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643 lines
20 KiB
643 lines
20 KiB
/*
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* Copyright (c) 2017-2023 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_TEST_UTILS_H
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#define ARM_COMPUTE_TEST_UTILS_H
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#include "arm_compute/core/Coordinates.h"
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#include "arm_compute/core/Error.h"
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#include "arm_compute/core/Size2D.h"
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#include "arm_compute/core/TensorInfo.h"
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#include "arm_compute/core/TensorShape.h"
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#include "arm_compute/core/Types.h"
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#include "support/StringSupport.h"
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#include "support/ToolchainSupport.h"
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#ifdef ARM_COMPUTE_CL
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#include "arm_compute/core/CL/OpenCL.h"
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#include "arm_compute/runtime/CL/CLScheduler.h"
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#endif /* ARM_COMPUTE_CL */
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#include <cmath>
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#include <cstddef>
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#include <limits>
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#include <memory>
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#include <random>
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#include <sstream>
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#include <string>
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#include <type_traits>
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#include <vector>
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#include "arm_compute/dynamic_fusion/sketch/attributes/Conv2dAttributes.h"
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#include "arm_compute/runtime/CPP/CPPScheduler.h"
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#include "arm_compute/runtime/RuntimeContext.h"
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namespace arm_compute
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{
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#ifdef ARM_COMPUTE_CL
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class CLTensor;
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#endif /* ARM_COMPUTE_CL */
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namespace test
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{
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/** Round floating-point value with half value rounding to positive infinity.
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*
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* @param[in] value floating-point value to be rounded.
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*
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* @return Floating-point value of rounded @p value.
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*/
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template <typename T, typename = typename std::enable_if<std::is_floating_point<T>::value>::type>
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inline T round_half_up(T value)
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{
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return std::floor(value + 0.5f);
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}
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/** Round floating-point value with half value rounding to nearest even.
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*
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* @param[in] value floating-point value to be rounded.
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* @param[in] epsilon precision.
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*
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* @return Floating-point value of rounded @p value.
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*/
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template <typename T, typename = typename std::enable_if<std::is_floating_point<T>::value>::type>
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inline T round_half_even(T value, T epsilon = std::numeric_limits<T>::epsilon())
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{
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T positive_value = std::abs(value);
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T ipart = 0;
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std::modf(positive_value, &ipart);
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// If 'value' is exactly halfway between two integers
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if(std::abs(positive_value - (ipart + 0.5f)) < epsilon)
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{
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// If 'ipart' is even then return 'ipart'
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if(std::fmod(ipart, 2.f) < epsilon)
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{
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return support::cpp11::copysign(ipart, value);
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}
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// Else return the nearest even integer
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return support::cpp11::copysign(std::ceil(ipart + 0.5f), value);
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}
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// Otherwise use the usual round to closest
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return support::cpp11::copysign(support::cpp11::round(positive_value), value);
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}
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namespace traits
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{
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// *INDENT-OFF*
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// clang-format off
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/** Promote a type */
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template <typename T> struct promote { };
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/** Promote uint8_t to uint16_t */
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template <> struct promote<uint8_t> { using type = uint16_t; /**< Promoted type */ };
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/** Promote int8_t to int16_t */
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template <> struct promote<int8_t> { using type = int16_t; /**< Promoted type */ };
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/** Promote uint16_t to uint32_t */
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template <> struct promote<uint16_t> { using type = uint32_t; /**< Promoted type */ };
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/** Promote int16_t to int32_t */
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template <> struct promote<int16_t> { using type = int32_t; /**< Promoted type */ };
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/** Promote uint32_t to uint64_t */
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template <> struct promote<uint32_t> { using type = uint64_t; /**< Promoted type */ };
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/** Promote int32_t to int64_t */
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template <> struct promote<int32_t> { using type = int64_t; /**< Promoted type */ };
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/** Promote float to float */
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template <> struct promote<float> { using type = float; /**< Promoted type */ };
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/** Promote half to half */
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template <> struct promote<half> { using type = half; /**< Promoted type */ };
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/** Get promoted type */
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template <typename T>
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using promote_t = typename promote<T>::type;
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template <typename T>
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using make_signed_conditional_t = typename std::conditional<std::is_integral<T>::value, std::make_signed<T>, std::common_type<T>>::type;
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template <typename T>
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using make_unsigned_conditional_t = typename std::conditional<std::is_integral<T>::value, std::make_unsigned<T>, std::common_type<T>>::type;
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// clang-format on
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// *INDENT-ON*
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} // namespace traits
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/** Look up the format corresponding to a channel.
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*
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* @param[in] channel Channel type.
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*
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* @return Format that contains the given channel.
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*/
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inline Format get_format_for_channel(Channel channel)
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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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case Channel::G:
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case Channel::B:
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return Format::RGB888;
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default:
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throw std::runtime_error("Unsupported channel");
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}
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}
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/** Return the format of a channel.
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*
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* @param[in] channel Channel type.
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*
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* @return Format of the given channel.
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*/
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inline Format get_channel_format(Channel channel)
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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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case Channel::G:
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case Channel::B:
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return Format::U8;
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default:
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throw std::runtime_error("Unsupported channel");
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}
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}
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/** Base case of foldl.
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*
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* @return value.
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*/
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template <typename F, typename T>
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inline T foldl(F &&, const T &value)
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{
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return value;
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}
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/** Base case of foldl.
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*
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* @return func(value1, value2).
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*/
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template <typename F, typename T, typename U>
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inline auto foldl(F &&func, T &&value1, U &&value2) -> decltype(func(value1, value2))
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{
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return func(value1, value2);
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}
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/** Fold left.
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*
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* @param[in] func Binary function to be called.
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* @param[in] initial Initial value.
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* @param[in] value Argument passed to the function.
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* @param[in] values Remaining arguments.
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*/
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template <typename F, typename I, typename T, typename... Vs>
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inline I foldl(F &&func, I &&initial, T &&value, Vs &&... values)
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{
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return foldl(std::forward<F>(func), func(std::forward<I>(initial), std::forward<T>(value)), std::forward<Vs>(values)...);
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}
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/** Create a valid region based on tensor shape, border mode and border size
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*
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* @param[in] a_shape Shape used as size of the valid region.
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* @param[in] border_undefined (Optional) Boolean indicating if the border mode is undefined.
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* @param[in] border_size (Optional) Border size used to specify the region to exclude.
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*
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* @return A valid region starting at (0, 0, ...) with size of @p shape if @p border_undefined is false; otherwise
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* return A valid region starting at (@p border_size.left, @p border_size.top, ...) with reduced size of @p shape.
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*/
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inline ValidRegion shape_to_valid_region(const TensorShape &a_shape, bool border_undefined = false, BorderSize border_size = BorderSize(0))
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{
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ValidRegion valid_region{ Coordinates(), a_shape };
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Coordinates &anchor = valid_region.anchor;
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TensorShape &shape = valid_region.shape;
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if(border_undefined)
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{
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ARM_COMPUTE_ERROR_ON(shape.num_dimensions() < 2);
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anchor.set(0, border_size.left);
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anchor.set(1, border_size.top);
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const int valid_shape_x = std::max(0, static_cast<int>(shape.x()) - static_cast<int>(border_size.left) - static_cast<int>(border_size.right));
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const int valid_shape_y = std::max(0, static_cast<int>(shape.y()) - static_cast<int>(border_size.top) - static_cast<int>(border_size.bottom));
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shape.set(0, valid_shape_x);
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shape.set(1, valid_shape_y);
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}
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return valid_region;
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}
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/** Write the value after casting the pointer according to @p data_type.
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*
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* @warning The type of the value must match the specified data type.
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*
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* @param[out] ptr Pointer to memory where the @p value will be written.
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* @param[in] value Value that will be written.
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* @param[in] data_type Data type that will be written.
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*/
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template <typename T>
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void store_value_with_data_type(void *ptr, T value, 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::QASYMM8:
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*reinterpret_cast<uint8_t *>(ptr) = value;
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break;
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case DataType::S8:
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case DataType::QASYMM8_SIGNED:
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case DataType::QSYMM8:
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case DataType::QSYMM8_PER_CHANNEL:
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*reinterpret_cast<int8_t *>(ptr) = value;
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break;
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case DataType::U16:
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case DataType::QASYMM16:
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*reinterpret_cast<uint16_t *>(ptr) = value;
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break;
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case DataType::S16:
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case DataType::QSYMM16:
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*reinterpret_cast<int16_t *>(ptr) = value;
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break;
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case DataType::U32:
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*reinterpret_cast<uint32_t *>(ptr) = value;
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break;
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case DataType::S32:
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*reinterpret_cast<int32_t *>(ptr) = value;
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break;
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case DataType::U64:
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*reinterpret_cast<uint64_t *>(ptr) = value;
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break;
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case DataType::S64:
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*reinterpret_cast<int64_t *>(ptr) = value;
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break;
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case DataType::BFLOAT16:
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*reinterpret_cast<bfloat16 *>(ptr) = bfloat16(value);
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break;
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case DataType::F16:
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*reinterpret_cast<half *>(ptr) = value;
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break;
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case DataType::F32:
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*reinterpret_cast<float *>(ptr) = value;
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break;
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case DataType::F64:
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*reinterpret_cast<double *>(ptr) = value;
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break;
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case DataType::SIZET:
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*reinterpret_cast<size_t *>(ptr) = value;
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break;
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default:
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ARM_COMPUTE_ERROR("NOT SUPPORTED!");
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}
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}
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/** Saturate a value of type T against the numeric limits of type U.
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*
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* @param[in] val Value to be saturated.
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*
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* @return saturated value.
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*/
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template <typename U, typename T>
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T saturate_cast(T val)
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{
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if(val > static_cast<T>(std::numeric_limits<U>::max()))
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{
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val = static_cast<T>(std::numeric_limits<U>::max());
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}
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if(val < static_cast<T>(std::numeric_limits<U>::lowest()))
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{
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val = static_cast<T>(std::numeric_limits<U>::lowest());
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}
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return val;
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}
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/** Find the signed promoted common type.
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*/
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template <typename... T>
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struct common_promoted_signed_type
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{
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/** Common type */
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using common_type = typename std::common_type<T...>::type;
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/** Promoted type */
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using promoted_type = traits::promote_t<common_type>;
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/** Intermediate type */
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using intermediate_type = typename traits::make_signed_conditional_t<promoted_type>::type;
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};
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/** Find the unsigned promoted common type.
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*/
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template <typename... T>
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struct common_promoted_unsigned_type
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{
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/** Common type */
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using common_type = typename std::common_type<T...>::type;
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/** Promoted type */
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using promoted_type = traits::promote_t<common_type>;
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/** Intermediate type */
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using intermediate_type = typename traits::make_unsigned_conditional_t<promoted_type>::type;
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};
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/** Convert a linear index into n-dimensional coordinates.
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*
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* @param[in] shape Shape of the n-dimensional tensor.
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* @param[in] index Linear index specifying the i-th element.
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*
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* @return n-dimensional coordinates.
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*/
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inline Coordinates index2coord(const TensorShape &shape, int index)
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{
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int num_elements = shape.total_size();
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ARM_COMPUTE_ERROR_ON_MSG(index < 0 || index >= num_elements, "Index has to be in [0, num_elements]");
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ARM_COMPUTE_ERROR_ON_MSG(num_elements == 0, "Cannot create coordinate from empty shape");
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Coordinates coord{ 0 };
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for(int d = shape.num_dimensions() - 1; d >= 0; --d)
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{
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num_elements /= shape[d];
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coord.set(d, index / num_elements);
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index %= num_elements;
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}
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return coord;
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}
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/** Linearise the given coordinate.
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*
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* Transforms the given coordinate into a linear offset in terms of
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* elements.
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*
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* @param[in] shape Shape of the n-dimensional tensor.
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* @param[in] coord The to be converted coordinate.
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*
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* @return Linear offset to the element.
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*/
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inline int coord2index(const TensorShape &shape, const Coordinates &coord)
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{
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ARM_COMPUTE_ERROR_ON_MSG(shape.total_size() == 0, "Cannot get index from empty shape");
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ARM_COMPUTE_ERROR_ON_MSG(coord.num_dimensions() == 0, "Cannot get index of empty coordinate");
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int index = 0;
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int dim_size = 1;
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for(unsigned int i = 0; i < coord.num_dimensions(); ++i)
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{
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index += coord[i] * dim_size;
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dim_size *= shape[i];
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}
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return index;
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}
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/** Check if a coordinate is within a valid region */
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inline bool is_in_valid_region(const ValidRegion &valid_region, Coordinates coord)
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{
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for(size_t d = 0; d < Coordinates::num_max_dimensions; ++d)
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{
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if(coord[d] < valid_region.start(d) || coord[d] >= valid_region.end(d))
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{
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return false;
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}
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}
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return true;
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}
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/** Create and initialize a tensor of the given type.
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*
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* @param[in] info Tensor information to be used to create the tensor
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* @param[in] ctx (Optional) Pointer to the runtime context.
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*
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* @return Initialized tensor of given type.
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*/
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template <typename T>
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inline T create_tensor(const TensorInfo &info, IRuntimeContext *ctx = nullptr)
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{
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T tensor(ctx);
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tensor.allocator()->init(info);
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return tensor;
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}
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/** Create and initialize a tensor of the given type.
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*
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* @param[in] shape Tensor shape.
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* @param[in] data_type Data type.
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* @param[in] num_channels (Optional) Number of channels.
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* @param[in] quantization_info (Optional) Quantization info for asymmetric quantized types.
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* @param[in] data_layout (Optional) Data layout. Default is NCHW.
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* @param[in] ctx (Optional) Pointer to the runtime context.
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*
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* @return Initialized tensor of given type.
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*/
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template <typename T>
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inline T create_tensor(const TensorShape &shape, DataType data_type, int num_channels = 1,
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QuantizationInfo quantization_info = QuantizationInfo(), DataLayout data_layout = DataLayout::NCHW, IRuntimeContext *ctx = nullptr)
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{
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T tensor(ctx);
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TensorInfo info(shape, num_channels, data_type);
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info.set_quantization_info(quantization_info);
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info.set_data_layout(data_layout);
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return create_tensor<T>(info, ctx);
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}
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/** Create and initialize a tensor of the given type.
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*
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* @param[in] shape Tensor shape.
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* @param[in] format Format type.
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* @param[in] ctx (Optional) Pointer to the runtime context.
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*
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* @return Initialized tensor of given type.
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*/
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template <typename T>
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inline T create_tensor(const TensorShape &shape, Format format, IRuntimeContext *ctx = nullptr)
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{
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TensorInfo info(shape, format);
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return create_tensor<T>(info, ctx);
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}
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/** Create a vector with a uniform distribution of floating point values across the specified range.
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*
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* @param[in] num_values The number of values to be created.
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* @param[in] min The minimum value in distribution (inclusive).
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* @param[in] max The maximum value in distribution (inclusive).
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* @param[in] seed The random seed to be used.
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*
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* @return A vector that contains the requested number of random floating point values
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*/
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template <typename T, typename = typename std::enable_if<std::is_floating_point<T>::value>::type>
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inline std::vector<T> generate_random_real(unsigned int num_values, T min, T max, std::random_device::result_type seed)
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{
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std::vector<T> v(num_values);
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std::mt19937 gen(seed);
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std::uniform_real_distribution<T> dist(min, max);
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for(unsigned int i = 0; i < num_values; ++i)
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{
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v.at(i) = dist(gen);
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}
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return v;
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}
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|
|
template <typename T, typename ArrayAccessor_T>
|
|
inline void fill_array(ArrayAccessor_T &&array, const std::vector<T> &v)
|
|
{
|
|
array.resize(v.size());
|
|
std::memcpy(array.buffer(), v.data(), v.size() * sizeof(T));
|
|
}
|
|
|
|
/** Obtain numpy type string from DataType.
|
|
*
|
|
* @param[in] data_type Data type.
|
|
*
|
|
* @return numpy type string.
|
|
*/
|
|
inline std::string get_typestring(DataType data_type)
|
|
{
|
|
// Check endianness
|
|
const unsigned int i = 1;
|
|
const char *c = reinterpret_cast<const char *>(&i);
|
|
std::string endianness;
|
|
if(*c == 1)
|
|
{
|
|
endianness = std::string("<");
|
|
}
|
|
else
|
|
{
|
|
endianness = std::string(">");
|
|
}
|
|
const std::string no_endianness("|");
|
|
|
|
switch(data_type)
|
|
{
|
|
case DataType::U8:
|
|
return no_endianness + "u" + support::cpp11::to_string(sizeof(uint8_t));
|
|
case DataType::S8:
|
|
return no_endianness + "i" + support::cpp11::to_string(sizeof(int8_t));
|
|
case DataType::U16:
|
|
return endianness + "u" + support::cpp11::to_string(sizeof(uint16_t));
|
|
case DataType::S16:
|
|
return endianness + "i" + support::cpp11::to_string(sizeof(int16_t));
|
|
case DataType::U32:
|
|
return endianness + "u" + support::cpp11::to_string(sizeof(uint32_t));
|
|
case DataType::S32:
|
|
return endianness + "i" + support::cpp11::to_string(sizeof(int32_t));
|
|
case DataType::U64:
|
|
return endianness + "u" + support::cpp11::to_string(sizeof(uint64_t));
|
|
case DataType::S64:
|
|
return endianness + "i" + support::cpp11::to_string(sizeof(int64_t));
|
|
case DataType::F32:
|
|
return endianness + "f" + support::cpp11::to_string(sizeof(float));
|
|
case DataType::F64:
|
|
return endianness + "f" + support::cpp11::to_string(sizeof(double));
|
|
case DataType::SIZET:
|
|
return endianness + "u" + support::cpp11::to_string(sizeof(size_t));
|
|
default:
|
|
ARM_COMPUTE_ERROR("NOT SUPPORTED!");
|
|
}
|
|
}
|
|
|
|
/** Sync if necessary.
|
|
*/
|
|
template <typename TensorType>
|
|
inline void sync_if_necessary()
|
|
{
|
|
#ifdef ARM_COMPUTE_CL
|
|
if(opencl_is_available() && std::is_same<typename std::decay<TensorType>::type, arm_compute::CLTensor>::value)
|
|
{
|
|
CLScheduler::get().sync();
|
|
}
|
|
#endif /* ARM_COMPUTE_CL */
|
|
}
|
|
|
|
/** Sync tensor if necessary.
|
|
*
|
|
* @note: If the destination tensor not being used on OpenGL ES, GPU will optimize out the operation.
|
|
*
|
|
* @param[in] tensor Tensor to be sync.
|
|
*/
|
|
template <typename TensorType>
|
|
inline void sync_tensor_if_necessary(TensorType &tensor)
|
|
{
|
|
ARM_COMPUTE_UNUSED(tensor);
|
|
}
|
|
|
|
/** Construct and return object for dimensions' state filled with the given value
|
|
*
|
|
* @param[in] value The value to fill
|
|
*
|
|
* @return Constructed class
|
|
*/
|
|
inline ITensorInfo::TensorDimsState construct_dims_state(int32_t value)
|
|
{
|
|
auto states = ITensorInfo::TensorDimsState{};
|
|
std::fill(states.begin(), states.end(), value);
|
|
return states;
|
|
}
|
|
|
|
/** Construct and return object for dimensions' state filled with the value for dynamic state
|
|
*
|
|
* @return Constructed class filled with the value for dynamic state
|
|
*/
|
|
inline ITensorInfo::TensorDimsState construct_dynamic_dims_state()
|
|
{
|
|
return construct_dims_state(ITensorInfo::get_dynamic_state_value());
|
|
}
|
|
|
|
/** Construct and return object for dimensions' state filled with the value for non-dynamic state
|
|
*
|
|
* @return Constructed class filled with the value for non-dynamic state
|
|
*/
|
|
inline ITensorInfo::TensorDimsState construct_static_dims_state()
|
|
{
|
|
return construct_dims_state(ITensorInfo::get_static_state_value());
|
|
}
|
|
|
|
/** Set the dimension states of the given tensor to dynamic
|
|
*
|
|
* @param[in] t The tensor to set to dynamic state
|
|
*
|
|
*/
|
|
template <typename TensorType>
|
|
void set_tensor_dynamic(TensorType &t)
|
|
{
|
|
t.info()->set_tensor_dims_state(construct_dynamic_dims_state());
|
|
}
|
|
|
|
/** Set the dimension states of the given tensor to state
|
|
*
|
|
* @param[in] t The tensor to set to static state
|
|
*
|
|
*/
|
|
template <typename TensorType>
|
|
void set_tensor_static(TensorType &t)
|
|
{
|
|
t.info()->set_tensor_dims_state(construct_static_dims_state());
|
|
}
|
|
|
|
inline experimental::dynamic_fusion::Conv2dAttributes convert_pad_stride_info_to_conv_attr(const PadStrideInfo &info, const Size2D &dialation)
|
|
{
|
|
const Padding2D info_pad(info.pad_left(), info.pad_right(), info.pad_top(), info.pad_bottom());
|
|
const Size2D info_stride(info.stride().first, info.stride().second);
|
|
return arm_compute::experimental::dynamic_fusion::Conv2dAttributes().pad(info_pad).stride(info_stride).dilation(dialation);
|
|
}
|
|
|
|
} // namespace test
|
|
} // namespace arm_compute
|
|
#endif /* ARM_COMPUTE_TEST_UTILS_H */
|