantization_info_output = QuantizationInfo());
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template SimpleTensor<int32_t> reduction_operation(const SimpleTensor<int8_t> &src, const TensorShape &dst_shape, unsigned int axis, ReductionOperation op,
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QuantizationInfo quantization_info_output = QuantizationInfo());
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} // namespace reference
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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