diff options
| author | Felix Thomasmathibalan <felixjohnny.thomasmathibalan@arm.com> | 2023-09-27 17:46:17 +0100 |
|---|---|---|
| committer | felixjohnny.thomasmathibalan <felixjohnny.thomasmathibalan@arm.com> | 2023-09-28 12:08:05 +0000 |
| commit | afd38f0c617d6f89b2b4532c6c44f116617e2b6f (patch) | |
| tree | 03bc7d5a762099989b16a656fa8d397b490ed70e /arm_compute/core | |
| parent | bdcb4c148ee2fdeaaddf4cf1e57bbb0de02bb894 (diff) | |
| download | ComputeLibrary-afd38f0c617d6f89b2b4532c6c44f116617e2b6f.tar.gz | |
Apply clang-format on repository
Code is formatted as per a revised clang format configuration
file(not part of this delivery). Version 14.0.6 is used.
Exclusion List:
- files with .cl extension
- files that are not strictly C/C++ (e.g. Android.bp, Sconscript ...)
And the following directories
- compute_kernel_writer/validation/
- tests/
- include/
- src/core/NEON/kernels/convolution/
- src/core/NEON/kernels/arm_gemm/
- src/core/NEON/kernels/arm_conv/
- data/
There will be a follow up for formatting of .cl files and the
files under tests/ and compute_kernel_writer/validation/.
Signed-off-by: Felix Thomasmathibalan <felixjohnny.thomasmathibalan@arm.com>
Change-Id: Ib7eb1fcf4e7537b9feaefcfc15098a804a3fde0a
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10391
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Diffstat (limited to 'arm_compute/core')
68 files changed, 1460 insertions, 1097 deletions
diff --git a/arm_compute/core/CL/CLCompileContext.h b/arm_compute/core/CL/CLCompileContext.h index 60e0f95f83..dcd3b45670 100644 --- a/arm_compute/core/CL/CLCompileContext.h +++ b/arm_compute/core/CL/CLCompileContext.h @@ -250,8 +250,12 @@ public: * * @return The created kernel. */ - Kernel create_kernel(const std::string &kernel_name, const std::string &program_name, const std::string &program_source, - const std::string &kernel_path, const StringSet &build_options_set, bool is_binary) const; + Kernel create_kernel(const std::string &kernel_name, + const std::string &program_name, + const std::string &program_source, + const std::string &kernel_path, + const StringSet &build_options_set, + bool is_binary) const; /** Clear the library's cache of binary programs */ @@ -323,7 +327,8 @@ private: * @param[in] program_source Source of the program. * @param[in] is_binary Flag to indicate if the program source is binary. */ - const Program &load_program(const std::string &program_name, const std::string &program_source, bool is_binary) const; + const Program & + load_program(const std::string &program_name, const std::string &program_source, bool is_binary) const; /** Generates the build options given a string of user defined ones * @@ -343,11 +348,11 @@ private: */ std::string stringify_set(const StringSet &s, const std::string &kernel_path) const; - cl::Context _context; /**< Underlying CL context. */ - CLDevice _device; /**< Underlying CL device. */ + cl::Context _context; /**< Underlying CL context. */ + CLDevice _device; /**< Underlying CL device. */ mutable std::map<std::string, const Program> _programs_map; /**< Map with all already loaded program data. */ mutable std::map<std::string, cl::Program> _built_programs_map; /**< Map with all already built program data. */ - bool _is_wbsm_supported; /**< Support of worksize batch size modifier support boolean*/ + bool _is_wbsm_supported; /**< Support of worksize batch size modifier support boolean*/ }; } // namespace arm_compute #endif /* ARM_COMPUTE_CLCOMPILECONTEXT_H */ diff --git a/arm_compute/core/CL/CLDevice.h b/arm_compute/core/CL/CLDevice.h index 5e0f86e6d9..ded6bb8493 100644 --- a/arm_compute/core/CL/CLDevice.h +++ b/arm_compute/core/CL/CLDevice.h @@ -44,8 +44,7 @@ class CLDevice : public IDevice { public: /** Default Constructor */ - CLDevice() - : _device(cl::Device()), _options() + CLDevice() : _device(cl::Device()), _options() { } @@ -53,8 +52,7 @@ public: * * @param[in] cl_device OpenCL device */ - CLDevice(const cl::Device &cl_device) - : _device(), _options() + CLDevice(const cl::Device &cl_device) : _device(), _options() { _device = cl_device; @@ -66,13 +64,13 @@ public: std::string extensions = _device.getInfo<CL_DEVICE_EXTENSIONS>(); std::istringstream iss(extensions); - for(std::string s; iss >> s;) + for (std::string s; iss >> s;) { _options.extensions.insert(s); } // SW workaround for G76 - if(_options.gpu_target == GPUTarget::G76) + if (_options.gpu_target == GPUTarget::G76) { _options.extensions.insert("cl_arm_integer_dot_product_int8"); } @@ -153,15 +151,15 @@ public: */ std::tuple<bool, std::string> is_non_uniform_workgroup_supported() const { - if(version() == CLVersion::CL30 && get_cl_non_uniform_work_group_supported(_device)) + if (version() == CLVersion::CL30 && get_cl_non_uniform_work_group_supported(_device)) { return {true, " -cl-std=CL3.0 "}; } - else if(version() == CLVersion::CL20) + else if (version() == CLVersion::CL20) { return {true, " -cl-std=CL2.0 "}; } - else if(supported("cl_arm_non_uniform_work_group_size")) + else if (supported("cl_arm_non_uniform_work_group_size")) { return {true, " -cl-arm-non-uniform-work-group-size "}; } diff --git a/arm_compute/core/CL/CLHelpers.h b/arm_compute/core/CL/CLHelpers.h index 20d93df5a1..1a639e47f9 100644 --- a/arm_compute/core/CL/CLHelpers.h +++ b/arm_compute/core/CL/CLHelpers.h @@ -179,7 +179,9 @@ bool dot8_acc_supported(const cl::Device &device); * * @return True if the configuration is supported */ -bool cl_winograd_convolution_layer_supported(const Size2D &output_tile, const Size2D &kernel_size, DataLayout data_layout); +bool cl_winograd_convolution_layer_supported(const Size2D &output_tile, + const Size2D &kernel_size, + DataLayout data_layout); /** Helper function to get the preferred native vector width size for built-in scalar types that can be put into vectors * @@ -215,7 +217,9 @@ bool image2d_from_buffer_supported(const cl::Device &device); * * @return An opencl kernel */ -cl::Kernel create_kernel(const CLCompileContext &ctx, const std::string &kernel_name, const std::set<std::string> &build_opts = std::set<std::string>()); +cl::Kernel create_kernel(const CLCompileContext &ctx, + const std::string &kernel_name, + const std::set<std::string> &build_opts = std::set<std::string>()); /** Creates a suitable LWS hint object for parallel implementations. Sets the number of WG based on the input size. * If input width is smaller than 128 we can use fewer threads than 8. diff --git a/arm_compute/core/CL/CLTypes.h b/arm_compute/core/CL/CLTypes.h index 00b7cda2e1..0f088e2b10 100644 --- a/arm_compute/core/CL/CLTypes.h +++ b/arm_compute/core/CL/CLTypes.h @@ -63,15 +63,13 @@ struct CLDeviceOptions struct CLQuantization { /** Default Constructor */ - CLQuantization() - : scale(nullptr), offset(nullptr) {}; + CLQuantization() : scale(nullptr), offset(nullptr){}; /** Constructor * * @param[in] scale OpenCL scale array * @param[in] offset OpenCL offset array */ - CLQuantization(const ICLFloatArray *scale, const ICLInt32Array *offset) - : scale(scale), offset(offset) {}; + CLQuantization(const ICLFloatArray *scale, const ICLInt32Array *offset) : scale(scale), offset(offset){}; const ICLFloatArray *scale; /**< Quantization scale array */ const ICLInt32Array *offset; /**< Quantization offset array */ diff --git a/arm_compute/core/CL/ICLArray.h b/arm_compute/core/CL/ICLArray.h index 57f842b6f9..a2b2baa5b3 100644 --- a/arm_compute/core/CL/ICLArray.h +++ b/arm_compute/core/CL/ICLArray.h @@ -40,8 +40,7 @@ public: * @param[in] max_num_values Maximum size of the array. * */ - explicit ICLArray(size_t max_num_values) - : IArray<T>(max_num_values), _mapping(nullptr) + explicit ICLArray(size_t max_num_values) : IArray<T>(max_num_values), _mapping(nullptr) { } @@ -125,5 +124,5 @@ using ICLInt16Array = ICLArray<cl_short>; using ICLInt32Array = ICLArray<cl_int>; /** Interface for OpenCL Array of floats. */ using ICLFloatArray = ICLArray<cl_float>; -} +} // namespace arm_compute #endif /*ARM_COMPUTE_ICLARRAY_H*/ diff --git a/arm_compute/core/CL/ICLTensor.h b/arm_compute/core/CL/ICLTensor.h index 78d3757e59..8de5423762 100644 --- a/arm_compute/core/CL/ICLTensor.h +++ b/arm_compute/core/CL/ICLTensor.h @@ -24,9 +24,8 @@ #ifndef ARM_COMPUTE_ICLTENSOR_H #define ARM_COMPUTE_ICLTENSOR_H -#include "arm_compute/core/ITensor.h" - #include "arm_compute/core/CL/CLTypes.h" +#include "arm_compute/core/ITensor.h" #include <cstdint> @@ -34,7 +33,7 @@ namespace cl { class Buffer; class CommandQueue; -} +} // namespace cl namespace arm_compute { @@ -113,5 +112,5 @@ private: }; using ICLImage = ICLTensor; -} +} // namespace arm_compute #endif /*ARM_COMPUTE_ICLTENSOR_H */ diff --git a/arm_compute/core/CL/OpenCL.h b/arm_compute/core/CL/OpenCL.h index f86d55a9ea..a5c4e39df2 100644 --- a/arm_compute/core/CL/OpenCL.h +++ b/arm_compute/core/CL/OpenCL.h @@ -31,8 +31,8 @@ #ifndef ARM_COMPUTE_NO_EXCEPTIONS #define CL_HPP_ENABLE_EXCEPTIONS #endif // ARM_COMPUTE_NO_EXCEPTIONS -#define CL_TARGET_OPENCL_VERSION 300 -#define CL_HPP_TARGET_OPENCL_VERSION 110 +#define CL_TARGET_OPENCL_VERSION 300 +#define CL_HPP_TARGET_OPENCL_VERSION 110 #define CL_HPP_MINIMUM_OPENCL_VERSION 110 #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Weffc++" @@ -40,7 +40,7 @@ #pragma GCC diagnostic ignored "-Wunused-parameter" #if defined(__GNUG__) && __GNUG__ >= 8 #pragma GCC diagnostic ignored "-Wcatch-value" -#endif // defined(__GNUG__) && __GNUG__ >= 8 +#endif // defined(__GNUG__) && __GNUG__ >= 8 #include <CL/opencl.hpp> // include new hpp header instead of cl2.hpp #pragma GCC diagnostic pop @@ -88,8 +88,7 @@ public: */ bool load_default(); -#define DECLARE_FUNCTION_PTR(func_name) \ - std::function<decltype(func_name)> func_name##_ptr = nullptr +#define DECLARE_FUNCTION_PTR(func_name) std::function<decltype(func_name)> func_name##_ptr = nullptr DECLARE_FUNCTION_PTR(clCreateContext); DECLARE_FUNCTION_PTR(clCreateContextFromType); diff --git a/arm_compute/core/CPP/CPPTypes.h b/arm_compute/core/CPP/CPPTypes.h index e4cbd9ff9b..b080a86938 100644 --- a/arm_compute/core/CPP/CPPTypes.h +++ b/arm_compute/core/CPP/CPPTypes.h @@ -78,10 +78,10 @@ public: /* Delete move and copy constructors and assignment operator s */ - CPUInfo(CPUInfo const &) = delete; // Copy construct - CPUInfo(CPUInfo &&) = delete; // Move construct + CPUInfo(CPUInfo const &) = delete; // Copy construct + CPUInfo(CPUInfo &&) = delete; // Move construct CPUInfo &operator=(CPUInfo const &) = delete; // Copy assign - CPUInfo &operator=(CPUInfo &&) = delete; // Move assign + CPUInfo &operator=(CPUInfo &&) = delete; // Move assign /** Checks if the cpu model supports fp16. * @@ -179,9 +179,9 @@ private: /** Information about executing thread and CPU. */ struct ThreadInfo { - int thread_id{ 0 }; - int num_threads{ 1 }; - const CPUInfo *cpu_info{ nullptr }; + int thread_id{0}; + int num_threads{1}; + const CPUInfo *cpu_info{nullptr}; }; } // namespace arm_compute #endif /* ARM_COMPUTE_CPP_TYPES_H */ diff --git a/arm_compute/core/CPP/ICPPKernel.h b/arm_compute/core/CPP/ICPPKernel.h index 00a10555e3..03967a536d 100644 --- a/arm_compute/core/CPP/ICPPKernel.h +++ b/arm_compute/core/CPP/ICPPKernel.h @@ -25,9 +25,9 @@ #define ARM_COMPUTE_ICPPKERNEL_H #include "arm_compute/core/CPP/CPPTypes.h" +#include "arm_compute/core/experimental/Types.h" #include "arm_compute/core/IKernel.h" #include "arm_compute/core/Types.h" -#include "arm_compute/core/experimental/Types.h" namespace arm_compute { @@ -38,7 +38,7 @@ class ITensor; class ICPPKernel : public IKernel { public: - static constexpr size_t default_mws = 1; /* Default minimum workload size value - no impact */ + static constexpr size_t default_mws = 1; /* Default minimum workload size value - no impact */ /** Default destructor */ virtual ~ICPPKernel() = default; diff --git a/arm_compute/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.h b/arm_compute/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.h index 068b37d80c..dd91595ea6 100644 --- a/arm_compute/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.h +++ b/arm_compute/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.h @@ -63,8 +63,16 @@ public: * @param[out] keeps_size (Optional) Number of filtered indices per class tensor of size [num_classes]. Data types supported: U32 * @param[in] info (Optional) BoxNMSLimitInfo information. */ - void configure(const ITensor *scores_in, const ITensor *boxes_in, const ITensor *batch_splits_in, ITensor *scores_out, ITensor *boxes_out, ITensor *classes, - ITensor *batch_splits_out = nullptr, ITensor *keeps = nullptr, ITensor *keeps_size = nullptr, const BoxNMSLimitInfo info = BoxNMSLimitInfo()); + void configure(const ITensor *scores_in, + const ITensor *boxes_in, + const ITensor *batch_splits_in, + ITensor *scores_out, + ITensor *boxes_out, + ITensor *classes, + ITensor *batch_splits_out = nullptr, + ITensor *keeps = nullptr, + ITensor *keeps_size = nullptr, + const BoxNMSLimitInfo info = BoxNMSLimitInfo()); // Inherited methods overridden: void run(const Window &window, const ThreadInfo &info) override; @@ -74,9 +82,9 @@ public: void run_nmslimit(); private: - const ITensor *_scores_in; - const ITensor *_boxes_in; - const ITensor *_batch_splits_in; + const ITensor *_scores_in; + const ITensor *_boxes_in; + const ITensor *_batch_splits_in; ITensor *_scores_out; ITensor *_boxes_out; ITensor *_classes; diff --git a/arm_compute/core/CPP/kernels/CPPNonMaximumSuppressionKernel.h b/arm_compute/core/CPP/kernels/CPPNonMaximumSuppressionKernel.h index e32b5d8f7b..d1f7f8670f 100644 --- a/arm_compute/core/CPP/kernels/CPPNonMaximumSuppressionKernel.h +++ b/arm_compute/core/CPP/kernels/CPPNonMaximumSuppressionKernel.h @@ -24,9 +24,8 @@ #ifndef ARM_COMPUTE_CPP_NONMAXIMUMSUPPRESSIONKERNEL_LAYER_H #define ARM_COMPUTE_CPP_NONMAXIMUMSUPPRESSIONKERNEL_LAYER_H -#include "arm_compute/runtime/CPP/ICPPSimpleFunction.h" - #include "arm_compute/core/Types.h" +#include "arm_compute/runtime/CPP/ICPPSimpleFunction.h" namespace arm_compute { @@ -65,7 +64,12 @@ public: * @param[in] iou_threshold The threshold used in non maximum suppression. * */ - void configure(const ITensor *input_bboxes, const ITensor *input_scores, ITensor *output_indices, unsigned int max_output_size, const float score_threshold, const float iou_threshold); + void configure(const ITensor *input_bboxes, + const ITensor *input_scores, + ITensor *output_indices, + unsigned int max_output_size, + const float score_threshold, + const float iou_threshold); /** Static function to check if given arguments will lead to a valid configuration of @ref CPPNonMaximumSuppressionKernel * @@ -77,8 +81,12 @@ public: * @param[in] iou_threshold The threshold used in non maximum suppression. * */ - static Status validate(const ITensorInfo *input_bboxes, const ITensorInfo *input_scores, const ITensorInfo *output_indices, unsigned int max_output_size, - const float score_threshold, const float iou_threshold); + static Status validate(const ITensorInfo *input_bboxes, + const ITensorInfo *input_scores, + const ITensorInfo *output_indices, + unsigned int max_output_size, + const float score_threshold, + const float iou_threshold); // Inherited methods overridden: void run(const Window &window, const ThreadInfo &info) override; diff --git a/arm_compute/core/CPP/kernels/CPPTopKVKernel.h b/arm_compute/core/CPP/kernels/CPPTopKVKernel.h index 1245dbc14c..7326a10e2f 100644 --- a/arm_compute/core/CPP/kernels/CPPTopKVKernel.h +++ b/arm_compute/core/CPP/kernels/CPPTopKVKernel.h @@ -69,7 +69,8 @@ public: * * @return a status */ - static Status validate(const ITensorInfo *predictions, const ITensorInfo *targets, ITensorInfo *output, const unsigned int k); + static Status + validate(const ITensorInfo *predictions, const ITensorInfo *targets, ITensorInfo *output, const unsigned int k); // Inherited methods overridden: void run(const Window &window, const ThreadInfo &info) override; diff --git a/arm_compute/core/Coordinates.h b/arm_compute/core/Coordinates.h index f6e1f4d282..d1240bb10a 100644 --- a/arm_compute/core/Coordinates.h +++ b/arm_compute/core/Coordinates.h @@ -42,8 +42,7 @@ public: * @param[in] coords Values to initialize the dimensions. */ template <typename... Ts> - constexpr Coordinates(Ts... coords) - : Dimensions{ coords... } + constexpr Coordinates(Ts... coords) : Dimensions{coords...} { } /** Allow instances of this class to be copy constructed */ @@ -57,5 +56,5 @@ public: /** Default destructor */ ~Coordinates() = default; }; -} +} // namespace arm_compute #endif /*ARM_COMPUTE_COORDINATES_H*/ diff --git a/arm_compute/core/CoreTypes.h b/arm_compute/core/CoreTypes.h index 4a48a36651..1a9db1937c 100644 --- a/arm_compute/core/CoreTypes.h +++ b/arm_compute/core/CoreTypes.h @@ -25,6 +25,7 @@ #define ACL_ARM_COMPUTE_CORE_CORETYPES #include "arm_compute/core/Strides.h" + #include "support/Half.h" /** CoreTypes.h groups together essential small types that are used across functions */ @@ -146,9 +147,11 @@ public: * @param[in] pad_y (Optional) Padding, in elements, across y. Defaults to 0. * @param[in] round (Optional) Dimensions rounding. Defaults to @ref DimensionRoundingType::FLOOR. */ - PadStrideInfo(unsigned int stride_x = 1, unsigned int stride_y = 1, - unsigned int pad_x = 0, unsigned int pad_y = 0, - DimensionRoundingType round = DimensionRoundingType::FLOOR) + PadStrideInfo(unsigned int stride_x = 1, + unsigned int stride_y = 1, + unsigned int pad_x = 0, + unsigned int pad_y = 0, + DimensionRoundingType round = DimensionRoundingType::FLOOR) : _stride(std::make_pair(stride_x, stride_y)), _pad_left(pad_x), _pad_top(pad_y), @@ -167,9 +170,12 @@ public: * @param[in] pad_bottom Padding across y on the bottom, in elements. * @param[in] round Dimensions rounding. */ - PadStrideInfo(unsigned int stride_x, unsigned int stride_y, - unsigned int pad_left, unsigned int pad_right, - unsigned int pad_top, unsigned int pad_bottom, + PadStrideInfo(unsigned int stride_x, + unsigned int stride_y, + unsigned int pad_left, + unsigned int pad_right, + unsigned int pad_top, + unsigned int pad_bottom, DimensionRoundingType round) : _stride(std::make_pair(stride_x, stride_y)), _pad_left(pad_left), @@ -243,10 +249,10 @@ public: private: std::pair<unsigned int, unsigned int> _stride; - unsigned int _pad_left; - unsigned int _pad_top; - unsigned int _pad_right; - unsigned int _pad_bottom; + unsigned int _pad_left; + unsigned int _pad_top; + unsigned int _pad_right; + unsigned int _pad_bottom; DimensionRoundingType _round_type; }; diff --git a/arm_compute/core/Dimensions.h b/arm_compute/core/Dimensions.h index 2ebfcd7f83..bb8692d70a 100644 --- a/arm_compute/core/Dimensions.h +++ b/arm_compute/core/Dimensions.h @@ -50,8 +50,7 @@ public: * @param[in] dims Values to initialize the dimensions. */ template <typename... Ts> - explicit Dimensions(Ts... dims) - : _id{ { static_cast<T>(dims)... } }, _num_dimensions{ sizeof...(dims) } + explicit Dimensions(Ts... dims) : _id{{static_cast<T>(dims)...}}, _num_dimensions{sizeof...(dims)} { } @@ -78,7 +77,7 @@ public: ARM_COMPUTE_ERROR_ON(dimension >= num_max_dimensions); _id[dimension] = value; // Don't increase the number of dimensions if the new dimension is 1 - if(increase_dim_unit || value != 1) + if (increase_dim_unit || value != 1) { _num_dimensions = std::max(_num_dimensions, dimension + 1); } @@ -108,7 +107,7 @@ public: void increment(size_t dim, T step = 1) { ARM_COMPUTE_ERROR_ON(dim >= _num_dimensions); - if((std::numeric_limits<T>::max() - _id[dim]) >= step) + if ((std::numeric_limits<T>::max() - _id[dim]) >= step) { _id[dim] += step; } @@ -162,7 +161,7 @@ public: const size_t last = std::min(_num_dimensions, first + n); - if(last > (first + 1)) + if (last > (first + 1)) { // Collapse dimensions into the first _id[first] = std::accumulate(&_id[first], &_id[last], 1, std::multiplies<T>()); @@ -196,7 +195,7 @@ public: void remove(size_t idx) { ARM_COMPUTE_ERROR_ON(_num_dimensions < 1); - if(idx >= _num_dimensions) + if (idx >= _num_dimensions) { return; } @@ -262,7 +261,7 @@ protected: ~Dimensions() = default; std::array<T, num_max_dimensions> _id; - size_t _num_dimensions{ 0 }; + size_t _num_dimensions{0}; }; /** Check that given dimensions are equal. @@ -289,5 +288,5 @@ inline bool operator!=(const Dimensions<T> &lhs, const Dimensions<T> &rhs) { return !(lhs == rhs); } -} +} // namespace arm_compute #endif /*ARM_COMPUTE_DIMENSIONS_H*/ diff --git a/arm_compute/core/Error.h b/arm_compute/core/Error.h index 0854f2c527..7a7033805a 100644 --- a/arm_compute/core/Error.h +++ b/arm_compute/core/Error.h @@ -53,8 +53,7 @@ class Status { public: /** Default Constructor **/ - Status() - : _code(ErrorCode::OK), _error_description(" ") + Status() : _code(ErrorCode::OK), _error_description(" ") { } /** Default Constructor @@ -101,7 +100,7 @@ public: /** Throws a runtime exception in case it contains a valid error status */ void throw_if_error() const { - if(!bool(*this)) + if (!bool(*this)) { internal_throw_on_error(); } @@ -141,7 +140,7 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file * @param[in] err Error status */ [[noreturn]] void throw_error(Status err); -} +} // namespace arm_compute /** To avoid unused variables warnings * * This is useful if for example a variable is only used @@ -156,7 +155,8 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file * @param[in] error_code Error code. * @param[in] msg Message to encapsulate. */ -#define ARM_COMPUTE_CREATE_ERROR(error_code, msg) arm_compute::create_error_msg(error_code, __func__, __FILE__, __LINE__, msg) +#define ARM_COMPUTE_CREATE_ERROR(error_code, msg) \ + arm_compute::create_error_msg(error_code, __func__, __FILE__, __LINE__, msg) /** Creates an error on location with a given message * @@ -166,7 +166,8 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file * @param[in] line Line in which the error occurred. * @param[in] msg Message to display before abandoning. */ -#define ARM_COMPUTE_CREATE_ERROR_LOC(error_code, func, file, line, msg) arm_compute::create_error_msg(error_code, func, file, line, msg) +#define ARM_COMPUTE_CREATE_ERROR_LOC(error_code, func, file, line, msg) \ + arm_compute::create_error_msg(error_code, func, file, line, msg) /** Creates an error on location with a given message. Accepts a message format * and a variable list of arguments matching the format description. @@ -178,14 +179,14 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file * @param[in] msg Error description message format. * @param[in] ... List of arguments matching the format description. */ -#define ARM_COMPUTE_CREATE_ERROR_LOC_VAR(error_code, func, file, line, msg, ...) \ - do \ - { \ - std::array<char, 512> out{ 0 }; \ - int offset = snprintf(out.data(), out.size(), "in %s %s:%d: ", func, file, line); \ - snprintf(out.data() + offset, out.size() - offset, msg, __VA_ARGS__); \ - arm_compute::create_error(error_code, std::string(out.data())); \ - } while(false) +#define ARM_COMPUTE_CREATE_ERROR_LOC_VAR(error_code, func, file, line, msg, ...) \ + do \ + { \ + std::array<char, 512> out{0}; \ + int offset = snprintf(out.data(), out.size(), "in %s %s:%d: ", func, file, line); \ + snprintf(out.data() + offset, out.size() - offset, msg, __VA_ARGS__); \ + arm_compute::create_error(error_code, std::string(out.data())); \ + } while (false) /** An error is returned with the given description. * @@ -195,7 +196,7 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file do \ { \ return ARM_COMPUTE_CREATE_ERROR(arm_compute::ErrorCode::RUNTIME_ERROR, __VA_ARGS__); \ - } while(false) + } while (false) /** Checks if a status contains an error and returns it * @@ -205,18 +206,17 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file do \ { \ const auto s = status; \ - if(!bool(s)) \ + if (!bool(s)) \ { \ return s; \ } \ - } while(false) + } while (false) /** Checks if an error value is valid if not throws an exception with the error * * @param[in] error Error value to check. */ -#define ARM_COMPUTE_THROW_ON_ERROR(error) \ - error.throw_if_error(); +#define ARM_COMPUTE_THROW_ON_ERROR(error) error.throw_if_error(); /** If the condition is true, an error is returned. Accepts a message format * and a variable list of arguments matching the format description. @@ -228,28 +228,29 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file #define ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(cond, msg, ...) \ do \ { \ - if(cond) \ + if (cond) \ { \ - std::array<char, 512> out{ 0 }; \ + std::array<char, 512> out{0}; \ int offset = snprintf(out.data(), out.size(), "in %s %s:%d: ", __func__, __FILE__, __LINE__); \ snprintf(out.data() + offset, out.size() - offset, msg, __VA_ARGS__); \ return arm_compute::create_error(arm_compute::ErrorCode::RUNTIME_ERROR, std::string(out.data())); \ } \ - } while(false) + } while (false) /** If the condition is true, an error is returned * * @param[in] cond Condition to evaluate. * @param[in] msg Error description message */ -#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, msg) \ - do \ - { \ - if(cond) \ - { \ - return arm_compute::create_error_msg(arm_compute::ErrorCode::RUNTIME_ERROR, __func__, __FILE__, __LINE__, msg); \ - } \ - } while(false) +#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, msg) \ + do \ + { \ + if (cond) \ + { \ + return arm_compute::create_error_msg(arm_compute::ErrorCode::RUNTIME_ERROR, __func__, __FILE__, __LINE__, \ + msg); \ + } \ + } while (false) /** If the condition is true, an error is thrown. Accepts a message format * and a variable list of arguments matching the format description. @@ -261,17 +262,17 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file * @param[in] msg Error description message format. * @param[in] ... List of arguments matching the format description. */ -#define ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG_VAR(cond, func, file, line, msg, ...) \ - do \ - { \ - if(cond) \ - { \ - std::array<char, 512> out{ 0 }; \ - int offset = snprintf(out.data(), out.size(), "in %s %s:%d: ", func, file, line); \ - snprintf(out.data() + offset, out.size() - offset, msg, __VA_ARGS__); \ - return arm_compute::create_error(ErrorCode::RUNTIME_ERROR, std::string(out.data())); \ - } \ - } while(false) +#define ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG_VAR(cond, func, file, line, msg, ...) \ + do \ + { \ + if (cond) \ + { \ + std::array<char, 512> out{0}; \ + int offset = snprintf(out.data(), out.size(), "in %s %s:%d: ", func, file, line); \ + snprintf(out.data() + offset, out.size() - offset, msg, __VA_ARGS__); \ + return arm_compute::create_error(ErrorCode::RUNTIME_ERROR, std::string(out.data())); \ + } \ + } while (false) /** If the condition is true, an error is thrown. * @@ -284,18 +285,17 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file #define ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG(cond, func, file, line, msg) \ do \ { \ - if(cond) \ + if (cond) \ { \ return arm_compute::create_error_msg(ErrorCode::RUNTIME_ERROR, func, file, line, msg); \ } \ - } while(false) + } while (false) /** If the condition is true, an error is returned * * @param[in] cond Condition to evaluate */ -#define ARM_COMPUTE_RETURN_ERROR_ON(cond) \ - ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, #cond) +#define ARM_COMPUTE_RETURN_ERROR_ON(cond) ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, #cond) /** If the condition is true, an error is returned * @@ -314,11 +314,12 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file * @param[in] line Line in which the error occurred. * @param[in] msg Message to display. */ -#define ARM_COMPUTE_THROW_ERROR(func, file, line, msg) \ - do \ - { \ - arm_compute::throw_error(arm_compute::create_error_msg(arm_compute::ErrorCode::RUNTIME_ERROR, func, file, line, msg)); \ - } while(false) +#define ARM_COMPUTE_THROW_ERROR(func, file, line, msg) \ + do \ + { \ + arm_compute::throw_error( \ + arm_compute::create_error_msg(arm_compute::ErrorCode::RUNTIME_ERROR, func, file, line, msg)); \ + } while (false) /** Print the given message then throw an std::runtime_error. Accepts a message format * and a variable list of arguments matching the format description. @@ -332,11 +333,11 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file #define ARM_COMPUTE_THROW_ERROR_VAR(func, file, line, msg, ...) \ do \ { \ - std::array<char, 512> out{ 0 }; \ - int offset = snprintf(out.data(), out.size(), "in %s %s:%d: ", func, file, line); \ + std::array<char, 512> out{0}; \ + int offset = snprintf(out.data(), out.size(), "in %s %s:%d: ", func, file, line); \ snprintf(out.data() + offset, out.size() - offset, msg, __VA_ARGS__); \ arm_compute::throw_error(arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR, std::string(out.data()))); \ - } while(false) + } while (false) /** Print the given message then throw an std::runtime_error. Accepts a message format * and a variable list of arguments matching the format description. @@ -361,7 +362,8 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file * @param[in] msg Error description message format. * @param[in] ... List of arguments matching the format description. */ -#define ARM_COMPUTE_ERROR_LOC_VAR(func, file, line, msg, ...) ARM_COMPUTE_THROW_ERROR_VAR(func, file, line, msg, __VA_ARGS__) // NOLINT +#define ARM_COMPUTE_ERROR_LOC_VAR(func, file, line, msg, ...) \ + ARM_COMPUTE_THROW_ERROR_VAR(func, file, line, msg, __VA_ARGS__) // NOLINT /** Print the given message then throw an std::runtime_error. * @@ -380,11 +382,11 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file #define ARM_COMPUTE_EXIT_ON_MSG(cond, msg) \ do \ { \ - if(cond) \ + if (cond) \ { \ ARM_COMPUTE_ERROR(msg); \ } \ - } while(false) + } while (false) /** If the condition is true, the given message is printed and program exits. Accepts a message format * and a variable list of arguments matching the format description. @@ -396,27 +398,25 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file #define ARM_COMPUTE_EXIT_ON_MSG_VAR(cond, msg, ...) \ do \ { \ - if(cond) \ + if (cond) \ { \ ARM_COMPUTE_ERROR_VAR(msg, __VA_ARGS__); \ } \ - } while(false) + } while (false) #ifdef ARM_COMPUTE_ASSERTS_ENABLED /** Checks if a status value is valid if not throws an exception with the error * * @param[in] status Status value to check. */ -#define ARM_COMPUTE_ERROR_THROW_ON(status) \ - status.throw_if_error() +#define ARM_COMPUTE_ERROR_THROW_ON(status) status.throw_if_error() /** If the condition is true, the given message is printed and an exception is thrown * * @param[in] cond Condition to evaluate. * @param[in] msg Message to display. */ -#define ARM_COMPUTE_ERROR_ON_MSG(cond, msg) \ - ARM_COMPUTE_EXIT_ON_MSG(cond, msg) +#define ARM_COMPUTE_ERROR_ON_MSG(cond, msg) ARM_COMPUTE_EXIT_ON_MSG(cond, msg) /** If the condition is true, the given message is printed and an exception is thrown. Accepts a message format * and a variable list of arguments matching the format description. @@ -425,8 +425,7 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file * @param[in] msg Error description message format. * @param[in] ... List of arguments matching the format description. */ -#define ARM_COMPUTE_ERROR_ON_MSG_VAR(cond, msg, ...) \ - ARM_COMPUTE_EXIT_ON_MSG_VAR(cond, msg, __VA_ARGS__) +#define ARM_COMPUTE_ERROR_ON_MSG_VAR(cond, msg, ...) ARM_COMPUTE_EXIT_ON_MSG_VAR(cond, msg, __VA_ARGS__) /** If the condition is true, the given message is printed and an exception is thrown. * @@ -439,11 +438,11 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file #define ARM_COMPUTE_ERROR_ON_LOC_MSG(cond, func, file, line, ...) \ do \ { \ - if(cond) \ + if (cond) \ { \ ARM_COMPUTE_ERROR_LOC_VAR(func, file, line, __VA_ARGS__); \ } \ - } while(false) + } while (false) /** If the condition is true, the given message is printed and an exception is thrown, otherwise value is returned * @@ -464,8 +463,7 @@ Status create_error_msg(ErrorCode error_code, const char *func, const char *file * * @param[in] cond Condition to evaluate. */ -#define ARM_COMPUTE_ERROR_ON(cond) \ - ARM_COMPUTE_ERROR_ON_MSG(cond, #cond) +#define ARM_COMPUTE_ERROR_ON(cond) ARM_COMPUTE_ERROR_ON_MSG(cond, #cond) /** If the condition is true then an error message is printed and an exception thrown * diff --git a/arm_compute/core/Helpers.h b/arm_compute/core/Helpers.h index f19e1e12e0..960201510a 100644 --- a/arm_compute/core/Helpers.h +++ b/arm_compute/core/Helpers.h @@ -96,7 +96,6 @@ public: void reset(size_t dimension); private: - /** Initialize a container iterator for the tensor with the specified number of dimensions, stride, buffer pointer and window. * * @param[in] num_dims The number of dimensions. @@ -112,8 +111,7 @@ private: class Dimension { public: - constexpr Dimension() - : _dim_start(0), _stride(0) + constexpr Dimension() : _dim_start(0), _stride(0) { } @@ -133,7 +131,7 @@ private: * @param[in,out] iterators Tensor iterators which will be updated by this function before calling lambda_function. */ template <typename L, typename... Ts> -inline void execute_window_loop(const Window &w, L &&lambda_function, Ts &&... iterators); +inline void execute_window_loop(const Window &w, L &&lambda_function, Ts &&...iterators); /** Permutes given Dimensions according to a permutation vector * @@ -146,7 +144,7 @@ template <typename T> inline void permute(Dimensions<T> &dimensions, const PermutationVector &perm) { auto dimensions_copy = utility::make_array<Dimensions<T>::num_max_dimensions>(dimensions.begin(), dimensions.end()); - for(unsigned int i = 0; i < perm.num_dimensions(); ++i) + for (unsigned int i = 0; i < perm.num_dimensions(); ++i) { T dimension_val = (perm[i] < dimensions.num_dimensions()) ? dimensions_copy[perm[i]] : 0; dimensions.set(i, dimension_val); @@ -163,7 +161,7 @@ inline void permute(Dimensions<T> &dimensions, const PermutationVector &perm) inline void permute(TensorShape &shape, const PermutationVector &perm) { TensorShape shape_copy = shape; - for(unsigned int i = 0; i < perm.num_dimensions(); ++i) + for (unsigned int i = 0; i < perm.num_dimensions(); ++i) { size_t dimension_val = (perm[i] < shape.num_dimensions()) ? shape_copy[perm[i]] : 1; shape.set(i, dimension_val, false, false); // Avoid changes in _num_dimension @@ -180,8 +178,11 @@ inline void permute(TensorShape &shape, const PermutationVector &perm) * * @return The corresponding valid region */ -ValidRegion calculate_valid_region_scale(const ITensorInfo &src_info, const TensorShape &dst_shape, - InterpolationPolicy interpolate_policy, SamplingPolicy sampling_policy, bool border_undefined); +ValidRegion calculate_valid_region_scale(const ITensorInfo &src_info, + const TensorShape &dst_shape, + InterpolationPolicy interpolate_policy, + SamplingPolicy sampling_policy, + bool border_undefined); /** Convert a linear index into n-dimensional coordinates. * @@ -224,7 +225,8 @@ const std::map<DataLayout, std::vector<DataLayoutDimension>> &get_layout_map(); * * @return The int conversion of the requested data layout index. */ -inline size_t get_data_layout_dimension_index(const DataLayout &data_layout, const DataLayoutDimension &data_layout_dimension); +inline size_t get_data_layout_dimension_index(const DataLayout &data_layout, + const DataLayoutDimension &data_layout_dimension); /** Get the DataLayoutDimension of a given index and layout. * @@ -245,10 +247,17 @@ inline DataLayoutDimension get_index_data_layout_dimension(const DataLayout &dat * * @return the number of output tiles along the x and y directions of size "output_tile_size" */ -inline Size2D compute_winograd_convolution_tiles(const Size2D &in_dims, const Size2D &kernel_size, const Size2D &output_tile_size, const PadStrideInfo &conv_info) +inline Size2D compute_winograd_convolution_tiles(const Size2D &in_dims, + const Size2D &kernel_size, + const Size2D &output_tile_size, + const PadStrideInfo &conv_info) { - int num_tiles_x = std::ceil((in_dims.width - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast<float>(output_tile_size.width)); - int num_tiles_y = std::ceil((in_dims.height - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast<float>(output_tile_size.height)); + int num_tiles_x = + std::ceil((in_dims.width - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / + static_cast<float>(output_tile_size.width)); + int num_tiles_y = + std::ceil((in_dims.height - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / + static_cast<float>(output_tile_size.height)); // Clamp in case we provide paddings but we have 1D convolution num_tiles_x = std::min(num_tiles_x, static_cast<int>(in_dims.width)); @@ -277,7 +286,7 @@ inline T wrap_around(T x, T m) */ inline Coordinates &convert_negative_axis(Coordinates &coords, int max_value) { - for(unsigned int i = 0; i < coords.num_dimensions(); ++i) + for (unsigned int i = 0; i < coords.num_dimensions(); ++i) { coords[i] = wrap_around(coords[i], max_value); } diff --git a/arm_compute/core/Helpers.inl b/arm_compute/core/Helpers.inl index ff902bba20..60a21e9418 100644 --- a/arm_compute/core/Helpers.inl +++ b/arm_compute/core/Helpers.inl @@ -32,12 +32,9 @@ template <size_t dimension> struct IncrementIterators { template <typename T, typename... Ts> - static void unroll(T &&it, Ts &&... iterators) + static void unroll(T &&it, Ts &&...iterators) { - auto increment = [](T && it) - { - it.increment(dimension); - }; + auto increment = [](T &&it) { it.increment(dimension); }; utility::for_each(increment, std::forward<T>(it), std::forward<Ts>(iterators)...); } static void unroll() @@ -50,14 +47,14 @@ template <size_t dim> struct ForEachDimension { template <typename L, typename... Ts> - static void unroll(const Window &w, Coordinates &id, L &&lambda_function, Ts &&... iterators) + static void unroll(const Window &w, Coordinates &id, L &&lambda_function, Ts &&...iterators) { const auto &d = w[dim - 1]; - for(auto v = d.start(); v < d.end(); v += d.step(), IncrementIterators < dim - 1 >::unroll(iterators...)) + for (auto v = d.start(); v < d.end(); v += d.step(), IncrementIterators<dim - 1>::unroll(iterators...)) { id.set(dim - 1, v); - ForEachDimension < dim - 1 >::unroll(w, id, lambda_function, iterators...); + ForEachDimension<dim - 1>::unroll(w, id, lambda_function, iterators...); } } }; @@ -66,7 +63,7 @@ template <> struct ForEachDimension<0> { template <typename L, typename... Ts> - static void unroll(const Window &w, Coordinates &id, L &&lambda_function, Ts &&... iterators) + static void unroll(const Window &w, Coordinates &id, L &&lambda_function, Ts &&...iterators) { ARM_COMPUTE_UNUSED(w, iterators...); lambda_function(id); @@ -74,31 +71,31 @@ struct ForEachDimension<0> }; template <typename L, typename... Ts> -inline void execute_window_loop(const Window &w, L &&lambda_function, Ts &&... iterators) +inline void execute_window_loop(const Window &w, L &&lambda_function, Ts &&...iterators) { w.validate(); - for(unsigned int i = 0; i < Coordinates::num_max_dimensions; ++i) + for (unsigned int i = 0; i < Coordinates::num_max_dimensions; ++i) { ARM_COMPUTE_ERROR_ON(w[i].step() == 0); } Coordinates id; - ForEachDimension<Coordinates::num_max_dimensions>::unroll(w, id, std::forward<L>(lambda_function), std::forward<Ts>(iterators)...); + ForEachDimension<Coordinates::num_max_dimensions>::unroll(w, id, std::forward<L>(lambda_function), + std::forward<Ts>(iterators)...); } -inline constexpr Iterator::Iterator() - : _ptr(nullptr), _dims() +inline constexpr Iterator::Iterator() : _ptr(nullptr), _dims() { } -inline Iterator::Iterator(const ITensor *tensor, const Window &win) - : Iterator() +inline Iterator::Iterator(const ITensor *tensor, const Window &win) : Iterator() { ARM_COMPUTE_ERROR_ON(tensor == nullptr); ARM_COMPUTE_ERROR_ON(tensor->info() == nullptr); - initialize(tensor->info()->num_dimensions(), tensor->info()->strides_in_bytes(), tensor->buffer(), tensor->info()->offset_first_element_in_bytes(), win); + initialize(tensor->info()->num_dimensions(), tensor->info()->strides_in_bytes(), tensor->buffer(), + tensor->info()->offset_first_element_in_bytes(), win); } inline Iterator::Iterator(size_t num_dims, const Strides &strides, uint8_t *buffer, size_t offset, const Window &win) @@ -107,21 +104,22 @@ inline Iterator::Iterator(size_t num_dims, const Strides &strides, uint8_t *buff initialize(num_dims, strides, buffer, offset, win); } -inline void Iterator::initialize(size_t num_dims, const Strides &strides, uint8_t *buffer, size_t offset, const Window &win) +inline void +Iterator::initialize(size_t num_dims, const Strides &strides, uint8_t *buffer, size_t offset, const Window &win) { ARM_COMPUTE_ERROR_ON(buffer == nullptr); _ptr = buffer + offset; //Initialize the stride for each dimension and calculate the position of the first element of the iteration: - for(unsigned int n = 0; n < num_dims; ++n) + for (unsigned int n = 0; n < num_dims; ++n) { _dims[n]._stride = win[n].step() * strides[n]; std::get<0>(_dims)._dim_start += static_cast<size_t>(strides[n]) * win[n].start(); } //Copy the starting point to all the dimensions: - for(unsigned int n = 1; n < Coordinates::num_max_dimensions; ++n) + for (unsigned int n = 1; n < Coordinates::num_max_dimensions; ++n) { _dims[n]._dim_start = std::get<0>(_dims)._dim_start; } @@ -135,7 +133,7 @@ inline void Iterator::increment(const size_t dimension) _dims[dimension]._dim_start += _dims[dimension]._stride; - for(unsigned int n = 0; n < dimension; ++n) + for (unsigned int n = 0; n < dimension; ++n) { _dims[n]._dim_start = _dims[dimension]._dim_start; } @@ -157,7 +155,7 @@ inline void Iterator::reset(const size_t dimension) _dims[dimension]._dim_start = _dims[dimension + 1]._dim_start; - for(unsigned int n = 0; n < dimension; ++n) + for (unsigned int n = 0; n < dimension; ++n) { _dims[n]._dim_start = _dims[dimension]._dim_start; } @@ -170,9 +168,9 @@ inline Coordinates index2coords(const TensorShape &shape, int index) 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 }; + Coordinates coord{0}; - for(int d = shape.num_dimensions() - 1; d >= 0; --d) + for (int d = shape.num_dimensions() - 1; d >= 0; --d) { num_elements /= shape[d]; coord.set(d, index / num_elements); @@ -191,7 +189,7 @@ inline int coords2index(const TensorShape &shape, const Coordinates &coord) int index = 0; int stride = 1; - for(unsigned int d = 0; d < coord.num_dimensions(); ++d) + for (unsigned int d = 0; d < coord.num_dimensions(); ++d) { index += coord[d] * stride; stride *= shape[d]; @@ -200,9 +198,11 @@ inline int coords2index(const TensorShape &shape, const Coordinates &coord) return index; } -inline size_t get_data_layout_dimension_index(const DataLayout &data_layout, const DataLayoutDimension &data_layout_dimension) +inline size_t get_data_layout_dimension_index(const DataLayout &data_layout, + const DataLayoutDimension &data_layout_dimension) { - ARM_COMPUTE_ERROR_ON_MSG(data_layout == DataLayout::UNKNOWN, "Cannot retrieve the dimension index for an unknown layout!"); + ARM_COMPUTE_ERROR_ON_MSG(data_layout == DataLayout::UNKNOWN, + "Cannot retrieve the dimension index for an unknown layout!"); const auto &dims = get_layout_map().at(data_layout); const auto &it = std::find(dims.cbegin(), dims.cend(), data_layout_dimension); ARM_COMPUTE_ERROR_ON_MSG(it == dims.cend(), "Invalid dimension for the given layout."); @@ -211,7 +211,8 @@ inline size_t get_data_layout_dimension_index(const DataLayout &data_layout, con inline DataLayoutDimension get_index_data_layout_dimension(const DataLayout &data_layout, const size_t index) { - ARM_COMPUTE_ERROR_ON_MSG(data_layout == DataLayout::UNKNOWN, "Cannot retrieve the layout dimension for an unknown layout!"); + ARM_COMPUTE_ERROR_ON_MSG(data_layout == DataLayout::UNKNOWN, + "Cannot retrieve the layout dimension for an unknown layout!"); const auto &dims = get_layout_map().at(data_layout); ARM_COMPUTE_ERROR_ON_MSG(index >= dims.size(), "Invalid index for the given layout."); return dims[index]; diff --git a/arm_compute/core/IAccessWindow.h b/arm_compute/core/IAccessWindow.h index 880f6d6b27..9c9fb90915 100644 --- a/arm_compute/core/IAccessWindow.h +++ b/arm_compute/core/IAccessWindow.h @@ -100,7 +100,10 @@ public: * @return a valid region. * */ - virtual ValidRegion compute_valid_region(const Window &window, ValidRegion input_valid_region, bool border_undefined, BorderSize border_size) const = 0; + virtual ValidRegion compute_valid_region(const Window &window, + ValidRegion input_valid_region, + bool border_undefined, + BorderSize border_size) const = 0; }; /** Implementation of a rectangular access pattern. */ @@ -161,7 +164,10 @@ public: * @param[in] border_undefined (Optional) Undefined borders are excluded from the valid region. * @param[in] border_size (Optional) Size of the border around the XY-plane of the tensor. */ - void set_valid_region(const Window &window, const ValidRegion &input_valid_region, bool border_undefined = false, const BorderSize &border_size = BorderSize(0)); + void set_valid_region(const Window &window, + const ValidRegion &input_valid_region, + bool border_undefined = false, + const BorderSize &border_size = BorderSize(0)); /** Compute the valid region based on access pattern, valid region of the inputs and border mode. * @@ -189,7 +195,10 @@ public: * @return a valid region. * */ - ValidRegion compute_valid_region(const Window &window, ValidRegion input_valid_region, bool border_undefined, BorderSize border_size) const override; + ValidRegion compute_valid_region(const Window &window, + ValidRegion input_valid_region, + bool border_undefined, + BorderSize border_size) const override; bool update_window_if_needed(Window &window) const override; bool update_padding_if_needed(const Window &window) override; diff --git a/arm_compute/core/IArray.h b/arm_compute/core/IArray.h index 6edbc1d5d5..3471fc9a86 100644 --- a/arm_compute/core/IArray.h +++ b/arm_compute/core/IArray.h @@ -25,6 +25,7 @@ #define ARM_COMPUTE_IARRAY_H #include "arm_compute/core/Error.h" + #include <cstddef> #include <cstdint> @@ -36,14 +37,12 @@ class IArray { public: /** Default constructor */ - IArray() - : _num_values(0), _max_size(0) {}; + IArray() : _num_values(0), _max_size(0){}; /** Constructor: initializes an array which can contain up to max_num_points values * * @param[in] max_num_values Maximum number of values the array will be able to stored */ - IArray(size_t max_num_values) - : _num_values(0), _max_size(max_num_values) + IArray(size_t max_num_values) : _num_values(0), _max_size(max_num_values) { } /** Maximum number of values which can be stored in this array @@ -73,7 +72,7 @@ public: bool push_back(const T &val) { ARM_COMPUTE_ERROR_ON(0 == _max_size); - if(_num_values >= max_num_values()) + if (_num_values >= max_num_values()) { _num_values = max_num_values() + 1; return false; @@ -142,5 +141,5 @@ using IInt16Array = IArray<int16_t>; using IInt32Array = IArray<int32_t>; /** Interface for Array of floats. */ using IFloatArray = IArray<float>; -} +} // namespace arm_compute #endif /* ARM_COMPUTE_IARRAY_H */ diff --git a/arm_compute/core/IKernel.h b/arm_compute/core/IKernel.h index 98fd18cc91..403a2c724e 100644 --- a/arm_compute/core/IKernel.h +++ b/arm_compute/core/IKernel.h @@ -73,5 +73,5 @@ protected: private: Window _window; }; -} +} // namespace arm_compute #endif /*ARM_COMPUTE_IKERNEL_H */ diff --git a/arm_compute/core/ITensor.h b/arm_compute/core/ITensor.h index 32b93576bd..aad8313261 100644 --- a/arm_compute/core/ITensor.h +++ b/arm_compute/core/ITensor.h @@ -94,9 +94,9 @@ public: void mark_as_used() const; private: - mutable bool _is_used = { true }; /**< Flag that marks if the tensor is used or not */ + mutable bool _is_used = {true}; /**< Flag that marks if the tensor is used or not */ }; using IImage = ITensor; -} +} // namespace arm_compute #endif /*ARM_COMPUTE_ITENSOR_H */ diff --git a/arm_compute/core/ITensorInfo.h b/arm_compute/core/ITensorInfo.h index e7c0b182c6..c42f4b57a1 100644 --- a/arm_compute/core/ITensorInfo.h +++ b/arm_compute/core/ITensorInfo.h @@ -29,6 +29,7 @@ #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/utils/misc/Utility.h" + #include "support/ICloneable.h" #include <cstddef> @@ -328,23 +329,23 @@ public: * not broadcast compatible. */ template <typename... Infos> - static std::pair<TensorShape, ValidRegion> broadcast_shape_and_valid_region(const Infos &... infos) + static std::pair<TensorShape, ValidRegion> broadcast_shape_and_valid_region(const Infos &...infos) { TensorShape bc_shape = TensorShape::broadcast_shape(infos.tensor_shape()...); - ValidRegion bc_valid_region{ Coordinates(), bc_shape }; + ValidRegion bc_valid_region{Coordinates(), bc_shape}; - auto broadcast_valid_region = [&bc_valid_region](const ITensorInfo & info) + auto broadcast_valid_region = [&bc_valid_region](const ITensorInfo &info) { - if(info.num_dimensions() != 0) + if (info.num_dimensions() != 0) { - for(size_t d = 0; d < bc_valid_region.shape.num_dimensions(); ++d) + for (size_t d = 0; d < bc_valid_region.shape.num_dimensions(); ++d) { const bool is_broadcast = (info.tensor_shape()[d] == 1); const int anchor_max = std::max(bc_valid_region.anchor[d], info.valid_region().anchor[d]); const size_t valid_min = std::min(bc_valid_region.shape[d], info.valid_region().shape[d]); - if(!is_broadcast || (valid_min == 0)) + if (!is_broadcast || (valid_min == 0)) { bc_valid_region.anchor.set(d, anchor_max); bc_valid_region.shape.set(d, valid_min); diff --git a/arm_compute/core/ITensorPack.h b/arm_compute/core/ITensorPack.h index 17b7241862..f456c50769 100644 --- a/arm_compute/core/ITensorPack.h +++ b/arm_compute/core/ITensorPack.h @@ -42,18 +42,16 @@ public: struct PackElement { PackElement() = default; - PackElement(int id, ITensor *tensor) - : id(id), tensor(tensor), ctensor(nullptr) + PackElement(int id, ITensor *tensor) : id(id), tensor(tensor), ctensor(nullptr) { } - PackElement(int id, const ITensor *ctensor) - : id(id), tensor(nullptr), ctensor(ctensor) + PackElement(int id, const ITensor *ctensor) : id(id), tensor(nullptr), ctensor(ctensor) { } - int id{ -1 }; - ITensor *tensor{ nullptr }; - const ITensor *ctensor{ nullptr }; + int id{-1}; + ITensor *tensor{nullptr}; + const ITensor *ctensor{nullptr}; }; public: diff --git a/arm_compute/core/KernelDescriptors.h b/arm_compute/core/KernelDescriptors.h index 2bf5dee18c..168a06a55c 100644 --- a/arm_compute/core/KernelDescriptors.h +++ b/arm_compute/core/KernelDescriptors.h @@ -33,24 +33,24 @@ namespace arm_compute /** Descriptor for FFT scale kernels */ struct FFTScaleKernelInfo { - float scale{ 0.f }; /**< Axis to perform the kernel on. */ - bool conjugate{ true }; /**< Flag to conjugate the output/ */ + float scale{0.f}; /**< Axis to perform the kernel on. */ + bool conjugate{true}; /**< Flag to conjugate the output/ */ }; /** Descriptor for FFT digit reverse kernels */ struct FFTDigitReverseKernelInfo { - unsigned int axis{ 0 }; /**< Axis to perform the kernel on. */ - bool conjugate{ false }; /**< Flag to conjugate the output/ */ + unsigned int axis{0}; /**< Axis to perform the kernel on. */ + bool conjugate{false}; /**< Flag to conjugate the output/ */ }; /** Descriptor used by the FFT core kernels */ struct FFTRadixStageKernelInfo { - unsigned int axis{ 0 }; /**< Axis to run the kernel on. */ - unsigned int radix{ 0 }; /**< Radix to use. */ - unsigned int Nx{ 0 }; /**< Nx coefficient. */ - bool is_first_stage{ false }; /**< Flags if the FFT kernels is the first stage of a decomposed FFT. */ + unsigned int axis{0}; /**< Axis to run the kernel on. */ + unsigned int radix{0}; /**< Radix to use. */ + unsigned int Nx{0}; /**< Nx coefficient. */ + bool is_first_stage{false}; /**< Flags if the FFT kernels is the first stage of a decomposed FFT. */ }; class ITensorInfo; @@ -58,89 +58,102 @@ class ITensorInfo; struct GEMMKernelInfo { GEMMKernelInfo() = default; - GEMMKernelInfo( - unsigned int im, - unsigned int in, - unsigned int ik, - unsigned int idepth_output_gemm3d, - bool ireinterpret_input_as_3d, - bool ibroadcast_bias, - bool ifp_mixed_precision, - bool ihas_pad_y, - ActivationLayerInfo iactivation_info, - int inmult_transpose1xW_width, - int imult_interleave4x4_height, - GEMMLHSMatrixInfo ilhs_info, - GEMMRHSMatrixInfo irhs_info, - int32_t ina_offset, - int32_t inb_offset) - : m(im), n(in), k(ik), depth_output_gemm3d(idepth_output_gemm3d), reinterpret_input_as_3d(ireinterpret_input_as_3d), broadcast_bias(ibroadcast_bias), fp_mixed_precision(ifp_mixed_precision), - has_pad_y(ihas_pad_y), activation_info(iactivation_info), mult_transpose1xW_width(inmult_transpose1xW_width), mult_interleave4x4_height(imult_interleave4x4_height), lhs_info(ilhs_info), - rhs_info(irhs_info), a_offset(ina_offset), b_offset(inb_offset) + GEMMKernelInfo(unsigned int im, + unsigned int in, + unsigned int ik, + unsigned int idepth_output_gemm3d, + bool ireinterpret_input_as_3d, + bool ibroadcast_bias, + bool ifp_mixed_precision, + bool ihas_pad_y, + ActivationLayerInfo iactivation_info, + int inmult_transpose1xW_width, + int imult_interleave4x4_height, + GEMMLHSMatrixInfo ilhs_info, + GEMMRHSMatrixInfo irhs_info, + int32_t ina_offset, + int32_t inb_offset) + : m(im), + n(in), + k(ik), + depth_output_gemm3d(idepth_output_gemm3d), + reinterpret_input_as_3d(ireinterpret_input_as_3d), + broadcast_bias(ibroadcast_bias), + fp_mixed_precision(ifp_mixed_precision), + has_pad_y(ihas_pad_y), + activation_info(iactivation_info), + mult_transpose1xW_width(inmult_transpose1xW_width), + mult_interleave4x4_height(imult_interleave4x4_height), + lhs_info(ilhs_info), + rhs_info(irhs_info), + a_offset(ina_offset), + b_offset(inb_offset) { } - unsigned int m{ 0 }; /**< Number of LHS rows*/ - unsigned int n{ 0 }; /**< Number of RHS columns*/ - unsigned int k{ 0 }; /**< Number of LHS columns or RHS rows */ - unsigned int depth_output_gemm3d{ 0 }; /**< Depth of the output tensor in case is reinterpreted as 3D */ - bool reinterpret_input_as_3d{ false }; /**< Flag used to reinterpret the input as 3D */ - bool broadcast_bias{ false }; /**< Flag used to broadcast the bias addition */ - bool fp_mixed_precision{ false }; /**< Flag used to indicate wider accumulators (32 bit instead of 16 for FP16). */ - bool has_pad_y{ false }; /**< Flag used to indicate if the input/output tensors have internal pad on the y direction */ - ActivationLayerInfo activation_info{}; /**< Activation function to perform after the matrix multiplication */ - int mult_transpose1xW_width{ 1 }; /**< Multiplication factor for the width of the 1xW transposed block */ - int mult_interleave4x4_height{ 1 }; /**< Multiplication factor for the height of the 4x4 interleaved block */ - GEMMLHSMatrixInfo lhs_info{}; /**< LHS matrix information used to retrieve the number of rows processed by each thread */ - GEMMRHSMatrixInfo rhs_info{}; /**< RHS matrix information used for reshaping the RHS matrix */ - int32_t a_offset{ 0 }; /**< Offset to be added to each element of the matrix A */ - int32_t b_offset{ 0 }; /**< Offset to be added to each element of the matrix B */ - GEMMLowpOutputStageInfo output_stage{}; /**< GEMMLowp output stage information */ + unsigned int m{0}; /**< Number of LHS rows*/ + unsigned int n{0}; /**< Number of RHS columns*/ + unsigned int k{0}; /**< Number of LHS columns or RHS rows */ + unsigned int depth_output_gemm3d{0}; /**< Depth of the output tensor in case is reinterpreted as 3D */ + bool reinterpret_input_as_3d{false}; /**< Flag used to reinterpret the input as 3D */ + bool broadcast_bias{false}; /**< Flag used to broadcast the bias addition */ + bool fp_mixed_precision{false}; /**< Flag used to indicate wider accumulators (32 bit instead of 16 for FP16). */ + bool has_pad_y{ + false}; /**< Flag used to indicate if the input/output tensors have internal pad on the y direction */ + ActivationLayerInfo activation_info{}; /**< Activation function to perform after the matrix multiplication */ + int mult_transpose1xW_width{1}; /**< Multiplication factor for the width of the 1xW transposed block */ + int mult_interleave4x4_height{1}; /**< Multiplication factor for the height of the 4x4 interleaved block */ + GEMMLHSMatrixInfo + lhs_info{}; /**< LHS matrix information used to retrieve the number of rows processed by each thread */ + GEMMRHSMatrixInfo rhs_info{}; /**< RHS matrix information used for reshaping the RHS matrix */ + int32_t a_offset{0}; /**< Offset to be added to each element of the matrix A */ + int32_t b_offset{0}; /**< Offset to be added to each element of the matrix B */ + GEMMLowpOutputStageInfo output_stage{}; /**< GEMMLowp output stage information */ }; /** Compute descriptor used by the depthwise convolution native kernel */ struct DWCComputeKernelInfo { - unsigned int n0{ 1 }; /**< Number of columns processed by each thread */ - unsigned int m0{ 1 }; /**< Number of rows processed by each thread */ - bool export_input_to_cl_image{ false }; /**< Export input to cl_image */ - bool export_weights_to_cl_image{ false }; /**< Export the weights to cl_image */ + unsigned int n0{1}; /**< Number of columns processed by each thread */ + unsigned int m0{1}; /**< Number of rows processed by each thread */ + bool export_input_to_cl_image{false}; /**< Export input to cl_image */ + bool export_weights_to_cl_image{false}; /**< Export the weights to cl_image */ }; /** Compute descriptor used by the direct convolution kernel */ struct DirectConvComputeKernelInfo { - int32_t m0{ 1 }; /**< Number of rows to be processed by the kernel */ - int32_t n0{ 1 }; /**< Number of columns to be processed by the kernel */ - int32_t k0{ 1 }; /**< Number of partial accumulations to be processed in a single iteration by the kernel */ - bool export_weights_to_cl_image{ false }; /**< Flag to export the weights to cl_image */ - bool export_output_to_cl_image{ false }; /**< Flag to export the output to cl_image */ - bool export_input_to_cl_image{ false }; /**< Flag to export the input to cl_image */ + int32_t m0{1}; /**< Number of rows to be processed by the kernel */ + int32_t n0{1}; /**< Number of columns to be processed by the kernel */ + int32_t k0{1}; /**< Number of partial accumulations to be processed in a single iteration by the kernel */ + bool export_weights_to_cl_image{false}; /**< Flag to export the weights to cl_image */ + bool export_output_to_cl_image{false}; /**< Flag to export the output to cl_image */ + bool export_input_to_cl_image{false}; /**< Flag to export the input to cl_image */ }; /** Descriptor used by the softmax kernels */ struct SoftmaxKernelInfo { - float beta{ 1.f }; /**< A scaling factor for the exponent with default value 1.0 */ - bool is_log{ false }; /**< Flag used to perform Log Softmax operation */ - DataType input_data_type{ DataType::UNKNOWN }; /**< Input tensor data type */ - int32_t axis{ 0 }; /**< The dimension in which to apply softmax. */ + float beta{1.f}; /**< A scaling factor for the exponent with default value 1.0 */ + bool is_log{false}; /**< Flag used to perform Log Softmax operation */ + DataType input_data_type{DataType::UNKNOWN}; /**< Input tensor data type */ + int32_t axis{0}; /**< The dimension in which to apply softmax. */ }; /** Descriptor used by the direct convolution layer output stage kernels */ struct DirectConvolutionLayerOutputStageKernelInfo { - int32_t result_fixedpoint_multiplier{ 0 }; /**< Result output stage multiplier used for quantizing */ - int32_t result_shift{ 0 }; /**< Result output stage shift used for quantizing */ - int32_t result_offset_after_shift{ 0 }; /**< Result offset used for quantizing */ - DataType output_data_type{ DataType::UNKNOWN }; /**< Output tensor data type to use if the output is not initialized */ + int32_t result_fixedpoint_multiplier{0}; /**< Result output stage multiplier used for quantizing */ + int32_t result_shift{0}; /**< Result output stage shift used for quantizing */ + int32_t result_offset_after_shift{0}; /**< Result offset used for quantizing */ + DataType output_data_type{ + DataType::UNKNOWN}; /**< Output tensor data type to use if the output is not initialized */ }; struct InstanceNormalizationLayerKernelInfo { /** Default constructor */ - InstanceNormalizationLayerKernelInfo() - : InstanceNormalizationLayerKernelInfo(1.f, 0.f, 1e-12, true) + InstanceNormalizationLayerKernelInfo() : InstanceNormalizationLayerKernelInfo(1.f, 0.f, 1e-12, true) { } /** Constructor @@ -177,10 +190,10 @@ struct GEMMLowpReductionKernelInfo { } - int32_t k{ 0 }; /**< Number of matrix columns/rows */ - bool is_reshaped{ false }; /**< True if the input tensor has been reshaped */ - int32_t scalar{ 0 }; /**< Scalar value to multiply each reduced column/row by */ - bool mul_by_scalar{ false }; /**< True if each column/row reduction has to be multiplied by a scalar value */ + int32_t k{0}; /**< Number of matrix columns/rows */ + bool is_reshaped{false}; /**< True if the input tensor has been reshaped */ + int32_t scalar{0}; /**< Scalar value to multiply each reduced column/row by */ + bool mul_by_scalar{false}; /**< True if each column/row reduction has to be multiplied by a scalar value */ }; struct ScaleKernelInfo @@ -202,13 +215,13 @@ struct ScaleKernelInfo bool use_padding = true, bool align_corners = false, DataLayout data_layout = DataLayout::UNKNOWN) noexcept - : interpolation_policy{ interpolation_policy }, - border_mode{ border_mode }, - constant_border_value{ constant_border_value }, - sampling_policy{ sampling_policy }, - use_padding{ use_padding }, - align_corners{ align_corners }, - data_layout{ data_layout } + : interpolation_policy{interpolation_policy}, + border_mode{border_mode}, + constant_border_value{constant_border_value}, + sampling_policy{sampling_policy}, + use_padding{use_padding}, + align_corners{align_corners}, + data_layout{data_layout} { } @@ -224,16 +237,17 @@ struct ScaleKernelInfo struct MatMulKernelInfo { MatMulKernelInfo() = default; - MatMulKernelInfo(bool adj_lhs, bool adj_rhs, int m0 = 1, int n0 = 1, int k0 = 1, bool export_rhs_to_cl_image = false) - : adj_lhs{ adj_lhs }, adj_rhs{ adj_rhs }, m0{ m0 }, n0{ n0 }, k0{ k0 }, export_rhs_to_cl_image{ export_rhs_to_cl_image } + MatMulKernelInfo( + bool adj_lhs, bool adj_rhs, int m0 = 1, int n0 = 1, int k0 = 1, bool export_rhs_to_cl_image = false) + : adj_lhs{adj_lhs}, adj_rhs{adj_rhs}, m0{m0}, n0{n0}, k0{k0}, export_rhs_to_cl_image{export_rhs_to_cl_image} { } - bool adj_lhs{ false }; /**< Get Adjoint LHS flag value */ - bool adj_rhs{ false }; /**< Get Adjoint RHS flag value */ - int m0{ 1 }; /**< Number of output rows processed by each work-item*/ - int n0{ 1 }; /**< Number of output columns processed by each work-item*/ - int k0{ 1 }; /**< Number of inner accumulations */ - bool export_rhs_to_cl_image{ false }; /**< Flag to know whether the RHS tensor should be exported to cl_image*/ + bool adj_lhs{false}; /**< Get Adjoint LHS flag value */ + bool adj_rhs{false}; /**< Get Adjoint RHS flag value */ + int m0{1}; /**< Number of output rows processed by each work-item*/ + int n0{1}; /**< Number of output columns processed by each work-item*/ + int k0{1}; /**< Number of inner accumulations */ + bool export_rhs_to_cl_image{false}; /**< Flag to know whether the RHS tensor should be exported to cl_image*/ }; } // namespace arm_compute #endif // ACL_ARM_COMPUTE_CORE_KERNELDESCRIPTORS_H diff --git a/arm_compute/core/Log.h b/arm_compute/core/Log.h index bc0ecb802e..03b861f765 100644 --- a/arm_compute/core/Log.h +++ b/arm_compute/core/Log.h @@ -34,11 +34,11 @@ #define ARM_COMPUTE_CREATE_DEFAULT_CORE_LOGGER() \ do \ { \ - if(arm_compute::logging::LoggerRegistry::get().logger("CORE") == nullptr) \ + if (arm_compute::logging::LoggerRegistry::get().logger("CORE") == nullptr) \ { \ arm_compute::logging::LoggerRegistry::get().create_reserved_loggers(); \ } \ - } while(false) + } while (false) #else /* ARM_COMPUTE_LOGGING_ENABLED */ #define ARM_COMPUTE_CREATE_DEFAULT_CORE_LOGGER() #endif /* ARM_COMPUTE_LOGGING_ENABLED */ @@ -53,7 +53,7 @@ { \ ARM_COMPUTE_CREATE_DEFAULT_CORE_LOGGER(); \ ARM_COMPUTE_LOG_MSG("CORE", log_level, msg); \ - } while(false) + } while (false) /** Log a message with format to the core system logger * @@ -66,7 +66,7 @@ { \ ARM_COMPUTE_CREATE_DEFAULT_CORE_LOGGER(); \ ARM_COMPUTE_LOG_MSG_WITH_FORMAT("CORE", log_level, fmt, __VA_ARGS__); \ - } while(false) + } while (false) /** Log a stream to the core system logger * @@ -78,7 +78,7 @@ { \ ARM_COMPUTE_CREATE_DEFAULT_CORE_LOGGER(); \ ARM_COMPUTE_LOG_STREAM("CORE", log_level, ss); \ - } while(false) + } while (false) /** Log information level message to the core system logger * @@ -89,7 +89,7 @@ { \ ARM_COMPUTE_CREATE_DEFAULT_CORE_LOGGER(); \ ARM_COMPUTE_LOG_MSG_CORE(arm_compute::logging::LogLevel::INFO, msg); \ - } while(false) + } while (false) /** Log information level formatted message to the core system logger * @@ -101,7 +101,7 @@ { \ ARM_COMPUTE_CREATE_DEFAULT_CORE_LOGGER(); \ ARM_COMPUTE_LOG_MSG_WITH_FORMAT_CORE(arm_compute::logging::LogLevel::INFO, #fmt, __VA_ARGS__); \ - } while(false) + } while (false) /** Log information level stream to the core system logger * @@ -112,6 +112,6 @@ { \ ARM_COMPUTE_CREATE_DEFAULT_CORE_LOGGER(); \ ARM_COMPUTE_LOG_STREAM_CORE(arm_compute::logging::LogLevel::INFO, ss); \ - } while(false) + } while (false) #endif /* ARM_COMPUTE_LOGGING_MACROS_H */ diff --git a/arm_compute/core/PixelValue.h b/arm_compute/core/PixelValue.h index 790f58a793..0b4df4f2e2 100644 --- a/arm_compute/core/PixelValue.h +++ b/arm_compute/core/PixelValue.h @@ -24,8 +24,8 @@ #ifndef ARM_COMPUTE_PIXELVALUE_H #define ARM_COMPUTE_PIXELVALUE_H -#include "arm_compute/core/Types.h" #include "arm_compute/core/QuantizationInfo.h" +#include "arm_compute/core/Types.h" #include <cstdint> @@ -36,11 +36,7 @@ class PixelValue { public: /** Default constructor: value initialized to 0 */ - PixelValue() noexcept - : value - { - int64_t(0) - } + PixelValue() noexcept : value{int64_t(0)} { } /** Initialize the union with a pixel value of chosen datatype @@ -49,10 +45,9 @@ public: * @param[in] datatype DataType that @p v have to be stored * @param[in] qinfo (Optional) QuantizationInfo to apply in case of quantized data types to @p v */ - PixelValue(double v, DataType datatype, QuantizationInfo qinfo = QuantizationInfo()) - : PixelValue() + PixelValue(double v, DataType datatype, QuantizationInfo qinfo = QuantizationInfo()) : PixelValue() { - switch(datatype) + switch (datatype) { case DataType::U8: value.u8 = static_cast<uint8_t>(v); @@ -112,8 +107,7 @@ public: * * @param[in] v S8 value. */ - PixelValue(int8_t v) - : PixelValue() + PixelValue(int8_t v) : PixelValue() { value.s8 = v; } @@ -121,8 +115,7 @@ public: * * @param[in] v U8 value. */ - PixelValue(uint8_t v) - : PixelValue() + PixelValue(uint8_t v) : PixelValue() { value.u8 = v; } @@ -130,8 +123,7 @@ public: * * @param[in] v U16 value. */ - PixelValue(uint16_t v) - : PixelValue() + PixelValue(uint16_t v) : PixelValue() { value.u16 = v; } @@ -139,8 +131,7 @@ public: * * @param[in] v S16 value. */ - PixelValue(int16_t v) - : PixelValue() + PixelValue(int16_t v) : PixelValue() { value.s16 = v; } @@ -148,8 +139,7 @@ public: * * @param[in] v U32 value. */ - PixelValue(uint32_t v) - : PixelValue() + PixelValue(uint32_t v) : PixelValue() { value.u32 = v; } @@ -157,8 +147,7 @@ public: * * @param[in] v S32 value. */ - PixelValue(int32_t v) - : PixelValue() + PixelValue(int32_t v) : PixelValue() { value.s32 = v; } @@ -167,8 +156,7 @@ public: * * @param[in] v U64 value. */ - PixelValue(uint64_t v) - : PixelValue() + PixelValue(uint64_t v) : PixelValue() { value.u64 = v; } @@ -176,8 +164,7 @@ public: * * @param[in] v S64 value. */ - PixelValue(int64_t v) - : PixelValue() + PixelValue(int64_t v) : PixelValue() { value.s64 = v; } @@ -185,8 +172,7 @@ public: * * @param[in] v F16 value. */ - PixelValue(bfloat16 v) - : PixelValue() + PixelValue(bfloat16 v) : PixelValue() { value.bf16 = v; } @@ -194,8 +180,7 @@ public: * * @param[in] v F16 value. */ - PixelValue(half v) - : PixelValue() + PixelValue(half v) : PixelValue() { value.f16 = v; } @@ -203,8 +188,7 @@ public: * * @param[in] v F32 value. */ - PixelValue(float v) - : PixelValue() + PixelValue(float v) : PixelValue() { value.f32 = v; } @@ -212,8 +196,7 @@ public: * * @param[in] v F64 value. */ - PixelValue(double v) - : PixelValue() + PixelValue(double v) : PixelValue() { value.f64 = v; } @@ -221,23 +204,23 @@ public: * Use the field corresponding to the image format */ union - { - uint64_t u64; /**< Single channel U64 */ - int64_t s64; /**< Single channel S64 */ - uint8_t rgb[3]; /**< 3 channels: RGB888 */ - uint8_t yuv[3]; /**< 3 channels: Any YUV format */ - uint8_t rgbx[4]; /**< 4 channels: RGBX8888 */ - double f64; /**< Single channel double */ - float f32; /**< Single channel float 32 */ - half f16; /**< Single channel F16 */ - bfloat16 bf16; /**< Single channel brain floating-point number */ - uint8_t u8; /**< Single channel U8 */ - int8_t s8; /**< Single channel S8 */ - uint16_t u16; /**< Single channel U16 */ - int16_t s16; /**< Single channel S16 */ - uint32_t u32; /**< Single channel U32 */ - int32_t s32; /**< Single channel S32 */ - } value; + { + uint64_t u64; /**< Single channel U64 */ + int64_t s64; /**< Single channel S64 */ + uint8_t rgb[3]; /**< 3 channels: RGB888 */ + uint8_t yuv[3]; /**< 3 channels: Any YUV format */ + uint8_t rgbx[4]; /**< 4 channels: RGBX8888 */ + double f64; /**< Single channel double */ + float f32; /**< Single channel float 32 */ + half f16; /**< Single channel F16 */ + bfloat16 bf16; /**< Single channel brain floating-point number */ + uint8_t u8; /**< Single channel U8 */ + int8_t s8; /**< Single channel S8 */ + uint16_t u16; /**< Single channel U16 */ + int16_t s16; /**< Single channel S16 */ + uint32_t u32; /**< Single channel U32 */ + int32_t s32; /**< Single channel S32 */ + } value; /** Interpret the pixel value as a U8 * * @param[out] v Returned value diff --git a/arm_compute/core/QuantizationInfo.h b/arm_compute/core/QuantizationInfo.h index 8fa513eee1..471b8c57ab 100644 --- a/arm_compute/core/QuantizationInfo.h +++ b/arm_compute/core/QuantizationInfo.h @@ -26,6 +26,7 @@ #include "arm_compute/core/Rounding.h" #include "arm_compute/core/utils/misc/Utility.h" + #include "support/ToolchainSupport.h" #include <vector> @@ -41,8 +42,7 @@ using qasymm16_t = uint16_t; /**< 16 bit quantized asymmetric scalar value struct UniformQuantizationInfo { /** Default constructor */ - UniformQuantizationInfo() - : scale(0.f), offset(0) + UniformQuantizationInfo() : scale(0.f), offset(0) { } /** Constructor @@ -50,8 +50,7 @@ struct UniformQuantizationInfo * @param[in] scale Quantization scale * @param[in] offset Quantization offset */ - UniformQuantizationInfo(float scale, int32_t offset) - : scale(scale), offset(offset) + UniformQuantizationInfo(float scale, int32_t offset) : scale(scale), offset(offset) { } /** Checks if the scale and offset are both zero */ @@ -69,9 +68,7 @@ class QuantizationInfo { public: /** Default constructor */ - QuantizationInfo() noexcept - : _scale(), - _offset() + QuantizationInfo() noexcept : _scale(), _offset() { } /** Construct quantization info. @@ -80,8 +77,7 @@ public: * * @param[in] scale Scale. */ - QuantizationInfo(float scale) - : _scale(1, scale), _offset() + QuantizationInfo(float scale) : _scale(1, scale), _offset() { } /** Construct quantization info. @@ -91,8 +87,7 @@ public: * @param[in] scale Scale. * @param[in] offset Offset. */ - QuantizationInfo(float scale, int offset) - : _scale(1, scale), _offset(1, offset) + QuantizationInfo(float scale, int offset) : _scale(1, scale), _offset(1, offset) { } /** Construct quantization info. @@ -101,8 +96,7 @@ public: * * @param[in] scale Scale. */ - QuantizationInfo(std::vector<float> scale) - : _scale(scale), _offset() + QuantizationInfo(std::vector<float> scale) : _scale(scale), _offset() { } /** Construct quantization info. @@ -112,8 +106,7 @@ public: * @param[in] scale Scale. * @param[in] offset Offset. */ - QuantizationInfo(std::vector<float> scale, std::vector<int32_t> offset) - : _scale(scale), _offset(offset) + QuantizationInfo(std::vector<float> scale, std::vector<int32_t> offset) : _scale(scale), _offset(offset) { } /** Scale vector accessor @@ -208,8 +201,7 @@ inline bool operator!=(const UniformQuantizationInfo &lhs, const UniformQuantiza template <typename QUANTIZED_TYPE = uint8_t> struct Qasymm8QuantizationHelper { - static_assert(std::is_same<QUANTIZED_TYPE, uint8_t>::value - || std::is_same<QUANTIZED_TYPE, int8_t>::value, + static_assert(std::is_same<QUANTIZED_TYPE, uint8_t>::value || std::is_same<QUANTIZED_TYPE, int8_t>::value, "quantized type should be either uint8_t or int8_t."); /** Quantize a value given a 8-bit asymmetric quantization scheme @@ -234,9 +226,10 @@ struct Qasymm8QuantizationHelper * * @return Quantized value */ - static inline QUANTIZED_TYPE quantize(float value, const UniformQuantizationInfo &qinfo, RoundingPolicy rounding_policy) + static inline QUANTIZED_TYPE + quantize(float value, const UniformQuantizationInfo &qinfo, RoundingPolicy rounding_policy) { - if(rounding_policy == RoundingPolicy::TO_NEAREST_UP) + if (rounding_policy == RoundingPolicy::TO_NEAREST_UP) { return quantize(value, qinfo); } @@ -254,7 +247,8 @@ struct Qasymm8QuantizationHelper * * @return Quantized value */ - static inline QUANTIZED_TYPE quantize(float value, const QuantizationInfo &qinfo, RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_UP) + static inline QUANTIZED_TYPE + quantize(float value, const QuantizationInfo &qinfo, RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_UP) { const UniformQuantizationInfo uqinfo = qinfo.uniform(); ARM_COMPUTE_ERROR_ON(uqinfo.scale == 0); @@ -297,7 +291,8 @@ struct Qasymm8QuantizationHelper * @return Quantized value */ template <typename INFO_TYPE> -inline uint8_t quantize_qasymm8(float value, const INFO_TYPE &qinfo, RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_UP) +inline uint8_t +quantize_qasymm8(float value, const INFO_TYPE &qinfo, RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_UP) { return Qasymm8QuantizationHelper<uint8_t>::quantize(value, qinfo, rounding_policy); } @@ -311,7 +306,9 @@ inline uint8_t quantize_qasymm8(float value, const INFO_TYPE &qinfo, RoundingPol * @return Quantized value */ template <typename INFO_TYPE> -inline int8_t quantize_qasymm8_signed(float value, const INFO_TYPE &qinfo, RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_UP) +inline int8_t quantize_qasymm8_signed(float value, + const INFO_TYPE &qinfo, + RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_UP) { return Qasymm8QuantizationHelper<int8_t>::quantize(value, qinfo, rounding_policy); } @@ -441,7 +438,9 @@ inline float dequantize(uint16_t value, float scale, int32_t offset) * * @return Quantized value */ -inline int16_t quantize_qsymm16(float value, const UniformQuantizationInfo &qinfo, RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_UP) +inline int16_t quantize_qsymm16(float value, + const UniformQuantizationInfo &qinfo, + RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_UP) { int quantized = arm_compute::round(value / qinfo.scale, rounding_policy); quantized = arm_compute::utility::clamp<int, int16_t>(quantized); @@ -492,7 +491,9 @@ inline float dequantize_qsymm16(int16_t value, const QuantizationInfo &qinfo) * * @return Quantized value */ -inline uint16_t quantize_qasymm16(float value, const UniformQuantizationInfo &qinfo, RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_UP) +inline uint16_t quantize_qasymm16(float value, + const UniformQuantizationInfo &qinfo, + RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_UP) { int quantized = arm_compute::round(value / qinfo.scale, rounding_policy) + qinfo.offset; quantized = arm_compute::utility::clamp<int, uint16_t>(quantized); @@ -565,7 +566,8 @@ inline float dequantize_qasymm16(uint16_t value, const QuantizationInfo &qinfo) * z_n = - z_i * s_i / s_o + z_o * */ -inline UniformQuantizationInfo compute_requantization_scale_offset(const UniformQuantizationInfo &uqinfo_in, const UniformQuantizationInfo &uqinfo_out) +inline UniformQuantizationInfo compute_requantization_scale_offset(const UniformQuantizationInfo &uqinfo_in, + const UniformQuantizationInfo &uqinfo_out) { float scale_to_apply = uqinfo_out.scale; int32_t offset_to_apply = uqinfo_out.offset; diff --git a/arm_compute/core/Rounding.h b/arm_compute/core/Rounding.h index b6817b5107..30a5a0fe9d 100644 --- a/arm_compute/core/Rounding.h +++ b/arm_compute/core/Rounding.h @@ -42,5 +42,5 @@ enum class RoundingPolicy * @return Rounded value of the argument x. */ int round(float x, RoundingPolicy rounding_policy); -} +} // namespace arm_compute #endif /*ARM_COMPUTE_ROUNDING_H */ diff --git a/arm_compute/core/Size2D.h b/arm_compute/core/Size2D.h index f3e9bea4c7..672b392050 100644 --- a/arm_compute/core/Size2D.h +++ b/arm_compute/core/Size2D.h @@ -41,9 +41,7 @@ public: * @param[in] w Width of the image or rectangle * @param[in] h Height of the image or rectangle */ - Size2D(size_t w, size_t h) noexcept - : width(w), - height(h) + Size2D(size_t w, size_t h) noexcept : width(w), height(h) { } /** The area of the image or rectangle calculated as (width * height) @@ -90,5 +88,5 @@ public: size_t width = {}; /**< Width of the image region or rectangle */ size_t height = {}; /**< Height of the image region or rectangle */ }; -} +} // namespace arm_compute #endif /*ARM_COMPUTE_SIZE2D_H */ diff --git a/arm_compute/core/Size3D.h b/arm_compute/core/Size3D.h index 4241ed4f7e..e2dc6fe012 100644 --- a/arm_compute/core/Size3D.h +++ b/arm_compute/core/Size3D.h @@ -40,8 +40,7 @@ public: * @param[in] h Height of the 3D shape or object * @param[in] d Depth of the 3D shape or object */ - Size3D(size_t w, size_t h, size_t d) noexcept - : width(w), height(h), depth(d) + Size3D(size_t w, size_t h, size_t d) noexcept : width(w), height(h), depth(d) { } diff --git a/arm_compute/core/Steps.h b/arm_compute/core/Steps.h index 208fc4b294..6b261becc0 100644 --- a/arm_compute/core/Steps.h +++ b/arm_compute/core/Steps.h @@ -45,8 +45,7 @@ public: * @param[in] steps Values to initialize the steps. */ template <typename... Ts> - Steps(Ts... steps) - : Dimensions{ steps... } + Steps(Ts... steps) : Dimensions{steps...} { // Initialize empty dimensions to 1 std::fill(_id.begin() + _num_dimensions, _id.end(), 1); @@ -62,5 +61,5 @@ public: /** Default destructor */ ~Steps() = default; }; -} +} // namespace arm_compute #endif /*ARM_COMPUTE_STEPS_H*/ diff --git a/arm_compute/core/Strides.h b/arm_compute/core/Strides.h index b582d066f7..627b219987 100644 --- a/arm_compute/core/Strides.h +++ b/arm_compute/core/Strides.h @@ -43,8 +43,7 @@ public: * @param[in] strides Values to initialize the strides. */ template <typename... Ts> - constexpr Strides(Ts... strides) - : Dimensions{ strides... } + constexpr Strides(Ts... strides) : Dimensions{strides...} { } /** Allow instances of this class to be copy constructed */ diff --git a/arm_compute/core/SubTensorInfo.h b/arm_compute/core/SubTensorInfo.h index 21703b0d93..7a3ee2cfd0 100644 --- a/arm_compute/core/SubTensorInfo.h +++ b/arm_compute/core/SubTensorInfo.h @@ -24,10 +24,9 @@ #ifndef ARM_COMPUTE_SUBTENSORINFO_H #define ARM_COMPUTE_SUBTENSORINFO_H -#include "arm_compute/core/ITensorInfo.h" - #include "arm_compute/core/Coordinates.h" #include "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensorInfo.h" #include "arm_compute/core/Strides.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/TensorShape.h" @@ -73,7 +72,7 @@ public: // Inherited methods overridden: std::unique_ptr<ITensorInfo> clone() const override; - ITensorInfo &set_data_type(DataType data_type) override + ITensorInfo &set_data_type(DataType data_type) override { ARM_COMPUTE_ERROR_ON(_parent == nullptr); _parent->set_data_type(data_type); @@ -143,7 +142,7 @@ public: return _parent->offset_element_in_bytes(_coords); } int32_t offset_element_in_bytes(const Coordinates &pos) const override; - size_t element_size() const override + size_t element_size() const override { ARM_COMPUTE_ERROR_ON(_parent == nullptr); return _parent->element_size(); @@ -227,7 +226,7 @@ public: { ARM_COMPUTE_ERROR_ON(_parent == nullptr); // Check if subtensor is valid if parent is configured - if(_parent->tensor_shape().total_size() != 0) + if (_parent->tensor_shape().total_size() != 0) { ARM_COMPUTE_ERROR_ON_INVALID_SUBTENSOR_VALID_REGION(_parent->valid_region(), valid_region); } diff --git a/arm_compute/core/TensorInfo.h b/arm_compute/core/TensorInfo.h index e738a797b2..b18f750427 100644 --- a/arm_compute/core/TensorInfo.h +++ b/arm_compute/core/TensorInfo.h @@ -24,15 +24,14 @@ #ifndef ARM_COMPUTE_TENSORINFO_H #define ARM_COMPUTE_TENSORINFO_H -#include "arm_compute/core/ITensorInfo.h" - -#include "ITensorInfo.h" #include "arm_compute/core/Coordinates.h" #include "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensorInfo.h" #include "arm_compute/core/Strides.h" #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/Types.h" +#include "ITensorInfo.h" #include <cstddef> #include <memory> @@ -112,7 +111,10 @@ public: * @param[in] data_type Data type to use for each tensor element * @param[in] quantization_info The quantization settings for the tensor data. */ - TensorInfo(const TensorShape &tensor_shape, size_t num_channels, DataType data_type, QuantizationInfo quantization_info); + TensorInfo(const TensorShape &tensor_shape, + size_t num_channels, + DataType data_type, + QuantizationInfo quantization_info); /** Initialize the tensor info with just a format. * @@ -136,7 +138,11 @@ public: * @param[in] offset_first_element_in_bytes Offset in bytes from the beginning of memory allocation to access the first element. * @param[in] total_size_in_bytes Size in bytes of the memory allocation (including the offset to the first element). */ - void init(const TensorShape &tensor_shape, Format format, const Strides &strides_in_bytes, size_t offset_first_element_in_bytes, size_t total_size_in_bytes); + void init(const TensorShape &tensor_shape, + Format format, + const Strides &strides_in_bytes, + size_t offset_first_element_in_bytes, + size_t total_size_in_bytes); /** Initialize the tensor info with just a format. * @@ -164,8 +170,12 @@ public: * @param[in] offset_first_element_in_bytes Offset in bytes from the beginning of memory allocation to access the first element. * @param[in] total_size_in_bytes Size in bytes of the memory allocation (including the offset to the first element). */ - void init(const TensorShape &tensor_shape, size_t num_channels, DataType data_type, const Strides &strides_in_bytes, size_t offset_first_element_in_bytes, - size_t total_size_in_bytes); + void init(const TensorShape &tensor_shape, + size_t num_channels, + DataType data_type, + const Strides &strides_in_bytes, + size_t offset_first_element_in_bytes, + size_t total_size_in_bytes); /** Initialize the metadata structure for the given tensor shape and single-plane format, (Padding is automatically calculated) * * @note The padding used by this method is really conservative so that the tensor can be used for most functions. @@ -191,19 +201,19 @@ public: // Inherited methods overridden: std::unique_ptr<ITensorInfo> clone() const override; - ITensorInfo &set_data_type(DataType data_type) override; - ITensorInfo &set_num_channels(int num_channels) override; - ITensorInfo &set_format(Format format) override; - ITensorInfo &set_tensor_shape(const TensorShape &shape) override; - ITensorInfo &set_tensor_dims_state(const TensorDimsState &state) override; - ITensorInfo &set_quantization_info(const QuantizationInfo &quantization_info) override; - ITensorInfo &set_data_layout(const DataLayout &data_layout) override; - ITensorInfo &reset_padding() override; - bool auto_padding() override; - ITensorInfo &set_lock_paddings(bool flag) override; - bool lock_paddings() const override; - bool extend_padding(const PaddingSize &padding) override; - size_t dimension(size_t index) const override + ITensorInfo &set_data_type(DataType data_type) override; + ITensorInfo &set_num_channels(int num_channels) override; + ITensorInfo &set_format(Format format) override; + ITensorInfo &set_tensor_shape(const TensorShape &shape) override; + ITensorInfo &set_tensor_dims_state(const TensorDimsState &state) override; + ITensorInfo &set_quantization_info(const QuantizationInfo &quantization_info) override; + ITensorInfo &set_data_layout(const DataLayout &data_layout) override; + ITensorInfo &reset_padding() override; + bool auto_padding() override; + ITensorInfo &set_lock_paddings(bool flag) override; + bool lock_paddings() const override; + bool extend_padding(const PaddingSize &padding) override; + size_t dimension(size_t index) const override { return _tensor_shape[index]; } @@ -220,7 +230,7 @@ public: return _offset_first_element_in_bytes; } int32_t offset_element_in_bytes(const Coordinates &pos) const override; - size_t element_size() const override + size_t element_size() const override { return data_size_from_type(_data_type) * _num_channels; } @@ -266,7 +276,8 @@ public: } bool is_dynamic() const override { - return std::find(std::cbegin(_dims_state), std::cend(_dims_state), get_dynamic_state_value()) != std::cend(_dims_state); + return std::find(std::cbegin(_dims_state), std::cend(_dims_state), get_dynamic_state_value()) != + std::cend(_dims_state); } bool are_values_constant() const override { @@ -343,11 +354,15 @@ private: */ inline bool operator==(const TensorInfo &lhs, const TensorInfo &rhs) { - return (lhs._total_size == rhs._total_size) && (lhs._offset_first_element_in_bytes == rhs._offset_first_element_in_bytes) && (lhs._strides_in_bytes == rhs._strides_in_bytes) - && (lhs._num_channels == rhs._num_channels) && (lhs._tensor_shape == rhs._tensor_shape) && (lhs._dims_state == rhs._dims_state) && (lhs._data_type == rhs._data_type) && (lhs._format == rhs._format) - && (lhs._is_resizable == rhs._is_resizable) && (lhs._valid_region == rhs._valid_region) && (lhs._padding == rhs._padding) && (lhs._quantization_info == rhs._quantization_info) - && (lhs._data_layout == rhs._data_layout) && (lhs._are_values_constant == rhs._are_values_constant) - && (lhs._id == rhs._id); + return (lhs._total_size == rhs._total_size) && + (lhs._offset_first_element_in_bytes == rhs._offset_first_element_in_bytes) && + (lhs._strides_in_bytes == rhs._strides_in_bytes) && (lhs._num_channels == rhs._num_channels) && + (lhs._tensor_shape == rhs._tensor_shape) && (lhs._dims_state == rhs._dims_state) && + (lhs._data_type == rhs._data_type) && (lhs._format == rhs._format) && + (lhs._is_resizable == rhs._is_resizable) && (lhs._valid_region == rhs._valid_region) && + (lhs._padding == rhs._padding) && (lhs._quantization_info == rhs._quantization_info) && + (lhs._data_layout == rhs._data_layout) && (lhs._are_values_constant == rhs._are_values_constant) && + (lhs._id == rhs._id); } } // namespace arm_compute #endif /*ARM_COMPUTE_TENSORINFO_H */ diff --git a/arm_compute/core/TensorShape.h b/arm_compute/core/TensorShape.h index 4c9186ac64..c1707e262f 100644 --- a/arm_compute/core/TensorShape.h +++ b/arm_compute/core/TensorShape.h @@ -44,11 +44,10 @@ public: * @param[in] dims Values to initialize the dimensions. */ template <typename... Ts> - TensorShape(Ts... dims) - : Dimensions{ dims... } + TensorShape(Ts... dims) : Dimensions{dims...} { // Initialize unspecified dimensions to 1 - if(_num_dimensions > 0) + if (_num_dimensions > 0) { std::fill(_id.begin() + _num_dimensions, _id.end(), 1); } @@ -79,7 +78,7 @@ public: TensorShape &set(size_t dimension, size_t value, bool apply_dim_correction = true, bool increase_dim_unit = true) { // Clear entire shape if one dimension is zero - if(value == 0) + if (value == 0) { _num_dimensions = 0; std::fill(_id.begin(), _id.end(), 0); @@ -94,7 +93,7 @@ public: Dimensions::set(dimension, value, increase_dim_unit); // Correct number dimensions to ignore trailing dimensions of size 1 - if(apply_dim_correction) + if (apply_dim_correction) { apply_dimension_correction(); } @@ -123,7 +122,7 @@ public: std::fill(_id.begin() + _num_dimensions, _id.end(), 1); // Correct number dimensions to ignore trailing dimensions of size 1 - if(apply_dim_correction) + if (apply_dim_correction) { apply_dimension_correction(); } @@ -212,26 +211,26 @@ public: * @return The broadcasted shape or an empty shape if the shapes are not broadcast compatible. */ template <typename... Shapes> - static TensorShape broadcast_shape(const Shapes &... shapes) + static TensorShape broadcast_shape(const Shapes &...shapes) { TensorShape bc_shape; - auto broadcast = [&bc_shape](const TensorShape & other) + auto broadcast = [&bc_shape](const TensorShape &other) { - if(bc_shape.num_dimensions() == 0) + if (bc_shape.num_dimensions() == 0) { bc_shape = other; } - else if(other.num_dimensions() != 0) + else if (other.num_dimensions() != 0) { - for(size_t d = 0; d < TensorShape::num_max_dimensions; ++d) + for (size_t d = 0; d < TensorShape::num_max_dimensions; ++d) { const size_t dim_min = std::min(bc_shape[d], other[d]); const size_t dim_max = std::max(bc_shape[d], other[d]); - if((dim_min != 1) && (dim_min != dim_max)) + if ((dim_min != 1) && (dim_min != dim_max)) { - bc_shape = TensorShape{ 0U }; + bc_shape = TensorShape{0U}; break; } @@ -249,9 +248,9 @@ private: /** Remove trailing dimensions of size 1 from the reported number of dimensions. */ void apply_dimension_correction() { - for(int i = static_cast<int>(_num_dimensions) - 1; i > 0; --i) + for (int i = static_cast<int>(_num_dimensions) - 1; i > 0; --i) { - if(_id[i] == 1) + if (_id[i] == 1) { --_num_dimensions; } @@ -262,5 +261,5 @@ private: } } }; -} +} // namespace arm_compute #endif /*ARM_COMPUTE_TENSORSHAPE_H*/ diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h index 9264cefe3e..6b51af17d4 100644 --- a/arm_compute/core/Types.h +++ b/arm_compute/core/Types.h @@ -59,13 +59,13 @@ /** The following symbols have been moved to: * MatMulInfo */ -#include "arm_compute/function_info/MatMulInfo.h" - #include "arm_compute/core/Coordinates.h" #include "arm_compute/core/Size2D.h" #include "arm_compute/core/Size3D.h" #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/utils/misc/Macros.h" +#include "arm_compute/function_info/MatMulInfo.h" + #include "support/Bfloat16.h" #include <cmath> @@ -143,8 +143,7 @@ enum class ComparisonOperation struct ValidRegion { /** Default constructor */ - ValidRegion() - : anchor{}, shape{} + ValidRegion() : anchor{}, shape{} { } @@ -165,8 +164,7 @@ struct ValidRegion * @param[in] a_shape Shape of the valid region. * */ - ValidRegion(const Coordinates &an_anchor, const TensorShape &a_shape) - : anchor{ an_anchor }, shape{ a_shape } + ValidRegion(const Coordinates &an_anchor, const TensorShape &a_shape) : anchor{an_anchor}, shape{a_shape} { anchor.set_num_dimensions(std::max(anchor.num_dimensions(), shape.num_dimensions())); } @@ -179,7 +177,7 @@ struct ValidRegion * */ ValidRegion(const Coordinates &an_anchor, const TensorShape &a_shape, size_t num_dimensions) - : anchor{ an_anchor }, shape{ a_shape } + : anchor{an_anchor}, shape{a_shape} { ARM_COMPUTE_ERROR_ON(num_dimensions < std::max(anchor.num_dimensions(), shape.num_dimensions())); anchor.set_num_dimensions(num_dimensions); @@ -241,32 +239,24 @@ enum class BorderMode struct BorderSize { /** Empty border, i.e. no border */ - constexpr BorderSize() noexcept - : top{ 0 }, - right{ 0 }, - bottom{ 0 }, - left{ 0 } + constexpr BorderSize() noexcept : top{0}, right{0}, bottom{0}, left{0} { } /** Border with equal size around the 2D plane */ - explicit constexpr BorderSize(unsigned int size) noexcept - : top{ size }, - right{ size }, - bottom{ size }, - left{ size } + explicit constexpr BorderSize(unsigned int size) noexcept : top{size}, right{size}, bottom{size}, left{size} { } /** Border with same size for top/bottom and left/right */ constexpr BorderSize(unsigned int top_bottom, unsigned int left_right) - : top{ top_bottom }, right{ left_right }, bottom{ top_bottom }, left{ left_right } + : top{top_bottom}, right{left_right}, bottom{top_bottom}, left{left_right} { } /** Border with different sizes */ constexpr BorderSize(unsigned int top, unsigned int right, unsigned int bottom, unsigned int left) - : top{ top }, right{ right }, bottom{ bottom }, left{ left } + : top{top}, right{right}, bottom{bottom}, left{left} { } @@ -371,7 +361,7 @@ enum class InterpolationPolicy { NEAREST_NEIGHBOR, /**< Output values are defined to match the source pixel whose center is nearest to the sample position */ BILINEAR, /**< Output values are defined by bilinear interpolation between the pixels */ - AREA, /**< Output values are determined by averaging the source pixels whose areas fall under the area of the destination pixel, projected onto the source image */ + AREA, /**< Output values are determined by averaging the source pixels whose areas fall under the area of the destination pixel, projected onto the source image */ }; /** Bilinear Interpolation method used by LKTracker */ @@ -478,12 +468,12 @@ enum class NormType */ struct DetectionWindow { - uint16_t x{ 0 }; /**< Top-left x coordinate */ - uint16_t y{ 0 }; /**< Top-left y coordinate */ - uint16_t width{ 0 }; /**< Width of the detection window */ - uint16_t height{ 0 }; /**< Height of the detection window */ - uint16_t idx_class{ 0 }; /**< Index of the class */ - float score{ 0.f }; /**< Confidence value for the detection window */ + uint16_t x{0}; /**< Top-left x coordinate */ + uint16_t y{0}; /**< Top-left y coordinate */ + uint16_t width{0}; /**< Width of the detection window */ + uint16_t height{0}; /**< Height of the detection window */ + uint16_t idx_class{0}; /**< Index of the class */ + float score{0.f}; /**< Confidence value for the detection window */ }; /** Available pooling types */ @@ -520,12 +510,28 @@ public: * @param[in] im_width (Optional) Boxes whose centers (on the x axis) is beyond im_width will be filtered. Defaults to 1 * @param[in] im_height (Optional) Boxes whose centers (on the y axis) is beyond im_height will be filtered. Defaults to 1 */ - BoxNMSLimitInfo(float score_thresh = 0.05f, float nms = 0.3f, - int detections = 100, bool soft_nms_enabled = false, - NMSType soft_nms_method = NMSType::LINEAR, - float soft_nms_sigma = 0.5f, float soft_nms_min_score_thres = 0.001f, bool suppress_size = false, float min_size = 1.0f, float im_width = 1.0f, float im_height = 1.0f) - : _score_thresh(score_thresh), _nms(nms), _detections_per_im(detections), _soft_nms_enabled(soft_nms_enabled), _soft_nms_method(soft_nms_method), _soft_nms_sigma(soft_nms_sigma), - _soft_nms_min_score_thres(soft_nms_min_score_thres), _suppress_size(suppress_size), _min_size(min_size), _im_width(im_width), _im_height(im_height) + BoxNMSLimitInfo(float score_thresh = 0.05f, + float nms = 0.3f, + int detections = 100, + bool soft_nms_enabled = false, + NMSType soft_nms_method = NMSType::LINEAR, + float soft_nms_sigma = 0.5f, + float soft_nms_min_score_thres = 0.001f, + bool suppress_size = false, + float min_size = 1.0f, + float im_width = 1.0f, + float im_height = 1.0f) + : _score_thresh(score_thresh), + _nms(nms), + _detections_per_im(detections), + _soft_nms_enabled(soft_nms_enabled), + _soft_nms_method(soft_nms_method), + _soft_nms_sigma(soft_nms_sigma), + _soft_nms_min_score_thres(soft_nms_min_score_thres), + _suppress_size(suppress_size), + _min_size(min_size), + _im_width(im_width), + _im_height(im_height) { } /** Get the score threshold */ @@ -603,14 +609,13 @@ private: struct Padding2D { Padding2D() = default; - Padding2D(size_t left, size_t right, size_t top, size_t bottom) - : left(left), right(right), top(top), bottom(bottom) + Padding2D(size_t left, size_t right, size_t top, size_t bottom) : left(left), right(right), top(top), bottom(bottom) { } - size_t left = { 0 }; /**< Padding across the width dimension on the left, in elements. */ - size_t right = { 0 }; /**< Padding across the width dimension on the right, in elements. */ - size_t top = { 0 }; /**< Padding across the height dimension on the top, in elements. */ - size_t bottom = { 0 }; /**< Padding across the height dimension on the bottom, in elements. */ + size_t left = {0}; /**< Padding across the width dimension on the left, in elements. */ + size_t right = {0}; /**< Padding across the width dimension on the right, in elements. */ + size_t top = {0}; /**< Padding across the height dimension on the top, in elements. */ + size_t bottom = {0}; /**< Padding across the height dimension on the bottom, in elements. */ }; /** Padding information for 3D operations like Conv3d */ @@ -630,12 +635,12 @@ struct Padding3D { } - size_t left = { 0 }; /**< Padding across the width dimenstion on the left, in elements. */ - size_t right = { 0 }; /**< Padding across the width dimenstion on the right, in elements. */ - size_t top = { 0 }; /**< Padding across the height dimenstion on the top, in elements. */ - size_t bottom = { 0 }; /**< Padding across the height dimenstion on the bottom, in elements. */ - size_t front = { 0 }; /**< Padding across the depth dimenstion on the front, in elements. */ - size_t back = { 0 }; /**< Padding across the depth dimenstion on the back, in elements. */ + size_t left = {0}; /**< Padding across the width dimenstion on the left, in elements. */ + size_t right = {0}; /**< Padding across the width dimenstion on the right, in elements. */ + size_t top = {0}; /**< Padding across the height dimenstion on the top, in elements. */ + size_t bottom = {0}; /**< Padding across the height dimenstion on the bottom, in elements. */ + size_t front = {0}; /**< Padding across the depth dimenstion on the front, in elements. */ + size_t back = {0}; /**< Padding across the depth dimenstion on the back, in elements. */ }; /** PriorBox layer info */ @@ -667,9 +672,15 @@ public: * @param[in] img_size (Optional) Image size. * @param[in] steps (Optional) Step values. */ - PriorBoxLayerInfo(const std::vector<float> &min_sizes, const std::vector<float> &variances, float offset, bool flip = true, bool clip = false, - const std::vector<float> &max_sizes = {}, const std::vector<float> &aspect_ratios = {}, - const Coordinates2D &img_size = Coordinates2D{ 0, 0 }, const std::array<float, 2> &steps = { { 0.f, 0.f } }) + PriorBoxLayerInfo(const std::vector<float> &min_sizes, + const std::vector<float> &variances, + float offset, + bool flip = true, + bool clip = false, + const std::vector<float> &max_sizes = {}, + const std::vector<float> &aspect_ratios = {}, + const Coordinates2D &img_size = Coordinates2D{0, 0}, + const std::array<float, 2> &steps = {{0.f, 0.f}}) : _min_sizes(min_sizes), _variances(variances), _offset(offset), @@ -681,22 +692,22 @@ public: _steps(steps) { _aspect_ratios.push_back(1.); - for(unsigned int i = 0; i < aspect_ratios.size(); ++i) + for (unsigned int i = 0; i < aspect_ratios.size(); ++i) { float ar = aspect_ratios[i]; bool already_exist = false; - for(auto ar_new : _aspect_ratios) + for (auto ar_new : _aspect_ratios) { - if(fabs(ar - ar_new) < 1e-6) + if (fabs(ar - ar_new) < 1e-6) { already_exist = true; break; } } - if(!already_exist) + if (!already_exist) { _aspect_ratios.push_back(ar); - if(flip) + if (flip) { _aspect_ratios.push_back(1.f / ar); } @@ -808,8 +819,16 @@ public: * @param[in] variance_encoded_in_target (Optional) If true, variance is encoded in target. Otherwise we need to adjust the predicted offset accordingly.Default set to false. * @param[in] eta (Optional) Eta. */ - DetectionOutputLayerInfo(int num_classes, bool share_location, DetectionOutputLayerCodeType code_type, int keep_top_k, float nms_threshold, int top_k = -1, int background_label_id = -1, - float confidence_threshold = std::numeric_limits<float>::lowest(), bool variance_encoded_in_target = false, float eta = 1) + DetectionOutputLayerInfo(int num_classes, + bool share_location, + DetectionOutputLayerCodeType code_type, + int keep_top_k, + float nms_threshold, + int top_k = -1, + int background_label_id = -1, + float confidence_threshold = std::numeric_limits<float>::lowest(), + bool variance_encoded_in_target = false, + float eta = 1) : _num_classes(num_classes), _share_location(share_location), _code_type(code_type), @@ -923,8 +942,15 @@ public: * @param[in] detection_per_class (Optional) Number of detection per class. Used in the Regular Non-Max-Suppression. Defaults to 100. * @param[in] dequantize_scores (Optional) If the scores need to be dequantized. Defaults to true. */ - DetectionPostProcessLayerInfo(unsigned int max_detections, unsigned int max_classes_per_detection, float nms_score_threshold, float iou_threshold, unsigned int num_classes, - std::array<float, 4> scales_values, bool use_regular_nms = false, unsigned int detection_per_class = 100, bool dequantize_scores = true) + DetectionPostProcessLayerInfo(unsigned int max_detections, + unsigned int max_classes_per_detection, + float nms_score_threshold, + float iou_threshold, + unsigned int num_classes, + std::array<float, 4> scales_values, + bool use_regular_nms = false, + unsigned int detection_per_class = 100, + bool dequantize_scores = true) : _max_detections(max_detections), _max_classes_per_detection(max_classes_per_detection), _nms_score_threshold(nms_score_threshold), @@ -1240,8 +1266,14 @@ public: * @param[in] spatial_scale Spatial scale to be applied to the ROI coordinates and dimensions. * @param[in] sampling_ratio Number of samples to include in each pooling region (if set to zero, a ceil(roi_dims/pooling_dims)) */ - ROIPoolingLayerInfo(unsigned int pooled_width, unsigned int pooled_height, float spatial_scale, unsigned int sampling_ratio = 0) - : _pooled_width(pooled_width), _pooled_height(pooled_height), _spatial_scale(spatial_scale), _sampling_ratio(sampling_ratio) + ROIPoolingLayerInfo(unsigned int pooled_width, + unsigned int pooled_height, + float spatial_scale, + unsigned int sampling_ratio = 0) + : _pooled_width(pooled_width), + _pooled_height(pooled_height), + _spatial_scale(spatial_scale), + _sampling_ratio(sampling_ratio) { } /** Get the pooled width of the layer */ @@ -1288,10 +1320,24 @@ public: * @param[in] min_size (Optional)Size used to validate the anchors produced. Defaults to 16. * @param[in] values_per_roi (Optional)Values used to represent a ROI(Region of interest). Defaults to 4. */ - GenerateProposalsInfo(float im_width, float im_height, float im_scale, float spatial_scale = 1.0, int pre_nms_topN = 6000, int post_nms_topN = 300, float nms_thres = 0.7, float min_size = 16.0, + GenerateProposalsInfo(float im_width, + float im_height, + float im_scale, + float spatial_scale = 1.0, + int pre_nms_topN = 6000, + int post_nms_topN = 300, + float nms_thres = 0.7, + float min_size = 16.0, size_t values_per_roi = 4) - : _im_height(im_height), _im_width(im_width), _im_scale(im_scale), _spatial_scale(spatial_scale), _pre_nms_topN(pre_nms_topN), _post_nms_topN(post_nms_topN), _nms_thres(nms_thres), - _min_size(min_size), _values_per_roi(values_per_roi) + : _im_height(im_height), + _im_width(im_width), + _im_scale(im_scale), + _spatial_scale(spatial_scale), + _pre_nms_topN(pre_nms_topN), + _post_nms_topN(post_nms_topN), + _nms_thres(nms_thres), + _min_size(min_size), + _values_per_roi(values_per_roi) { } @@ -1417,11 +1463,20 @@ public: * @param[in] correct_transform_coords (Optional)Correct bounding box transform coordinates. Defaults to false * @param[in] bbox_xform_clip (Optional)Minimum bounding box width and height after bounding box transformation in log-space. Defaults to log(1000/16) */ - BoundingBoxTransformInfo(float img_width, float img_height, float scale, bool apply_scale = false, const std::array<float, 4> weights = { { 1.f, 1.f, 1.f, 1.f } }, bool correct_transform_coords = - false, - float bbox_xform_clip = - 4.135166556742356f) - : _img_width(img_width), _img_height(img_height), _scale(scale), _apply_scale(apply_scale), _correct_transform_coords(correct_transform_coords), _weights(weights), _bbox_xform_clip(bbox_xform_clip) + BoundingBoxTransformInfo(float img_width, + float img_height, + float scale, + bool apply_scale = false, + const std::array<float, 4> weights = {{1.f, 1.f, 1.f, 1.f}}, + bool correct_transform_coords = false, + float bbox_xform_clip = 4.135166556742356f) + : _img_width(img_width), + _img_height(img_height), + _scale(scale), + _apply_scale(apply_scale), + _correct_transform_coords(correct_transform_coords), + _weights(weights), + _bbox_xform_clip(bbox_xform_clip) { } @@ -1484,7 +1539,12 @@ public: * @param[in] is_scaled (Optional) Boolean that specifies if alpha will be scaled by the normalization size or not. * Should be false to follow [Krichevksy 2012]. */ - NormalizationLayerInfo(NormType type, uint32_t norm_size = 5, float alpha = 0.0001f, float beta = 0.5f, float kappa = 1.f, bool is_scaled = true) + NormalizationLayerInfo(NormType type, + uint32_t norm_size = 5, + float alpha = 0.0001f, + float beta = 0.5f, + float kappa = 1.f, + bool is_scaled = true) : _type(type), _norm_size(norm_size), _alpha(alpha), _beta(beta), _kappa(kappa), _is_scaled(is_scaled) { } @@ -1612,7 +1672,12 @@ class WeightsInfo public: /** Default constructor */ WeightsInfo() - : _are_reshaped(false), _kernel_width(0), _kernel_height(0), _num_kernels(0), _retain_internal_weights(false), _weight_format(arm_compute::WeightFormat::UNSPECIFIED) + : _are_reshaped(false), + _kernel_width(0), + _kernel_height(0), + _num_kernels(0), + _retain_internal_weights(false), + _weight_format(arm_compute::WeightFormat::UNSPECIFIED) { } /** Constructor @@ -1624,9 +1689,18 @@ public: * @param[in] retain_internal_weights (Optional) True if internal reshaped weights must be retained. Used for reconfiguration purposes. Default is false. * @param[in] weight_format (Optional) arm_gemm:WeightFormat enumeration requested by the user. Default is arm_compute::WeightFormat::UNSPECIFIED. */ - WeightsInfo(bool are_reshaped, unsigned int kernel_width, unsigned int kernel_height, unsigned int num_kernels, bool retain_internal_weights = false, - arm_compute::WeightFormat weight_format = arm_compute::WeightFormat::UNSPECIFIED) - : _are_reshaped(are_reshaped), _kernel_width(kernel_width), _kernel_height(kernel_height), _num_kernels(num_kernels), _retain_internal_weights(retain_internal_weights), _weight_format(weight_format) + WeightsInfo(bool are_reshaped, + unsigned int kernel_width, + unsigned int kernel_height, + unsigned int num_kernels, + bool retain_internal_weights = false, + arm_compute::WeightFormat weight_format = arm_compute::WeightFormat::UNSPECIFIED) + : _are_reshaped(are_reshaped), + _kernel_width(kernel_width), + _kernel_height(kernel_height), + _num_kernels(num_kernels), + _retain_internal_weights(retain_internal_weights), + _weight_format(weight_format) { } /** Flag which specifies if the weights tensor has been reshaped. @@ -1698,7 +1772,14 @@ class GEMMReshapeInfo final public: /** Default constructor */ GEMMReshapeInfo() - : _m(1), _n(1), _k(1), _mult_transpose1xW_width(1), _mult_interleave4x4_height(1), _depth_output_gemm3d(0), _reinterpret_input_as_3d(false), _broadcast_bias(false) + : _m(1), + _n(1), + _k(1), + _mult_transpose1xW_width(1), + _mult_interleave4x4_height(1), + _depth_output_gemm3d(0), + _reinterpret_input_as_3d(false), + _broadcast_bias(false) { } /** Constructor @@ -1714,9 +1795,22 @@ public: * to perform 1x1 convolutions with the NHWC data layout) * @param[in] broadcast_bias (Optional) Broadcast the shape of the bias tensor from a vector to a matrix. */ - GEMMReshapeInfo(int m, int n, int k, int mult_transpose1xW_width = 1, int mult_interleave4x4_height = 1, int depth_output_gemm3d = 0, bool reinterpret_input_as_3d = false, bool broadcast_bias = false) - : _m(m), _n(n), _k(k), _mult_transpose1xW_width(mult_transpose1xW_width), _mult_interleave4x4_height(mult_interleave4x4_height), _depth_output_gemm3d(depth_output_gemm3d), - _reinterpret_input_as_3d(reinterpret_input_as_3d), _broadcast_bias(broadcast_bias) + GEMMReshapeInfo(int m, + int n, + int k, + int mult_transpose1xW_width = 1, + int mult_interleave4x4_height = 1, + int depth_output_gemm3d = 0, + bool reinterpret_input_as_3d = false, + bool broadcast_bias = false) + : _m(m), + _n(n), + _k(k), + _mult_transpose1xW_width(mult_transpose1xW_width), + _mult_interleave4x4_height(mult_interleave4x4_height), + _depth_output_gemm3d(depth_output_gemm3d), + _reinterpret_input_as_3d(reinterpret_input_as_3d), + _broadcast_bias(broadcast_bias) { } /** Number of matrix A rows @@ -1806,11 +1900,11 @@ struct GEMMLHSMatrixInfo : m0(m), k0(k), v0(v), transpose(trans), interleave(inter) { } - unsigned int m0{ 1 }; /**< Number of rows processed by the matrix multiplication */ - unsigned int k0{ 1 }; /**< Number of partial accumulations performed by the matrix multiplication */ - unsigned int v0{ 1 }; /**< Number of vertical blocks of size (m0xk0) stored on the same output row */ - bool transpose{ true }; /**< True if the (m0xk0) block has to be transposed before been stored */ - bool interleave{ true }; /**< True if the v0 (m0xk0) blocks have to be interleaved in the output row */ + unsigned int m0{1}; /**< Number of rows processed by the matrix multiplication */ + unsigned int k0{1}; /**< Number of partial accumulations performed by the matrix multiplication */ + unsigned int v0{1}; /**< Number of vertical blocks of size (m0xk0) stored on the same output row */ + bool transpose{true}; /**< True if the (m0xk0) block has to be transposed before been stored */ + bool interleave{true}; /**< True if the v0 (m0xk0) blocks have to be interleaved in the output row */ }; /** GEMM RHS (Right Hand Side) matrix information */ @@ -1821,12 +1915,13 @@ struct GEMMRHSMatrixInfo : n0(n), k0(k), h0(h), transpose(trans), interleave(inter), export_to_cl_image(export_to_cl_img) { } - unsigned int n0{ 1 }; /**< Number of columns processed by the matrix multiplication */ - unsigned int k0{ 1 }; /**< Number of partial accumulations performed by the matrix multiplication */ - unsigned int h0{ 1 }; /**< Number of horizontal blocks of size (k0xn0) stored on the same output row */ - bool transpose{ true }; /**< True if the (k0xn0) block has to be transposed before been stored */ - bool interleave{ true }; /**< True if the h0 (k0xn0) blocks have to be interleaved in the output row */ - bool export_to_cl_image{ false }; /**< True if the reshaped rhs has to be exported to cl_image. n0 must be equal to 4 */ + unsigned int n0{1}; /**< Number of columns processed by the matrix multiplication */ + unsigned int k0{1}; /**< Number of partial accumulations performed by the matrix multiplication */ + unsigned int h0{1}; /**< Number of horizontal blocks of size (k0xn0) stored on the same output row */ + bool transpose{true}; /**< True if the (k0xn0) block has to be transposed before been stored */ + bool interleave{true}; /**< True if the h0 (k0xn0) blocks have to be interleaved in the output row */ + bool export_to_cl_image{ + false}; /**< True if the reshaped rhs has to be exported to cl_image. n0 must be equal to 4 */ }; class ITensorInfo; @@ -1842,16 +1937,23 @@ struct WinogradInfo * @param[in] conv_info Convolution info (Pads, strides) * @param[in] data_layout Data layout to use for the output tensor once the convolution has been applied */ - WinogradInfo(Size2D output_tile_sz, Size2D kernel_sz, Size2D input_dims, PadStrideInfo conv_info, DataLayout data_layout) - : output_tile_size(output_tile_sz), kernel_size(kernel_sz), input_dimensions(input_dims), convolution_info(conv_info), output_data_layout(data_layout) - { - } - - Size2D output_tile_size{}; /**< Width and height of the output tile */ - Size2D kernel_size{}; /**< Width and height of the kernel*/ - Size2D input_dimensions{}; /**< Width and height of the input tensor before the convolution is applied */ - PadStrideInfo convolution_info{}; /**< Convolution info (Pads, strides,...) */ - DataLayout output_data_layout{ DataLayout::NCHW }; /**< Data layout to use for the output tensor once the convolution has been applied (NCHW or NHWC) */ + WinogradInfo( + Size2D output_tile_sz, Size2D kernel_sz, Size2D input_dims, PadStrideInfo conv_info, DataLayout data_layout) + : output_tile_size(output_tile_sz), + kernel_size(kernel_sz), + input_dimensions(input_dims), + convolution_info(conv_info), + output_data_layout(data_layout) + { + } + + Size2D output_tile_size{}; /**< Width and height of the output tile */ + Size2D kernel_size{}; /**< Width and height of the kernel*/ + Size2D input_dimensions{}; /**< Width and height of the input tensor before the convolution is applied */ + PadStrideInfo convolution_info{}; /**< Convolution info (Pads, strides,...) */ + DataLayout output_data_layout{ + DataLayout:: + NCHW}; /**< Data layout to use for the output tensor once the convolution has been applied (NCHW or NHWC) */ }; /** IO formatting information class*/ diff --git a/arm_compute/core/Utils.h b/arm_compute/core/Utils.h index c5b50167bf..a2146522f7 100644 --- a/arm_compute/core/Utils.h +++ b/arm_compute/core/Utils.h @@ -69,7 +69,7 @@ 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) + for (unsigned int i = 0; i < perm.num_dimensions(); ++i) { T dimension_val = old_dim[i]; dimensions.set(perm[i], dimension_val); @@ -87,7 +87,11 @@ inline void permute_strides(Dimensions<T> &dimensions, const PermutationVector & * * @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), +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. @@ -100,8 +104,10 @@ PadStrideInfo calculate_same_pad(TensorShape input_shape, TensorShape weights_sh * * @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, +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. @@ -115,8 +121,10 @@ std::pair<unsigned int, unsigned int> deconvolution_output_dimensions(unsigned i * * @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, +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)); @@ -130,9 +138,8 @@ std::pair<unsigned int, unsigned int> scaled_dimensions(int width, int height, * * @return A pair with the new width in the first position and the new height in the second, returned values can be < 1 */ -std::pair<int, int> scaled_dimensions_signed(int width, int height, - int kernel_width, int kernel_height, - const PadStrideInfo &pad_stride_info); +std::pair<int, int> scaled_dimensions_signed( + int width, int height, int kernel_width, int kernel_height, const PadStrideInfo &pad_stride_info); /** Returns calculated width, height and depth of output scaled tensor depending on dimensions rounding mode. * @@ -147,8 +154,12 @@ std::pair<int, int> scaled_dimensions_signed(int width, int height, * @return A tuple with the new width in the first position, the new height in the second, and the new depth in the third. * Returned values can be < 1 */ -std::tuple<int, int, int> scaled_3d_dimensions_signed(int width, int height, int depth, - int kernel_width, int kernel_height, int kernel_depth, +std::tuple<int, int, int> scaled_3d_dimensions_signed(int width, + int height, + int depth, + int kernel_width, + int kernel_height, + int kernel_depth, const Pooling3dLayerInfo &pool3d_info); /** Check if the given reduction operation should be handled in a serial way. @@ -178,7 +189,9 @@ QuantizationInfo get_softmax_output_quantization_info(DataType input_type, bool * * @return The pair with minimum and maximum values */ -std::pair<int32_t, int32_t> get_quantized_activation_min_max(const ActivationLayerInfo &act_info, DataType data_type, UniformQuantizationInfo oq_info); +std::pair<int32_t, int32_t> get_quantized_activation_min_max(const ActivationLayerInfo &act_info, + DataType data_type, + UniformQuantizationInfo oq_info); /** Convert a channel identity into a string. * @@ -295,26 +308,27 @@ inline size_t num_of_elements_in_range(const float start, const float end, const * @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 = " ") +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) + for (unsigned int i = 0; i < n; ++i) { // Set stream width as it is not a "sticky" stream manipulator - if(stream_width != 0) + if (stream_width != 0) { s.width(stream_width); } - if(std::is_same<typename std::decay<T>::type, half>::value) + 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) + 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; @@ -343,17 +357,17 @@ int max_consecutive_elements_display_width_impl(std::ostream &s, const T *ptr, u 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) + 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) + 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) + 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]); @@ -377,7 +391,12 @@ int max_consecutive_elements_display_width_impl(std::ostream &s, const T *ptr, u * @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 = " "); +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. * @@ -390,5 +409,5 @@ void print_consecutive_elements(std::ostream &s, DataType dt, const uint8_t *ptr */ int max_consecutive_elements_display_width(std::ostream &s, DataType dt, const uint8_t *ptr, unsigned int n); #endif /* ARM_COMPUTE_ASSERTS_ENABLED */ -} +} // namespace arm_compute #endif /*ARM_COMPUTE_UTILS_H */ diff --git a/arm_compute/core/Validate.h b/arm_compute/core/Validate.h index 5bffc16f3b..5550560aff 100644 --- a/arm_compute/core/Validate.h +++ b/arm_compute/core/Validate.h @@ -24,13 +24,13 @@ #ifndef ARM_COMPUTE_VALIDATE_H #define ARM_COMPUTE_VALIDATE_H -#include "arm_compute/core/utils/DataLayoutUtils.h" -#include "arm_compute/core/utils/DataTypeUtils.h" #include "arm_compute/core/Error.h" -#include "arm_compute/core/utils/FormatUtils.h" #include "arm_compute/core/IKernel.h" #include "arm_compute/core/ITensor.h" #include "arm_compute/core/QuantizationInfo.h" +#include "arm_compute/core/utils/DataLayoutUtils.h" +#include "arm_compute/core/utils/DataTypeUtils.h" +#include "arm_compute/core/utils/FormatUtils.h" #include "arm_compute/core/Window.h" #include <algorithm> @@ -50,9 +50,9 @@ namespace detail template <typename T> inline bool have_different_dimensions(const Dimensions<T> &dim1, const Dimensions<T> &dim2, unsigned int upper_dim) { - for(unsigned int i = upper_dim; i < arm_compute::Dimensions<T>::num_max_dimensions; ++i) + for (unsigned int i = upper_dim; i < arm_compute::Dimensions<T>::num_max_dimensions; ++i) { - if(dim1[i] != dim2[i]) + if (dim1[i] != dim2[i]) { return true; } @@ -80,7 +80,7 @@ public: * @param[in] line Source code line. Used for error reporting. */ compare_dimension(const Dimensions<T> &dim, const char *function, const char *file, int line) - : _dim{ dim }, _function{ function }, _file{ file }, _line{ line } + : _dim{dim}, _function{function}, _file{file}, _line{line} { } @@ -111,7 +111,7 @@ inline arm_compute::Status for_each_error(F &&) } template <typename F, typename T, typename... Ts> -inline arm_compute::Status for_each_error(F &&func, T &&arg, Ts &&... args) +inline arm_compute::Status for_each_error(F &&func, T &&arg, Ts &&...args) { ARM_COMPUTE_RETURN_ON_ERROR(func(arg)); ARM_COMPUTE_RETURN_ON_ERROR(for_each_error(func, args...)); @@ -148,13 +148,11 @@ struct get_tensor_info_t<ITensorInfo *> * @return Status */ template <typename... Ts> -inline arm_compute::Status error_on_nullptr(const char *function, const char *file, const int line, Ts &&... pointers) +inline arm_compute::Status error_on_nullptr(const char *function, const char *file, const int line, Ts &&...pointers) { - const std::array<const void *, sizeof...(Ts)> pointers_array{ { std::forward<Ts>(pointers)... } }; - bool has_nullptr = std::any_of(pointers_array.begin(), pointers_array.end(), [&](const void *ptr) - { - return (ptr == nullptr); - }); + const std::array<const void *, sizeof...(Ts)> pointers_array{{std::forward<Ts>(pointers)...}}; + bool has_nullptr = + std::any_of(pointers_array.begin(), pointers_array.end(), [&](const void *ptr) { return (ptr == nullptr); }); ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG(has_nullptr, function, file, line, "Nullptr object!"); return arm_compute::Status{}; } @@ -178,8 +176,8 @@ inline arm_compute::Status error_on_nullptr(const char *function, const char *fi * * @return Status */ -arm_compute::Status error_on_mismatching_windows(const char *function, const char *file, const int line, - const Window &full, const Window &win); +arm_compute::Status error_on_mismatching_windows( + const char *function, const char *file, const int line, const Window &full, const Window &win); #define ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(f, w) \ ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_mismatching_windows(__func__, __FILE__, __LINE__, f, w)) #define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_WINDOWS(f, w) \ @@ -200,8 +198,8 @@ arm_compute::Status error_on_mismatching_windows(const char *function, const cha * * @return Status */ -arm_compute::Status error_on_invalid_subwindow(const char *function, const char *file, const int line, - const Window &full, const Window &sub); +arm_compute::Status error_on_invalid_subwindow( + const char *function, const char *file, const int line, const Window &full, const Window &sub); #define ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(f, s) \ ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_invalid_subwindow(__func__, __FILE__, __LINE__, f, s)) #define ARM_COMPUTE_RETURN_ERROR_ON_INVALID_SUBWINDOW(f, s) \ @@ -220,12 +218,14 @@ arm_compute::Status error_on_invalid_subwindow(const char *function, const char * * @return Status */ -arm_compute::Status error_on_window_not_collapsable_at_dimension(const char *function, const char *file, const int line, - const Window &full, const Window &window, const int dim); +arm_compute::Status error_on_window_not_collapsable_at_dimension( + const char *function, const char *file, const int line, const Window &full, const Window &window, const int dim); #define ARM_COMPUTE_ERROR_ON_WINDOW_NOT_COLLAPSABLE_AT_DIMENSION(f, w, d) \ - ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_window_not_collapsable_at_dimension(__func__, __FILE__, __LINE__, f, w, d)) + ARM_COMPUTE_ERROR_THROW_ON( \ + ::arm_compute::error_on_window_not_collapsable_at_dimension(__func__, __FILE__, __LINE__, f, w, d)) #define ARM_COMPUTE_RETURN_ERROR_ON_WINDOW_NOT_COLLAPSABLE_AT_DIMENSION(f, w, d) \ - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_window_not_collapsable_at_dimension(__func__, __FILE__, __LINE__, f, w, d)) + ARM_COMPUTE_RETURN_ON_ERROR( \ + ::arm_compute::error_on_window_not_collapsable_at_dimension(__func__, __FILE__, __LINE__, f, w, d)) /** Return an error if the passed coordinates have too many dimensions. * @@ -239,8 +239,8 @@ arm_compute::Status error_on_window_not_collapsable_at_dimension(const char *fun * * @return Status */ -arm_compute::Status error_on_coordinates_dimensions_gte(const char *function, const char *file, const int line, - const Coordinates &pos, unsigned int max_dim); +arm_compute::Status error_on_coordinates_dimensions_gte( + const char *function, const char *file, const int line, const Coordinates &pos, unsigned int max_dim); #define ARM_COMPUTE_ERROR_ON_COORDINATES_DIMENSIONS_GTE(p, md) \ ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_coordinates_dimensions_gte(__func__, __FILE__, __LINE__, p, md)) #define ARM_COMPUTE_RETURN_ERROR_ON_COORDINATES_DIMENSIONS_GTE(p, md) \ @@ -258,8 +258,8 @@ arm_compute::Status error_on_coordinates_dimensions_gte(const char *function, co * * @return Status */ -arm_compute::Status error_on_window_dimensions_gte(const char *function, const char *file, const int line, - const Window &win, unsigned int max_dim); +arm_compute::Status error_on_window_dimensions_gte( + const char *function, const char *file, const int line, const Window &win, unsigned int max_dim); #define ARM_COMPUTE_ERROR_ON_WINDOW_DIMENSIONS_GTE(w, md) \ ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_window_dimensions_gte(__func__, __FILE__, __LINE__, w, md)) #define ARM_COMPUTE_RETURN_ERROR_ON_WINDOW_DIMENSIONS_GTE(w, md) \ @@ -277,16 +277,23 @@ arm_compute::Status error_on_window_dimensions_gte(const char *function, const c * @return Status */ template <typename T, typename... Ts> -arm_compute::Status error_on_mismatching_dimensions(const char *function, const char *file, int line, - const Dimensions<T> &dim1, const Dimensions<T> &dim2, Ts &&... dims) +arm_compute::Status error_on_mismatching_dimensions(const char *function, + const char *file, + int line, + const Dimensions<T> &dim1, + const Dimensions<T> &dim2, + Ts &&...dims) { - ARM_COMPUTE_RETURN_ON_ERROR(detail::for_each_error(detail::compare_dimension<T>(dim1, function, file, line), dim2, std::forward<Ts>(dims)...)); + ARM_COMPUTE_RETURN_ON_ERROR(detail::for_each_error(detail::compare_dimension<T>(dim1, function, file, line), dim2, + std::forward<Ts>(dims)...)); return arm_compute::Status{}; } #define ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(...) \ - ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_mismatching_dimensions(__func__, __FILE__, __LINE__, __VA_ARGS__)) + ARM_COMPUTE_ERROR_THROW_ON( \ + ::arm_compute::error_on_mismatching_dimensions(__func__, __FILE__, __LINE__, __VA_ARGS__)) #define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(...) \ - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_mismatching_dimensions(__func__, __FILE__, __LINE__, __VA_ARGS__)) + ARM_COMPUTE_RETURN_ON_ERROR( \ + ::arm_compute::error_on_mismatching_dimensions(__func__, __FILE__, __LINE__, __VA_ARGS__)) /** Return true if the given format has horizontal subsampling. * @@ -296,7 +303,10 @@ arm_compute::Status error_on_mismatching_dimensions(const char *function, const */ 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 (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. @@ -307,7 +317,9 @@ inline bool has_format_horizontal_subsampling(Format format) */ inline bool has_format_vertical_subsampling(Format format) { - return (format == Format::NV12 || format == Format::NV21 || format == Format::IYUV || format == Format::UV88) ? true : false; + return (format == Format::NV12 || format == Format::NV21 || format == Format::IYUV || format == Format::UV88) + ? true + : false; } /** Adjust tensor shape size if width or height are odd for a given multi-planar format. No modification is done for other formats. @@ -325,16 +337,16 @@ inline bool has_format_vertical_subsampling(Format format) */ inline TensorShape adjust_odd_shape(const TensorShape &shape, Format format) { - TensorShape output{ shape }; + 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)) + 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)) + if (has_format_vertical_subsampling(format)) { output.set(1, (output.y() + 1) & ~1U); } @@ -354,18 +366,20 @@ inline TensorShape adjust_odd_shape(const TensorShape &shape, Format format) * @return Status */ template <typename... Ts> -arm_compute::Status error_on_tensors_not_even(const char *function, const char *file, int line, - const Format &format, const ITensor *tensor1, Ts... tensors) +arm_compute::Status error_on_tensors_not_even( + const char *function, const char *file, int line, const Format &format, const ITensor *tensor1, Ts... tensors) { ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor1 == nullptr, function, file, line); ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_nullptr(function, file, line, std::forward<Ts>(tensors)...)); - const std::array < const ITensor *, 1 + sizeof...(Ts) > tensors_info_array{ { tensor1, std::forward<Ts>(tensors)... } }; - ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG(std::any_of(tensors_info_array.cbegin(), tensors_info_array.cend(), [&](const ITensor * tensor) - { - const TensorShape correct_shape = adjust_odd_shape(tensor->info()->tensor_shape(), format); - return detail::have_different_dimensions(tensor->info()->tensor_shape(), correct_shape, 2); - }), - function, file, line, "Tensor shape has odd dimensions"); + const std::array<const ITensor *, 1 + sizeof...(Ts)> tensors_info_array{{tensor1, std::forward<Ts>(tensors)...}}; + ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG( + std::any_of(tensors_info_array.cbegin(), tensors_info_array.cend(), + [&](const ITensor *tensor) + { + const TensorShape correct_shape = adjust_odd_shape(tensor->info()->tensor_shape(), format); + return detail::have_different_dimensions(tensor->info()->tensor_shape(), correct_shape, 2); + }), + function, file, line, "Tensor shape has odd dimensions"); return arm_compute::Status{}; } @@ -382,21 +396,22 @@ arm_compute::Status error_on_tensors_not_even(const char *function, const char * * * @return The subsampled tensor shape. */ -inline TensorShape calculate_subsampled_shape(const TensorShape &shape, Format format, Channel channel = Channel::UNKNOWN) +inline TensorShape +calculate_subsampled_shape(const TensorShape &shape, Format format, Channel channel = Channel::UNKNOWN) { - TensorShape output{ shape }; + TensorShape output{shape}; // Subsample shape only for U or V channel - if(Channel::U == channel || Channel::V == channel || Channel::UNKNOWN == 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)) + 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)) + if (has_format_vertical_subsampling(format)) { output.set(1, output.y() / 2U); } @@ -418,25 +433,32 @@ inline TensorShape calculate_subsampled_shape(const TensorShape &shape, Format f * @return Status */ template <typename... Ts> -arm_compute::Status error_on_tensors_not_subsampled(const char *function, const char *file, int line, - const Format &format, const TensorShape &shape, const ITensor *tensor1, Ts... tensors) +arm_compute::Status error_on_tensors_not_subsampled(const char *function, + const char *file, + int line, + const Format &format, + const TensorShape &shape, + const ITensor *tensor1, + Ts... tensors) { ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor1 == nullptr, function, file, line); ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_nullptr(function, file, line, std::forward<Ts>(tensors)...)); - const TensorShape sub2_shape = calculate_subsampled_shape(shape, format); - const std::array < const ITensor *, 1 + sizeof...(Ts) > tensors_info_array{ { tensor1, std::forward<Ts>(tensors)... } }; - ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG(std::any_of(tensors_info_array.cbegin(), tensors_info_array.cend(), [&](const ITensor * tensor) - { - return detail::have_different_dimensions(tensor->info()->tensor_shape(), sub2_shape, 2); - }), - function, file, line, "Tensor shape has mismatch dimensions for sub-sampling"); + const TensorShape sub2_shape = calculate_subsampled_shape(shape, format); + const std::array<const ITensor *, 1 + sizeof...(Ts)> tensors_info_array{{tensor1, std::forward<Ts>(tensors)...}}; + ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG( + std::any_of(tensors_info_array.cbegin(), tensors_info_array.cend(), + [&](const ITensor *tensor) + { return detail::have_different_dimensions(tensor->info()->tensor_shape(), sub2_shape, 2); }), + function, file, line, "Tensor shape has mismatch dimensions for sub-sampling"); return arm_compute::Status{}; } #define ARM_COMPUTE_ERROR_ON_TENSORS_NOT_SUBSAMPLED(...) \ - ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_tensors_not_subsampled(__func__, __FILE__, __LINE__, __VA_ARGS__)) + ARM_COMPUTE_ERROR_THROW_ON( \ + ::arm_compute::error_on_tensors_not_subsampled(__func__, __FILE__, __LINE__, __VA_ARGS__)) #define ARM_COMPUTE_RETURN_ERROR_ON_TENSORS_NOT_SUBSAMPLED(...) \ - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_tensors_not_subsampled(__func__, __FILE__, __LINE__, __VA_ARGS__)) + ARM_COMPUTE_RETURN_ON_ERROR( \ + ::arm_compute::error_on_tensors_not_subsampled(__func__, __FILE__, __LINE__, __VA_ARGS__)) /** Return an error if the passed two tensor infos have different shapes from the given dimension * @@ -450,10 +472,15 @@ arm_compute::Status error_on_tensors_not_subsampled(const char *function, const * @return Status */ template <typename... Ts> -inline arm_compute::Status error_on_mismatching_shapes(const char *function, const char *file, const int line, - const ITensorInfo *tensor_info_1, const ITensorInfo *tensor_info_2, Ts... tensor_infos) +inline arm_compute::Status error_on_mismatching_shapes(const char *function, + const char *file, + const int line, + const ITensorInfo *tensor_info_1, + const ITensorInfo *tensor_info_2, + Ts... tensor_infos) { - return error_on_mismatching_shapes(function, file, line, 0U, tensor_info_1, tensor_info_2, std::forward<Ts>(tensor_infos)...); + return error_on_mismatching_shapes(function, file, line, 0U, tensor_info_1, tensor_info_2, + std::forward<Ts>(tensor_infos)...); } /** Return an error if the passed two tensors have different shapes from the given dimension * @@ -467,8 +494,12 @@ inline arm_compute::Status error_on_mismatching_shapes(const char *function, con * @return Status */ template <typename... Ts> -inline arm_compute::Status error_on_mismatching_shapes(const char *function, const char *file, const int line, - const ITensor *tensor_1, const ITensor *tensor_2, Ts... tensors) +inline arm_compute::Status error_on_mismatching_shapes(const char *function, + const char *file, + const int line, + const ITensor *tensor_1, + const ITensor *tensor_2, + Ts... tensors) { return error_on_mismatching_shapes(function, file, line, 0U, tensor_1, tensor_2, std::forward<Ts>(tensors)...); } @@ -485,19 +516,28 @@ inline arm_compute::Status error_on_mismatching_shapes(const char *function, con * @return Status */ template <typename... Ts> -inline arm_compute::Status error_on_mismatching_shapes(const char *function, const char *file, const int line, - unsigned int upper_dim, const ITensorInfo *tensor_info_1, const ITensorInfo *tensor_info_2, Ts... tensor_infos) +inline arm_compute::Status error_on_mismatching_shapes(const char *function, + const char *file, + const int line, + unsigned int upper_dim, + const ITensorInfo *tensor_info_1, + const ITensorInfo *tensor_info_2, + Ts... tensor_infos) { ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor_info_1 == nullptr, function, file, line); ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor_info_2 == nullptr, function, file, line); ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_nullptr(function, file, line, tensor_infos...)); - const std::array < const ITensorInfo *, 2 + sizeof...(Ts) > tensors_info_array{ { tensor_info_1, tensor_info_2, tensor_infos... } }; - ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG(std::any_of(std::next(tensors_info_array.cbegin()), tensors_info_array.cend(), [&](const ITensorInfo * tensor_info) - { - return detail::have_different_dimensions((*tensors_info_array.cbegin())->tensor_shape(), tensor_info->tensor_shape(), upper_dim); - }), - function, file, line, "Tensors have different shapes"); + const std::array<const ITensorInfo *, 2 + sizeof...(Ts)> tensors_info_array{ + {tensor_info_1, tensor_info_2, tensor_infos...}}; + ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG(std::any_of(std::next(tensors_info_array.cbegin()), tensors_info_array.cend(), + [&](const ITensorInfo *tensor_info) + { + return detail::have_different_dimensions( + (*tensors_info_array.cbegin())->tensor_shape(), + tensor_info->tensor_shape(), upper_dim); + }), + function, file, line, "Tensors have different shapes"); return arm_compute::Status{}; } /** Return an error if the passed two tensors have different shapes from the given dimension @@ -513,14 +553,20 @@ inline arm_compute::Status error_on_mismatching_shapes(const char *function, con * @return Status */ template <typename... Ts> -inline arm_compute::Status error_on_mismatching_shapes(const char *function, const char *file, const int line, - unsigned int upper_dim, const ITensor *tensor_1, const ITensor *tensor_2, Ts... tensors) +inline arm_compute::Status error_on_mismatching_shapes(const char *function, + const char *file, + const int line, + unsigned int upper_dim, + const ITensor *tensor_1, + const ITensor *tensor_2, + Ts... tensors) { ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor_1 == nullptr, function, file, line); ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor_2 == nullptr, function, file, line); ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_nullptr(function, file, line, tensors...)); - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_mismatching_shapes(function, file, line, upper_dim, tensor_1->info(), tensor_2->info(), - detail::get_tensor_info_t<ITensorInfo *>()(tensors)...)); + ARM_COMPUTE_RETURN_ON_ERROR( + ::arm_compute::error_on_mismatching_shapes(function, file, line, upper_dim, tensor_1->info(), tensor_2->info(), + detail::get_tensor_info_t<ITensorInfo *>()(tensors)...)); return arm_compute::Status{}; } #define ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(...) \ @@ -539,19 +585,18 @@ inline arm_compute::Status error_on_mismatching_shapes(const char *function, con * @return Status */ template <typename... Ts> -inline arm_compute::Status error_on_mismatching_data_layouts(const char *function, const char *file, const int line, - const ITensorInfo *tensor_info, Ts... tensor_infos) +inline arm_compute::Status error_on_mismatching_data_layouts( + const char *function, const char *file, const int line, const ITensorInfo *tensor_info, Ts... tensor_infos) { ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor_info == nullptr, function, file, line); ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_nullptr(function, file, line, tensor_infos...)); - DataLayout &&tensor_data_layout = tensor_info->data_layout(); - const std::array<const ITensorInfo *, sizeof...(Ts)> tensors_infos_array{ { tensor_infos... } }; - ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG(std::any_of(tensors_infos_array.begin(), tensors_infos_array.end(), [&](const ITensorInfo * tensor_info_obj) - { - return tensor_info_obj->data_layout() != tensor_data_layout; - }), - function, file, line, "Tensors have different data layouts"); + DataLayout &&tensor_data_layout = tensor_info->data_layout(); + const std::array<const ITensorInfo *, sizeof...(Ts)> tensors_infos_array{{tensor_infos...}}; + ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG(std::any_of(tensors_infos_array.begin(), tensors_infos_array.end(), + [&](const ITensorInfo *tensor_info_obj) + { return tensor_info_obj->data_layout() != tensor_data_layout; }), + function, file, line, "Tensors have different data layouts"); return arm_compute::Status{}; } /** Return an error if the passed tensors have different data layouts @@ -565,19 +610,21 @@ inline arm_compute::Status error_on_mismatching_data_layouts(const char *functio * @return Status */ template <typename... Ts> -inline arm_compute::Status error_on_mismatching_data_layouts(const char *function, const char *file, const int line, - const ITensor *tensor, Ts... tensors) +inline arm_compute::Status error_on_mismatching_data_layouts( + const char *function, const char *file, const int line, const ITensor *tensor, Ts... tensors) { ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor == nullptr, function, file, line); ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_nullptr(function, file, line, std::forward<Ts>(tensors)...)); - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_mismatching_data_layouts(function, file, line, tensor->info(), - detail::get_tensor_info_t<ITensorInfo *>()(tensors)...)); + ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_mismatching_data_layouts( + function, file, line, tensor->info(), detail::get_tensor_info_t<ITensorInfo *>()(tensors)...)); return arm_compute::Status{}; } #define ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_LAYOUT(...) \ - ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_mismatching_data_layouts(__func__, __FILE__, __LINE__, __VA_ARGS__)) + ARM_COMPUTE_ERROR_THROW_ON( \ + ::arm_compute::error_on_mismatching_data_layouts(__func__, __FILE__, __LINE__, __VA_ARGS__)) #define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(...) \ - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_mismatching_data_layouts(__func__, __FILE__, __LINE__, __VA_ARGS__)) + ARM_COMPUTE_RETURN_ON_ERROR( \ + ::arm_compute::error_on_mismatching_data_layouts(__func__, __FILE__, __LINE__, __VA_ARGS__)) /** Return an error if the passed two tensor infos have different data types * @@ -590,19 +637,18 @@ inline arm_compute::Status error_on_mismatching_data_layouts(const char *functio * @return Status */ template <typename... Ts> -inline arm_compute::Status error_on_mismatching_data_types(const char *function, const char *file, const int line, - const ITensorInfo *tensor_info, Ts... tensor_infos) +inline arm_compute::Status error_on_mismatching_data_types( + const char *function, const char *file, const int line, const ITensorInfo *tensor_info, Ts... tensor_infos) { ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor_info == nullptr, function, file, line); ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_nullptr(function, file, line, tensor_infos...)); - DataType &&tensor_data_type = tensor_info->data_type(); - const std::array<const ITensorInfo *, sizeof...(Ts)> tensors_infos_array{ { tensor_infos... } }; - ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG(std::any_of(tensors_infos_array.begin(), tensors_infos_array.end(), [&](const ITensorInfo * tensor_info_obj) - { - return tensor_info_obj->data_type() != tensor_data_type; - }), - function, file, line, "Tensors have different data types"); + DataType &&tensor_data_type = tensor_info->data_type(); + const std::array<const ITensorInfo *, sizeof...(Ts)> tensors_infos_array{{tensor_infos...}}; + ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG(std::any_of(tensors_infos_array.begin(), tensors_infos_array.end(), + [&](const ITensorInfo *tensor_info_obj) + { return tensor_info_obj->data_type() != tensor_data_type; }), + function, file, line, "Tensors have different data types"); return arm_compute::Status{}; } /** Return an error if the passed two tensors have different data types @@ -616,19 +662,21 @@ inline arm_compute::Status error_on_mismatching_data_types(const char *function, * @return Status */ template <typename... Ts> -inline arm_compute::Status error_on_mismatching_data_types(const char *function, const char *file, const int line, - const ITensor *tensor, Ts... tensors) +inline arm_compute::Status error_on_mismatching_data_types( + const char *function, const char *file, const int line, const ITensor *tensor, Ts... tensors) { ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor == nullptr, function, file, line); ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_nullptr(function, file, line, tensors...)); - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_mismatching_data_types(function, file, line, tensor->info(), - detail::get_tensor_info_t<ITensorInfo *>()(tensors)...)); + ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_mismatching_data_types( + function, file, line, tensor->info(), detail::get_tensor_info_t<ITensorInfo *>()(tensors)...)); return arm_compute::Status{}; } #define ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(...) \ - ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_mismatching_data_types(__func__, __FILE__, __LINE__, __VA_ARGS__)) + ARM_COMPUTE_ERROR_THROW_ON( \ + ::arm_compute::error_on_mismatching_data_types(__func__, __FILE__, __LINE__, __VA_ARGS__)) #define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...) \ - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_mismatching_data_types(__func__, __FILE__, __LINE__, __VA_ARGS__)) + ARM_COMPUTE_RETURN_ON_ERROR( \ + ::arm_compute::error_on_mismatching_data_types(__func__, __FILE__, __LINE__, __VA_ARGS__)) /** Return an error if the passed tensor infos have different asymmetric quantized data types or different quantization info * @@ -644,28 +692,32 @@ inline arm_compute::Status error_on_mismatching_data_types(const char *function, * @return Status */ template <typename... Ts> -inline arm_compute::Status error_on_mismatching_quantization_info(const char *function, const char *file, const int line, - const ITensorInfo *tensor_info_1, const ITensorInfo *tensor_info_2, Ts... tensor_infos) +inline arm_compute::Status error_on_mismatching_quantization_info(const char *function, + const char *file, + const int line, + const ITensorInfo *tensor_info_1, + const ITensorInfo *tensor_info_2, + Ts... tensor_infos) { DataType &&first_data_type = tensor_info_1->data_type(); const QuantizationInfo first_quantization_info = tensor_info_1->quantization_info(); - if(!is_data_type_quantized(first_data_type)) + if (!is_data_type_quantized(first_data_type)) { return arm_compute::Status{}; } - const std::array < const ITensorInfo *, 1 + sizeof...(Ts) > tensor_infos_array{ { tensor_info_2, std::forward<Ts>(tensor_infos)... } }; - ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG(std::any_of(tensor_infos_array.begin(), tensor_infos_array.end(), [&](const ITensorInfo * tensor_info) - { - return tensor_info->data_type() != first_data_type; - }), - function, file, line, "Tensors have different asymmetric quantized data types"); - ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG(std::any_of(tensor_infos_array.begin(), tensor_infos_array.end(), [&](const ITensorInfo * tensor_info) - { - return tensor_info->quantization_info() != first_quantization_info; - }), - function, file, line, "Tensors have different quantization information"); + const std::array<const ITensorInfo *, 1 + sizeof...(Ts)> tensor_infos_array{ + {tensor_info_2, std::forward<Ts>(tensor_infos)...}}; + ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG(std::any_of(tensor_infos_array.begin(), tensor_infos_array.end(), + [&](const ITensorInfo *tensor_info) + { return tensor_info->data_type() != first_data_type; }), + function, file, line, "Tensors have different asymmetric quantized data types"); + ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG( + std::any_of(tensor_infos_array.begin(), tensor_infos_array.end(), + [&](const ITensorInfo *tensor_info) + { return tensor_info->quantization_info() != first_quantization_info; }), + function, file, line, "Tensors have different quantization information"); return arm_compute::Status{}; } @@ -683,17 +735,24 @@ inline arm_compute::Status error_on_mismatching_quantization_info(const char *fu * @return Status */ template <typename... Ts> -inline arm_compute::Status error_on_mismatching_quantization_info(const char *function, const char *file, const int line, - const ITensor *tensor_1, const ITensor *tensor_2, Ts... tensors) +inline arm_compute::Status error_on_mismatching_quantization_info(const char *function, + const char *file, + const int line, + const ITensor *tensor_1, + const ITensor *tensor_2, + Ts... tensors) { - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_mismatching_quantization_info(function, file, line, tensor_1->info(), tensor_2->info(), - detail::get_tensor_info_t<ITensorInfo *>()(tensors)...)); + ARM_COMPUTE_RETURN_ON_ERROR( + ::arm_compute::error_on_mismatching_quantization_info(function, file, line, tensor_1->info(), tensor_2->info(), + detail::get_tensor_info_t<ITensorInfo *>()(tensors)...)); return arm_compute::Status{}; } #define ARM_COMPUTE_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(...) \ - ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_mismatching_quantization_info(__func__, __FILE__, __LINE__, __VA_ARGS__)) + ARM_COMPUTE_ERROR_THROW_ON( \ + ::arm_compute::error_on_mismatching_quantization_info(__func__, __FILE__, __LINE__, __VA_ARGS__)) #define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(...) \ - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_mismatching_quantization_info(__func__, __FILE__, __LINE__, __VA_ARGS__)) + ARM_COMPUTE_RETURN_ON_ERROR( \ + ::arm_compute::error_on_mismatching_quantization_info(__func__, __FILE__, __LINE__, __VA_ARGS__)) /** Throw an error if the format of the passed tensor/multi-image does not match any of the formats provided. * @@ -705,8 +764,8 @@ inline arm_compute::Status error_on_mismatching_quantization_info(const char *fu * @param[in] formats (Optional) Further allowed formats. */ template <typename T, typename F, typename... Fs> -void error_on_format_not_in(const char *function, const char *file, const int line, - const T *object, F &&format, Fs &&... formats) +void error_on_format_not_in( + const char *function, const char *file, const int line, const T *object, F &&format, Fs &&...formats) { ARM_COMPUTE_ERROR_ON_LOC(object == nullptr, function, file, line); @@ -715,17 +774,17 @@ void error_on_format_not_in(const char *function, const char *file, const int li ARM_COMPUTE_ERROR_ON_LOC(object_format == Format::UNKNOWN, function, file, line); - const std::array<F, sizeof...(Fs)> formats_array{ { std::forward<Fs>(formats)... } }; + const std::array<F, sizeof...(Fs)> formats_array{{std::forward<Fs>(formats)...}}; ARM_COMPUTE_UNUSED(formats_array); - ARM_COMPUTE_ERROR_ON_LOC_MSG(object_format != format && std::none_of(formats_array.begin(), formats_array.end(), [&](const F & f) - { - return f == object_format; - }), - function, file, line, "Format %s not supported by this kernel", string_from_format(object_format).c_str()); + ARM_COMPUTE_ERROR_ON_LOC_MSG( + object_format != format && + std::none_of(formats_array.begin(), formats_array.end(), [&](const F &f) { return f == object_format; }), + function, file, line, "Format %s not supported by this kernel", string_from_format(object_format).c_str()); ARM_COMPUTE_UNUSED(function, format, file, line); } -#define ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(t, ...) ::arm_compute::error_on_format_not_in(__func__, __FILE__, __LINE__, t, __VA_ARGS__) +#define ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(t, ...) \ + ::arm_compute::error_on_format_not_in(__func__, __FILE__, __LINE__, t, __VA_ARGS__) /** Return an error if the data type of the passed tensor info does not match any of the data types provided. * @@ -739,20 +798,19 @@ void error_on_format_not_in(const char *function, const char *file, const int li * @return Status */ template <typename T, typename... Ts> -inline arm_compute::Status error_on_data_type_not_in(const char *function, const char *file, const int line, - const ITensorInfo *tensor_info, T &&dt, Ts &&... dts) +inline arm_compute::Status error_on_data_type_not_in( + const char *function, const char *file, const int line, const ITensorInfo *tensor_info, T &&dt, Ts &&...dts) { ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor_info == nullptr, function, file, line); const DataType &tensor_dt = tensor_info->data_type(); //NOLINT ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor_dt == DataType::UNKNOWN, function, file, line); - const std::array<T, sizeof...(Ts)> dts_array{ { std::forward<Ts>(dts)... } }; - ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG_VAR(tensor_dt != dt && std::none_of(dts_array.begin(), dts_array.end(), [&](const T & d) - { - return d == tensor_dt; - }), - function, file, line, "ITensor data type %s not supported by this kernel", string_from_data_type(tensor_dt).c_str()); + const std::array<T, sizeof...(Ts)> dts_array{{std::forward<Ts>(dts)...}}; + ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG_VAR( + tensor_dt != dt && std::none_of(dts_array.begin(), dts_array.end(), [&](const T &d) { return d == tensor_dt; }), + function, file, line, "ITensor data type %s not supported by this kernel", + string_from_data_type(tensor_dt).c_str()); return arm_compute::Status{}; } /** Return an error if the data type of the passed tensor does not match any of the data types provided. @@ -767,11 +825,12 @@ inline arm_compute::Status error_on_data_type_not_in(const char *function, const * @return Status */ template <typename T, typename... Ts> -inline arm_compute::Status error_on_data_type_not_in(const char *function, const char *file, const int line, - const ITensor *tensor, T &&dt, Ts &&... dts) +inline arm_compute::Status error_on_data_type_not_in( + const char *function, const char *file, const int line, const ITensor *tensor, T &&dt, Ts &&...dts) { ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor == nullptr, function, file, line); - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_data_type_not_in(function, file, line, tensor->info(), std::forward<T>(dt), std::forward<Ts>(dts)...)); + ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_data_type_not_in( + function, file, line, tensor->info(), std::forward<T>(dt), std::forward<Ts>(dts)...)); return arm_compute::Status{}; } #define ARM_COMPUTE_ERROR_ON_DATA_TYPE_NOT_IN(t, ...) \ @@ -791,20 +850,19 @@ inline arm_compute::Status error_on_data_type_not_in(const char *function, const * @return Status */ template <typename T, typename... Ts> -inline arm_compute::Status error_on_data_layout_not_in(const char *function, const char *file, const int line, - const ITensorInfo *tensor_info, T &&dl, Ts &&... dls) +inline arm_compute::Status error_on_data_layout_not_in( + const char *function, const char *file, const int line, const ITensorInfo *tensor_info, T &&dl, Ts &&...dls) { ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor_info == nullptr, function, file, line); const DataLayout &tensor_dl = tensor_info->data_layout(); //NOLINT ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor_dl == DataLayout::UNKNOWN, function, file, line); - const std::array<T, sizeof...(Ts)> dls_array{ { std::forward<Ts>(dls)... } }; - ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG_VAR(tensor_dl != dl && std::none_of(dls_array.begin(), dls_array.end(), [&](const T & l) - { - return l == tensor_dl; - }), - function, file, line, "ITensor data layout %s not supported by this kernel", string_from_data_layout(tensor_dl).c_str()); + const std::array<T, sizeof...(Ts)> dls_array{{std::forward<Ts>(dls)...}}; + ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG_VAR( + tensor_dl != dl && std::none_of(dls_array.begin(), dls_array.end(), [&](const T &l) { return l == tensor_dl; }), + function, file, line, "ITensor data layout %s not supported by this kernel", + string_from_data_layout(tensor_dl).c_str()); return arm_compute::Status{}; } /** Return an error if the data layout of the passed tensor does not match any of the data layout provided. @@ -819,17 +877,19 @@ inline arm_compute::Status error_on_data_layout_not_in(const char *function, con * @return Status */ template <typename T, typename... Ts> -inline arm_compute::Status error_on_data_layout_not_in(const char *function, const char *file, const int line, - const ITensor *tensor, T &&dl, Ts &&... dls) +inline arm_compute::Status error_on_data_layout_not_in( + const char *function, const char *file, const int line, const ITensor *tensor, T &&dl, Ts &&...dls) { ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor == nullptr, function, file, line); - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_data_layout_not_in(function, file, line, tensor->info(), std::forward<T>(dl), std::forward<Ts>(dls)...)); + ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_data_layout_not_in( + function, file, line, tensor->info(), std::forward<T>(dl), std::forward<Ts>(dls)...)); return arm_compute::Status{}; } #define ARM_COMPUTE_ERROR_ON_DATA_LAYOUT_NOT_IN(t, ...) \ ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_data_layout_not_in(__func__, __FILE__, __LINE__, t, __VA_ARGS__)) #define ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(t, ...) \ - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_data_layout_not_in(__func__, __FILE__, __LINE__, t, __VA_ARGS__)) + ARM_COMPUTE_RETURN_ON_ERROR( \ + ::arm_compute::error_on_data_layout_not_in(__func__, __FILE__, __LINE__, t, __VA_ARGS__)) /** Return an error if the data type or the number of channels of the passed tensor info does not match any of the data types and number of channels provided. * @@ -844,12 +904,20 @@ inline arm_compute::Status error_on_data_layout_not_in(const char *function, con * @return Status */ template <typename T, typename... Ts> -inline arm_compute::Status error_on_data_type_channel_not_in(const char *function, const char *file, const int line, - const ITensorInfo *tensor_info, size_t num_channels, T &&dt, Ts &&... dts) +inline arm_compute::Status error_on_data_type_channel_not_in(const char *function, + const char *file, + const int line, + const ITensorInfo *tensor_info, + size_t num_channels, + T &&dt, + Ts &&...dts) { - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_data_type_not_in(function, file, line, tensor_info, std::forward<T>(dt), std::forward<Ts>(dts)...)); + ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_data_type_not_in( + function, file, line, tensor_info, std::forward<T>(dt), std::forward<Ts>(dts)...)); const size_t tensor_nc = tensor_info->num_channels(); - ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG_VAR(tensor_nc != num_channels, function, file, line, "Number of channels %zu. Required number of channels %zu", tensor_nc, num_channels); + ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG_VAR(tensor_nc != num_channels, function, file, line, + "Number of channels %zu. Required number of channels %zu", tensor_nc, + num_channels); return arm_compute::Status{}; } /** Return an error if the data type or the number of channels of the passed tensor does not match any of the data types and number of channels provided. @@ -865,17 +933,25 @@ inline arm_compute::Status error_on_data_type_channel_not_in(const char *functio * @return Status */ template <typename T, typename... Ts> -inline arm_compute::Status error_on_data_type_channel_not_in(const char *function, const char *file, const int line, - const ITensor *tensor, size_t num_channels, T &&dt, Ts &&... dts) +inline arm_compute::Status error_on_data_type_channel_not_in(const char *function, + const char *file, + const int line, + const ITensor *tensor, + size_t num_channels, + T &&dt, + Ts &&...dts) { ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor == nullptr, function, file, line); - ARM_COMPUTE_RETURN_ON_ERROR(error_on_data_type_channel_not_in(function, file, line, tensor->info(), num_channels, std::forward<T>(dt), std::forward<Ts>(dts)...)); + ARM_COMPUTE_RETURN_ON_ERROR(error_on_data_type_channel_not_in(function, file, line, tensor->info(), num_channels, + std::forward<T>(dt), std::forward<Ts>(dts)...)); return arm_compute::Status{}; } #define ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c, ...) \ - ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_data_type_channel_not_in(__func__, __FILE__, __LINE__, t, c, __VA_ARGS__)) + ARM_COMPUTE_ERROR_THROW_ON( \ + ::arm_compute::error_on_data_type_channel_not_in(__func__, __FILE__, __LINE__, t, c, __VA_ARGS__)) #define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c, ...) \ - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_data_type_channel_not_in(__func__, __FILE__, __LINE__, t, c, __VA_ARGS__)) + ARM_COMPUTE_RETURN_ON_ERROR( \ + ::arm_compute::error_on_data_type_channel_not_in(__func__, __FILE__, __LINE__, t, c, __VA_ARGS__)) /** Return an error if the data type of the passed tensor info is FP16 and FP16 extension is not supported by the device. * @@ -887,12 +963,12 @@ inline arm_compute::Status error_on_data_type_channel_not_in(const char *functio * * @return Status */ -inline arm_compute::Status error_on_unsupported_fp16(const char *function, const char *file, const int line, - const ITensorInfo *tensor_info, bool is_fp16_supported) +inline arm_compute::Status error_on_unsupported_fp16( + const char *function, const char *file, const int line, const ITensorInfo *tensor_info, bool is_fp16_supported) { ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor_info == nullptr, function, file, line); - ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG((tensor_info->data_type() == DataType::F16 && !is_fp16_supported), - function, file, line, "FP16 not supported by the device"); + ARM_COMPUTE_RETURN_ERROR_ON_LOC_MSG((tensor_info->data_type() == DataType::F16 && !is_fp16_supported), function, + file, line, "FP16 not supported by the device"); return arm_compute::Status{}; } @@ -906,11 +982,12 @@ inline arm_compute::Status error_on_unsupported_fp16(const char *function, const * * @return Status */ -inline arm_compute::Status error_on_unsupported_fp16(const char *function, const char *file, const int line, - const ITensor *tensor, bool is_fp16_supported) +inline arm_compute::Status error_on_unsupported_fp16( + const char *function, const char *file, const int line, const ITensor *tensor, bool is_fp16_supported) { ARM_COMPUTE_RETURN_ERROR_ON_LOC(tensor == nullptr, function, file, line); - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_unsupported_fp16(function, file, line, tensor->info(), is_fp16_supported)); + ARM_COMPUTE_RETURN_ON_ERROR( + ::arm_compute::error_on_unsupported_fp16(function, file, line, tensor->info(), is_fp16_supported)); return arm_compute::Status{}; } @@ -923,8 +1000,8 @@ inline arm_compute::Status error_on_unsupported_fp16(const char *function, const * * @return Status */ -arm_compute::Status error_on_tensor_not_2d(const char *function, const char *file, const int line, - const ITensor *tensor); +arm_compute::Status +error_on_tensor_not_2d(const char *function, const char *file, const int line, const ITensor *tensor); /** Return an error if the tensor info is not 2D. * @@ -935,8 +1012,8 @@ arm_compute::Status error_on_tensor_not_2d(const char *function, const char *fil * * @return Status */ -arm_compute::Status error_on_tensor_not_2d(const char *function, const char *file, const int line, - const ITensorInfo *tensor); +arm_compute::Status +error_on_tensor_not_2d(const char *function, const char *file, const int line, const ITensorInfo *tensor); #define ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(t) \ ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_tensor_not_2d(__func__, __FILE__, __LINE__, t)) @@ -955,17 +1032,15 @@ arm_compute::Status error_on_tensor_not_2d(const char *function, const char *fil * @return Status */ template <typename T, typename... Ts> -inline arm_compute::Status error_on_channel_not_in(const char *function, const char *file, const int line, - T cn, T &&channel, Ts &&... channels) +inline arm_compute::Status +error_on_channel_not_in(const char *function, const char *file, const int line, T cn, T &&channel, Ts &&...channels) { ARM_COMPUTE_RETURN_ERROR_ON_LOC(cn == Channel::UNKNOWN, function, file, line); - const std::array<T, sizeof...(Ts)> channels_array{ { std::forward<Ts>(channels)... } }; - ARM_COMPUTE_RETURN_ERROR_ON_LOC(channel != cn && std::none_of(channels_array.begin(), channels_array.end(), [&](const T & f) - { - return f == cn; - }), - function, file, line); + const std::array<T, sizeof...(Ts)> channels_array{{std::forward<Ts>(channels)...}}; + ARM_COMPUTE_RETURN_ERROR_ON_LOC(channel != cn && std::none_of(channels_array.begin(), channels_array.end(), + [&](const T &f) { return f == cn; }), + function, file, line); return arm_compute::Status{}; } #define ARM_COMPUTE_ERROR_ON_CHANNEL_NOT_IN(c, ...) \ @@ -983,8 +1058,8 @@ inline arm_compute::Status error_on_channel_not_in(const char *function, const c * * @return Status */ -arm_compute::Status error_on_channel_not_in_known_format(const char *function, const char *file, const int line, - Format fmt, Channel cn); +arm_compute::Status +error_on_channel_not_in_known_format(const char *function, const char *file, const int line, Format fmt, Channel cn); #define ARM_COMPUTE_ERROR_ON_CHANNEL_NOT_IN_KNOWN_FORMAT(f, c) \ ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_channel_not_in_known_format(__func__, __FILE__, __LINE__, f, c)) #define ARM_COMPUTE_RETURN_ERROR_ON_CHANNEL_NOT_IN_KNOWN_FORMAT(f, c) \ @@ -999,8 +1074,8 @@ arm_compute::Status error_on_channel_not_in_known_format(const char *function, c * * @return Status */ -arm_compute::Status error_on_unconfigured_kernel(const char *function, const char *file, const int line, - const IKernel *kernel); +arm_compute::Status +error_on_unconfigured_kernel(const char *function, const char *file, const int line, const IKernel *kernel); #define ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(k) \ ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_unconfigured_kernel(__func__, __FILE__, __LINE__, k)) #define ARM_COMPUTE_RETURN_ERROR_ON_UNCONFIGURED_KERNEL(k) \ @@ -1017,8 +1092,12 @@ arm_compute::Status error_on_unconfigured_kernel(const char *function, const cha * * @return Status */ -arm_compute::Status error_on_invalid_subtensor(const char *function, const char *file, const int line, - const TensorShape &parent_shape, const Coordinates &coords, const TensorShape &shape); +arm_compute::Status error_on_invalid_subtensor(const char *function, + const char *file, + const int line, + const TensorShape &parent_shape, + const Coordinates &coords, + const TensorShape &shape); #define ARM_COMPUTE_ERROR_ON_INVALID_SUBTENSOR(p, c, s) \ ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_invalid_subtensor(__func__, __FILE__, __LINE__, p, c, s)) #define ARM_COMPUTE_RETURN_ERROR_ON_INVALID_SUBTENSOR(p, c, s) \ @@ -1034,11 +1113,16 @@ arm_compute::Status error_on_invalid_subtensor(const char *function, const char * * @return Status */ -arm_compute::Status error_on_invalid_subtensor_valid_region(const char *function, const char *file, const int line, - const ValidRegion &parent_valid_region, const ValidRegion &valid_region); +arm_compute::Status error_on_invalid_subtensor_valid_region(const char *function, + const char *file, + const int line, + const ValidRegion &parent_valid_region, + const ValidRegion &valid_region); #define ARM_COMPUTE_ERROR_ON_INVALID_SUBTENSOR_VALID_REGION(pv, sv) \ - ARM_COMPUTE_ERROR_THROW_ON(::arm_compute::error_on_invalid_subtensor_valid_region(__func__, __FILE__, __LINE__, pv, sv)) + ARM_COMPUTE_ERROR_THROW_ON( \ + ::arm_compute::error_on_invalid_subtensor_valid_region(__func__, __FILE__, __LINE__, pv, sv)) #define ARM_COMPUTE_RETURN_ERROR_ON_INVALID_SUBTENSOR_VALID_REGION(pv, sv) \ - ARM_COMPUTE_RETURN_ON_ERROR(::arm_compute::error_on_invalid_subtensor_valid_region(__func__, __FILE__, __LINE__, pv, sv)) -} + ARM_COMPUTE_RETURN_ON_ERROR( \ + ::arm_compute::error_on_invalid_subtensor_valid_region(__func__, __FILE__, __LINE__, pv, sv)) +} // namespace arm_compute #endif /* ARM_COMPUTE_VALIDATE_H*/ diff --git a/arm_compute/core/Version.h b/arm_compute/core/Version.h index a4d307950a..44d400bad8 100644 --- a/arm_compute/core/Version.h +++ b/arm_compute/core/Version.h @@ -28,7 +28,7 @@ /* Macro utilities */ #define ARM_COMPUTE_STRINGIFY2(s) #s -#define ARM_COMPUTE_STRINGIFY(s) ARM_COMPUTE_STRINGIFY2(s) +#define ARM_COMPUTE_STRINGIFY(s) ARM_COMPUTE_STRINGIFY2(s) #define ARM_COMPUTE_VERSION_STR \ ARM_COMPUTE_STRINGIFY(ARM_COMPUTE_VERSION_MAJOR) \ diff --git a/arm_compute/core/Window.h b/arm_compute/core/Window.h index 8ae859f4b3..4863b95045 100644 --- a/arm_compute/core/Window.h +++ b/arm_compute/core/Window.h @@ -24,15 +24,15 @@ #ifndef ARM_COMPUTE_WINDOW_H #define ARM_COMPUTE_WINDOW_H -#include <algorithm> -#include <array> -#include <cstddef> - #include "arm_compute/core/Coordinates.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/ITensorInfo.h" #include "arm_compute/core/utils/math/Math.h" +#include <algorithm> +#include <array> +#include <cstddef> + namespace arm_compute { /** Describe a multidimensional execution window. */ @@ -86,8 +86,7 @@ public: * @param[in] step Step between two elements of the dimension when iterating. * */ - constexpr Dimension(int start = 0, int end = 1, int step = 1) - : _start(start), _end(end), _step(step) + constexpr Dimension(int start = 0, int end = 1, int step = 1) : _start(start), _end(end), _step(step) { } Dimension(const Dimension &d) = default; @@ -373,7 +372,8 @@ public: * * @return Collapsed window. */ - Window collapse_if_possible(const Window &full_window, size_t first, size_t last, bool *has_collapsed = nullptr) const; + Window + collapse_if_possible(const Window &full_window, size_t first, size_t last, bool *has_collapsed = nullptr) const; /** Collapse the dimensions higher than @p first if possible. * @@ -441,7 +441,7 @@ private: * @return The first slice of the window. */ template <unsigned int window_dimension> - Window first_slice_window() const; + Window first_slice_window() const; /** Slide the passed window slice. * diff --git a/arm_compute/core/Window.inl b/arm_compute/core/Window.inl index 5ee4b57145..d935507b1d 100644 --- a/arm_compute/core/Window.inl +++ b/arm_compute/core/Window.inl @@ -26,7 +26,7 @@ namespace arm_compute inline Window::Window(const Window &src) : _dims(), _is_broadcasted(utility::generate_array<bool, Coordinates::num_max_dimensions, false>::value) { - for(size_t i = 0; i < Coordinates::num_max_dimensions; ++i) + for (size_t i = 0; i < Coordinates::num_max_dimensions; ++i) { set(i, src[i]); _is_broadcasted[i] = src.is_broadcasted(i); @@ -65,32 +65,34 @@ inline bool Window::is_broadcasted(size_t dimension) const return _is_broadcasted[dimension]; } -inline Window Window::collapse_if_possible(const Window &full_window, const size_t first, - const size_t last, bool *has_collapsed) const +inline Window Window::collapse_if_possible(const Window &full_window, + const size_t first, + const size_t last, + bool *has_collapsed) const { Window collapsed(*this); bool is_collapsable = true; int collapsed_end = _dims[first].end(); - for(size_t d = first + 1; is_collapsable && (d < last); ++d) + for (size_t d = first + 1; is_collapsable && (d < last); ++d) { // The _dims's dimension must match the full _dims dimension to be collapsable: - is_collapsable = (_dims[d].start() == 0) && (full_window[d].start() == 0) && (_dims[d].step() <= 1) - && (full_window[d].end() == _dims[d].end()); + is_collapsable = (_dims[d].start() == 0) && (full_window[d].start() == 0) && (_dims[d].step() <= 1) && + (full_window[d].end() == _dims[d].end()); collapsed_end *= _dims[d].end(); } - if(is_collapsable) + if (is_collapsable) { collapsed._dims.at(first).set_end(collapsed_end); - for(size_t d = first + 1; is_collapsable && (d < last); ++d) + for (size_t d = first + 1; is_collapsable && (d < last); ++d) { collapsed.set(d, Dimension()); } } - if(has_collapsed != nullptr) + if (has_collapsed != nullptr) { *has_collapsed = is_collapsable; } @@ -101,7 +103,7 @@ inline Window Window::collapse_if_possible(const Window &full_window, const size inline Window Window::shift_dimensions(unsigned int shift_value) const { Window shifted_window; - for(size_t n = 0; n < (Coordinates::num_max_dimensions - shift_value); n++) + for (size_t n = 0; n < (Coordinates::num_max_dimensions - shift_value); n++) { shifted_window.set(n, _dims[n + shift_value]); } @@ -120,9 +122,9 @@ inline Window Window::collapse(const Window &full_window, const size_t first, co inline Window Window::broadcast_if_dimension_le_one(const TensorShape &shape) const { Window broadcastWin(*this); - for(size_t d = 0; d < TensorShape::num_max_dimensions; ++d) + for (size_t d = 0; d < TensorShape::num_max_dimensions; ++d) { - if(shape[d] <= 1) + if (shape[d] <= 1) { broadcastWin.set_broadcasted(d); } @@ -142,7 +144,7 @@ inline void Window::adjust(size_t dimension, int adjust_value, bool is_at_start) ARM_COMPUTE_ERROR_ON(dimension >= Coordinates::num_max_dimensions); Window::Dimension &d = _dims[dimension]; - if(is_at_start) + if (is_at_start) { d = Window::Dimension(d.start() + adjust_value, d.end(), d.step()); } @@ -172,7 +174,7 @@ inline void Window::set_dimension_step(size_t dimension, int step) inline void Window::validate() const { - for(size_t i = 0; i < Coordinates::num_max_dimensions; ++i) + for (size_t i = 0; i < Coordinates::num_max_dimensions; ++i) { ARM_COMPUTE_ERROR_ON(_dims[i].end() < _dims[i].start()); ARM_COMPUTE_ERROR_ON((_dims[i].step() != 0) && (((_dims[i].end() - _dims[i].start()) % _dims[i].step()) != 0)); @@ -193,9 +195,9 @@ inline Window Window::split_window(size_t dimension, size_t id, size_t total) co Window out; - for(size_t d = 0; d < Coordinates::num_max_dimensions; ++d) + for (size_t d = 0; d < Coordinates::num_max_dimensions; ++d) { - if(d == dimension) + if (d == dimension) { int start = _dims[d].start(); int end = _dims[d].end(); @@ -207,7 +209,7 @@ inline Window Window::split_window(size_t dimension, size_t id, size_t total) co int it_start = work * id; - if(int(id) < rem) + if (int(id) < rem) { ++work; it_start += id; @@ -234,18 +236,18 @@ inline Window Window::split_window(size_t dimension, size_t id, size_t total) co template <unsigned int window_dimension> inline bool Window::slide_window_slice(Window &slice) const { - for(unsigned int n = window_dimension; n < Coordinates::num_max_dimensions; ++n) + for (unsigned int n = window_dimension; n < Coordinates::num_max_dimensions; ++n) { // Did we reach the end of this dimension? const int v = slice._dims[n].start() + 1; - if(v < _dims[n].end()) + if (v < _dims[n].end()) { // No: increment slice._dims[n] = Dimension(v, v + 1, 1); // Reset lower dimensions: - for(unsigned int lower = window_dimension; lower < n; ++lower) + for (unsigned int lower = window_dimension; lower < n; ++lower) { slice._dims[lower] = Dimension(_dims[lower].start(), _dims[lower].start() + 1, 1); } @@ -258,14 +260,14 @@ inline bool Window::slide_window_slice(Window &slice) const } template <unsigned int window_dimension> -inline Window Window::first_slice_window() const +inline Window Window::first_slice_window() const { Window slice; std::copy_n(_dims.begin(), window_dimension, slice._dims.begin()); //Initialise higher dimensions to be the first slice. - for(unsigned int n = window_dimension; n < Coordinates::num_max_dimensions; ++n) + for (unsigned int n = window_dimension; n < Coordinates::num_max_dimensions; ++n) { slice._dims[n] = Dimension(_dims[n].start(), _dims[n].start() + 1, 1); } @@ -275,7 +277,7 @@ inline Window Window::first_slice_window() const inline void Window::use_tensor_dimensions(const TensorShape &shape, size_t first_dimension) { - for(unsigned int n = first_dimension; n < shape.num_dimensions(); ++n) + for (unsigned int n = first_dimension; n < shape.num_dimensions(); ++n) { set(n, Window::Dimension(0, std::max(shape[n], static_cast<size_t>(1)))); } @@ -284,7 +286,7 @@ inline void Window::use_tensor_dimensions(const TensorShape &shape, size_t first inline TensorShape Window::shape() const { TensorShape shape; - for(size_t d = 0; d < TensorShape::num_max_dimensions; ++d) + for (size_t d = 0; d < TensorShape::num_max_dimensions; ++d) { shape.set(d, (_dims[d].end() - _dims[d].start()) / _dims[d].step()); } @@ -294,7 +296,7 @@ inline TensorShape Window::shape() const inline size_t Window::num_iterations_total() const { size_t total = 1; - for(size_t d = 0; d < Coordinates::num_max_dimensions; ++d) + for (size_t d = 0; d < Coordinates::num_max_dimensions; ++d) { total *= num_iterations(d); } diff --git a/arm_compute/core/WindowIterator.h b/arm_compute/core/WindowIterator.h index b1e399c872..29302c410a 100644 --- a/arm_compute/core/WindowIterator.h +++ b/arm_compute/core/WindowIterator.h @@ -28,7 +28,6 @@ #include "arm_compute/core/ITensor.h" #include "arm_compute/core/Window.h" - namespace arm_compute { /** Convert an offset in window steps into absolute coordinates. @@ -41,7 +40,7 @@ namespace arm_compute inline Coordinates convert_window_coord_to_position(const Window &w, const Coordinates &offset) { Coordinates position; - for(unsigned int i = 0; i < Coordinates::num_max_dimensions; ++i) + for (unsigned int i = 0; i < Coordinates::num_max_dimensions; ++i) { position.set(i, w[i].start() + offset[i] * w[i].step()); } @@ -165,7 +164,7 @@ public: template <typename M> void iterate_3D(M &&on_new_row_size) { - while(_end.z() != _position.z()) + while (_end.z() != _position.z()) { iterate_2D_internal(on_new_row_size, _w.x().end() - _w.x().step(), _w.y().end() - _w.y().step()); _position[2] += _w.z().step(); @@ -212,7 +211,7 @@ private: void iterate_2D_internal(M &&on_new_row_size, int end_x, int end_y) { //Is there more than one row to process ? - if(end_y == _position.y()) + if (end_y == _position.y()) { // Both start and end belong to the same row: iterate_over_dim0(end_x + _w.x().step(), on_new_row_size); @@ -220,7 +219,7 @@ private: else { // Do we start from the beginning of the row ? - if(_w.x().start() != _position.x()) + if (_w.x().start() != _position.x()) { //Start in the middle of a row: process left-over X iterate_over_dim0(_w.x().end(), on_new_row_size); @@ -229,7 +228,7 @@ private: //Middle rows bool no_leftover = end_x + _w.x().step() == _w.x().end(); - if(no_leftover) + if (no_leftover) { //Switch to full row size: on_new_row_size(_w[0].start(), _w.x().end()); @@ -241,7 +240,7 @@ private: else { // Are there full rows to process ? - if(_position[1] != end_y) + if (_position[1] != end_y) { //Switch to full row size: on_new_row_size(_w[0].start(), _w.x().end()); @@ -261,7 +260,7 @@ private: */ void iterate_over_dim1(int end) { - for(; _position[1] != end; _position[1] += _w[1].step()) + for (; _position[1] != end; _position[1] += _w[1].step()) { _position[0] = _w[0].start(); iterate_over_dim0(_w[0].end()); @@ -288,7 +287,7 @@ private: { // Both start and end belong to the same row: ARM_COMPUTE_ERROR_ON(_position[0] > end); - for(; _position.x() < end; _position[0] += _w[0].step()) + for (; _position.x() < end; _position[0] += _w[0].step()) { _lambda_function(_position); } @@ -310,9 +309,10 @@ private: * @return A WindowIterator object. */ template <typename L> -WindowIterator<L> create_window_iterator(const Window &w, const Coordinates &start, const Coordinates &end, L &&lambda_function) +WindowIterator<L> +create_window_iterator(const Window &w, const Coordinates &start, const Coordinates &end, L &&lambda_function) { return WindowIterator<L>(w, start, end, std::move(lambda_function)); } -} +} // namespace arm_compute #endif /*ARM_COMPUTE_WINDOW_ITERATOR_H*/ diff --git a/arm_compute/core/experimental/Types.h b/arm_compute/core/experimental/Types.h index 8dd6812b58..63a3a1a1ec 100644 --- a/arm_compute/core/experimental/Types.h +++ b/arm_compute/core/experimental/Types.h @@ -92,24 +92,18 @@ struct MemoryInfo { MemoryInfo() = default; - MemoryInfo(int slot, size_t size, size_t alignment = 0) noexcept - : slot(slot), - size(size), - alignment(alignment) + MemoryInfo(int slot, size_t size, size_t alignment = 0) noexcept : slot(slot), size(size), alignment(alignment) { } MemoryInfo(int slot, MemoryLifetime lifetime, size_t size, size_t alignment = 0) noexcept - : slot(slot), - lifetime(lifetime), - size(size), - alignment(alignment) + : slot(slot), lifetime(lifetime), size(size), alignment(alignment) { } bool merge(int slot, size_t new_size, size_t new_alignment = 0) noexcept { - if(slot != this->slot) + if (slot != this->slot) { return false; } @@ -120,10 +114,10 @@ struct MemoryInfo return true; } - int slot{ ACL_UNKNOWN }; - MemoryLifetime lifetime{ MemoryLifetime::Temporary }; - size_t size{ 0 }; - size_t alignment{ 64 }; + int slot{ACL_UNKNOWN}; + MemoryLifetime lifetime{MemoryLifetime::Temporary}; + size_t size{0}; + size_t alignment{64}; }; using MemoryRequirements = std::vector<MemoryInfo>; diff --git a/arm_compute/core/utils/ActivationFunctionUtils.h b/arm_compute/core/utils/ActivationFunctionUtils.h index 1cb66da13d..c988efa256 100644 --- a/arm_compute/core/utils/ActivationFunctionUtils.h +++ b/arm_compute/core/utils/ActivationFunctionUtils.h @@ -37,5 +37,5 @@ namespace arm_compute * @return The string describing the activation function. */ const std::string &string_from_activation_func(const ActivationFunction &act); -} +} // namespace arm_compute #endif /*ARM_COMPUTE_CORE_UTILS_ACTIVATIONFUNCTIONUTILS_H */ diff --git a/arm_compute/core/utils/DataLayoutUtils.h b/arm_compute/core/utils/DataLayoutUtils.h index 399f55c63f..61839c9f91 100644 --- a/arm_compute/core/utils/DataLayoutUtils.h +++ b/arm_compute/core/utils/DataLayoutUtils.h @@ -36,5 +36,5 @@ namespace arm_compute * @return The string describing the data layout. */ const std::string &string_from_data_layout(DataLayout dl); -} +} // namespace arm_compute #endif /*ARM_COMPUTE_CORE_UTILS_DATALAYOUTUTILS_H */ diff --git a/arm_compute/core/utils/DataTypeUtils.h b/arm_compute/core/utils/DataTypeUtils.h index cbb409c8a1..7ea5eb7670 100644 --- a/arm_compute/core/utils/DataTypeUtils.h +++ b/arm_compute/core/utils/DataTypeUtils.h @@ -37,7 +37,7 @@ namespace arm_compute */ inline size_t data_size_from_type(DataType data_type) { - switch(data_type) + switch (data_type) { case DataType::U8: case DataType::S8: @@ -77,7 +77,7 @@ inline size_t data_size_from_type(DataType data_type) */ inline size_t element_size_from_data_type(DataType dt) { - switch(dt) + switch (dt) { case DataType::S8: case DataType::U8: @@ -114,7 +114,7 @@ inline size_t element_size_from_data_type(DataType dt) */ inline DataType data_type_from_format(Format format) { - switch(format) + switch (format) { case Format::U8: case Format::UV88: @@ -158,7 +158,7 @@ inline DataType data_type_from_format(Format format) */ inline DataType get_promoted_data_type(DataType dt) { - switch(dt) + switch (dt) { case DataType::U8: return DataType::U16; @@ -196,7 +196,7 @@ inline std::tuple<PixelValue, PixelValue> get_min_max(DataType dt) { PixelValue min{}; PixelValue max{}; - switch(dt) + switch (dt) { case DataType::U8: case DataType::QASYMM8: @@ -303,7 +303,7 @@ inline ::std::istream &operator>>(::std::istream &stream, DataType &data_type) */ inline bool is_data_type_float(DataType dt) { - switch(dt) + switch (dt) { case DataType::F16: case DataType::F32: @@ -323,7 +323,7 @@ inline bool is_data_type_float(DataType dt) */ inline bool is_data_type_quantized(DataType dt) { - switch(dt) + switch (dt) { case DataType::QSYMM8: case DataType::QASYMM8: @@ -345,7 +345,7 @@ inline bool is_data_type_quantized(DataType dt) */ inline bool is_data_type_quantized_asymmetric(DataType dt) { - switch(dt) + switch (dt) { case DataType::QASYMM8: case DataType::QASYMM8_SIGNED: @@ -364,7 +364,7 @@ inline bool is_data_type_quantized_asymmetric(DataType dt) */ inline bool is_data_type_quantized_asymmetric_signed(DataType dt) { - switch(dt) + switch (dt) { case DataType::QASYMM8_SIGNED: return true; @@ -381,7 +381,7 @@ inline bool is_data_type_quantized_asymmetric_signed(DataType dt) */ inline bool is_data_type_quantized_symmetric(DataType dt) { - switch(dt) + switch (dt) { case DataType::QSYMM8: case DataType::QSYMM8_PER_CHANNEL: @@ -400,7 +400,7 @@ inline bool is_data_type_quantized_symmetric(DataType dt) */ inline bool is_data_type_quantized_per_channel(DataType dt) { - switch(dt) + switch (dt) { case DataType::QSYMM8_PER_CHANNEL: return true; @@ -420,12 +420,13 @@ inline bool is_data_type_quantized_per_channel(DataType dt) template <typename T> bool check_value_range(T val, DataType dt, QuantizationInfo qinfo = QuantizationInfo()) { - switch(dt) + switch (dt) { case DataType::U8: { const auto val_u8 = static_cast<uint8_t>(val); - return ((val_u8 == val) && val >= std::numeric_limits<uint8_t>::lowest() && val <= std::numeric_limits<uint8_t>::max()); + return ((val_u8 == val) && val >= std::numeric_limits<uint8_t>::lowest() && + val <= std::numeric_limits<uint8_t>::max()); } case DataType::QASYMM8: { @@ -436,29 +437,34 @@ bool check_value_range(T val, DataType dt, QuantizationInfo qinfo = Quantization case DataType::S8: { const auto val_s8 = static_cast<int8_t>(val); - return ((val_s8 == val) && val >= std::numeric_limits<int8_t>::lowest() && val <= std::numeric_limits<int8_t>::max()); + return ((val_s8 == val) && val >= std::numeric_limits<int8_t>::lowest() && + val <= std::numeric_limits<int8_t>::max()); } case DataType::U16: { const auto val_u16 = static_cast<uint16_t>(val); - return ((val_u16 == val) && val >= std::numeric_limits<uint16_t>::lowest() && val <= std::numeric_limits<uint16_t>::max()); + return ((val_u16 == val) && val >= std::numeric_limits<uint16_t>::lowest() && + val <= std::numeric_limits<uint16_t>::max()); } case DataType::S16: { const auto val_s16 = static_cast<int16_t>(val); - return ((val_s16 == val) && val >= std::numeric_limits<int16_t>::lowest() && val <= std::numeric_limits<int16_t>::max()); + return ((val_s16 == val) && val >= std::numeric_limits<int16_t>::lowest() && + val <= std::numeric_limits<int16_t>::max()); } case DataType::U32: { const auto val_d64 = static_cast<double>(val); const auto val_u32 = static_cast<uint32_t>(val); - return ((val_u32 == val_d64) && val_d64 >= std::numeric_limits<uint32_t>::lowest() && val_d64 <= std::numeric_limits<uint32_t>::max()); + return ((val_u32 == val_d64) && val_d64 >= std::numeric_limits<uint32_t>::lowest() && + val_d64 <= std::numeric_limits<uint32_t>::max()); } case DataType::S32: { const auto val_d64 = static_cast<double>(val); const auto val_s32 = static_cast<int32_t>(val); - return ((val_s32 == val_d64) && val_d64 >= std::numeric_limits<int32_t>::lowest() && val_d64 <= std::numeric_limits<int32_t>::max()); + return ((val_s32 == val_d64) && val_d64 >= std::numeric_limits<int32_t>::lowest() && + val_d64 <= std::numeric_limits<int32_t>::max()); } case DataType::BFLOAT16: return (val >= bfloat16::lowest() && val <= bfloat16::max()); @@ -482,7 +488,7 @@ inline std::string cpu_impl_dt(const DataType &data_type) { std::string ret = ""; - switch(data_type) + switch (data_type) { case DataType::F32: ret = "fp32"; @@ -521,5 +527,5 @@ inline std::string cpu_impl_dt(const DataType &data_type) return ret; } -} +} // namespace arm_compute #endif /*ARM_COMPUTE_CORE_UTILS_DATATYPEUTILS_H */ diff --git a/arm_compute/core/utils/FormatUtils.h b/arm_compute/core/utils/FormatUtils.h index afb0f78255..a8e96bd361 100644 --- a/arm_compute/core/utils/FormatUtils.h +++ b/arm_compute/core/utils/FormatUtils.h @@ -37,7 +37,7 @@ namespace arm_compute */ inline size_t pixel_size_from_format(Format format) { - switch(format) + switch (format) { case Format::U8: return 1; @@ -77,7 +77,7 @@ inline size_t pixel_size_from_format(Format format) */ inline int plane_idx_from_channel(Format format, Channel channel) { - switch(format) + switch (format) { // Single planar formats have a single plane case Format::U8: @@ -99,7 +99,7 @@ inline int plane_idx_from_channel(Format format, Channel channel) case Format::NV21: { // Channel U and V share the same plane of format UV88 - switch(channel) + switch (channel) { case Channel::Y: return 0; @@ -114,7 +114,7 @@ inline int plane_idx_from_channel(Format format, Channel channel) case Format::IYUV: case Format::YUV444: { - switch(channel) + switch (channel) { case Channel::Y: return 0; @@ -142,11 +142,11 @@ inline int plane_idx_from_channel(Format format, Channel channel) */ inline int channel_idx_from_format(Format format, Channel channel) { - switch(format) + switch (format) { case Format::RGB888: { - switch(channel) + switch (channel) { case Channel::R: return 0; @@ -161,7 +161,7 @@ inline int channel_idx_from_format(Format format, Channel channel) } case Format::RGBA8888: { - switch(channel) + switch (channel) { case Channel::R: return 0; @@ -178,7 +178,7 @@ inline int channel_idx_from_format(Format format, Channel channel) } case Format::YUYV422: { - switch(channel) + switch (channel) { case Channel::Y: return 0; @@ -193,7 +193,7 @@ inline int channel_idx_from_format(Format format, Channel channel) } case Format::UYVY422: { - switch(channel) + switch (channel) { case Channel::Y: return 1; @@ -208,7 +208,7 @@ inline int channel_idx_from_format(Format format, Channel channel) } case Format::NV12: { - switch(channel) + switch (channel) { case Channel::Y: return 0; @@ -223,7 +223,7 @@ inline int channel_idx_from_format(Format format, Channel channel) } case Format::NV21: { - switch(channel) + switch (channel) { case Channel::Y: return 0; @@ -239,7 +239,7 @@ inline int channel_idx_from_format(Format format, Channel channel) case Format::YUV444: case Format::IYUV: { - switch(channel) + switch (channel) { case Channel::Y: return 0; @@ -266,7 +266,7 @@ inline int channel_idx_from_format(Format format, Channel channel) */ inline size_t num_planes_from_format(Format format) { - switch(format) + switch (format) { case Format::U8: case Format::S16: @@ -301,7 +301,7 @@ inline size_t num_planes_from_format(Format format) */ inline size_t num_channels_from_format(Format format) { - switch(format) + switch (format) { case Format::U8: case Format::U16: @@ -340,5 +340,5 @@ inline size_t num_channels_from_format(Format format) * @return The string describing the format. */ const std::string &string_from_format(Format format); -} +} // namespace arm_compute #endif /*ARM_COMPUTE_CORE_UTILS_FORMATUTILS_H */ diff --git a/arm_compute/core/utils/InterpolationPolicyUtils.h b/arm_compute/core/utils/InterpolationPolicyUtils.h index 79f6e3aa5f..8d4ae4321c 100644 --- a/arm_compute/core/utils/InterpolationPolicyUtils.h +++ b/arm_compute/core/utils/InterpolationPolicyUtils.h @@ -37,5 +37,5 @@ namespace arm_compute * @return The string describing the interpolation policy. */ const std::string &string_from_interpolation_policy(InterpolationPolicy policy); -} +} // namespace arm_compute #endif /*ARM_COMPUTE_CORE_UTILS_INTERPOLATIONPOLICYUTILS_H */ diff --git a/arm_compute/core/utils/StringUtils.h b/arm_compute/core/utils/StringUtils.h index 41f29b0901..c13cbaa334 100644 --- a/arm_compute/core/utils/StringUtils.h +++ b/arm_compute/core/utils/StringUtils.h @@ -61,5 +61,5 @@ std::string float_to_string_with_full_precision(float val); * @return std::string */ std::string join(const std::vector<std::string> strings, const std::string &sep); -} +} // namespace arm_compute #endif /*ARM_COMPUTE_CORE_UTILS_STRINGUTILS_H */ diff --git a/arm_compute/core/utils/helpers/AdjustVecSize.h b/arm_compute/core/utils/helpers/AdjustVecSize.h index bbb3048b84..842e3b57d6 100644 --- a/arm_compute/core/utils/helpers/AdjustVecSize.h +++ b/arm_compute/core/utils/helpers/AdjustVecSize.h @@ -39,17 +39,17 @@ 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)) + if ((vec_size >= dim0) && (dim0 == 3)) { return dim0; } - while(vec_size > dim0) + while (vec_size > dim0) { vec_size >>= 1; } return vec_size; } -} +} // namespace arm_compute #endif /*ARM_COMPUTE_UTILS_H */ diff --git a/arm_compute/core/utils/helpers/tensor_transform.h b/arm_compute/core/utils/helpers/tensor_transform.h index faa5b4433c..7a61fa192a 100644 --- a/arm_compute/core/utils/helpers/tensor_transform.h +++ b/arm_compute/core/utils/helpers/tensor_transform.h @@ -52,7 +52,8 @@ int calculate_stride_on_index(int index, Coordinates strides); * * @return Absolute start position of a given index */ -int calculate_start_on_index(TensorShape input_shape, int index, Coordinates starts, Coordinates strides, int32_t begin_mask); +int calculate_start_on_index( + TensorShape input_shape, int index, Coordinates starts, Coordinates strides, int32_t begin_mask); /** Returns the absolute end position of a given index for a strided slice operation * @@ -68,8 +69,13 @@ int calculate_start_on_index(TensorShape input_shape, int index, Coordinates sta * * @return Absolute end position of a given index */ -int calculate_end_on_index(TensorShape input_shape, int index, int start_on_index, Coordinates ends, Coordinates strides, - int32_t end_mask = 0, int32_t shrink_axis_mask = 0); +int calculate_end_on_index(TensorShape input_shape, + int index, + int start_on_index, + Coordinates ends, + Coordinates strides, + int32_t end_mask = 0, + int32_t shrink_axis_mask = 0); /** Calculate start, end and stride coordinates for a strided slice * @@ -87,8 +93,12 @@ int calculate_end_on_index(TensorShape input_shape, int index, int start_on_inde * @return A tuple with <Start,End,Strides> */ std::tuple<Coordinates, Coordinates, Coordinates> calculate_strided_slice_coords(TensorShape input_shape, - Coordinates starts, Coordinates ends, Coordinates strides, - int32_t begin_mask = 0, int32_t end_mask = 0, int32_t shrink_axis_mask = 0); + Coordinates starts, + Coordinates ends, + Coordinates strides, + int32_t begin_mask = 0, + int32_t end_mask = 0, + int32_t shrink_axis_mask = 0); /** Computes output shape of strided slice * @@ -109,9 +119,14 @@ std::tuple<Coordinates, Coordinates, Coordinates> calculate_strided_slice_coords * * @return The output tensor shape */ -TensorShape compute_strided_slice_output_shape(TensorShape input_shape, Coordinates starts, Coordinates ends, Coordinates strides, - int32_t begin_mask = 0, int32_t end_mask = 0, int32_t shrink_axis_mask = 0, - bool return_unshrinked = false); +TensorShape compute_strided_slice_output_shape(TensorShape input_shape, + Coordinates starts, + Coordinates ends, + Coordinates strides, + int32_t begin_mask = 0, + int32_t end_mask = 0, + int32_t shrink_axis_mask = 0, + bool return_unshrinked = false); /** Constructs end mask in case we want to perform a slice operation using the strided slice interface * @@ -122,7 +137,7 @@ TensorShape compute_strided_slice_output_shape(TensorShape input_shape, Coordina * @return End mask */ int32_t construct_slice_end_mask(Coordinates ends); -} // namespace tensor_tranform +} // namespace tensor_transform } // namespace helpers } // namespace arm_compute #endif /* ARM_COMPUTE_UTILS_HELPERS_TENSOR_TRANSFORM_H */ diff --git a/arm_compute/core/utils/logging/FilePrinter.h b/arm_compute/core/utils/logging/FilePrinter.h index 0e5b84f084..a865aadddb 100644 --- a/arm_compute/core/utils/logging/FilePrinter.h +++ b/arm_compute/core/utils/logging/FilePrinter.h @@ -24,9 +24,8 @@ #ifndef ARM_COMPUTE_LOGGING_FILE_PRINTER_H #define ARM_COMPUTE_LOGGING_FILE_PRINTER_H -#include "arm_compute/core/utils/logging/IPrinter.h" - #include "arm_compute/core/utils/io/FileHandler.h" +#include "arm_compute/core/utils/logging/IPrinter.h" namespace arm_compute { diff --git a/arm_compute/core/utils/logging/Helpers.h b/arm_compute/core/utils/logging/Helpers.h index 5f8b948592..c3c2f0f0b8 100644 --- a/arm_compute/core/utils/logging/Helpers.h +++ b/arm_compute/core/utils/logging/Helpers.h @@ -25,6 +25,7 @@ #define ARM_COMPUTE_LOGGING_HELPERS_H #include "arm_compute/core/utils/logging/Types.h" + #include "support/ToolchainSupport.h" #include <cstddef> @@ -45,7 +46,7 @@ namespace logging * @return The formatted string */ template <typename... Ts> -inline std::string string_with_format(const std::string &fmt, Ts &&... args) +inline std::string string_with_format(const std::string &fmt, Ts &&...args) { size_t size = support::cpp11::snprintf(nullptr, 0, fmt.c_str(), args...) + 1; auto char_str = std::make_unique<char[]>(size); diff --git a/arm_compute/core/utils/logging/IPrinter.h b/arm_compute/core/utils/logging/IPrinter.h index 42dca58ea1..7fde4d9302 100644 --- a/arm_compute/core/utils/logging/IPrinter.h +++ b/arm_compute/core/utils/logging/IPrinter.h @@ -35,8 +35,7 @@ class Printer { public: /** Default Constructor */ - Printer() noexcept - : _mtx() + Printer() noexcept : _mtx() { } /** Prevent instances of this class from being copied */ diff --git a/arm_compute/core/utils/logging/LogMsgDecorators.h b/arm_compute/core/utils/logging/LogMsgDecorators.h index 9c9e62740f..66a8180e21 100644 --- a/arm_compute/core/utils/logging/LogMsgDecorators.h +++ b/arm_compute/core/utils/logging/LogMsgDecorators.h @@ -63,8 +63,7 @@ public: * * @param str Sting to append */ - StringDecorator(const std::string &str) - : _str(str) + StringDecorator(const std::string &str) : _str(str) { _str = angle_wrap_value(str); } @@ -103,7 +102,7 @@ private: auto time = std::chrono::system_clock::to_time_t(now); // TODO: use put_time for gcc > 4.9 - char buf[100] = { 0 }; + char buf[100] = {0}; std::strftime(buf, sizeof(buf), "%d-%m-%Y %I:%M:%S", std::localtime(&time)); return buf; } diff --git a/arm_compute/core/utils/logging/Logger.h b/arm_compute/core/utils/logging/Logger.h index 4fc9bb7dbf..608db39138 100644 --- a/arm_compute/core/utils/logging/Logger.h +++ b/arm_compute/core/utils/logging/Logger.h @@ -88,7 +88,7 @@ public: * @param[in] args Message arguments */ template <typename... Ts> - void log(LogLevel log_level, const std::string &fmt, Ts &&... args); + void log(LogLevel log_level, const std::string &fmt, Ts &&...args); /** Sets log level of the logger * * @warning Not thread-safe @@ -159,11 +159,11 @@ private: }; template <typename... Ts> -inline void Logger::log(LogLevel log_level, const std::string &fmt, Ts &&... args) +inline void Logger::log(LogLevel log_level, const std::string &fmt, Ts &&...args) { // Return if message shouldn't be logged // i.e. if log level does not match the logger's - if(!is_loggable(log_level)) + if (!is_loggable(log_level)) { return; } diff --git a/arm_compute/core/utils/logging/LoggerRegistry.h b/arm_compute/core/utils/logging/LoggerRegistry.h index 7c9931a260..4e52a10935 100644 --- a/arm_compute/core/utils/logging/LoggerRegistry.h +++ b/arm_compute/core/utils/logging/LoggerRegistry.h @@ -27,6 +27,7 @@ #include "arm_compute/core/utils/logging/Logger.h" #include "arm_compute/core/utils/logging/Printers.h" #include "arm_compute/core/utils/logging/Types.h" + #include "support/Mutex.h" #include <memory> @@ -54,8 +55,9 @@ public: * @param[in] log_level Logger's log level. Defaults to INFO * @param[in] printers Printers to attach to the system loggers. Defaults with a @ref StdPrinter. */ - void create_logger(const std::string &name, LogLevel log_level = LogLevel::INFO, - const std::vector<std::shared_ptr<Printer>> &printers = { std::make_shared<StdPrinter>() }); + void create_logger(const std::string &name, + LogLevel log_level = LogLevel::INFO, + const std::vector<std::shared_ptr<Printer>> &printers = {std::make_shared<StdPrinter>()}); /** Remove a logger * * @param name Logger's name @@ -74,16 +76,17 @@ public: * @param[in] printers (Optional) Printers to attach to the system loggers. Defaults with a @ref StdPrinter. */ void create_reserved_loggers(LogLevel log_level = LogLevel::INFO, - const std::vector<std::shared_ptr<Printer>> &printers = { std::make_shared<StdPrinter>() }); + const std::vector<std::shared_ptr<Printer>> &printers = { + std::make_shared<StdPrinter>()}); private: /** Default constructor */ LoggerRegistry(); private: - arm_compute::Mutex _mtx; + arm_compute::Mutex _mtx; std::unordered_map<std::string, std::shared_ptr<Logger>> _loggers; - static std::set<std::string> _reserved_loggers; + static std::set<std::string> _reserved_loggers; }; } // namespace logging } // namespace arm_compute diff --git a/arm_compute/core/utils/logging/Macros.h b/arm_compute/core/utils/logging/Macros.h index 0ab17c4464..4d5aa5fe2c 100644 --- a/arm_compute/core/utils/logging/Macros.h +++ b/arm_compute/core/utils/logging/Macros.h @@ -48,48 +48,48 @@ inline std::string signature_name(const std::string &pretty_func) do \ { \ auto __logger = arm_compute::logging::LoggerRegistry::get().logger(logger_name); \ - if(__logger != nullptr) \ + if (__logger != nullptr) \ { \ __logger->log(log_level, msg); \ } \ - } while(false) + } while (false) #define ARM_COMPUTE_LOG_MSG_WITH_FUNCNAME(logger_name, log_level, msg) \ do \ { \ auto __logger = arm_compute::logging::LoggerRegistry::get().logger(logger_name); \ - if(__logger != nullptr) \ + if (__logger != nullptr) \ { \ std::ostringstream s; \ s << ARM_COMPUTE_SIGNATURE_NAME << " : " << msg; \ __logger->log(log_level, s.str()); \ } \ - } while(false) + } while (false) #define ARM_COMPUTE_LOG_MSG_WITH_FORMAT(logger_name, log_level, fmt, ...) \ do \ { \ auto __logger = arm_compute::logging::LoggerRegistry::get().logger(logger_name); \ - if(__logger != nullptr) \ + if (__logger != nullptr) \ { \ size_t size = ::snprintf(nullptr, 0, fmt, __VA_ARGS__) + 1; \ auto char_str = std::make_unique<char[]>(size); \ ::snprintf(char_str.get(), size, fmt, __VA_ARGS__); \ __logger->log(log_level, std::string(char_str.get(), char_str.get() + size - 1)); \ } \ - } while(false) + } while (false) #define ARM_COMPUTE_LOG_STREAM(logger_name, log_level, stream) \ do \ { \ auto __logger = arm_compute::logging::LoggerRegistry::get().logger(logger_name); \ - if(__logger != nullptr) \ + if (__logger != nullptr) \ { \ std::ostringstream s; \ s << stream; \ __logger->log(log_level, s.str()); \ } \ - } while(false) + } while (false) #else /* ARM_COMPUTE_LOGGING_ENABLED */ diff --git a/arm_compute/core/utils/logging/Types.h b/arm_compute/core/utils/logging/Types.h index f0ddae6c84..64c567b984 100644 --- a/arm_compute/core/utils/logging/Types.h +++ b/arm_compute/core/utils/logging/Types.h @@ -44,8 +44,7 @@ enum class LogLevel struct LogMsg { /** Default constructor */ - LogMsg() - : raw_(), log_level_(LogLevel::OFF) + LogMsg() : raw_(), log_level_(LogLevel::OFF) { } /** Construct a log message @@ -53,8 +52,7 @@ struct LogMsg * @param[in] msg Message to log. * @param[in] log_level Logging level. Default: OFF */ - LogMsg(std::string msg, LogLevel log_level = LogLevel::OFF) - : raw_(msg), log_level_(log_level) + LogMsg(std::string msg, LogLevel log_level = LogLevel::OFF) : raw_(msg), log_level_(log_level) { } diff --git a/arm_compute/core/utils/math/Math.h b/arm_compute/core/utils/math/Math.h index c1dce7ff08..e70337ba0f 100644 --- a/arm_compute/core/utils/math/Math.h +++ b/arm_compute/core/utils/math/Math.h @@ -67,5 +67,5 @@ inline auto floor_to_multiple(S value, T divisor) -> decltype((value / divisor) return (value / divisor) * divisor; } -} +} // namespace arm_compute #endif /*ARM_COMPUTE_UTILS_MATH_H */ diff --git a/arm_compute/core/utils/math/SafeOps.h b/arm_compute/core/utils/math/SafeOps.h index dc928a0e5d..ef8bcf7e14 100644 --- a/arm_compute/core/utils/math/SafeOps.h +++ b/arm_compute/core/utils/math/SafeOps.h @@ -25,6 +25,7 @@ #define ARM_COMPUTE_UTILS_MATH_SAFE_OPS #include "arm_compute/core/Error.h" + #include "support/AclRequires.h" #include <limits> @@ -51,11 +52,11 @@ T safe_integer_add(T val_a, T val_b) { T result = 0; - if((val_b > 0) && (val_a > std::numeric_limits<T>::max() - val_b)) + if ((val_b > 0) && (val_a > std::numeric_limits<T>::max() - val_b)) { result = std::numeric_limits<T>::max(); } - else if((val_b < 0) && (val_a < std::numeric_limits<T>::min() - val_b)) + else if ((val_b < 0) && (val_a < std::numeric_limits<T>::min() - val_b)) { result = std::numeric_limits<T>::min(); } @@ -83,11 +84,11 @@ T safe_integer_sub(T val_a, T val_b) { T result = 0; - if((val_b < 0) && (val_a > std::numeric_limits<T>::max() + val_b)) + if ((val_b < 0) && (val_a > std::numeric_limits<T>::max() + val_b)) { result = std::numeric_limits<T>::max(); } - else if((val_b > 0) && (val_a < std::numeric_limits<T>::min() + val_b)) + else if ((val_b > 0) && (val_a < std::numeric_limits<T>::min() + val_b)) { result = std::numeric_limits<T>::min(); } @@ -115,13 +116,13 @@ T safe_integer_mul(T val_a, T val_b) { T result = 0; - if(val_a > 0) + if (val_a > 0) { - if((val_b > 0) && (val_a > (std::numeric_limits<T>::max() / val_b))) + if ((val_b > 0) && (val_a > (std::numeric_limits<T>::max() / val_b))) { result = std::numeric_limits<T>::max(); } - else if(val_b < (std::numeric_limits<T>::min() / val_a)) + else if (val_b < (std::numeric_limits<T>::min() / val_a)) { result = std::numeric_limits<T>::min(); } @@ -132,11 +133,11 @@ T safe_integer_mul(T val_a, T val_b) } else { - if((val_b > 0) && (val_a < (std::numeric_limits<T>::min() / val_b))) + if ((val_b > 0) && (val_a < (std::numeric_limits<T>::min() / val_b))) { result = std::numeric_limits<T>::max(); } - else if((val_a != 0) && (val_b < (std::numeric_limits<T>::max() / val_a))) + else if ((val_a != 0) && (val_b < (std::numeric_limits<T>::max() / val_a))) { result = std::numeric_limits<T>::min(); } @@ -165,7 +166,7 @@ T safe_integer_div(T val_a, T val_b) { T result = 0; - if((val_b == 0) || ((val_a == std::numeric_limits<T>::min()) && (val_b == -1))) + if ((val_b == 0) || ((val_a == std::numeric_limits<T>::min()) && (val_b == -1))) { result = std::numeric_limits<T>::min(); } @@ -176,7 +177,7 @@ T safe_integer_div(T val_a, T val_b) return result; } -} // namespace cast +} // namespace math } // namespace utils } // namespace arm_compute #endif /* ARM_COMPUTE_UTILS_MATH_SAFE_OPS */ diff --git a/arm_compute/core/utils/misc/InfoHelpers.h b/arm_compute/core/utils/misc/InfoHelpers.h index ced0d24b56..1d1b4ea8d7 100644 --- a/arm_compute/core/utils/misc/InfoHelpers.h +++ b/arm_compute/core/utils/misc/InfoHelpers.h @@ -53,10 +53,12 @@ inline bool is_relu(ActivationLayerInfo activation_info) */ inline bool is_relu6(ActivationLayerInfo activation_info) { - const bool is_lu_bounded_relu = activation_info.activation() == ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU - && activation_info.a() == 6.f && activation_info.b() == 0.f; - const bool is_bounded_relu = activation_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU - && activation_info.a() == 6.f; + const bool is_lu_bounded_relu = + activation_info.activation() == ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU && + activation_info.a() == 6.f && activation_info.b() == 0.f; + const bool is_bounded_relu = + activation_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU && + activation_info.a() == 6.f; return activation_info.enabled() && (is_lu_bounded_relu || is_bounded_relu); } @@ -68,34 +70,37 @@ inline bool is_relu6(ActivationLayerInfo activation_info) * */ template <typename T> -inline void build_lstm_params_tensor_info(const LSTMParams<T> &lstm_params, - LSTMParams<ITensorInfo> *lstm_params_info) +inline void build_lstm_params_tensor_info(const LSTMParams<T> &lstm_params, LSTMParams<ITensorInfo> *lstm_params_info) { - if(lstm_params.has_peephole_opt()) + if (lstm_params.has_peephole_opt()) { ARM_COMPUTE_ERROR_ON_NULLPTR(lstm_params.cell_to_forget_weights(), lstm_params.cell_to_output_weights()); - lstm_params_info->set_peephole_params(lstm_params.cell_to_forget_weights()->info(), lstm_params.cell_to_output_weights()->info()); + lstm_params_info->set_peephole_params(lstm_params.cell_to_forget_weights()->info(), + lstm_params.cell_to_output_weights()->info()); } - if(lstm_params.has_projection()) + if (lstm_params.has_projection()) { ARM_COMPUTE_ERROR_ON_NULLPTR(lstm_params.projection_weights()); - lstm_params_info->set_projection_params(lstm_params.projection_weights()->info(), - lstm_params.projection_bias() != nullptr ? lstm_params.projection_bias()->info() : nullptr); + lstm_params_info->set_projection_params( + lstm_params.projection_weights()->info(), + lstm_params.projection_bias() != nullptr ? lstm_params.projection_bias()->info() : nullptr); } - if(!lstm_params.has_cifg_opt()) + if (!lstm_params.has_cifg_opt()) { - ARM_COMPUTE_ERROR_ON_NULLPTR(lstm_params.input_to_input_weights(), lstm_params.recurrent_to_input_weights(), lstm_params.input_gate_bias()); + ARM_COMPUTE_ERROR_ON_NULLPTR(lstm_params.input_to_input_weights(), lstm_params.recurrent_to_input_weights(), + lstm_params.input_gate_bias()); - ITensorInfo *cell_to_input_weights_info = (lstm_params.has_peephole_opt()) ? lstm_params.cell_to_input_weights()->info() : nullptr; - lstm_params_info->set_cifg_params(lstm_params.input_to_input_weights()->info(), lstm_params.recurrent_to_input_weights()->info(), - cell_to_input_weights_info, lstm_params.input_gate_bias()->info()); + ITensorInfo *cell_to_input_weights_info = + (lstm_params.has_peephole_opt()) ? lstm_params.cell_to_input_weights()->info() : nullptr; + lstm_params_info->set_cifg_params(lstm_params.input_to_input_weights()->info(), + lstm_params.recurrent_to_input_weights()->info(), cell_to_input_weights_info, + lstm_params.input_gate_bias()->info()); } - if(lstm_params.use_layer_norm()) + if (lstm_params.use_layer_norm()) { - ARM_COMPUTE_ERROR_ON_NULLPTR(lstm_params.forget_layer_norm_weights(), - lstm_params.output_layer_norm_weights(), + ARM_COMPUTE_ERROR_ON_NULLPTR(lstm_params.forget_layer_norm_weights(), lstm_params.output_layer_norm_weights(), lstm_params.cell_layer_norm_weights()); - if(!lstm_params.has_cifg_opt()) + if (!lstm_params.has_cifg_opt()) { ARM_COMPUTE_ERROR_ON_NULLPTR(lstm_params.input_layer_norm_weights()); } @@ -103,15 +108,14 @@ inline void build_lstm_params_tensor_info(const LSTMParams<T> &lstm_params, ITensorInfo *forget_info = lstm_params.forget_layer_norm_weights()->info(); ITensorInfo *cell_info = lstm_params.cell_layer_norm_weights()->info(); ITensorInfo *output_info = lstm_params.output_layer_norm_weights()->info(); - ITensorInfo *input_info = lstm_params.has_cifg_opt() ? nullptr : lstm_params.input_layer_norm_weights()->info(); + ITensorInfo *input_info = lstm_params.has_cifg_opt() ? nullptr : lstm_params.input_layer_norm_weights()->info(); lstm_params_info->set_layer_normalization_params(input_info, forget_info, cell_info, output_info); } - lstm_params_info->set_matmul_scale_params(lstm_params.input_intermediate_scale(), - lstm_params.forget_intermediate_scale(), - lstm_params.cell_intermediate_scale(), - lstm_params.output_intermediate_scale()); + lstm_params_info->set_matmul_scale_params( + lstm_params.input_intermediate_scale(), lstm_params.forget_intermediate_scale(), + lstm_params.cell_intermediate_scale(), lstm_params.output_intermediate_scale()); lstm_params_info->set_hidden_state_params(lstm_params.hidden_state_zero(), lstm_params.hidden_state_scale()); } diff --git a/arm_compute/core/utils/misc/Macros.h b/arm_compute/core/utils/misc/Macros.h index de66b6a52f..fa861fa442 100644 --- a/arm_compute/core/utils/misc/Macros.h +++ b/arm_compute/core/utils/misc/Macros.h @@ -26,15 +26,16 @@ #if defined(__cplusplus) && (__cplusplus >= 201402L) -#define ARM_COMPUTE_DEPRECATED [[deprecated]] -#define ARM_COMPUTE_DEPRECATED_REL(rel) [[deprecated("Deprecated in : " #rel)]] +#define ARM_COMPUTE_DEPRECATED [[deprecated]] +#define ARM_COMPUTE_DEPRECATED_REL(rel) [[deprecated("Deprecated in : " #rel)]] #define ARM_COMPUTE_DEPRECATED_REL_REPLACE(rel, replace) [[deprecated("Deprecated in : " #rel " - Use : " #replace)]] #elif defined(__GNUC__) || defined(__clang__) -#define ARM_COMPUTE_DEPRECATED __attribute__((deprecated)) +#define ARM_COMPUTE_DEPRECATED __attribute__((deprecated)) #define ARM_COMPUTE_DEPRECATED_REL(rel) __attribute__((deprecated("Deprecated in : " #rel))) -#define ARM_COMPUTE_DEPRECATED_REL_REPLACE(rel, replace) __attribute__((deprecated("Deprecated in : " #rel " - Use : " #replace))) +#define ARM_COMPUTE_DEPRECATED_REL_REPLACE(rel, replace) \ + __attribute__((deprecated("Deprecated in : " #rel " - Use : " #replace))) #else // defined(__cplusplus) && (__cplusplus >= 201402L) diff --git a/arm_compute/core/utils/misc/ShapeCalculator.h b/arm_compute/core/utils/misc/ShapeCalculator.h index 4c2037ab8d..31362f1ac4 100644 --- a/arm_compute/core/utils/misc/ShapeCalculator.h +++ b/arm_compute/core/utils/misc/ShapeCalculator.h @@ -28,11 +28,10 @@ #include "arm_compute/core/ITensorInfo.h" #include "arm_compute/core/KernelDescriptors.h" #include "arm_compute/core/Utils.h" +#include "arm_compute/core/utils/helpers/tensor_transform.h" #include "arm_compute/function_info/ConvolutionInfo.h" #include "arm_compute/runtime/FunctionDescriptors.h" -#include "arm_compute/core/utils/helpers/tensor_transform.h" - #include <cmath> namespace arm_compute @@ -57,12 +56,12 @@ inline TensorShape calculate_reduce_mean_shape(ITensorInfo *input, const Coordin convert_negative_axis(axis_local, input_dims); TensorShape out_shape = input->tensor_shape(); // Configure reshape layer if we want to drop the dimensions - if(!keep_dims) + if (!keep_dims) { // We have to sort the reduction axis vectors in order for remove_dimension // to work properly std::sort(axis_local.begin(), axis_local.begin() + reduction_ops); - for(int i = 0; i < reduction_ops; ++i) + for (int i = 0; i < reduction_ops; ++i) { out_shape.remove_dimension(axis_local[i] - i, false); } @@ -70,7 +69,7 @@ inline TensorShape calculate_reduce_mean_shape(ITensorInfo *input, const Coordin } else { - for(int i = 0; i < reduction_ops; ++i) + for (int i = 0; i < reduction_ops; ++i) { out_shape.set(axis_local[i], 1); } @@ -86,7 +85,10 @@ inline TensorShape calculate_reduce_mean_shape(ITensorInfo *input, const Coordin * * @return the calculated shape */ -inline TensorShape compute_vector_to_tensor_output_shape(const TensorShape &input, size_t conv_w, size_t conv_h, const DataLayout &data_layout) +inline TensorShape compute_vector_to_tensor_output_shape(const TensorShape &input, + size_t conv_w, + size_t conv_h, + const DataLayout &data_layout) { const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); @@ -128,10 +130,12 @@ inline TensorShape compute_reorg_output_shape(const ITensorInfo &input, int32_t const size_t idx_channel = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::CHANNEL); ARM_COMPUTE_ERROR_ON(stride <= 0); - ARM_COMPUTE_ERROR_ON_MSG((input.tensor_shape()[idx_width] % stride != 0), "The width of the input tensor must be a multiple of stride"); - ARM_COMPUTE_ERROR_ON_MSG((input.tensor_shape()[idx_height] % stride != 0), "The height of the input tensor must be a multiple of stride"); + ARM_COMPUTE_ERROR_ON_MSG((input.tensor_shape()[idx_width] % stride != 0), + "The width of the input tensor must be a multiple of stride"); + ARM_COMPUTE_ERROR_ON_MSG((input.tensor_shape()[idx_height] % stride != 0), + "The height of the input tensor must be a multiple of stride"); - TensorShape output_shape{ input.tensor_shape() }; + TensorShape output_shape{input.tensor_shape()}; output_shape.set(idx_width, output_shape[idx_width] / stride); output_shape.set(idx_height, output_shape[idx_height] / stride); @@ -148,7 +152,8 @@ inline TensorShape compute_reorg_output_shape(const ITensorInfo &input, int32_t * * @return the calculated shape of the reshaped weights */ -inline TensorShape compute_weights_reshaped_shape(const ITensorInfo &weights, bool has_bias = false, unsigned int num_groups = 1) +inline TensorShape +compute_weights_reshaped_shape(const ITensorInfo &weights, bool has_bias = false, unsigned int num_groups = 1) { // Number of groups greater than one are only supported for NCHW data layout, and the number of weights must be a multiple of it. ARM_COMPUTE_ERROR_ON(num_groups == 0); @@ -156,14 +161,14 @@ inline TensorShape compute_weights_reshaped_shape(const ITensorInfo &weights, bo ARM_COMPUTE_ERROR_ON((weights.dimension(3) % num_groups) != 0); // Calculate output shape - TensorShape weights_reshaped{ weights.tensor_shape() }; + TensorShape weights_reshaped{weights.tensor_shape()}; weights_reshaped.set(3, weights_reshaped[3] / num_groups); weights_reshaped.collapse(3); const size_t tmp_dim = weights_reshaped[0]; weights_reshaped.set(0, weights_reshaped[1]); weights_reshaped.set(1, tmp_dim + (has_bias ? 1 : 0)); - if(weights.num_dimensions() < 5) + if (weights.num_dimensions() < 5) { weights_reshaped.set(2, num_groups); } @@ -179,7 +184,9 @@ inline TensorShape compute_weights_reshaped_shape(const ITensorInfo &weights, bo * * @return the calculated shape */ -inline TensorShape compute_lhs_reshaped_shape(const ITensorInfo &a, const GEMMLHSMatrixInfo &lhs_info, bool reinterpret_input_as_3d = false) +inline TensorShape compute_lhs_reshaped_shape(const ITensorInfo &a, + const GEMMLHSMatrixInfo &lhs_info, + bool reinterpret_input_as_3d = false) { ARM_COMPUTE_ERROR_ON(lhs_info.m0 == 0); ARM_COMPUTE_ERROR_ON(lhs_info.k0 == 0); @@ -200,11 +207,11 @@ inline TensorShape compute_lhs_reshaped_shape(const ITensorInfo &a, const GEMMLH const unsigned int output_width = block_size * num_horiz_blocks * lhs_info.v0; const unsigned int output_height = std::ceil(num_vert_blocks / static_cast<float>(lhs_info.v0)); - TensorShape lhs_shape{ a.tensor_shape() }; + TensorShape lhs_shape{a.tensor_shape()}; lhs_shape.set(0, output_width); lhs_shape.set(1, output_height); - if((reinterpret_input_as_3d) && (lhs_shape.num_dimensions() > 2)) + if ((reinterpret_input_as_3d) && (lhs_shape.num_dimensions() > 2)) { // When the data format is NHWC and the shapes are Nx1x1 // the tensor shape num_dimensions is automatically set to 1 instead of 3. @@ -244,7 +251,7 @@ inline TensorShape compute_rhs_reshaped_shape(const ITensorInfo &a, const GEMMRH const unsigned int output_width = block_size * num_vert_blocks * rhs_info.h0; const unsigned int output_height = std::ceil(num_horiz_blocks / static_cast<float>(rhs_info.h0)); - TensorShape rhs_shape{ a.tensor_shape() }; + TensorShape rhs_shape{a.tensor_shape()}; rhs_shape.set(0, output_width); rhs_shape.set(1, output_height); @@ -259,14 +266,15 @@ inline TensorShape compute_rhs_reshaped_shape(const ITensorInfo &a, const GEMMRH * * @return the calculated shape */ -inline TensorShape compute_interleaved_shape(const ITensorInfo &a, int mult_interleave4x4_height = 1, bool reinterpret_input_as_3d = false) +inline TensorShape +compute_interleaved_shape(const ITensorInfo &a, int mult_interleave4x4_height = 1, bool reinterpret_input_as_3d = false) { // The interleaved output matrix will have the following shape: [ a_height * W, ceil(a_width / W) ] where W = 4 * mult_interleave4x4_height ARM_COMPUTE_ERROR_ON(mult_interleave4x4_height < 1); const int interleave_width = 4 * mult_interleave4x4_height; - TensorShape shape_interleaved_a{ a.tensor_shape() }; + TensorShape shape_interleaved_a{a.tensor_shape()}; shape_interleaved_a.set(0, a.dimension(0) * interleave_width); - if(reinterpret_input_as_3d) + if (reinterpret_input_as_3d) { const int M = a.dimension(1) * a.dimension(2); const int height = std::ceil(M / static_cast<float>(interleave_width)); @@ -276,7 +284,7 @@ inline TensorShape compute_interleaved_shape(const ITensorInfo &a, int mult_inte // the tensor shape num_dimensions is automatically set to 1 instead of 3. // To avoid failures by removing a dimension that doesn't exist // check if the number of dimensions is greater than 2. - if(shape_interleaved_a.num_dimensions() > 2) + if (shape_interleaved_a.num_dimensions() > 2) { shape_interleaved_a.remove_dimension(2); } @@ -298,7 +306,7 @@ inline TensorShape compute_interleaved_shape(const ITensorInfo &a, int mult_inte inline TensorShape compute_transpose1xW_shape(const ITensorInfo &b) { // The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ] - TensorShape shape_transposed1xW_b{ b.tensor_shape() }; + TensorShape shape_transposed1xW_b{b.tensor_shape()}; shape_transposed1xW_b.set(0, b.dimension(1) * 16); shape_transposed1xW_b.set(1, std::ceil(b.dimension(0) / 16.f)); @@ -318,7 +326,7 @@ inline TensorShape compute_transpose1xW_with_element_size_shape(const ITensorInf // The transpose1xW output matrix will have the following shape: // [ b_height * W, ceil(b_width / W) ] where W = (16 / element size of the tensor) * mult_transpose1xW_width ARM_COMPUTE_ERROR_ON(mult_transpose1xW_width < 1); - TensorShape shape_transposed1xW_b{ b.tensor_shape() }; + TensorShape shape_transposed1xW_b{b.tensor_shape()}; const size_t transpose_width = (16 / b.element_size()) * mult_transpose1xW_width; shape_transposed1xW_b.set(0, b.dimension(1) * transpose_width); shape_transposed1xW_b.set(1, static_cast<size_t>(std::ceil(b.dimension(0) / static_cast<float>(transpose_width)))); @@ -334,8 +342,8 @@ inline TensorShape compute_transpose1xW_with_element_size_shape(const ITensorInf */ inline TensorShape compute_reductionA_shape(const ITensorInfo &b) { - TensorShape shape_vector_sum_col{ b.tensor_shape() }; - if(shape_vector_sum_col.num_dimensions() > 1) + TensorShape shape_vector_sum_col{b.tensor_shape()}; + if (shape_vector_sum_col.num_dimensions() > 1) { shape_vector_sum_col.remove_dimension(1); } @@ -351,9 +359,9 @@ inline TensorShape compute_reductionA_shape(const ITensorInfo &b) */ inline TensorShape compute_reductionB_shape(const ITensorInfo &a) { - TensorShape shape_vector_sum_row{ a.tensor_shape() }; + TensorShape shape_vector_sum_row{a.tensor_shape()}; shape_vector_sum_row.set(Window::DimX, a.dimension(1)); - if(shape_vector_sum_row.num_dimensions() > 1) + if (shape_vector_sum_row.num_dimensions() > 1) { shape_vector_sum_row.remove_dimension(1); } @@ -370,7 +378,10 @@ inline TensorShape compute_reductionB_shape(const ITensorInfo &a) * * @return the calculated shape */ -inline TensorShape compute_col2im_shape(const ITensorInfo &input, const Size2D &convolved_dims, bool batch_size_on_z, unsigned int num_groups = 1) +inline TensorShape compute_col2im_shape(const ITensorInfo &input, + const Size2D &convolved_dims, + bool batch_size_on_z, + unsigned int num_groups = 1) { ARM_COMPUTE_ERROR_ON(num_groups == 0); ARM_COMPUTE_ERROR_ON(input.tensor_shape()[1] != (convolved_dims.area())); @@ -381,10 +392,10 @@ inline TensorShape compute_col2im_shape(const ITensorInfo &input, const Size2D & const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); - TensorShape col2im_shape{ input.tensor_shape() }; + TensorShape col2im_shape{input.tensor_shape()}; // If batches start on 3rd dimension shift dimensions right by 1 to retain upper tensor shape, // as first three will be override by H,W,C data - if(batch_size_on_z && num_groups == 1) + if (batch_size_on_z && num_groups == 1) { col2im_shape.shift_right(1); } @@ -403,7 +414,7 @@ inline TensorShape compute_col2im_shape(const ITensorInfo &input, const Size2D & */ inline TensorShape compute_transposed_shape(const ITensorInfo &input) { - TensorShape shape_transposed{ input.tensor_shape() }; + TensorShape shape_transposed{input.tensor_shape()}; shape_transposed.set(0, input.dimension(1), false); shape_transposed.set(1, input.dimension(0), false); @@ -419,10 +430,11 @@ inline TensorShape compute_transposed_shape(const ITensorInfo &input) * * @return the calculated shape */ -inline TensorShape compute_depthwise_convolution_shape(const ITensorInfo &input, const ITensorInfo &weights, const ConvolutionInfo &info) +inline TensorShape +compute_depthwise_convolution_shape(const ITensorInfo &input, const ITensorInfo &weights, const ConvolutionInfo &info) { - const TensorShape input_shape{ input.tensor_shape() }; - const TensorShape weights_shape{ weights.tensor_shape() }; + const TensorShape input_shape{input.tensor_shape()}; + const TensorShape weights_shape{weights.tensor_shape()}; const DataLayout data_layout = input.data_layout(); const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); @@ -430,16 +442,16 @@ inline TensorShape compute_depthwise_convolution_shape(const ITensorInfo &input, const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); const DataLayout weights_data_layout = weights.data_layout(); - const int weights_width_idx = get_data_layout_dimension_index(weights_data_layout, DataLayoutDimension::WIDTH); - const int weights_height_idx = get_data_layout_dimension_index(weights_data_layout, DataLayoutDimension::HEIGHT); + const int weights_width_idx = get_data_layout_dimension_index(weights_data_layout, DataLayoutDimension::WIDTH); + const int weights_height_idx = get_data_layout_dimension_index(weights_data_layout, DataLayoutDimension::HEIGHT); unsigned int output_width = 0; unsigned int output_height = 0; - std::tie(output_width, output_height) = scaled_dimensions(input_shape[width_idx], input_shape[height_idx], - weights_shape[weights_width_idx], weights_shape[weights_height_idx], - info.pad_stride_info, info.dilation); + std::tie(output_width, output_height) = + scaled_dimensions(input_shape[width_idx], input_shape[height_idx], weights_shape[weights_width_idx], + weights_shape[weights_height_idx], info.pad_stride_info, info.dilation); - TensorShape output_shape{ input_shape }; + TensorShape output_shape{input_shape}; output_shape.set(width_idx, output_width); output_shape.set(height_idx, output_height); output_shape.set(channel_idx, input_shape[channel_idx] * info.depth_multiplier); @@ -459,8 +471,13 @@ inline TensorShape compute_depthwise_convolution_shape(const ITensorInfo &input, * * @return the calculated shape */ -inline TensorShape compute_deconvolution_upsampled_shape(const ITensorInfo &input, const ITensorInfo &weights, unsigned int sx, unsigned int sy, - std::pair<unsigned int, unsigned int> &out_dims, uint32_t &padx, uint32_t &pady) +inline TensorShape compute_deconvolution_upsampled_shape(const ITensorInfo &input, + const ITensorInfo &weights, + unsigned int sx, + unsigned int sy, + std::pair<unsigned int, unsigned int> &out_dims, + uint32_t &padx, + uint32_t &pady) { const DataLayout data_layout = input.data_layout(); const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); @@ -491,10 +508,12 @@ inline TensorShape compute_deconvolution_upsampled_shape(const ITensorInfo &inpu * * @return the calculated shape */ -inline TensorShape compute_deconvolution_output_shape(const std::pair<unsigned int, unsigned int> &out_dims, const ITensorInfo &input, const ITensorInfo &weights) +inline TensorShape compute_deconvolution_output_shape(const std::pair<unsigned int, unsigned int> &out_dims, + const ITensorInfo &input, + const ITensorInfo &weights) { - const TensorShape input_shape{ input.tensor_shape() }; - const TensorShape weights_shape{ weights.tensor_shape() }; + const TensorShape input_shape{input.tensor_shape()}; + const TensorShape weights_shape{weights.tensor_shape()}; const DataLayout data_layout = input.data_layout(); const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); @@ -502,7 +521,7 @@ inline TensorShape compute_deconvolution_output_shape(const std::pair<unsigned i const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); const int batch_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); - TensorShape out_shape{ input_shape }; + TensorShape out_shape{input_shape}; out_shape.set(width_idx, out_dims.first); out_shape.set(height_idx, out_dims.second); out_shape.set(channel_idx, weights_shape[batch_idx]); @@ -522,8 +541,14 @@ inline TensorShape compute_deconvolution_output_shape(const std::pair<unsigned i * * @return the calculated shape */ -inline TensorShape compute_im2col_conv_shape(const ITensorInfo *input, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation, bool batch_size_on_z, - unsigned int num_groups = 1, unsigned int input_pad_right = 0) +inline TensorShape compute_im2col_conv_shape(const ITensorInfo *input, + const Size2D &kernel_dims, + const PadStrideInfo &conv_info, + bool has_bias, + const Size2D &dilation, + bool batch_size_on_z, + unsigned int num_groups = 1, + unsigned int input_pad_right = 0) { // The output shape will be the 3D shape [ out_channels * kernel_area, num_elems_per_out_channel, batches ] if batch_size_on_z == true // or the 4D shape [ out_channels * kernel_area / num_groups, num_elems_per_out_channel, num_groups, batches ] if batch_size_on_z == false @@ -532,17 +557,19 @@ inline TensorShape compute_im2col_conv_shape(const ITensorInfo *input, const Siz ARM_COMPUTE_ERROR_ON(num_groups > 1 && input->data_layout() != DataLayout::NCHW); ARM_COMPUTE_ERROR_ON(num_groups > 1 && batch_size_on_z); - TensorShape output_shape{ input->tensor_shape() }; + TensorShape output_shape{input->tensor_shape()}; const DataLayout data_layout = input->data_layout(); const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); - std::pair<unsigned int, unsigned int> out_dims = scaled_dimensions(output_shape[width_idx], output_shape[height_idx], kernel_dims.width, kernel_dims.height, conv_info, dilation); - output_shape.set(0, ((output_shape[channel_idx] + input_pad_right) / num_groups * kernel_dims.area() + (has_bias ? 1 : 0))); // NOLINT + std::pair<unsigned int, unsigned int> out_dims = scaled_dimensions( + output_shape[width_idx], output_shape[height_idx], kernel_dims.width, kernel_dims.height, conv_info, dilation); + output_shape.set(0, ((output_shape[channel_idx] + input_pad_right) / num_groups * kernel_dims.area() + + (has_bias ? 1 : 0))); // NOLINT output_shape.set(1, (out_dims.first * out_dims.second)); - if(batch_size_on_z && output_shape.num_dimensions() >= 3) + if (batch_size_on_z && output_shape.num_dimensions() >= 3) { output_shape.remove_dimension(2); } @@ -564,7 +591,7 @@ inline TensorShape compute_flatten_shape(const ITensorInfo *input) { // The output shape will be the flatten version of the input (i.e. [ width * height * channels, num_batches, ... ] ). Used for FlattenLayer and FullyConnectedLayer. - TensorShape output_shape{ input->tensor_shape() }; + TensorShape output_shape{input->tensor_shape()}; output_shape.collapse(3); @@ -586,7 +613,7 @@ inline TensorShape compute_softmax_shape(const ITensorInfo *input, size_t axis = // - [x,y,z,w] and axis 3 will return [x*y*z, w] TensorShape shape2D = input->tensor_shape(); - if(axis < input->num_dimensions()) + if (axis < input->num_dimensions()) { // Collapse from axis onward (this changes the shape) shape2D.collapse_from(axis); @@ -600,7 +627,7 @@ inline TensorShape compute_softmax_shape(const ITensorInfo *input, size_t axis = shape2D.collapse(shape2D.num_dimensions()); } - if(axis == 0) + if (axis == 0) { // If axis is zero the first dim should be one. Since // collapse is an inclusive operation we need to shift @@ -619,15 +646,17 @@ inline TensorShape compute_softmax_shape(const ITensorInfo *input, size_t axis = */ inline TensorShape compute_winograd_filter_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info) { - TensorShape tensor_shape{ input.tensor_shape() }; + TensorShape tensor_shape{input.tensor_shape()}; const Size2D kernel_size = winograd_info.kernel_size; const Size2D output_tile_size = winograd_info.output_tile_size; - const Size2D input_tile_size = Size2D(output_tile_size.width + kernel_size.width - 1, output_tile_size.height + kernel_size.height - 1); + const Size2D input_tile_size = + Size2D(output_tile_size.width + kernel_size.width - 1, output_tile_size.height + kernel_size.height - 1); tensor_shape.remove_dimension(get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH)); tensor_shape.set(Window::DimX, input.dimension(3)); - tensor_shape.set(Window::DimY, input.dimension(get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::CHANNEL))); + tensor_shape.set(Window::DimY, input.dimension(get_data_layout_dimension_index(input.data_layout(), + DataLayoutDimension::CHANNEL))); tensor_shape.set(Window::DimZ, input_tile_size.area()); return tensor_shape; @@ -645,23 +674,22 @@ inline TensorShape compute_winograd_input_transform_shape(const ITensorInfo &inp const PadStrideInfo conv_info = winograd_info.convolution_info; const Size2D kernel_size = winograd_info.kernel_size; const Size2D output_tile_size = winograd_info.output_tile_size; - const Size2D input_tile_size = Size2D(output_tile_size.width + kernel_size.width - 1, output_tile_size.height + kernel_size.height - 1); + const Size2D input_tile_size = + Size2D(output_tile_size.width + kernel_size.width - 1, output_tile_size.height + kernel_size.height - 1); const size_t idx_w = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH); const size_t idx_h = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::HEIGHT); const size_t idx_c = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::CHANNEL); // Compute the number of output tiles along the x and y direction of size "output_tile_size" - const Size2D num_tiles = compute_winograd_convolution_tiles(Size2D(input.tensor_shape()[idx_w], input.tensor_shape()[idx_h]), - kernel_size, - output_tile_size, - conv_info); + const Size2D num_tiles = compute_winograd_convolution_tiles( + Size2D(input.tensor_shape()[idx_w], input.tensor_shape()[idx_h]), kernel_size, output_tile_size, conv_info); const unsigned int width = input.tensor_shape()[idx_c]; const unsigned int height = num_tiles.area(); const unsigned int depth = input_tile_size.area(); - TensorShape output_shape{ input.tensor_shape() }; + TensorShape output_shape{input.tensor_shape()}; output_shape.set(0, width); output_shape.set(1, height); output_shape.set(2, depth); @@ -684,12 +712,12 @@ inline TensorShape compute_winograd_output_transform_shape(const ITensorInfo &in const DataLayout data_layout = winograd_info.output_data_layout; // Compute output shape - unsigned int output_width = 0; - unsigned int output_height = 0; + unsigned int output_width = 0; + unsigned int output_height = 0; std::tie(output_width, output_height) = scaled_dimensions(input_dimensions.width, input_dimensions.height, kernel_size.width, kernel_size.height, conv_info); - TensorShape tensor_shape{ input.tensor_shape() }; + TensorShape tensor_shape{input.tensor_shape()}; // Output dimension const unsigned int out_w = output_width; @@ -712,7 +740,10 @@ inline TensorShape compute_winograd_output_transform_shape(const ITensorInfo &in * * @return the calculated shape */ -inline TensorShape compute_deep_convolution_shape(const TensorShape &input_shape, DataLayout input_data_layout, const TensorShape &weights_shape, const PadStrideInfo &conv_info) +inline TensorShape compute_deep_convolution_shape(const TensorShape &input_shape, + DataLayout input_data_layout, + const TensorShape &weights_shape, + const PadStrideInfo &conv_info) { const size_t idx_width = get_data_layout_dimension_index(input_data_layout, DataLayoutDimension::WIDTH); const size_t idx_height = get_data_layout_dimension_index(input_data_layout, DataLayoutDimension::HEIGHT); @@ -725,9 +756,10 @@ inline TensorShape compute_deep_convolution_shape(const TensorShape &input_shape const unsigned int weights_out_channel = weights_shape[3]; unsigned int output_width = 0; unsigned int output_height = 0; - std::tie(output_width, output_height) = scaled_dimensions(input_width, input_height, weights_width, weights_height, conv_info); + std::tie(output_width, output_height) = + scaled_dimensions(input_width, input_height, weights_width, weights_height, conv_info); - TensorShape output_shape{ input_shape }; + TensorShape output_shape{input_shape}; output_shape.set(idx_width, output_width); output_shape.set(idx_height, output_height); output_shape.set(idx_channel, weights_out_channel); @@ -743,7 +775,8 @@ inline TensorShape compute_deep_convolution_shape(const TensorShape &input_shape * * @return the calculated shape */ -inline TensorShape compute_deep_convolution_shape(const ITensorInfo &input, const ITensorInfo &weights, const PadStrideInfo &conv_info) +inline TensorShape +compute_deep_convolution_shape(const ITensorInfo &input, const ITensorInfo &weights, const PadStrideInfo &conv_info) { return compute_deep_convolution_shape(input.tensor_shape(), input.data_layout(), weights.tensor_shape(), conv_info); } @@ -758,7 +791,10 @@ inline TensorShape compute_deep_convolution_shape(const ITensorInfo &input, cons * * @return the calculated shape */ -inline TensorShape compute_indirect_buffer_shape(const TensorShape &input_shape, DataLayout input_data_layout, const TensorShape &weights_shape, const PadStrideInfo &conv_info, +inline TensorShape compute_indirect_buffer_shape(const TensorShape &input_shape, + DataLayout input_data_layout, + const TensorShape &weights_shape, + const PadStrideInfo &conv_info, const DirectConvComputeKernelInfo &desc) { ARM_COMPUTE_ERROR_ON_MSG(input_data_layout != DataLayout::NHWC, "The data layout can only be NHWC"); @@ -768,7 +804,8 @@ inline TensorShape compute_indirect_buffer_shape(const TensorShape &input_shape, const unsigned int kw = weights_shape[1]; const unsigned int kh = weights_shape[2]; - TensorShape output_conv2d_shape = compute_deep_convolution_shape(input_shape, input_data_layout, weights_shape, conv_info); + TensorShape output_conv2d_shape = + compute_deep_convolution_shape(input_shape, input_data_layout, weights_shape, conv_info); const unsigned int output_w = m0 * kw * kh; const unsigned int output_h = DIV_CEIL(output_conv2d_shape[1] * output_conv2d_shape[2], m0); @@ -785,7 +822,7 @@ inline TensorShape compute_indirect_buffer_shape(const TensorShape &input_shape, */ inline TensorShape compute_min_max_shape(const ITensorInfo *input) { - TensorShape output_shape{ input->tensor_shape() }; + TensorShape output_shape{input->tensor_shape()}; output_shape.set(Window::DimX, 2); output_shape.remove_dimension(1); output_shape.remove_dimension(1); @@ -805,7 +842,7 @@ inline TensorShape compute_pool_shape(const ITensorInfo &input, PoolingLayerInfo int pooled_w = 0; int pooled_h = 0; - TensorShape output_shape{ input.tensor_shape() }; + TensorShape output_shape{input.tensor_shape()}; const bool is_global_pooling = pool_info.is_global_pooling; const int idx_width = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH); @@ -815,9 +852,8 @@ inline TensorShape compute_pool_shape(const ITensorInfo &input, PoolingLayerInfo const int pool_size_x = is_global_pooling ? output_shape[idx_width] : pool_info.pool_size.width; const int pool_size_y = is_global_pooling ? output_shape[idx_height] : pool_info.pool_size.height; - std::tie(pooled_w, pooled_h) = scaled_dimensions_signed(input_width, input_height, - pool_size_x, pool_size_y, - pool_info.pad_stride_info); + std::tie(pooled_w, pooled_h) = + scaled_dimensions_signed(input_width, input_height, pool_size_x, pool_size_y, pool_info.pad_stride_info); ARM_COMPUTE_ERROR_ON_MSG((pooled_w < 1 || pooled_h < 1), "Calculated output dimension size is invalid"); @@ -850,8 +886,10 @@ inline TensorShape compute_unpool_shape(const ITensorInfo &input, PoolingLayerIn const int pad_bottom = pad_stride_info.pad_bottom(); TensorShape output_shape = input_shape; - const unsigned int out_width = (input_shape[idx_width] - 1) * stride_x - pad_left - pad_right + pool_info.pool_size.width; - const unsigned int out_height = (input_shape[idx_height] - 1) * stride_y - pad_top - pad_bottom + pool_info.pool_size.height; + const unsigned int out_width = + (input_shape[idx_width] - 1) * stride_x - pad_left - pad_right + pool_info.pool_size.width; + const unsigned int out_height = + (input_shape[idx_height] - 1) * stride_y - pad_top - pad_bottom + pool_info.pool_size.height; output_shape.set(idx_width, out_width); output_shape.set(idx_height, out_height); @@ -866,9 +904,10 @@ inline TensorShape compute_unpool_shape(const ITensorInfo &input, PoolingLayerIn * * @return the calculated shape */ -inline TensorShape compute_roi_align_shape(const ITensorInfo &input, const ITensorInfo &rois, ROIPoolingLayerInfo pool_info) +inline TensorShape +compute_roi_align_shape(const ITensorInfo &input, const ITensorInfo &rois, ROIPoolingLayerInfo pool_info) { - TensorShape output_shape{ input.tensor_shape() }; + TensorShape output_shape{input.tensor_shape()}; const unsigned int idx_width = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH); const unsigned int idx_height = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::HEIGHT); @@ -889,7 +928,7 @@ inline TensorShape compute_roi_align_shape(const ITensorInfo &input, const ITens */ inline TensorShape compute_rnn_shape(const ITensorInfo *input, const unsigned int batch_size) { - TensorShape output_shape{ input->tensor_shape() }; + TensorShape output_shape{input->tensor_shape()}; output_shape.set(1, batch_size); return output_shape; @@ -904,15 +943,21 @@ inline TensorShape compute_rnn_shape(const ITensorInfo *input, const unsigned in * * @return the calculated shape */ -inline TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo &input1, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info) +inline TensorShape compute_mm_shape(const ITensorInfo &input0, + const ITensorInfo &input1, + bool is_interleaved_transposed, + const GEMMReshapeInfo &reshape_info) { ARM_COMPUTE_ERROR_ON_MSG(input0.num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4"); - ARM_COMPUTE_ERROR_ON_MSG(is_interleaved_transposed && reshape_info.reinterpret_input_as_3d(), "The first input tensor cannot be reinterpreted as 3D if is_interleaved_transposed is true"); + ARM_COMPUTE_ERROR_ON_MSG( + is_interleaved_transposed && reshape_info.reinterpret_input_as_3d(), + "The first input tensor cannot be reinterpreted as 3D if is_interleaved_transposed is true"); const bool reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d(); const bool reinterpret_output_as_3d = reshape_info.depth_output_gemm3d() != 0; const int depth_output_gemm3d = reinterpret_output_as_3d ? reshape_info.depth_output_gemm3d() : 1; - const int m = reshape_info.reinterpret_input_as_3d() ? input0.dimension(1) * input0.dimension(2) : input0.dimension(1); + const int m = + reshape_info.reinterpret_input_as_3d() ? input0.dimension(1) * input0.dimension(2) : input0.dimension(1); // If the output of GEMM has to be reinterpreted as 3D, the number of input0 rows (M) is obtained collapsing the second and third // dimension of the output tensor @@ -921,7 +966,7 @@ inline TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo const int dim2 = reinterpret_input_as_3d ? input0.tensor_shape()[3] : input0.tensor_shape()[2]; const int dim3 = reinterpret_input_as_3d ? 1 : input0.tensor_shape()[3]; - TensorShape output_shape{ input0.tensor_shape() }; + TensorShape output_shape{input0.tensor_shape()}; output_shape.set(0, dim0); output_shape.set(1, dim1); @@ -940,7 +985,8 @@ inline TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo * * @return the calculated shape */ -inline TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo &input1, const GEMMReshapeInfo &gemm_info) +inline TensorShape +compute_mm_shape(const ITensorInfo &input0, const ITensorInfo &input1, const GEMMReshapeInfo &gemm_info) { ARM_COMPUTE_UNUSED(input1); ARM_COMPUTE_ERROR_ON_MSG(input0.num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4"); @@ -949,9 +995,9 @@ inline TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo const bool reinterpret_output_as_3d = gemm_info.depth_output_gemm3d() != 0; const int depth_output_gemm3d = reinterpret_output_as_3d ? gemm_info.depth_output_gemm3d() : 1; - TensorShape output_shape{ input0.tensor_shape() }; + TensorShape output_shape{input0.tensor_shape()}; - if(!reinterpret_input_as_3d && !reinterpret_output_as_3d) + if (!reinterpret_input_as_3d && !reinterpret_output_as_3d) { output_shape.set(0, gemm_info.n()); output_shape.set(1, gemm_info.m()); @@ -978,7 +1024,8 @@ inline TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo * * @return the calculated shape */ -inline TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo &input1, const GEMMKernelInfo &gemm_info) +inline TensorShape +compute_mm_shape(const ITensorInfo &input0, const ITensorInfo &input1, const GEMMKernelInfo &gemm_info) { ARM_COMPUTE_UNUSED(input1); ARM_COMPUTE_ERROR_ON_MSG(input0.num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4"); @@ -987,9 +1034,9 @@ inline TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo const bool reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0; const unsigned int depth_output_gemm3d = reinterpret_output_as_3d ? gemm_info.depth_output_gemm3d : 1; - TensorShape output_shape{ input0.tensor_shape() }; + TensorShape output_shape{input0.tensor_shape()}; - if(!reinterpret_input_as_3d && !reinterpret_output_as_3d) + if (!reinterpret_input_as_3d && !reinterpret_output_as_3d) { output_shape.set(0, gemm_info.n); output_shape.set(1, gemm_info.m); @@ -1016,16 +1063,17 @@ inline TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo * * @return the calculated shape */ -inline TensorShape compute_matmul_shape(const TensorShape &input0, const TensorShape &input1, const MatMulKernelInfo &matmul_info) +inline TensorShape +compute_matmul_shape(const TensorShape &input0, const TensorShape &input1, const MatMulKernelInfo &matmul_info) { - TensorShape output_shape{ input0 }; + TensorShape output_shape{input0}; - if(matmul_info.adj_lhs) + if (matmul_info.adj_lhs) { output_shape.set(1, input0[0]); // The vertical (M) dimension } - if(matmul_info.adj_rhs) + if (matmul_info.adj_rhs) { output_shape.set(0, input1[1]); // The horizontal (N) dimension } @@ -1044,14 +1092,15 @@ inline TensorShape compute_matmul_shape(const TensorShape &input0, const TensorS * * @return the calculated shape */ -inline TensorShape compute_output_stage_shape(const ITensorInfo &input, unsigned int gemm_3d_depth = 1, bool batch_size_on_z = false) +inline TensorShape +compute_output_stage_shape(const ITensorInfo &input, unsigned int gemm_3d_depth = 1, bool batch_size_on_z = false) { ARM_COMPUTE_ERROR_ON(input.data_layout() != DataLayout::NHWC && gemm_3d_depth > 1); TensorShape output_shape = input.tensor_shape(); - if(gemm_3d_depth > 1) + if (gemm_3d_depth > 1) { - if(batch_size_on_z) + if (batch_size_on_z) { output_shape.shift_right(1); } @@ -1076,11 +1125,16 @@ inline TensorShape compute_output_stage_shape(const ITensorInfo &input, unsigned * @return the calculated shape */ inline TensorShape compute_strided_slice_shape(const ITensorInfo &input, - const Coordinates &starts, const Coordinates &ends, const Coordinates &strides, - int32_t begin_mask, int32_t end_mask, int32_t shrink_axis_mask) + const Coordinates &starts, + const Coordinates &ends, + const Coordinates &strides, + int32_t begin_mask, + int32_t end_mask, + int32_t shrink_axis_mask) { using namespace arm_compute::helpers::tensor_transform; - return compute_strided_slice_output_shape(input.tensor_shape(), starts, ends, strides, begin_mask, end_mask, shrink_axis_mask); + return compute_strided_slice_output_shape(input.tensor_shape(), starts, ends, strides, begin_mask, end_mask, + shrink_axis_mask); } /** Calculate the slice output shape of a tensor @@ -1091,13 +1145,13 @@ inline TensorShape compute_strided_slice_shape(const ITensorInfo &input, * * @return the calculated shape */ -inline TensorShape compute_slice_shape(const TensorShape &input_shape, const Coordinates &starts, const Coordinates &ends) +inline TensorShape +compute_slice_shape(const TensorShape &input_shape, const Coordinates &starts, const Coordinates &ends) { using namespace arm_compute::helpers::tensor_transform; - return compute_strided_slice_output_shape(input_shape, - starts, ends, BiStrides(), - 0, construct_slice_end_mask(ends), 0); + return compute_strided_slice_output_shape(input_shape, starts, ends, BiStrides(), 0, construct_slice_end_mask(ends), + 0); } /** Calculate the batch to space output shape of a tensor @@ -1110,7 +1164,8 @@ inline TensorShape compute_slice_shape(const TensorShape &input_shape, const Coo * * @return the calculated shape */ -inline TensorShape compute_batch_to_space_shape(DataLayout data_layout, const TensorShape &input, int block_x, int block_y, const CropInfo &crop_info = CropInfo{}) +inline TensorShape compute_batch_to_space_shape( + DataLayout data_layout, const TensorShape &input, int block_x, int block_y, const CropInfo &crop_info = CropInfo{}) { ARM_COMPUTE_ERROR_ON(block_x < 1 || block_y < 1); @@ -1118,7 +1173,7 @@ inline TensorShape compute_batch_to_space_shape(DataLayout data_layout, const Te const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); const int idx_batch = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); - TensorShape output_shape{ input }; + TensorShape output_shape{input}; unsigned int new_width = input[idx_width] * static_cast<unsigned int>(block_x); unsigned int new_height = input[idx_height] * static_cast<unsigned int>(block_y); @@ -1152,7 +1207,7 @@ inline TensorShape compute_depth_to_space_shape(const TensorShape &input_shape, const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); - TensorShape output_shape{ input_shape }; + TensorShape output_shape{input_shape}; output_shape.set(idx_width, input_shape[idx_width] * block); output_shape.set(idx_height, input_shape[idx_height] * block); output_shape.set(idx_channel, input_shape[idx_channel] / (block * block)); @@ -1173,10 +1228,10 @@ inline TensorShape compute_split_shape(const ITensorInfo *input, unsigned int ax TensorShape empty_shape; empty_shape.set(0, 0); - TensorShape out_shape{ input->tensor_shape() }; + TensorShape out_shape{input->tensor_shape()}; // Return empty shape if axis is invalid - if(axis > input->tensor_shape().num_dimensions()) + if (axis > input->tensor_shape().num_dimensions()) { return empty_shape; } @@ -1184,7 +1239,7 @@ inline TensorShape compute_split_shape(const ITensorInfo *input, unsigned int ax size_t axis_size = out_shape[axis]; // Return empty shape if num_split is not valid - if(axis_size % num_splits) + if (axis_size % num_splits) { return empty_shape; } @@ -1203,9 +1258,10 @@ inline TensorShape compute_split_shape(const ITensorInfo *input, unsigned int ax * * @return the calculated shape */ -inline TensorShape compute_space_to_batch_shape(const ITensorInfo *input, int block_x, int block_y, const Size2D &padding_left, const Size2D &padding_right) +inline TensorShape compute_space_to_batch_shape( + const ITensorInfo *input, int block_x, int block_y, const Size2D &padding_left, const Size2D &padding_right) { - TensorShape output_shape{ input->tensor_shape() }; + TensorShape output_shape{input->tensor_shape()}; const DataLayout data_layout = input->data_layout(); const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); @@ -1231,7 +1287,7 @@ inline TensorShape compute_space_to_batch_shape(const ITensorInfo *input, int bl */ inline TensorShape compute_space_to_depth_shape(const ITensorInfo *input, int32_t block_shape) { - TensorShape output_shape{ input->tensor_shape() }; + TensorShape output_shape{input->tensor_shape()}; const DataLayout data_layout = input->data_layout(); const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); @@ -1276,7 +1332,7 @@ inline TensorShape compute_prior_box_shape(const ITensorInfo &input, const Prior inline TensorShape compute_padded_shape(const TensorShape &input_shape, const PaddingList &padding) { TensorShape padded_shape = input_shape; - for(size_t dim = 0; dim < padding.size(); ++dim) + for (size_t dim = 0; dim < padding.size(); ++dim) { const auto &padding_pair = padding[dim]; const uint32_t shape_on_index = (padded_shape.num_dimensions() <= dim) ? 1 : input_shape[dim]; @@ -1295,7 +1351,7 @@ inline TensorShape compute_padded_shape(const TensorShape &input_shape, const Pa inline TensorShape compute_tiled_shape(const TensorShape &input_shape, const Multiples &multiples) { TensorShape tiled_shape = input_shape; - for(size_t dim = 0; dim < multiples.size(); ++dim) + for (size_t dim = 0; dim < multiples.size(); ++dim) { tiled_shape.set(dim, input_shape[dim] * multiples[dim]); } @@ -1312,9 +1368,9 @@ inline TensorShape compute_tiled_shape(const TensorShape &input_shape, const Mul */ inline TensorShape compute_reduced_shape(const TensorShape &input, unsigned int axis, bool keep_dims = true) { - TensorShape output_shape{ input }; + TensorShape output_shape{input}; - if(!keep_dims) + if (!keep_dims) { output_shape.remove_dimension(axis); } @@ -1407,14 +1463,14 @@ inline TensorShape calculate_concatenate_shape(const std::vector<T *> &input, si #if defined(ARM_COMPUTE_ASSERTS_ENABLED) // All dimensions must match except the axis one - for(unsigned int i = 0; i < MAX_DIMS; ++i) + for (unsigned int i = 0; i < MAX_DIMS; ++i) { - if(i == axis) + if (i == axis) { continue; } - for(const auto &tensor : input) + for (const auto &tensor : input) { ARM_COMPUTE_ERROR_ON(tensor == nullptr); const TensorShape shape = extract_shape(tensor); @@ -1425,7 +1481,7 @@ inline TensorShape calculate_concatenate_shape(const std::vector<T *> &input, si // Calculate output shape size_t new_size = 0; - for(const auto &tensor : input) + for (const auto &tensor : input) { const TensorShape shape = extract_shape(tensor); new_size += shape[axis]; @@ -1448,14 +1504,14 @@ inline TensorShape compute_stack_shape(const ITensorInfo &a, unsigned int axis, ARM_COMPUTE_ERROR_ON(axis > a.num_dimensions()); ARM_COMPUTE_ERROR_ON(a.num_dimensions() > 4); - TensorShape shape_out{ a.tensor_shape() }; + TensorShape shape_out{a.tensor_shape()}; shape_out.set(axis, num_tensors); unsigned int i_shift = 0; - for(unsigned int i = 0; i < a.num_dimensions(); ++i) + for (unsigned int i = 0; i < a.num_dimensions(); ++i) { - if(i == axis) + if (i == axis) { i_shift++; } @@ -1473,7 +1529,8 @@ inline TensorShape compute_stack_shape(const ITensorInfo &a, unsigned int axis, * * @return the calculated shape */ -inline TensorShape compute_conv3d_shape(const TensorShape &src, const TensorShape &weights, const Conv3dInfo &conv3d_info) +inline TensorShape +compute_conv3d_shape(const TensorShape &src, const TensorShape &weights, const Conv3dInfo &conv3d_info) { // Weight tensor shape indices (D H W Cin Cout) constexpr unsigned int weights_depth_dim = 4u; @@ -1488,7 +1545,7 @@ inline TensorShape compute_conv3d_shape(const TensorShape &src, const TensorShap constexpr unsigned int width_dim = 1u; constexpr unsigned int channel_dim = 0u; - TensorShape output_shape{ src }; + TensorShape output_shape{src}; const size_t pad_left = conv3d_info.padding.left; const size_t pad_right = conv3d_info.padding.right; const size_t pad_top = conv3d_info.padding.top; @@ -1506,17 +1563,41 @@ inline TensorShape compute_conv3d_shape(const TensorShape &src, const TensorShap int output_height_size = 0; int output_depth_size = 0; - switch(conv3d_info.round_type) + switch (conv3d_info.round_type) { case DimensionRoundingType::FLOOR: - output_width_size = static_cast<int>(std::floor((static_cast<float>(src[width_dim] + pad_left + pad_right - (dilation_x * (weights[weights_width_dim] - 1) + 1)) / stride_x) + 1)); - output_height_size = static_cast<int>(std::floor((static_cast<float>(src[height_dim] + pad_top + pad_bottom - (dilation_y * (weights[weights_height_dim] - 1) + 1)) / stride_y) + 1)); - output_depth_size = static_cast<int>(std::floor((static_cast<float>(src[depth_dim] + pad_front + pad_back - (dilation_z * (weights[weights_depth_dim] - 1) + 1)) / stride_z) + 1)); + output_width_size = + static_cast<int>(std::floor((static_cast<float>(src[width_dim] + pad_left + pad_right - + (dilation_x * (weights[weights_width_dim] - 1) + 1)) / + stride_x) + + 1)); + output_height_size = + static_cast<int>(std::floor((static_cast<float>(src[height_dim] + pad_top + pad_bottom - + (dilation_y * (weights[weights_height_dim] - 1) + 1)) / + stride_y) + + 1)); + output_depth_size = + static_cast<int>(std::floor((static_cast<float>(src[depth_dim] + pad_front + pad_back - + (dilation_z * (weights[weights_depth_dim] - 1) + 1)) / + stride_z) + + 1)); break; case DimensionRoundingType::CEIL: - output_width_size = static_cast<int>(std::ceil((static_cast<float>(src[width_dim] + pad_left + pad_right - (dilation_x * (weights[weights_width_dim] - 1) + 1)) / stride_x) + 1)); - output_height_size = static_cast<int>(std::ceil((static_cast<float>(src[height_dim] + pad_top + pad_bottom - (dilation_y * (weights[weights_height_dim] - 1) + 1)) / stride_y) + 1)); - output_depth_size = static_cast<int>(std::ceil((static_cast<float>(src[depth_dim] + pad_front + pad_back - (dilation_z * (weights[weights_depth_dim] - 1) + 1)) / stride_z) + 1)); + output_width_size = + static_cast<int>(std::ceil((static_cast<float>(src[width_dim] + pad_left + pad_right - + (dilation_x * (weights[weights_width_dim] - 1) + 1)) / + stride_x) + + 1)); + output_height_size = + static_cast<int>(std::ceil((static_cast<float>(src[height_dim] + pad_top + pad_bottom - + (dilation_y * (weights[weights_height_dim] - 1) + 1)) / + stride_y) + + 1)); + output_depth_size = + static_cast<int>(std::ceil((static_cast<float>(src[depth_dim] + pad_front + pad_back - + (dilation_z * (weights[weights_depth_dim] - 1) + 1)) / + stride_z) + + 1)); break; default: ARM_COMPUTE_ERROR("Unsupported rounding type"); @@ -1539,7 +1620,7 @@ inline TensorShape compute_conv3d_shape(const TensorShape &src, const TensorShap */ inline TensorShape compute_pool3d_shape(const TensorShape &src, Pooling3dLayerInfo pool3d_info) { - TensorShape output_shape{ src }; + TensorShape output_shape{src}; const auto data_layout = DataLayout::NDHWC; const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); @@ -1552,10 +1633,12 @@ inline TensorShape compute_pool3d_shape(const TensorShape &src, Pooling3dLayerIn int output_height = 0; int output_depth = 0; - std::tie(output_width, output_height, output_depth) = scaled_3d_dimensions_signed(src[idx_width], src[idx_height], src[idx_depth], pool_size_width, pool_size_height, - pool_size_depth, pool3d_info); + std::tie(output_width, output_height, output_depth) = + scaled_3d_dimensions_signed(src[idx_width], src[idx_height], src[idx_depth], pool_size_width, pool_size_height, + pool_size_depth, pool3d_info); - ARM_COMPUTE_ERROR_ON_MSG((output_width < 1 || output_height < 1 || output_depth < 1), "Calculated output dimension size is invalid"); + ARM_COMPUTE_ERROR_ON_MSG((output_width < 1 || output_height < 1 || output_depth < 1), + "Calculated output dimension size is invalid"); output_shape.set(idx_width, static_cast<size_t>(output_width)); output_shape.set(idx_height, static_cast<size_t>(output_height)); @@ -1576,7 +1659,8 @@ inline TensorShape compute_pool3d_shape(const TensorShape &src, Pooling3dLayerIn * * @return the calculated shape */ -inline TensorShape compute_gather_shape(const TensorShape &input_shape, const TensorShape &indices_shape, uint32_t actual_axis) +inline TensorShape +compute_gather_shape(const TensorShape &input_shape, const TensorShape &indices_shape, uint32_t actual_axis) { const auto input_num_dims = input_shape.num_dimensions(); const auto indices_num_dims = indices_shape.num_dimensions(); @@ -1587,22 +1671,23 @@ inline TensorShape compute_gather_shape(const TensorShape &input_shape, const Te TensorShape output_shape; size_t dim_no = 0; - for(; dim_no < actual_axis; ++dim_no) + for (; dim_no < actual_axis; ++dim_no) { output_shape.set(dim_no, input_shape[dim_no]); } - for(; dim_no < actual_axis + indices_num_dims; ++dim_no) + for (; dim_no < actual_axis + indices_num_dims; ++dim_no) { output_shape.set(dim_no, indices_shape[dim_no - actual_axis]); } - for(; dim_no < input_num_dims + indices_num_dims - 1; ++dim_no) + for (; dim_no < input_num_dims + indices_num_dims - 1; ++dim_no) { output_shape.set(dim_no, input_shape[dim_no + 1 - indices_num_dims]); } - ARM_COMPUTE_ERROR_ON(input_shape.total_size() * indices_shape.total_size() != output_shape.total_size() * input_shape[actual_axis]); + ARM_COMPUTE_ERROR_ON(input_shape.total_size() * indices_shape.total_size() != + output_shape.total_size() * input_shape[actual_axis]); return output_shape; } diff --git a/arm_compute/core/utils/misc/Traits.h b/arm_compute/core/utils/misc/Traits.h index 933922f63c..944fcb95f9 100644 --- a/arm_compute/core/utils/misc/Traits.h +++ b/arm_compute/core/utils/misc/Traits.h @@ -25,6 +25,7 @@ #define ARM_COMPUTE_UTILS_TRAITS_TRAITS_H #include "arm_compute/core/Types.h" + #include <type_traits> namespace arm_compute diff --git a/arm_compute/core/utils/misc/Utility.h b/arm_compute/core/utils/misc/Utility.h index e3e20d719f..22f10d74cc 100644 --- a/arm_compute/core/utils/misc/Utility.h +++ b/arm_compute/core/utils/misc/Utility.h @@ -44,7 +44,7 @@ struct index_sequence }; template <std::size_t N, std::size_t... S> -struct index_sequence_generator : index_sequence_generator < N - 1, N - 1, S... > +struct index_sequence_generator : index_sequence_generator<N - 1, N - 1, S...> { }; @@ -58,17 +58,17 @@ template <std::size_t N> using index_sequence_t = typename index_sequence_generator<N>::type; template <typename T, std::size_t N, T val, T... vals> -struct generate_array : generate_array < T, N - 1, val, val, vals... > +struct generate_array : generate_array<T, N - 1, val, val, vals...> { }; template <typename T, T val, T... vals> struct generate_array<T, 0, val, vals...> { - static constexpr std::array<T, sizeof...(vals)> value{ vals... }; + static constexpr std::array<T, sizeof...(vals)> value{vals...}; }; -template <typename T, T val, T... vals> +template <typename T, T val, T... vals> constexpr std::array<T, sizeof...(vals)> generate_array<T, 0, val, vals...>::value; /** @endcond */ @@ -79,7 +79,7 @@ template <std::size_t... S, typename T = std::array<typename std::iterator_traits<Iterator>::value_type, sizeof...(S)>> T make_array(Iterator first, index_sequence<S...>) { - return T{ { first[S]... } }; + return T{{first[S]...}}; } } // namespace detail @@ -87,7 +87,7 @@ template <std::size_t N, typename Iterator> std::array<typename std::iterator_traits<Iterator>::value_type, N> make_array(Iterator first, Iterator last) { ARM_COMPUTE_UNUSED(last); - return detail::make_array(first, index_sequence_t<N> {}); + return detail::make_array(first, index_sequence_t<N>{}); } /** Performs clamping among a lower and upper value. @@ -119,7 +119,7 @@ inline void for_each(F &&) * @param[in] args Remaining arguments */ template <typename F, typename T, typename... Ts> -inline void for_each(F &&func, T &&arg, Ts &&... args) +inline void for_each(F &&func, T &&arg, Ts &&...args) { func(std::forward<T>(arg)); for_each(std::forward<F>(func), std::forward<Ts>(args)...); @@ -143,9 +143,11 @@ inline T &&foldl(F &&, T &&value) * @param[in] values Remaining arguments */ template <typename F, typename T, typename U, typename... Us> -inline auto foldl(F &&func, T &&initial, U &&value, Us &&... values) -> decltype(func(std::forward<T>(initial), std::forward<U>(value))) +inline auto foldl(F &&func, T &&initial, U &&value, Us &&...values) + -> decltype(func(std::forward<T>(initial), std::forward<U>(value))) { - return foldl(std::forward<F>(func), func(std::forward<T>(initial), std::forward<U>(value)), std::forward<Us>(values)...); + return foldl(std::forward<F>(func), func(std::forward<T>(initial), std::forward<U>(value)), + std::forward<Us>(values)...); } /** Perform an index sort of a given vector. @@ -160,11 +162,7 @@ std::vector<size_t> sort_indices(const std::vector<T> &v) std::vector<size_t> idx(v.size()); std::iota(idx.begin(), idx.end(), 0); - std::sort(idx.begin(), idx.end(), - [&v](size_t i1, size_t i2) - { - return v[i1] < v[i2]; - }); + std::sort(idx.begin(), idx.end(), [&v](size_t i1, size_t i2) { return v[i1] < v[i2]; }); return idx; } @@ -178,7 +176,7 @@ std::vector<size_t> sort_indices(const std::vector<T> &v) */ inline bool endswith(const std::string &str, const std::string &suffix) { - if(str.size() < suffix.size()) + if (str.size() < suffix.size()) { return false; } @@ -205,10 +203,7 @@ inline bool check_aligned(void *ptr, const size_t alignment) */ inline std::string tolower(std::string string) { - std::transform(string.begin(), string.end(), string.begin(), [](unsigned char c) - { - return std::tolower(c); - }); + std::transform(string.begin(), string.end(), string.begin(), [](unsigned char c) { return std::tolower(c); }); return string; } @@ -227,7 +222,7 @@ inline std::string getenv(const std::string &env_name) return std::string{}; #else // BARE_METAL const auto env_chr = std::getenv(env_name.c_str()); - return env_chr == nullptr ? std::string{} : std::string{ env_chr }; + return env_chr == nullptr ? std::string{} : std::string{env_chr}; #endif // BARE_METAL } } // namespace utility diff --git a/arm_compute/core/utils/quantization/AsymmHelpers.h b/arm_compute/core/utils/quantization/AsymmHelpers.h index a15f3e5cde..2324fe1838 100644 --- a/arm_compute/core/utils/quantization/AsymmHelpers.h +++ b/arm_compute/core/utils/quantization/AsymmHelpers.h @@ -41,7 +41,10 @@ namespace quantization * * @return a status */ -Status calculate_quantized_multiplier(float multiplier, int32_t *quant_multiplier, int32_t *shift, bool ignore_epsilon = false); +Status calculate_quantized_multiplier(float multiplier, + int32_t *quant_multiplier, + int32_t *shift, + bool ignore_epsilon = false); /** Calculate quantized representation of multiplier with value less than one. * * @param[in] multiplier Real multiplier. @@ -51,7 +54,10 @@ Status calculate_quantized_multiplier(float multiplier, int32_t *quant_multiplie * * @return a status */ -Status calculate_quantized_multiplier_less_than_one(float multiplier, int32_t *quant_multiplier, int32_t *right_shift, bool ignore_epsilon = false); +Status calculate_quantized_multiplier_less_than_one(float multiplier, + int32_t *quant_multiplier, + int32_t *right_shift, + bool ignore_epsilon = false); /** Calculate quantized representation of multiplier having value greater than one. * * @param[in] multiplier Real multiplier. @@ -60,7 +66,8 @@ Status calculate_quantized_multiplier_less_than_one(float multiplier, int32_t *q * * @return a status */ -Status calculate_quantized_multiplier_greater_than_one(float multiplier, int32_t *quantized_multiplier, int32_t *left_shift); +Status +calculate_quantized_multiplier_greater_than_one(float multiplier, int32_t *quantized_multiplier, int32_t *left_shift); /** Calculate quantized representation of per-channel multipliers * @@ -71,9 +78,9 @@ Status calculate_quantized_multiplier_greater_than_one(float multiplier, int32_t * * @return a status */ -Status calculate_quantized_multipliers(const QuantizationInfo &iq_info, - const QuantizationInfo &wq_info, - const QuantizationInfo &oq_info, +Status calculate_quantized_multipliers(const QuantizationInfo &iq_info, + const QuantizationInfo &wq_info, + const QuantizationInfo &oq_info, GEMMLowpOutputStageInfo &stage_info); /** Get minimum and maximum values for the input quantized data type @@ -147,7 +154,10 @@ int32_t saturating_rounding_multiply_by_pow2(int32_t exponent, int32_t v); * @param[out] output_shift Shift for inverse square root * */ -void get_invsqrt_quantized_multiplier_exp(int32_t input, int32_t reverse_shift, int32_t &output_inv_sqrt, int32_t &output_shift); +void get_invsqrt_quantized_multiplier_exp(int32_t input, + int32_t reverse_shift, + int32_t &output_inv_sqrt, + int32_t &output_shift); } // namespace quantization } // namespace arm_compute |