| /* |
| * Copyright (c) 2017-2019 ARM Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #ifndef __ARM_COMPUTE_TEST_UTILS_H__ |
| #define __ARM_COMPUTE_TEST_UTILS_H__ |
| |
| #include "arm_compute/core/Coordinates.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/HOGInfo.h" |
| #include "arm_compute/core/PyramidInfo.h" |
| #include "arm_compute/core/Size2D.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/TensorShape.h" |
| #include "arm_compute/core/Types.h" |
| #include "support/ToolchainSupport.h" |
| |
| #ifdef ARM_COMPUTE_CL |
| #include "arm_compute/core/CL/OpenCL.h" |
| #include "arm_compute/runtime/CL/CLScheduler.h" |
| #endif /* ARM_COMPUTE_CL */ |
| |
| #ifdef ARM_COMPUTE_GC |
| #include "arm_compute/core/GLES_COMPUTE/OpenGLES.h" |
| #include "arm_compute/runtime/GLES_COMPUTE/GCTensor.h" |
| #endif /* ARM_COMPUTE_GC */ |
| |
| #include <cmath> |
| #include <cstddef> |
| #include <limits> |
| #include <memory> |
| #include <random> |
| #include <sstream> |
| #include <string> |
| #include <type_traits> |
| #include <vector> |
| |
| #include "arm_compute/runtime/CPP/CPPScheduler.h" |
| #include "arm_compute/runtime/RuntimeContext.h" |
| |
| namespace arm_compute |
| { |
| #ifdef ARM_COMPUTE_CL |
| class CLTensor; |
| #endif /* ARM_COMPUTE_CL */ |
| namespace test |
| { |
| /** Round floating-point value with half value rounding to positive infinity. |
| * |
| * @param[in] value floating-point value to be rounded. |
| * |
| * @return Floating-point value of rounded @p value. |
| */ |
| template <typename T, typename = typename std::enable_if<std::is_floating_point<T>::value>::type> |
| inline T round_half_up(T value) |
| { |
| return std::floor(value + 0.5f); |
| } |
| |
| /** Round floating-point value with half value rounding to nearest even. |
| * |
| * @param[in] value floating-point value to be rounded. |
| * @param[in] epsilon precision. |
| * |
| * @return Floating-point value of rounded @p value. |
| */ |
| template <typename T, typename = typename std::enable_if<std::is_floating_point<T>::value>::type> |
| inline T round_half_even(T value, T epsilon = std::numeric_limits<T>::epsilon()) |
| { |
| T positive_value = std::abs(value); |
| T ipart = 0; |
| std::modf(positive_value, &ipart); |
| // If 'value' is exactly halfway between two integers |
| if(std::abs(positive_value - (ipart + 0.5f)) < epsilon) |
| { |
| // If 'ipart' is even then return 'ipart' |
| if(std::fmod(ipart, 2.f) < epsilon) |
| { |
| return support::cpp11::copysign(ipart, value); |
| } |
| // Else return the nearest even integer |
| return support::cpp11::copysign(std::ceil(ipart + 0.5f), value); |
| } |
| // Otherwise use the usual round to closest |
| return support::cpp11::copysign(support::cpp11::round(positive_value), value); |
| } |
| |
| namespace traits |
| { |
| // *INDENT-OFF* |
| // clang-format off |
| /** Promote a type */ |
| template <typename T> struct promote { }; |
| /** Promote uint8_t to uint16_t */ |
| template <> struct promote<uint8_t> { using type = uint16_t; /**< Promoted type */ }; |
| /** Promote int8_t to int16_t */ |
| template <> struct promote<int8_t> { using type = int16_t; /**< Promoted type */ }; |
| /** Promote uint16_t to uint32_t */ |
| template <> struct promote<uint16_t> { using type = uint32_t; /**< Promoted type */ }; |
| /** Promote int16_t to int32_t */ |
| template <> struct promote<int16_t> { using type = int32_t; /**< Promoted type */ }; |
| /** Promote uint32_t to uint64_t */ |
| template <> struct promote<uint32_t> { using type = uint64_t; /**< Promoted type */ }; |
| /** Promote int32_t to int64_t */ |
| template <> struct promote<int32_t> { using type = int64_t; /**< Promoted type */ }; |
| /** Promote float to float */ |
| template <> struct promote<float> { using type = float; /**< Promoted type */ }; |
| /** Promote half to half */ |
| template <> struct promote<half> { using type = half; /**< Promoted type */ }; |
| |
| /** Get promoted type */ |
| template <typename T> |
| using promote_t = typename promote<T>::type; |
| |
| template <typename T> |
| using make_signed_conditional_t = typename std::conditional<std::is_integral<T>::value, std::make_signed<T>, std::common_type<T>>::type; |
| |
| template <typename T> |
| using make_unsigned_conditional_t = typename std::conditional<std::is_integral<T>::value, std::make_unsigned<T>, std::common_type<T>>::type; |
| |
| // clang-format on |
| // *INDENT-ON* |
| } |
| |
| /** Look up the format corresponding to a channel. |
| * |
| * @param[in] channel Channel type. |
| * |
| * @return Format that contains the given channel. |
| */ |
| inline Format get_format_for_channel(Channel channel) |
| { |
| switch(channel) |
| { |
| case Channel::R: |
| case Channel::G: |
| case Channel::B: |
| return Format::RGB888; |
| default: |
| throw std::runtime_error("Unsupported channel"); |
| } |
| } |
| |
| /** Return the format of a channel. |
| * |
| * @param[in] channel Channel type. |
| * |
| * @return Format of the given channel. |
| */ |
| inline Format get_channel_format(Channel channel) |
| { |
| switch(channel) |
| { |
| case Channel::R: |
| case Channel::G: |
| case Channel::B: |
| return Format::U8; |
| default: |
| throw std::runtime_error("Unsupported channel"); |
| } |
| } |
| |
| /** Base case of foldl. |
| * |
| * @return value. |
| */ |
| template <typename F, typename T> |
| inline T foldl(F &&, const T &value) |
| { |
| return value; |
| } |
| |
| /** Base case of foldl. |
| * |
| * @return func(value1, value2). |
| */ |
| template <typename F, typename T, typename U> |
| inline auto foldl(F &&func, T &&value1, U &&value2) -> decltype(func(value1, value2)) |
| { |
| return func(value1, value2); |
| } |
| |
| /** Fold left. |
| * |
| * @param[in] func Binary function to be called. |
| * @param[in] initial Initial value. |
| * @param[in] value Argument passed to the function. |
| * @param[in] values Remaining arguments. |
| */ |
| template <typename F, typename I, typename T, typename... Vs> |
| inline I foldl(F &&func, I &&initial, T &&value, Vs &&... values) |
| { |
| return foldl(std::forward<F>(func), func(std::forward<I>(initial), std::forward<T>(value)), std::forward<Vs>(values)...); |
| } |
| |
| /** Create a valid region based on tensor shape, border mode and border size |
| * |
| * @param[in] a_shape Shape used as size of the valid region. |
| * @param[in] border_undefined (Optional) Boolean indicating if the border mode is undefined. |
| * @param[in] border_size (Optional) Border size used to specify the region to exclude. |
| * |
| * @return A valid region starting at (0, 0, ...) with size of @p shape if @p border_undefined is false; otherwise |
| * return A valid region starting at (@p border_size.left, @p border_size.top, ...) with reduced size of @p shape. |
| */ |
| inline ValidRegion shape_to_valid_region(const TensorShape &a_shape, bool border_undefined = false, BorderSize border_size = BorderSize(0)) |
| { |
| ValidRegion valid_region{ Coordinates(), a_shape }; |
| |
| Coordinates &anchor = valid_region.anchor; |
| TensorShape &shape = valid_region.shape; |
| |
| if(border_undefined) |
| { |
| ARM_COMPUTE_ERROR_ON(shape.num_dimensions() < 2); |
| |
| anchor.set(0, border_size.left); |
| anchor.set(1, border_size.top); |
| |
| 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)); |
| 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)); |
| |
| shape.set(0, valid_shape_x); |
| shape.set(1, valid_shape_y); |
| } |
| |
| return valid_region; |
| } |
| |
| /** Create a valid region for Gaussian Pyramid Half based on tensor shape and valid region at level "i - 1" and border mode |
| * |
| * @note The border size is 2 in case of Gaussian Pyramid Half |
| * |
| * @param[in] a_shape Shape used at level "i - 1" of Gaussian Pyramid Half |
| * @param[in] a_valid_region Valid region used at level "i - 1" of Gaussian Pyramid Half |
| * @param[in] border_undefined (Optional) Boolean indicating if the border mode is undefined. |
| * |
| * return The valid region for the level "i" of Gaussian Pyramid Half |
| */ |
| inline ValidRegion shape_to_valid_region_gaussian_pyramid_half(const TensorShape &a_shape, const ValidRegion &a_valid_region, bool border_undefined = false) |
| { |
| constexpr int border_size = 2; |
| |
| ValidRegion valid_region{ Coordinates(), a_shape }; |
| |
| Coordinates &anchor = valid_region.anchor; |
| TensorShape &shape = valid_region.shape; |
| |
| // Compute tensor shape for level "i" of Gaussian Pyramid Half |
| // dst_width = (src_width + 1) * 0.5f |
| // dst_height = (src_height + 1) * 0.5f |
| shape.set(0, (a_shape[0] + 1) * 0.5f); |
| shape.set(1, (a_shape[1] + 1) * 0.5f); |
| |
| if(border_undefined) |
| { |
| ARM_COMPUTE_ERROR_ON(shape.num_dimensions() < 2); |
| |
| // Compute the left and top invalid borders |
| float invalid_border_left = static_cast<float>(a_valid_region.anchor.x() + border_size) / 2.0f; |
| float invalid_border_top = static_cast<float>(a_valid_region.anchor.y() + border_size) / 2.0f; |
| |
| // For the new anchor point we can have 2 cases: |
| // 1) If the width/height of the tensor shape is odd, we have to take the ceil value of (a_valid_region.anchor.x() + border_size) / 2.0f or (a_valid_region.anchor.y() + border_size / 2.0f |
| // 2) If the width/height of the tensor shape is even, we have to take the floor value of (a_valid_region.anchor.x() + border_size) / 2.0f or (a_valid_region.anchor.y() + border_size) / 2.0f |
| // In this manner we should be able to propagate correctly the valid region along all levels of the pyramid |
| invalid_border_left = (a_shape[0] % 2) ? std::ceil(invalid_border_left) : std::floor(invalid_border_left); |
| invalid_border_top = (a_shape[1] % 2) ? std::ceil(invalid_border_top) : std::floor(invalid_border_top); |
| |
| // Set the anchor point |
| anchor.set(0, static_cast<int>(invalid_border_left)); |
| anchor.set(1, static_cast<int>(invalid_border_top)); |
| |
| // Compute shape |
| // Calculate the right and bottom invalid borders at the previous level of the pyramid |
| const float prev_invalid_border_right = static_cast<float>(a_shape[0] - (a_valid_region.anchor.x() + a_valid_region.shape[0])); |
| const float prev_invalid_border_bottom = static_cast<float>(a_shape[1] - (a_valid_region.anchor.y() + a_valid_region.shape[1])); |
| |
| // Calculate the right and bottom invalid borders at the current level of the pyramid |
| const float invalid_border_right = std::ceil((prev_invalid_border_right + static_cast<float>(border_size)) / 2.0f); |
| const float invalid_border_bottom = std::ceil((prev_invalid_border_bottom + static_cast<float>(border_size)) / 2.0f); |
| |
| const int valid_shape_x = std::max(0, static_cast<int>(shape.x()) - static_cast<int>(invalid_border_left) - static_cast<int>(invalid_border_right)); |
| const int valid_shape_y = std::max(0, static_cast<int>(shape.y()) - static_cast<int>(invalid_border_top) - static_cast<int>(invalid_border_bottom)); |
| |
| shape.set(0, valid_shape_x); |
| shape.set(1, valid_shape_y); |
| } |
| |
| return valid_region; |
| } |
| |
| /** Create a valid region for Laplacian Pyramid based on tensor shape and valid region at level "i - 1" and border mode |
| * |
| * @note The border size is 2 in case of Laplacian Pyramid |
| * |
| * @param[in] a_shape Shape used at level "i - 1" of Laplacian Pyramid |
| * @param[in] a_valid_region Valid region used at level "i - 1" of Laplacian Pyramid |
| * @param[in] border_undefined (Optional) Boolean indicating if the border mode is undefined. |
| * |
| * return The valid region for the level "i" of Laplacian Pyramid |
| */ |
| inline ValidRegion shape_to_valid_region_laplacian_pyramid(const TensorShape &a_shape, const ValidRegion &a_valid_region, bool border_undefined = false) |
| { |
| ValidRegion valid_region = shape_to_valid_region_gaussian_pyramid_half(a_shape, a_valid_region, border_undefined); |
| |
| if(border_undefined) |
| { |
| const BorderSize gaussian5x5_border(2); |
| |
| auto border_left = static_cast<int>(gaussian5x5_border.left); |
| auto border_right = static_cast<int>(gaussian5x5_border.right); |
| auto border_top = static_cast<int>(gaussian5x5_border.top); |
| auto border_bottom = static_cast<int>(gaussian5x5_border.bottom); |
| |
| valid_region.anchor.set(0, valid_region.anchor[0] + border_left); |
| valid_region.anchor.set(1, valid_region.anchor[1] + border_top); |
| valid_region.shape.set(0, std::max(0, static_cast<int>(valid_region.shape[0]) - border_right - border_left)); |
| valid_region.shape.set(1, std::max(0, static_cast<int>(valid_region.shape[1]) - border_top - border_bottom)); |
| } |
| |
| return valid_region; |
| } |
| |
| /** Write the value after casting the pointer according to @p data_type. |
| * |
| * @warning The type of the value must match the specified data type. |
| * |
| * @param[out] ptr Pointer to memory where the @p value will be written. |
| * @param[in] value Value that will be written. |
| * @param[in] data_type Data type that will be written. |
| */ |
| template <typename T> |
| void store_value_with_data_type(void *ptr, T value, DataType data_type) |
| { |
| switch(data_type) |
| { |
| case DataType::U8: |
| case DataType::QASYMM8: |
| case DataType::QASYMM8_PER_CHANNEL: |
| *reinterpret_cast<uint8_t *>(ptr) = value; |
| break; |
| case DataType::S8: |
| case DataType::QSYMM8: |
| case DataType::QSYMM8_PER_CHANNEL: |
| *reinterpret_cast<int8_t *>(ptr) = value; |
| break; |
| case DataType::U16: |
| case DataType::QASYMM16: |
| *reinterpret_cast<uint16_t *>(ptr) = value; |
| break; |
| case DataType::S16: |
| case DataType::QSYMM16: |
| *reinterpret_cast<int16_t *>(ptr) = value; |
| break; |
| case DataType::U32: |
| *reinterpret_cast<uint32_t *>(ptr) = value; |
| break; |
| case DataType::S32: |
| *reinterpret_cast<int32_t *>(ptr) = value; |
| break; |
| case DataType::U64: |
| *reinterpret_cast<uint64_t *>(ptr) = value; |
| break; |
| case DataType::S64: |
| *reinterpret_cast<int64_t *>(ptr) = value; |
| break; |
| case DataType::F16: |
| *reinterpret_cast<half *>(ptr) = value; |
| break; |
| case DataType::F32: |
| *reinterpret_cast<float *>(ptr) = value; |
| break; |
| case DataType::F64: |
| *reinterpret_cast<double *>(ptr) = value; |
| break; |
| case DataType::SIZET: |
| *reinterpret_cast<size_t *>(ptr) = value; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| } |
| } |
| |
| /** Saturate a value of type T against the numeric limits of type U. |
| * |
| * @param[in] val Value to be saturated. |
| * |
| * @return saturated value. |
| */ |
| template <typename U, typename T> |
| T saturate_cast(T val) |
| { |
| if(val > static_cast<T>(std::numeric_limits<U>::max())) |
| { |
| val = static_cast<T>(std::numeric_limits<U>::max()); |
| } |
| if(val < static_cast<T>(std::numeric_limits<U>::lowest())) |
| { |
| val = static_cast<T>(std::numeric_limits<U>::lowest()); |
| } |
| return val; |
| } |
| |
| /** Find the signed promoted common type. |
| */ |
| template <typename... T> |
| struct common_promoted_signed_type |
| { |
| /** Common type */ |
| using common_type = typename std::common_type<T...>::type; |
| /** Promoted type */ |
| using promoted_type = traits::promote_t<common_type>; |
| /** Intermediate type */ |
| using intermediate_type = typename traits::make_signed_conditional_t<promoted_type>::type; |
| }; |
| |
| /** Find the unsigned promoted common type. |
| */ |
| template <typename... T> |
| struct common_promoted_unsigned_type |
| { |
| /** Common type */ |
| using common_type = typename std::common_type<T...>::type; |
| /** Promoted type */ |
| using promoted_type = traits::promote_t<common_type>; |
| /** Intermediate type */ |
| using intermediate_type = typename traits::make_unsigned_conditional_t<promoted_type>::type; |
| }; |
| |
| /** Convert a linear index into n-dimensional coordinates. |
| * |
| * @param[in] shape Shape of the n-dimensional tensor. |
| * @param[in] index Linear index specifying the i-th element. |
| * |
| * @return n-dimensional coordinates. |
| */ |
| inline Coordinates index2coord(const TensorShape &shape, int index) |
| { |
| int num_elements = shape.total_size(); |
| |
| ARM_COMPUTE_ERROR_ON_MSG(index < 0 || index >= num_elements, "Index has to be in [0, num_elements]"); |
| ARM_COMPUTE_ERROR_ON_MSG(num_elements == 0, "Cannot create coordinate from empty shape"); |
| |
| Coordinates coord{ 0 }; |
| |
| for(int d = shape.num_dimensions() - 1; d >= 0; --d) |
| { |
| num_elements /= shape[d]; |
| coord.set(d, index / num_elements); |
| index %= num_elements; |
| } |
| |
| return coord; |
| } |
| |
| /** Linearise the given coordinate. |
| * |
| * Transforms the given coordinate into a linear offset in terms of |
| * elements. |
| * |
| * @param[in] shape Shape of the n-dimensional tensor. |
| * @param[in] coord The to be converted coordinate. |
| * |
| * @return Linear offset to the element. |
| */ |
| inline int coord2index(const TensorShape &shape, const Coordinates &coord) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(shape.total_size() == 0, "Cannot get index from empty shape"); |
| ARM_COMPUTE_ERROR_ON_MSG(coord.num_dimensions() == 0, "Cannot get index of empty coordinate"); |
| |
| int index = 0; |
| int dim_size = 1; |
| |
| for(unsigned int i = 0; i < coord.num_dimensions(); ++i) |
| { |
| index += coord[i] * dim_size; |
| dim_size *= shape[i]; |
| } |
| |
| return index; |
| } |
| |
| /** Check if a coordinate is within a valid region */ |
| inline bool is_in_valid_region(const ValidRegion &valid_region, Coordinates coord) |
| { |
| for(size_t d = 0; d < Coordinates::num_max_dimensions; ++d) |
| { |
| if(coord[d] < valid_region.start(d) || coord[d] >= valid_region.end(d)) |
| { |
| return false; |
| } |
| } |
| |
| return true; |
| } |
| |
| /** Create and initialize a tensor of the given type. |
| * |
| * @param[in] shape Tensor shape. |
| * @param[in] data_type Data type. |
| * @param[in] num_channels (Optional) Number of channels. |
| * @param[in] quantization_info (Optional) Quantization info for asymmetric quantized types. |
| * @param[in] data_layout (Optional) Data layout. Default is NCHW. |
| * @param[in] ctx (Optional) Pointer to the runtime context. |
| * |
| * @return Initialized tensor of given type. |
| */ |
| template <typename T> |
| inline T create_tensor(const TensorShape &shape, DataType data_type, int num_channels = 1, |
| QuantizationInfo quantization_info = QuantizationInfo(), DataLayout data_layout = DataLayout::NCHW, IRuntimeContext *ctx = nullptr) |
| { |
| T tensor(ctx); |
| TensorInfo info(shape, num_channels, data_type); |
| info.set_quantization_info(quantization_info); |
| info.set_data_layout(data_layout); |
| tensor.allocator()->init(info); |
| |
| return tensor; |
| } |
| |
| /** Create and initialize a tensor of the given type. |
| * |
| * @param[in] shape Tensor shape. |
| * @param[in] format Format type. |
| * @param[in] ctx (Optional) Pointer to the runtime context. |
| * |
| * @return Initialized tensor of given type. |
| */ |
| template <typename T> |
| inline T create_tensor(const TensorShape &shape, Format format, IRuntimeContext *ctx = nullptr) |
| { |
| TensorInfo info(shape, format); |
| |
| T tensor(ctx); |
| tensor.allocator()->init(info); |
| |
| return tensor; |
| } |
| |
| /** Create and initialize a multi-image of the given type. |
| * |
| * @param[in] shape Tensor shape. |
| * @param[in] format Format type. |
| * |
| * @return Initialized tensor of given type. |
| */ |
| template <typename T> |
| inline T create_multi_image(const TensorShape &shape, Format format) |
| { |
| T multi_image; |
| multi_image.init(shape.x(), shape.y(), format); |
| |
| return multi_image; |
| } |
| |
| /** Create and initialize a HOG (Histogram of Oriented Gradients) of the given type. |
| * |
| * @param[in] hog_info HOGInfo object |
| * |
| * @return Initialized HOG of given type. |
| */ |
| template <typename T> |
| inline T create_HOG(const HOGInfo &hog_info) |
| { |
| T hog; |
| hog.init(hog_info); |
| |
| return hog; |
| } |
| |
| /** Create and initialize a Pyramid of the given type. |
| * |
| * @param[in] pyramid_info The PyramidInfo object. |
| * |
| * @return Initialized Pyramid of given type. |
| */ |
| template <typename T> |
| inline T create_pyramid(const PyramidInfo &pyramid_info) |
| { |
| T pyramid; |
| pyramid.init_auto_padding(pyramid_info); |
| |
| return pyramid; |
| } |
| |
| /** Initialize a convolution matrix. |
| * |
| * @param[in, out] conv The input convolution matrix. |
| * @param[in] width The width of the convolution matrix. |
| * @param[in] height The height of the convolution matrix. |
| * @param[in] seed The random seed to be used. |
| */ |
| inline void init_conv(int16_t *conv, unsigned int width, unsigned int height, std::random_device::result_type seed) |
| { |
| std::mt19937 gen(seed); |
| std::uniform_int_distribution<int16_t> distribution_int16(-32768, 32767); |
| |
| for(unsigned int i = 0; i < width * height; ++i) |
| { |
| conv[i] = distribution_int16(gen); |
| } |
| } |
| |
| /** Initialize a separable convolution matrix. |
| * |
| * @param[in, out] conv The input convolution matrix. |
| * @param[in] width The width of the convolution matrix. |
| * @param[in] height The height of the convolution matrix. |
| * @param[in] seed The random seed to be used. |
| */ |
| inline void init_separable_conv(int16_t *conv, unsigned int width, unsigned int height, std::random_device::result_type seed) |
| { |
| std::mt19937 gen(seed); |
| // Set it between -128 and 127 to ensure the matrix does not overflow |
| std::uniform_int_distribution<int16_t> distribution_int16(-128, 127); |
| |
| int16_t conv_row[width]; |
| int16_t conv_col[height]; |
| |
| conv_row[0] = conv_col[0] = 1; |
| for(unsigned int i = 1; i < width; ++i) |
| { |
| conv_row[i] = distribution_int16(gen); |
| } |
| |
| for(unsigned int i = 1; i < height; ++i) |
| { |
| conv_col[i] = distribution_int16(gen); |
| } |
| |
| // Multiply two matrices |
| for(unsigned int i = 0; i < width; ++i) |
| { |
| for(unsigned int j = 0; j < height; ++j) |
| { |
| conv[i * width + j] = conv_col[i] * conv_row[j]; |
| } |
| } |
| } |
| |
| /** Create a vector with a uniform distribution of floating point values across the specified range. |
| * |
| * @param[in] num_values The number of values to be created. |
| * @param[in] min The minimum value in distribution (inclusive). |
| * @param[in] max The maximum value in distribution (inclusive). |
| * @param[in] seed The random seed to be used. |
| * |
| * @return A vector that contains the requested number of random floating point values |
| */ |
| template <typename T, typename = typename std::enable_if<std::is_floating_point<T>::value>::type> |
| inline std::vector<T> generate_random_real(unsigned int num_values, T min, T max, std::random_device::result_type seed) |
| { |
| std::vector<T> v(num_values); |
| std::mt19937 gen(seed); |
| std::uniform_real_distribution<T> dist(min, max); |
| |
| for(unsigned int i = 0; i < num_values; ++i) |
| { |
| v.at(i) = dist(gen); |
| } |
| |
| return v; |
| } |
| |
| /** Create a vector of random keypoints for pyramid representation. |
| * |
| * @param[in] shape The shape of the input tensor. |
| * @param[in] num_keypoints The number of keypoints to be created. |
| * @param[in] seed The random seed to be used. |
| * @param[in] num_levels The number of pyramid levels. |
| * |
| * @return A vector that contains the requested number of random keypoints |
| */ |
| inline std::vector<KeyPoint> generate_random_keypoints(const TensorShape &shape, size_t num_keypoints, std::random_device::result_type seed, size_t num_levels = 1) |
| { |
| std::vector<KeyPoint> keypoints; |
| std::mt19937 gen(seed); |
| |
| // Calculate distribution bounds |
| const auto min = static_cast<int>(std::pow(2, num_levels)); |
| const auto max_width = static_cast<int>(shape.x()); |
| const auto max_height = static_cast<int>(shape.y()); |
| |
| ARM_COMPUTE_ERROR_ON(min > max_width || min > max_height); |
| |
| // Create distributions |
| std::uniform_int_distribution<> dist_w(min, max_width); |
| std::uniform_int_distribution<> dist_h(min, max_height); |
| |
| for(unsigned int i = 0; i < num_keypoints; i++) |
| { |
| KeyPoint keypoint; |
| keypoint.x = dist_w(gen); |
| keypoint.y = dist_h(gen); |
| keypoint.tracking_status = 1; |
| |
| keypoints.push_back(keypoint); |
| } |
| |
| return keypoints; |
| } |
| |
| 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) |
| { |
| #ifdef ARM_COMPUTE_GC |
| if(opengles31_is_available() && std::is_same<typename std::decay<TensorType>::type, arm_compute::GCTensor>::value) |
| { |
| // Force sync the tensor by calling map and unmap. |
| IGCTensor &t = dynamic_cast<IGCTensor &>(tensor); |
| t.map(); |
| t.unmap(); |
| } |
| #else /* ARM_COMPUTE_GC */ |
| ARM_COMPUTE_UNUSED(tensor); |
| #endif /* ARM_COMPUTE_GC */ |
| } |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* __ARM_COMPUTE_TEST_UTILS_H__ */ |