1482 lines
47 KiB
C++
1482 lines
47 KiB
C++
/*
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* Copyright (c) 2016-2020 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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#ifndef ARM_COMPUTE_UTILS_H
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#define ARM_COMPUTE_UTILS_H
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#include "arm_compute/core/Error.h"
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#include "arm_compute/core/PixelValue.h"
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#include "arm_compute/core/Rounding.h"
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#include "arm_compute/core/Types.h"
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#include "arm_compute/core/Version.h"
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#include <algorithm>
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#include <cstdint>
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#include <cstdlib>
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#include <iomanip>
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#include <numeric>
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#include <sstream>
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#include <string>
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#include <type_traits>
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#include <unordered_map>
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#include <utility>
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#include <vector>
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namespace arm_compute
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{
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class ITensor;
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class ITensorInfo;
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/** Calculate the rounded up quotient of val / m.
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*
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* @param[in] val Value to divide and round up.
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* @param[in] m Value to divide by.
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*
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* @return the result.
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*/
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template <typename S, typename T>
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constexpr auto DIV_CEIL(S val, T m) -> decltype((val + m - 1) / m)
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{
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return (val + m - 1) / m;
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}
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/** Computes the smallest number larger or equal to value that is a multiple of divisor.
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*
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* @param[in] value Lower bound value
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* @param[in] divisor Value to compute multiple of.
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*
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* @return the result.
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*/
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template <typename S, typename T>
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inline auto ceil_to_multiple(S value, T divisor) -> decltype(((value + divisor - 1) / divisor) * divisor)
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{
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ARM_COMPUTE_ERROR_ON(value < 0 || divisor <= 0);
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return DIV_CEIL(value, divisor) * divisor;
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}
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/** Computes the largest number smaller or equal to value that is a multiple of divisor.
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*
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* @param[in] value Upper bound value
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* @param[in] divisor Value to compute multiple of.
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*
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* @return the result.
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*/
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template <typename S, typename T>
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inline auto floor_to_multiple(S value, T divisor) -> decltype((value / divisor) * divisor)
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{
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ARM_COMPUTE_ERROR_ON(value < 0 || divisor <= 0);
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return (value / divisor) * divisor;
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}
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/** Load an entire file in memory
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*
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* @param[in] filename Name of the file to read.
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* @param[in] binary Is it a binary file ?
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*
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* @return The content of the file.
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*/
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std::string read_file(const std::string &filename, bool binary);
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/** The size in bytes of the data type
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*
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* @param[in] data_type Input data type
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*
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* @return The size in bytes of the data type
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*/
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inline size_t data_size_from_type(DataType data_type)
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{
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switch(data_type)
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{
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case DataType::U8:
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case DataType::S8:
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case DataType::QSYMM8:
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case DataType::QASYMM8:
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case DataType::QASYMM8_SIGNED:
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case DataType::QSYMM8_PER_CHANNEL:
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return 1;
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case DataType::U16:
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case DataType::S16:
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case DataType::QSYMM16:
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case DataType::QASYMM16:
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case DataType::BFLOAT16:
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case DataType::F16:
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return 2;
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case DataType::F32:
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case DataType::U32:
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case DataType::S32:
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return 4;
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case DataType::F64:
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case DataType::U64:
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case DataType::S64:
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return 8;
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case DataType::SIZET:
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return sizeof(size_t);
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default:
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ARM_COMPUTE_ERROR("Invalid data type");
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return 0;
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}
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}
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/** The size in bytes of the pixel format
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*
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* @param[in] format Input format
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*
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* @return The size in bytes of the pixel format
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*/
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inline size_t pixel_size_from_format(Format format)
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{
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switch(format)
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{
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case Format::U8:
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return 1;
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case Format::U16:
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case Format::S16:
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case Format::BFLOAT16:
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case Format::F16:
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case Format::UV88:
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case Format::YUYV422:
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case Format::UYVY422:
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return 2;
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case Format::RGB888:
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return 3;
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case Format::RGBA8888:
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return 4;
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case Format::U32:
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case Format::S32:
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case Format::F32:
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return 4;
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//Doesn't make sense for planar formats:
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case Format::NV12:
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case Format::NV21:
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case Format::IYUV:
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case Format::YUV444:
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default:
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ARM_COMPUTE_ERROR("Undefined pixel size for given format");
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return 0;
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}
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}
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/** The size in bytes of the data type
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*
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* @param[in] dt Input data type
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*
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* @return The size in bytes of the data type
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*/
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inline size_t element_size_from_data_type(DataType dt)
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{
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switch(dt)
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{
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case DataType::S8:
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case DataType::U8:
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case DataType::QSYMM8:
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case DataType::QASYMM8:
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case DataType::QASYMM8_SIGNED:
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case DataType::QSYMM8_PER_CHANNEL:
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return 1;
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case DataType::U16:
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case DataType::S16:
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case DataType::QSYMM16:
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case DataType::QASYMM16:
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case DataType::BFLOAT16:
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case DataType::F16:
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return 2;
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case DataType::U32:
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case DataType::S32:
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case DataType::F32:
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return 4;
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default:
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ARM_COMPUTE_ERROR("Undefined element size for given data type");
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return 0;
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}
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}
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/** Return the data type used by a given single-planar pixel format
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*
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* @param[in] format Input format
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*
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* @return The size in bytes of the pixel format
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*/
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inline DataType data_type_from_format(Format format)
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{
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switch(format)
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{
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case Format::U8:
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case Format::UV88:
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case Format::RGB888:
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case Format::RGBA8888:
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case Format::YUYV422:
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case Format::UYVY422:
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return DataType::U8;
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case Format::U16:
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return DataType::U16;
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case Format::S16:
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return DataType::S16;
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case Format::U32:
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return DataType::U32;
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case Format::S32:
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return DataType::S32;
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case Format::BFLOAT16:
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return DataType::BFLOAT16;
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case Format::F16:
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return DataType::F16;
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case Format::F32:
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return DataType::F32;
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//Doesn't make sense for planar formats:
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case Format::NV12:
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case Format::NV21:
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case Format::IYUV:
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case Format::YUV444:
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default:
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ARM_COMPUTE_ERROR("Not supported data_type for given format");
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return DataType::UNKNOWN;
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}
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}
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/** Return the plane index of a given channel given an input format.
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*
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* @param[in] format Input format
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* @param[in] channel Input channel
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*
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* @return The plane index of the specific channel of the specific format
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*/
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inline int plane_idx_from_channel(Format format, Channel channel)
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{
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switch(format)
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{
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// Single planar formats have a single plane
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case Format::U8:
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case Format::U16:
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case Format::S16:
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case Format::U32:
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case Format::S32:
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case Format::BFLOAT16:
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case Format::F16:
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case Format::F32:
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case Format::UV88:
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case Format::RGB888:
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case Format::RGBA8888:
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case Format::YUYV422:
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case Format::UYVY422:
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return 0;
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// Multi planar formats
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case Format::NV12:
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case Format::NV21:
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{
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// Channel U and V share the same plane of format UV88
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switch(channel)
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{
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case Channel::Y:
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return 0;
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case Channel::U:
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case Channel::V:
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return 1;
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default:
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ARM_COMPUTE_ERROR("Not supported channel");
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return 0;
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}
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}
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case Format::IYUV:
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case Format::YUV444:
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{
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switch(channel)
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{
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case Channel::Y:
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return 0;
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case Channel::U:
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return 1;
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case Channel::V:
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return 2;
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default:
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ARM_COMPUTE_ERROR("Not supported channel");
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return 0;
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}
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}
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default:
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ARM_COMPUTE_ERROR("Not supported format");
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return 0;
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}
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}
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/** Return the channel index of a given channel given an input format.
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*
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* @param[in] format Input format
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* @param[in] channel Input channel
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*
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* @return The channel index of the specific channel of the specific format
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*/
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inline int channel_idx_from_format(Format format, Channel channel)
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{
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switch(format)
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{
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case Format::RGB888:
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{
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switch(channel)
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{
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case Channel::R:
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return 0;
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case Channel::G:
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return 1;
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case Channel::B:
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return 2;
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default:
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ARM_COMPUTE_ERROR("Not supported channel");
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return 0;
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}
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}
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case Format::RGBA8888:
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{
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switch(channel)
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{
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case Channel::R:
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return 0;
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case Channel::G:
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return 1;
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case Channel::B:
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return 2;
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case Channel::A:
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return 3;
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default:
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ARM_COMPUTE_ERROR("Not supported channel");
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return 0;
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}
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}
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case Format::YUYV422:
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{
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switch(channel)
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{
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case Channel::Y:
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return 0;
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case Channel::U:
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return 1;
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case Channel::V:
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return 3;
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default:
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ARM_COMPUTE_ERROR("Not supported channel");
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return 0;
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}
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}
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case Format::UYVY422:
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{
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switch(channel)
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{
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case Channel::Y:
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return 1;
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case Channel::U:
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return 0;
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case Channel::V:
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return 2;
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default:
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ARM_COMPUTE_ERROR("Not supported channel");
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return 0;
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}
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}
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case Format::NV12:
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{
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switch(channel)
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{
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case Channel::Y:
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return 0;
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case Channel::U:
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return 0;
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case Channel::V:
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return 1;
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default:
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ARM_COMPUTE_ERROR("Not supported channel");
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return 0;
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}
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}
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case Format::NV21:
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{
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switch(channel)
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{
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case Channel::Y:
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return 0;
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case Channel::U:
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return 1;
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case Channel::V:
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return 0;
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default:
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ARM_COMPUTE_ERROR("Not supported channel");
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return 0;
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}
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}
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case Format::YUV444:
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case Format::IYUV:
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{
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switch(channel)
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{
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case Channel::Y:
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return 0;
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case Channel::U:
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return 0;
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case Channel::V:
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return 0;
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default:
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ARM_COMPUTE_ERROR("Not supported channel");
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return 0;
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}
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}
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default:
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ARM_COMPUTE_ERROR("Not supported format");
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return 0;
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}
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}
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/** Return the number of planes for a given format
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*
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* @param[in] format Input format
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*
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* @return The number of planes for a given image format.
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*/
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inline size_t num_planes_from_format(Format format)
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{
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switch(format)
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{
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case Format::U8:
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case Format::S16:
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case Format::U16:
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case Format::S32:
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case Format::U32:
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case Format::BFLOAT16:
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case Format::F16:
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case Format::F32:
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case Format::RGB888:
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case Format::RGBA8888:
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case Format::YUYV422:
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case Format::UYVY422:
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return 1;
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case Format::NV12:
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case Format::NV21:
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return 2;
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case Format::IYUV:
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case Format::YUV444:
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return 3;
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default:
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ARM_COMPUTE_ERROR("Not supported format");
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return 0;
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}
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}
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/** Return the number of channels for a given single-planar pixel format
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*
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* @param[in] format Input format
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*
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* @return The number of channels for a given image format.
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*/
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inline size_t num_channels_from_format(Format format)
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{
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switch(format)
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{
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case Format::U8:
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case Format::U16:
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case Format::S16:
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case Format::U32:
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case Format::S32:
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case Format::BFLOAT16:
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case Format::F16:
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case Format::F32:
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return 1;
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// Because the U and V channels are subsampled
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// these formats appear like having only 2 channels:
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case Format::YUYV422:
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case Format::UYVY422:
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return 2;
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case Format::UV88:
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return 2;
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case Format::RGB888:
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return 3;
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case Format::RGBA8888:
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return 4;
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//Doesn't make sense for planar formats:
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case Format::NV12:
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case Format::NV21:
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case Format::IYUV:
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case Format::YUV444:
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default:
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return 0;
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}
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}
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/** Return the promoted data type of a given data type.
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*
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* @note If promoted data type is not supported an error will be thrown
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*
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* @param[in] dt Data type to get the promoted type of.
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*
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* @return Promoted data type
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*/
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inline DataType get_promoted_data_type(DataType dt)
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{
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switch(dt)
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{
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case DataType::U8:
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return DataType::U16;
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case DataType::S8:
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return DataType::S16;
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case DataType::U16:
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return DataType::U32;
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case DataType::S16:
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return DataType::S32;
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case DataType::QSYMM8:
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case DataType::QASYMM8:
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case DataType::QASYMM8_SIGNED:
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case DataType::QSYMM8_PER_CHANNEL:
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case DataType::QSYMM16:
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case DataType::QASYMM16:
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case DataType::BFLOAT16:
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case DataType::F16:
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case DataType::U32:
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case DataType::S32:
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case DataType::F32:
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ARM_COMPUTE_ERROR("Unsupported data type promotions!");
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default:
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ARM_COMPUTE_ERROR("Undefined data type!");
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}
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return DataType::UNKNOWN;
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}
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|
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/** Compute the mininum and maximum values a data type can take
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|
*
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|
* @param[in] dt Data type to get the min/max bounds of
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*
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|
* @return A tuple (min,max) with the minimum and maximum values respectively wrapped in PixelValue.
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|
*/
|
|
inline std::tuple<PixelValue, PixelValue> get_min_max(DataType dt)
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|
{
|
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PixelValue min{};
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|
PixelValue max{};
|
|
switch(dt)
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|
{
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|
case DataType::U8:
|
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case DataType::QASYMM8:
|
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{
|
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min = PixelValue(static_cast<int32_t>(std::numeric_limits<uint8_t>::lowest()));
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max = PixelValue(static_cast<int32_t>(std::numeric_limits<uint8_t>::max()));
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break;
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}
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|
case DataType::S8:
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|
case DataType::QSYMM8:
|
|
case DataType::QASYMM8_SIGNED:
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|
case DataType::QSYMM8_PER_CHANNEL:
|
|
{
|
|
min = PixelValue(static_cast<int32_t>(std::numeric_limits<int8_t>::lowest()));
|
|
max = PixelValue(static_cast<int32_t>(std::numeric_limits<int8_t>::max()));
|
|
break;
|
|
}
|
|
case DataType::U16:
|
|
case DataType::QASYMM16:
|
|
{
|
|
min = PixelValue(static_cast<int32_t>(std::numeric_limits<uint16_t>::lowest()));
|
|
max = PixelValue(static_cast<int32_t>(std::numeric_limits<uint16_t>::max()));
|
|
break;
|
|
}
|
|
case DataType::S16:
|
|
case DataType::QSYMM16:
|
|
{
|
|
min = PixelValue(static_cast<int32_t>(std::numeric_limits<int16_t>::lowest()));
|
|
max = PixelValue(static_cast<int32_t>(std::numeric_limits<int16_t>::max()));
|
|
break;
|
|
}
|
|
case DataType::U32:
|
|
{
|
|
min = PixelValue(std::numeric_limits<uint32_t>::lowest());
|
|
max = PixelValue(std::numeric_limits<uint32_t>::max());
|
|
break;
|
|
}
|
|
case DataType::S32:
|
|
{
|
|
min = PixelValue(std::numeric_limits<int32_t>::lowest());
|
|
max = PixelValue(std::numeric_limits<int32_t>::max());
|
|
break;
|
|
}
|
|
case DataType::BFLOAT16:
|
|
{
|
|
min = PixelValue(bfloat16::lowest());
|
|
max = PixelValue(bfloat16::max());
|
|
break;
|
|
}
|
|
case DataType::F16:
|
|
{
|
|
min = PixelValue(std::numeric_limits<half>::lowest());
|
|
max = PixelValue(std::numeric_limits<half>::max());
|
|
break;
|
|
}
|
|
case DataType::F32:
|
|
{
|
|
min = PixelValue(std::numeric_limits<float>::lowest());
|
|
max = PixelValue(std::numeric_limits<float>::max());
|
|
break;
|
|
}
|
|
default:
|
|
ARM_COMPUTE_ERROR("Undefined data type!");
|
|
}
|
|
return std::make_tuple(min, max);
|
|
}
|
|
|
|
/** Return true if the given format has horizontal subsampling.
|
|
*
|
|
* @param[in] format Format to determine subsampling.
|
|
*
|
|
* @return True if the format can be subsampled horizontaly.
|
|
*/
|
|
inline bool has_format_horizontal_subsampling(Format format)
|
|
{
|
|
return (format == Format::YUYV422 || format == Format::UYVY422 || format == Format::NV12 || format == Format::NV21 || format == Format::IYUV || format == Format::UV88) ? true : false;
|
|
}
|
|
|
|
/** Return true if the given format has vertical subsampling.
|
|
*
|
|
* @param[in] format Format to determine subsampling.
|
|
*
|
|
* @return True if the format can be subsampled verticaly.
|
|
*/
|
|
inline bool has_format_vertical_subsampling(Format format)
|
|
{
|
|
return (format == Format::NV12 || format == Format::NV21 || format == Format::IYUV || format == Format::UV88) ? true : false;
|
|
}
|
|
|
|
/** Separate a 2D convolution into two 1D convolutions
|
|
*
|
|
* @param[in] conv 2D convolution
|
|
* @param[out] conv_col 1D vertical convolution
|
|
* @param[out] conv_row 1D horizontal convolution
|
|
* @param[in] size Size of the 2D convolution
|
|
*
|
|
* @return true if the separation was successful
|
|
*/
|
|
inline bool separate_matrix(const int16_t *conv, int16_t *conv_col, int16_t *conv_row, uint8_t size)
|
|
{
|
|
int32_t min_col = -1;
|
|
int16_t min_col_val = -1;
|
|
|
|
for(int32_t i = 0; i < size; ++i)
|
|
{
|
|
if(conv[i] != 0 && (min_col < 0 || abs(min_col_val) > abs(conv[i])))
|
|
{
|
|
min_col = i;
|
|
min_col_val = conv[i];
|
|
}
|
|
}
|
|
|
|
if(min_col < 0)
|
|
{
|
|
return false;
|
|
}
|
|
|
|
for(uint32_t j = 0; j < size; ++j)
|
|
{
|
|
conv_col[j] = conv[min_col + j * size];
|
|
}
|
|
|
|
for(uint32_t i = 0; i < size; i++)
|
|
{
|
|
if(static_cast<int>(i) == min_col)
|
|
{
|
|
conv_row[i] = 1;
|
|
}
|
|
else
|
|
{
|
|
int16_t coeff = conv[i] / conv[min_col];
|
|
|
|
for(uint32_t j = 1; j < size; ++j)
|
|
{
|
|
if(conv[i + j * size] != (conv_col[j] * coeff))
|
|
{
|
|
return false;
|
|
}
|
|
}
|
|
|
|
conv_row[i] = coeff;
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
/** Calculate the scale of the given square matrix
|
|
*
|
|
* The scale is the absolute value of the sum of all the coefficients in the matrix.
|
|
*
|
|
* @note If the coefficients add up to 0 then the scale is set to 1.
|
|
*
|
|
* @param[in] matrix Matrix coefficients
|
|
* @param[in] matrix_size Number of elements per side of the square matrix. (Number of coefficients = matrix_size * matrix_size).
|
|
*
|
|
* @return The absolute value of the sum of the coefficients if they don't add up to 0, otherwise 1.
|
|
*/
|
|
inline uint32_t calculate_matrix_scale(const int16_t *matrix, unsigned int matrix_size)
|
|
{
|
|
const size_t size = matrix_size * matrix_size;
|
|
|
|
return std::max(1, std::abs(std::accumulate(matrix, matrix + size, 0)));
|
|
}
|
|
|
|
/** Adjust tensor shape size if width or height are odd for a given multi-planar format. No modification is done for other formats.
|
|
*
|
|
* @note Adding here a few links discussing the issue of odd size and sharing the same solution:
|
|
* <a href="https://android.googlesource.com/platform/frameworks/base/+/refs/heads/master/graphics/java/android/graphics/YuvImage.java">Android Source</a>
|
|
* <a href="https://groups.google.com/a/webmproject.org/forum/#!topic/webm-discuss/LaCKpqiDTXM">WebM</a>
|
|
* <a href="https://bugs.chromium.org/p/libyuv/issues/detail?id=198&can=1&q=odd%20width">libYUV</a>
|
|
* <a href="https://sourceforge.net/p/raw-yuvplayer/bugs/1/">YUVPlayer</a> *
|
|
*
|
|
* @param[in, out] shape Tensor shape of 2D size
|
|
* @param[in] format Format of the tensor
|
|
*
|
|
* @return The adjusted tensor shape.
|
|
*/
|
|
inline TensorShape adjust_odd_shape(const TensorShape &shape, Format format)
|
|
{
|
|
TensorShape output{ shape };
|
|
|
|
// Force width to be even for formats which require subsampling of the U and V channels
|
|
if(has_format_horizontal_subsampling(format))
|
|
{
|
|
output.set(0, (output.x() + 1) & ~1U);
|
|
}
|
|
|
|
// Force height to be even for formats which require subsampling of the U and V channels
|
|
if(has_format_vertical_subsampling(format))
|
|
{
|
|
output.set(1, (output.y() + 1) & ~1U);
|
|
}
|
|
|
|
return output;
|
|
}
|
|
|
|
/** Calculate subsampled shape for a given format and channel
|
|
*
|
|
* @param[in] shape Shape of the tensor to calculate the extracted channel.
|
|
* @param[in] format Format of the tensor.
|
|
* @param[in] channel Channel to create tensor shape to be extracted.
|
|
*
|
|
* @return The subsampled tensor shape.
|
|
*/
|
|
inline TensorShape calculate_subsampled_shape(const TensorShape &shape, Format format, Channel channel = Channel::UNKNOWN)
|
|
{
|
|
TensorShape output{ shape };
|
|
|
|
// Subsample shape only for U or V channel
|
|
if(Channel::U == channel || Channel::V == channel || Channel::UNKNOWN == channel)
|
|
{
|
|
// Subsample width for the tensor shape when channel is U or V
|
|
if(has_format_horizontal_subsampling(format))
|
|
{
|
|
output.set(0, output.x() / 2U);
|
|
}
|
|
|
|
// Subsample height for the tensor shape when channel is U or V
|
|
if(has_format_vertical_subsampling(format))
|
|
{
|
|
output.set(1, output.y() / 2U);
|
|
}
|
|
}
|
|
|
|
return output;
|
|
}
|
|
|
|
/** Calculate accurary required by the horizontal and vertical convolution computations
|
|
*
|
|
* @param[in] conv_col Pointer to the vertical vector of the separated convolution filter
|
|
* @param[in] conv_row Pointer to the horizontal vector of the convolution filter
|
|
* @param[in] size Number of elements per vector of the separated matrix
|
|
*
|
|
* @return The return type is a pair. The first element of the pair is the biggest data type needed for the first stage. The second
|
|
* element of the pair is the biggest data type needed for the second stage.
|
|
*/
|
|
inline std::pair<DataType, DataType> data_type_for_convolution(const int16_t *conv_col, const int16_t *conv_row, size_t size)
|
|
{
|
|
DataType first_stage = DataType::UNKNOWN;
|
|
DataType second_stage = DataType::UNKNOWN;
|
|
|
|
auto gez = [](const int16_t &v)
|
|
{
|
|
return v >= 0;
|
|
};
|
|
|
|
auto accu_neg = [](const int &first, const int &second)
|
|
{
|
|
return first + (second < 0 ? second : 0);
|
|
};
|
|
|
|
auto accu_pos = [](const int &first, const int &second)
|
|
{
|
|
return first + (second > 0 ? second : 0);
|
|
};
|
|
|
|
const bool only_positive_coefficients = std::all_of(conv_row, conv_row + size, gez) && std::all_of(conv_col, conv_col + size, gez);
|
|
|
|
if(only_positive_coefficients)
|
|
{
|
|
const int max_row_value = std::accumulate(conv_row, conv_row + size, 0) * UINT8_MAX;
|
|
const int max_value = std::accumulate(conv_col, conv_col + size, 0) * max_row_value;
|
|
|
|
first_stage = (max_row_value <= UINT16_MAX) ? DataType::U16 : DataType::S32;
|
|
|
|
second_stage = (max_value <= UINT16_MAX) ? DataType::U16 : DataType::S32;
|
|
}
|
|
else
|
|
{
|
|
const int min_row_value = std::accumulate(conv_row, conv_row + size, 0, accu_neg) * UINT8_MAX;
|
|
const int max_row_value = std::accumulate(conv_row, conv_row + size, 0, accu_pos) * UINT8_MAX;
|
|
const int neg_coeffs_sum = std::accumulate(conv_col, conv_col + size, 0, accu_neg);
|
|
const int pos_coeffs_sum = std::accumulate(conv_col, conv_col + size, 0, accu_pos);
|
|
const int min_value = neg_coeffs_sum * max_row_value + pos_coeffs_sum * min_row_value;
|
|
const int max_value = neg_coeffs_sum * min_row_value + pos_coeffs_sum * max_row_value;
|
|
|
|
first_stage = ((INT16_MIN <= min_row_value) && (max_row_value <= INT16_MAX)) ? DataType::S16 : DataType::S32;
|
|
|
|
second_stage = ((INT16_MIN <= min_value) && (max_value <= INT16_MAX)) ? DataType::S16 : DataType::S32;
|
|
}
|
|
|
|
return std::make_pair(first_stage, second_stage);
|
|
}
|
|
|
|
/** Calculate the accuracy required by the squared convolution calculation.
|
|
*
|
|
*
|
|
* @param[in] conv Pointer to the squared convolution matrix
|
|
* @param[in] size The total size of the convolution matrix
|
|
*
|
|
* @return The return is the biggest data type needed to do the convolution
|
|
*/
|
|
inline DataType data_type_for_convolution_matrix(const int16_t *conv, size_t size)
|
|
{
|
|
auto gez = [](const int16_t v)
|
|
{
|
|
return v >= 0;
|
|
};
|
|
|
|
const bool only_positive_coefficients = std::all_of(conv, conv + size, gez);
|
|
|
|
if(only_positive_coefficients)
|
|
{
|
|
const int max_conv_value = std::accumulate(conv, conv + size, 0) * UINT8_MAX;
|
|
if(max_conv_value <= UINT16_MAX)
|
|
{
|
|
return DataType::U16;
|
|
}
|
|
else
|
|
{
|
|
return DataType::S32;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
const int min_value = std::accumulate(conv, conv + size, 0, [](int a, int b)
|
|
{
|
|
return b < 0 ? a + b : a;
|
|
})
|
|
* UINT8_MAX;
|
|
|
|
const int max_value = std::accumulate(conv, conv + size, 0, [](int a, int b)
|
|
{
|
|
return b > 0 ? a + b : a;
|
|
})
|
|
* UINT8_MAX;
|
|
|
|
if((INT16_MIN <= min_value) && (INT16_MAX >= max_value))
|
|
{
|
|
return DataType::S16;
|
|
}
|
|
else
|
|
{
|
|
return DataType::S32;
|
|
}
|
|
}
|
|
}
|
|
|
|
/** Permutes the given dimensions according the permutation vector
|
|
*
|
|
* @param[in,out] dimensions Dimensions to be permuted.
|
|
* @param[in] perm Vector describing the permutation.
|
|
*
|
|
*/
|
|
template <typename T>
|
|
inline void permute_strides(Dimensions<T> &dimensions, const PermutationVector &perm)
|
|
{
|
|
const auto old_dim = utility::make_array<Dimensions<T>::num_max_dimensions>(dimensions.begin(), dimensions.end());
|
|
for(unsigned int i = 0; i < perm.num_dimensions(); ++i)
|
|
{
|
|
T dimension_val = old_dim[i];
|
|
dimensions.set(perm[i], dimension_val);
|
|
}
|
|
}
|
|
|
|
/** Calculate padding requirements in case of SAME padding
|
|
*
|
|
* @param[in] input_shape Input shape
|
|
* @param[in] weights_shape Weights shape
|
|
* @param[in] conv_info Convolution information (containing strides)
|
|
* @param[in] data_layout (Optional) Data layout of the input and weights tensor
|
|
* @param[in] dilation (Optional) Dilation factor used in the convolution.
|
|
* @param[in] rounding_type (Optional) Dimension rounding type when down-scaling.
|
|
*
|
|
* @return PadStrideInfo for SAME padding
|
|
*/
|
|
PadStrideInfo calculate_same_pad(TensorShape input_shape, TensorShape weights_shape, PadStrideInfo conv_info, DataLayout data_layout = DataLayout::NCHW, const Size2D &dilation = Size2D(1u, 1u),
|
|
const DimensionRoundingType &rounding_type = DimensionRoundingType::FLOOR);
|
|
|
|
/** Returns expected width and height of the deconvolution's output tensor.
|
|
*
|
|
* @param[in] in_width Width of input tensor (Number of columns)
|
|
* @param[in] in_height Height of input tensor (Number of rows)
|
|
* @param[in] kernel_width Kernel width.
|
|
* @param[in] kernel_height Kernel height.
|
|
* @param[in] pad_stride_info Pad and stride information.
|
|
*
|
|
* @return A pair with the new width in the first position and the new height in the second.
|
|
*/
|
|
std::pair<unsigned int, unsigned int> deconvolution_output_dimensions(unsigned int in_width, unsigned int in_height,
|
|
unsigned int kernel_width, unsigned int kernel_height,
|
|
const PadStrideInfo &pad_stride_info);
|
|
|
|
/** Returns expected width and height of output scaled tensor depending on dimensions rounding mode.
|
|
*
|
|
* @param[in] width Width of input tensor (Number of columns)
|
|
* @param[in] height Height of input tensor (Number of rows)
|
|
* @param[in] kernel_width Kernel width.
|
|
* @param[in] kernel_height Kernel height.
|
|
* @param[in] pad_stride_info Pad and stride information.
|
|
* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
|
|
*
|
|
* @return A pair with the new width in the first position and the new height in the second.
|
|
*/
|
|
std::pair<unsigned int, unsigned int> scaled_dimensions(int width, int height,
|
|
int kernel_width, int kernel_height,
|
|
const PadStrideInfo &pad_stride_info,
|
|
const Size2D &dilation = Size2D(1U, 1U));
|
|
|
|
/** Check if the given reduction operation should be handled in a serial way.
|
|
*
|
|
* @param[in] op Reduction operation to perform
|
|
* @param[in] dt Data type
|
|
* @param[in] axis Axis along which to reduce
|
|
*
|
|
* @return True if the given reduction operation should be handled in a serial way.
|
|
*/
|
|
bool needs_serialized_reduction(ReductionOperation op, DataType dt, unsigned int axis);
|
|
|
|
/** Returns output quantization information for softmax layer
|
|
*
|
|
* @param[in] input_type The data type of the input tensor
|
|
* @param[in] is_log True for log softmax
|
|
*
|
|
* @return Quantization information for the output tensor
|
|
*/
|
|
QuantizationInfo get_softmax_output_quantization_info(DataType input_type, bool is_log);
|
|
|
|
/** Returns a pair of minimum and maximum values for a quantized activation
|
|
*
|
|
* @param[in] act_info The information for activation
|
|
* @param[in] data_type The used data type
|
|
* @param[in] oq_info The output quantization information
|
|
*
|
|
* @return The pair with minimum and maximum values
|
|
*/
|
|
std::pair<int32_t, int32_t> get_quantized_activation_min_max(ActivationLayerInfo act_info, DataType data_type, UniformQuantizationInfo oq_info);
|
|
|
|
/** Convert a tensor format into a string.
|
|
*
|
|
* @param[in] format @ref Format to be translated to string.
|
|
*
|
|
* @return The string describing the format.
|
|
*/
|
|
const std::string &string_from_format(Format format);
|
|
|
|
/** Convert a channel identity into a string.
|
|
*
|
|
* @param[in] channel @ref Channel to be translated to string.
|
|
*
|
|
* @return The string describing the channel.
|
|
*/
|
|
const std::string &string_from_channel(Channel channel);
|
|
/** Convert a data layout identity into a string.
|
|
*
|
|
* @param[in] dl @ref DataLayout to be translated to string.
|
|
*
|
|
* @return The string describing the data layout.
|
|
*/
|
|
const std::string &string_from_data_layout(DataLayout dl);
|
|
/** Convert a data type identity into a string.
|
|
*
|
|
* @param[in] dt @ref DataType to be translated to string.
|
|
*
|
|
* @return The string describing the data type.
|
|
*/
|
|
const std::string &string_from_data_type(DataType dt);
|
|
/** Convert a matrix pattern into a string.
|
|
*
|
|
* @param[in] pattern @ref MatrixPattern to be translated to string.
|
|
*
|
|
* @return The string describing the matrix pattern.
|
|
*/
|
|
const std::string &string_from_matrix_pattern(MatrixPattern pattern);
|
|
/** Translates a given activation function to a string.
|
|
*
|
|
* @param[in] act @ref ActivationLayerInfo::ActivationFunction to be translated to string.
|
|
*
|
|
* @return The string describing the activation function.
|
|
*/
|
|
const std::string &string_from_activation_func(ActivationLayerInfo::ActivationFunction act);
|
|
/** Translates a given non linear function to a string.
|
|
*
|
|
* @param[in] function @ref NonLinearFilterFunction to be translated to string.
|
|
*
|
|
* @return The string describing the non linear function.
|
|
*/
|
|
const std::string &string_from_non_linear_filter_function(NonLinearFilterFunction function);
|
|
/** Translates a given interpolation policy to a string.
|
|
*
|
|
* @param[in] policy @ref InterpolationPolicy to be translated to string.
|
|
*
|
|
* @return The string describing the interpolation policy.
|
|
*/
|
|
const std::string &string_from_interpolation_policy(InterpolationPolicy policy);
|
|
/** Translates a given border mode policy to a string.
|
|
*
|
|
* @param[in] border_mode @ref BorderMode to be translated to string.
|
|
*
|
|
* @return The string describing the border mode.
|
|
*/
|
|
const std::string &string_from_border_mode(BorderMode border_mode);
|
|
/** Translates a given normalization type to a string.
|
|
*
|
|
* @param[in] type @ref NormType to be translated to string.
|
|
*
|
|
* @return The string describing the normalization type.
|
|
*/
|
|
const std::string &string_from_norm_type(NormType type);
|
|
/** Translates a given pooling type to a string.
|
|
*
|
|
* @param[in] type @ref PoolingType to be translated to string.
|
|
*
|
|
* @return The string describing the pooling type.
|
|
*/
|
|
const std::string &string_from_pooling_type(PoolingType type);
|
|
/** Translates a given GEMMLowp output stage to a string.
|
|
*
|
|
* @param[in] output_stage @ref GEMMLowpOutputStageInfo to be translated to string.
|
|
*
|
|
* @return The string describing the GEMMLowp output stage
|
|
*/
|
|
const std::string &string_from_gemmlowp_output_stage(GEMMLowpOutputStageType output_stage);
|
|
/** Convert a PixelValue to a string, represented through the specific data type
|
|
*
|
|
* @param[in] value The PixelValue to convert
|
|
* @param[in] data_type The type to be used to convert the @p value
|
|
*
|
|
* @return String representation of the PixelValue through the given data type.
|
|
*/
|
|
std::string string_from_pixel_value(const PixelValue &value, const DataType data_type);
|
|
/** Convert a string to DataType
|
|
*
|
|
* @param[in] name The name of the data type
|
|
*
|
|
* @return DataType
|
|
*/
|
|
DataType data_type_from_name(const std::string &name);
|
|
/** Stores padding information before configuring a kernel
|
|
*
|
|
* @param[in] infos list of tensor infos to store the padding info for
|
|
*
|
|
* @return An unordered map where each tensor info pointer is paired with its original padding info
|
|
*/
|
|
std::unordered_map<const ITensorInfo *, PaddingSize> get_padding_info(std::initializer_list<const ITensorInfo *> infos);
|
|
/** Stores padding information before configuring a kernel
|
|
*
|
|
* @param[in] tensors list of tensors to store the padding info for
|
|
*
|
|
* @return An unordered map where each tensor info pointer is paired with its original padding info
|
|
*/
|
|
std::unordered_map<const ITensorInfo *, PaddingSize> get_padding_info(std::initializer_list<const ITensor *> tensors);
|
|
/** Check if the previously stored padding info has changed after configuring a kernel
|
|
*
|
|
* @param[in] padding_map an unordered map where each tensor info pointer is paired with its original padding info
|
|
*
|
|
* @return true if any of the tensor infos has changed its paddings
|
|
*/
|
|
bool has_padding_changed(const std::unordered_map<const ITensorInfo *, PaddingSize> &padding_map);
|
|
|
|
/** Input Stream operator for @ref DataType
|
|
*
|
|
* @param[in] stream Stream to parse
|
|
* @param[out] data_type Output data type
|
|
*
|
|
* @return Updated stream
|
|
*/
|
|
inline ::std::istream &operator>>(::std::istream &stream, DataType &data_type)
|
|
{
|
|
std::string value;
|
|
stream >> value;
|
|
data_type = data_type_from_name(value);
|
|
return stream;
|
|
}
|
|
/** Lower a given string.
|
|
*
|
|
* @param[in] val Given string to lower.
|
|
*
|
|
* @return The lowered string
|
|
*/
|
|
std::string lower_string(const std::string &val);
|
|
|
|
/** Check if a given data type is of floating point type
|
|
*
|
|
* @param[in] dt Input data type.
|
|
*
|
|
* @return True if data type is of floating point type, else false.
|
|
*/
|
|
inline bool is_data_type_float(DataType dt)
|
|
{
|
|
switch(dt)
|
|
{
|
|
case DataType::F16:
|
|
case DataType::F32:
|
|
return true;
|
|
default:
|
|
return false;
|
|
}
|
|
}
|
|
|
|
/** Check if a given data type is of quantized type
|
|
*
|
|
* @note Quantized is considered a super-set of fixed-point and asymmetric data types.
|
|
*
|
|
* @param[in] dt Input data type.
|
|
*
|
|
* @return True if data type is of quantized type, else false.
|
|
*/
|
|
inline bool is_data_type_quantized(DataType dt)
|
|
{
|
|
switch(dt)
|
|
{
|
|
case DataType::QSYMM8:
|
|
case DataType::QASYMM8:
|
|
case DataType::QASYMM8_SIGNED:
|
|
case DataType::QSYMM8_PER_CHANNEL:
|
|
case DataType::QSYMM16:
|
|
case DataType::QASYMM16:
|
|
return true;
|
|
default:
|
|
return false;
|
|
}
|
|
}
|
|
|
|
/** Check if a given data type is of asymmetric quantized type
|
|
*
|
|
* @param[in] dt Input data type.
|
|
*
|
|
* @return True if data type is of asymmetric quantized type, else false.
|
|
*/
|
|
inline bool is_data_type_quantized_asymmetric(DataType dt)
|
|
{
|
|
switch(dt)
|
|
{
|
|
case DataType::QASYMM8:
|
|
case DataType::QASYMM8_SIGNED:
|
|
case DataType::QASYMM16:
|
|
return true;
|
|
default:
|
|
return false;
|
|
}
|
|
}
|
|
|
|
/** Check if a given data type is of asymmetric quantized signed type
|
|
*
|
|
* @param[in] dt Input data type.
|
|
*
|
|
* @return True if data type is of asymmetric quantized signed type, else false.
|
|
*/
|
|
inline bool is_data_type_quantized_asymmetric_signed(DataType dt)
|
|
{
|
|
switch(dt)
|
|
{
|
|
case DataType::QASYMM8_SIGNED:
|
|
return true;
|
|
default:
|
|
return false;
|
|
}
|
|
}
|
|
|
|
/** Check if a given data type is of symmetric quantized type
|
|
*
|
|
* @param[in] dt Input data type.
|
|
*
|
|
* @return True if data type is of symmetric quantized type, else false.
|
|
*/
|
|
inline bool is_data_type_quantized_symmetric(DataType dt)
|
|
{
|
|
switch(dt)
|
|
{
|
|
case DataType::QSYMM8:
|
|
case DataType::QSYMM8_PER_CHANNEL:
|
|
case DataType::QSYMM16:
|
|
return true;
|
|
default:
|
|
return false;
|
|
}
|
|
}
|
|
|
|
/** Check if a given data type is of per channel type
|
|
*
|
|
* @param[in] dt Input data type.
|
|
*
|
|
* @return True if data type is of per channel type, else false.
|
|
*/
|
|
inline bool is_data_type_quantized_per_channel(DataType dt)
|
|
{
|
|
switch(dt)
|
|
{
|
|
case DataType::QSYMM8_PER_CHANNEL:
|
|
return true;
|
|
default:
|
|
return false;
|
|
}
|
|
}
|
|
|
|
/** Create a string with the float in full precision.
|
|
*
|
|
* @param val Floating point value
|
|
*
|
|
* @return String with the floating point value.
|
|
*/
|
|
inline std::string float_to_string_with_full_precision(float val)
|
|
{
|
|
std::stringstream ss;
|
|
ss.precision(std::numeric_limits<float>::max_digits10);
|
|
ss << val;
|
|
|
|
if(val != static_cast<int>(val))
|
|
{
|
|
ss << "f";
|
|
}
|
|
|
|
return ss.str();
|
|
}
|
|
|
|
/** Returns the number of elements required to go from start to end with the wanted step
|
|
*
|
|
* @param[in] start start value
|
|
* @param[in] end end value
|
|
* @param[in] step step value between each number in the wanted sequence
|
|
*
|
|
* @return number of elements to go from start value to end value using the wanted step
|
|
*/
|
|
inline size_t num_of_elements_in_range(const float start, const float end, const float step)
|
|
{
|
|
ARM_COMPUTE_ERROR_ON_MSG(step == 0, "Range Step cannot be 0");
|
|
return size_t(std::ceil((end - start) / step));
|
|
}
|
|
|
|
/** Returns true if the value can be represented by the given data type
|
|
*
|
|
* @param[in] val value to be checked
|
|
* @param[in] dt data type that is checked
|
|
* @param[in] qinfo (Optional) quantization info if the data type is QASYMM8
|
|
*
|
|
* @return true if the data type can hold the value.
|
|
*/
|
|
template <typename T>
|
|
bool check_value_range(T val, DataType dt, QuantizationInfo qinfo = QuantizationInfo())
|
|
{
|
|
switch(dt)
|
|
{
|
|
case DataType::U8:
|
|
{
|
|
const auto val_u8 = static_cast<uint8_t>(val);
|
|
return ((val_u8 == val) && val_u8 >= std::numeric_limits<uint8_t>::lowest() && val_u8 <= std::numeric_limits<uint8_t>::max());
|
|
}
|
|
case DataType::QASYMM8:
|
|
{
|
|
double min = static_cast<double>(dequantize_qasymm8(0, qinfo));
|
|
double max = static_cast<double>(dequantize_qasymm8(std::numeric_limits<uint8_t>::max(), qinfo));
|
|
return ((double)val >= min && (double)val <= max);
|
|
}
|
|
case DataType::S8:
|
|
{
|
|
const auto val_s8 = static_cast<int8_t>(val);
|
|
return ((val_s8 == val) && val_s8 >= std::numeric_limits<int8_t>::lowest() && val_s8 <= std::numeric_limits<int8_t>::max());
|
|
}
|
|
case DataType::U16:
|
|
{
|
|
const auto val_u16 = static_cast<uint16_t>(val);
|
|
return ((val_u16 == val) && val_u16 >= std::numeric_limits<uint16_t>::lowest() && val_u16 <= std::numeric_limits<uint16_t>::max());
|
|
}
|
|
case DataType::S16:
|
|
{
|
|
const auto val_s16 = static_cast<int16_t>(val);
|
|
return ((val_s16 == val) && val_s16 >= std::numeric_limits<int16_t>::lowest() && val_s16 <= std::numeric_limits<int16_t>::max());
|
|
}
|
|
case DataType::U32:
|
|
{
|
|
const auto val_u32 = static_cast<uint32_t>(val);
|
|
return ((val_u32 == val) && val_u32 >= std::numeric_limits<uint32_t>::lowest() && val_u32 <= std::numeric_limits<uint32_t>::max());
|
|
}
|
|
case DataType::S32:
|
|
{
|
|
const auto val_s32 = static_cast<int32_t>(val);
|
|
return ((val_s32 == val) && val_s32 >= std::numeric_limits<int32_t>::lowest() && val_s32 <= std::numeric_limits<int32_t>::max());
|
|
}
|
|
case DataType::BFLOAT16:
|
|
return (val >= bfloat16::lowest() && val <= bfloat16::max());
|
|
case DataType::F16:
|
|
return (val >= std::numeric_limits<half>::lowest() && val <= std::numeric_limits<half>::max());
|
|
case DataType::F32:
|
|
return (val >= std::numeric_limits<float>::lowest() && val <= std::numeric_limits<float>::max());
|
|
default:
|
|
ARM_COMPUTE_ERROR("Data type not supported");
|
|
return false;
|
|
}
|
|
}
|
|
|
|
/** Returns the adjusted vector size in case it is less than the input's first dimension, getting rounded down to its closest valid vector size
|
|
*
|
|
* @param[in] vec_size vector size to be adjusted
|
|
* @param[in] dim0 size of the first dimension
|
|
*
|
|
* @return the number of element processed along the X axis per thread
|
|
*/
|
|
inline unsigned int adjust_vec_size(unsigned int vec_size, size_t dim0)
|
|
{
|
|
ARM_COMPUTE_ERROR_ON(vec_size > 16);
|
|
|
|
if((vec_size >= dim0) && (dim0 == 3))
|
|
{
|
|
return dim0;
|
|
}
|
|
|
|
while(vec_size > dim0)
|
|
{
|
|
vec_size >>= 1;
|
|
}
|
|
|
|
return vec_size;
|
|
}
|
|
|
|
#ifdef ARM_COMPUTE_ASSERTS_ENABLED
|
|
/** Print consecutive elements to an output stream.
|
|
*
|
|
* @param[out] s Output stream to print the elements to.
|
|
* @param[in] ptr Pointer to print the elements from.
|
|
* @param[in] n Number of elements to print.
|
|
* @param[in] stream_width (Optional) Width of the stream. If set to 0 the element's width is used. Defaults to 0.
|
|
* @param[in] element_delim (Optional) Delimeter among the consecutive elements. Defaults to space delimeter
|
|
*/
|
|
template <typename T>
|
|
void print_consecutive_elements_impl(std::ostream &s, const T *ptr, unsigned int n, int stream_width = 0, const std::string &element_delim = " ")
|
|
{
|
|
using print_type = typename std::conditional<std::is_floating_point<T>::value, T, int>::type;
|
|
std::ios stream_status(nullptr);
|
|
stream_status.copyfmt(s);
|
|
|
|
for(unsigned int i = 0; i < n; ++i)
|
|
{
|
|
// Set stream width as it is not a "sticky" stream manipulator
|
|
if(stream_width != 0)
|
|
{
|
|
s.width(stream_width);
|
|
}
|
|
|
|
if(std::is_same<typename std::decay<T>::type, half>::value)
|
|
{
|
|
// We use T instead of print_type here is because the std::is_floating_point<half> returns false and then the print_type becomes int.
|
|
s << std::right << static_cast<T>(ptr[i]) << element_delim;
|
|
}
|
|
else if(std::is_same<typename std::decay<T>::type, bfloat16>::value)
|
|
{
|
|
// We use T instead of print_type here is because the std::is_floating_point<bfloat16> returns false and then the print_type becomes int.
|
|
s << std::right << float(ptr[i]) << element_delim;
|
|
}
|
|
else
|
|
{
|
|
s << std::right << static_cast<print_type>(ptr[i]) << element_delim;
|
|
}
|
|
}
|
|
|
|
// Restore output stream flags
|
|
s.copyfmt(stream_status);
|
|
}
|
|
|
|
/** Identify the maximum width of n consecutive elements.
|
|
*
|
|
* @param[in] s The output stream which will be used to print the elements. Used to extract the stream format.
|
|
* @param[in] ptr Pointer to the elements.
|
|
* @param[in] n Number of elements.
|
|
*
|
|
* @return The maximum width of the elements.
|
|
*/
|
|
template <typename T>
|
|
int max_consecutive_elements_display_width_impl(std::ostream &s, const T *ptr, unsigned int n)
|
|
{
|
|
using print_type = typename std::conditional<std::is_floating_point<T>::value, T, int>::type;
|
|
|
|
int max_width = -1;
|
|
for(unsigned int i = 0; i < n; ++i)
|
|
{
|
|
std::stringstream ss;
|
|
ss.copyfmt(s);
|
|
|
|
if(std::is_same<typename std::decay<T>::type, half>::value)
|
|
{
|
|
// We use T instead of print_type here is because the std::is_floating_point<half> returns false and then the print_type becomes int.
|
|
ss << static_cast<T>(ptr[i]);
|
|
}
|
|
else if(std::is_same<typename std::decay<T>::type, bfloat16>::value)
|
|
{
|
|
// We use T instead of print_type here is because the std::is_floating_point<bfloat> returns false and then the print_type becomes int.
|
|
ss << float(ptr[i]);
|
|
}
|
|
else
|
|
{
|
|
ss << static_cast<print_type>(ptr[i]);
|
|
}
|
|
|
|
max_width = std::max<int>(max_width, ss.str().size());
|
|
}
|
|
return max_width;
|
|
}
|
|
|
|
/** Print consecutive elements to an output stream.
|
|
*
|
|
* @param[out] s Output stream to print the elements to.
|
|
* @param[in] dt Data type of the elements
|
|
* @param[in] ptr Pointer to print the elements from.
|
|
* @param[in] n Number of elements to print.
|
|
* @param[in] stream_width (Optional) Width of the stream. If set to 0 the element's width is used. Defaults to 0.
|
|
* @param[in] element_delim (Optional) Delimeter among the consecutive elements. Defaults to space delimeter
|
|
*/
|
|
void print_consecutive_elements(std::ostream &s, DataType dt, const uint8_t *ptr, unsigned int n, int stream_width, const std::string &element_delim = " ");
|
|
|
|
/** Identify the maximum width of n consecutive elements.
|
|
*
|
|
* @param[in] s Output stream to print the elements to.
|
|
* @param[in] dt Data type of the elements
|
|
* @param[in] ptr Pointer to print the elements from.
|
|
* @param[in] n Number of elements to print.
|
|
*
|
|
* @return The maximum width of the elements.
|
|
*/
|
|
int max_consecutive_elements_display_width(std::ostream &s, DataType dt, const uint8_t *ptr, unsigned int n);
|
|
#endif /* ARM_COMPUTE_ASSERTS_ENABLED */
|
|
}
|
|
#endif /*ARM_COMPUTE_UTILS_H */
|