/* * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #ifndef __ARM_COMPUTE_GRAPH_UTILS_H__ #define __ARM_COMPUTE_GRAPH_UTILS_H__ #include "arm_compute/core/PixelValue.h" #include "arm_compute/graph/Graph.h" #include "arm_compute/graph/ITensorAccessor.h" #include "arm_compute/graph/Types.h" #include "arm_compute/core/CL/OpenCL.h" #include "arm_compute/graph2/Types.h" #include #include #include #include namespace arm_compute { namespace graph_utils { /** Preprocessor interface **/ class IPreprocessor { public: virtual ~IPreprocessor() = default; virtual void preprocess(ITensor &tensor) = 0; }; /** Caffe preproccessor */ class CaffePreproccessor : public IPreprocessor { public: /** Default Constructor * * @param mean Mean array in RGB ordering * @param bgr Boolean specifying if the preprocessing should assume BGR format */ CaffePreproccessor(std::array mean = std::array { { 0, 0, 0 } }, bool bgr = true); void preprocess(ITensor &tensor) override; private: std::array _mean; bool _bgr; }; /** TF preproccessor */ class TFPreproccessor : public IPreprocessor { public: void preprocess(ITensor &tensor) override; }; /** PPM writer class */ class PPMWriter : public graph::ITensorAccessor { public: /** Constructor * * @param[in] name PPM file name * @param[in] maximum Maximum elements to access */ PPMWriter(std::string name, unsigned int maximum = 1); /** Allows instances to move constructed */ PPMWriter(PPMWriter &&) = default; // Inherited methods overriden: bool access_tensor(ITensor &tensor) override; private: const std::string _name; unsigned int _iterator; unsigned int _maximum; }; /** Dummy accessor class */ class DummyAccessor final : public graph::ITensorAccessor { public: /** Constructor * * @param[in] maximum Maximum elements to write */ DummyAccessor(unsigned int maximum = 1); /** Allows instances to move constructed */ DummyAccessor(DummyAccessor &&) = default; // Inherited methods overriden: bool access_tensor(ITensor &tensor) override; private: unsigned int _iterator; unsigned int _maximum; }; /** PPM accessor class */ class PPMAccessor final : public graph::ITensorAccessor { public: /** Constructor * * @param[in] ppm_path Path to PPM file * @param[in] bgr (Optional) Fill the first plane with blue channel (default = false) * @param[in] preprocessor (Optional) PPM pre-processing object */ PPMAccessor(std::string ppm_path, bool bgr = true, std::unique_ptr preprocessor = nullptr); /** Allow instances of this class to be move constructed */ PPMAccessor(PPMAccessor &&) = default; // Inherited methods overriden: bool access_tensor(ITensor &tensor) override; private: const std::string _ppm_path; const bool _bgr; std::unique_ptr _preprocessor; }; /** Result accessor class */ class TopNPredictionsAccessor final : public graph::ITensorAccessor { public: /** Constructor * * @param[in] labels_path Path to labels text file. * @param[in] top_n (Optional) Number of output classes to print * @param[out] output_stream (Optional) Output stream */ TopNPredictionsAccessor(const std::string &labels_path, size_t top_n = 5, std::ostream &output_stream = std::cout); /** Allow instances of this class to be move constructed */ TopNPredictionsAccessor(TopNPredictionsAccessor &&) = default; /** Prevent instances of this class from being copied (As this class contains pointers) */ TopNPredictionsAccessor(const TopNPredictionsAccessor &) = delete; /** Prevent instances of this class from being copied (As this class contains pointers) */ TopNPredictionsAccessor &operator=(const TopNPredictionsAccessor &) = delete; // Inherited methods overriden: bool access_tensor(ITensor &tensor) override; private: template void access_predictions_tensor(ITensor &tensor); std::vector _labels; std::ostream &_output_stream; size_t _top_n; }; /** Random accessor class */ class RandomAccessor final : public graph::ITensorAccessor { public: /** Constructor * * @param[in] lower Lower bound value. * @param[in] upper Upper bound value. * @param[in] seed (Optional) Seed used to initialise the random number generator. */ RandomAccessor(PixelValue lower, PixelValue upper, const std::random_device::result_type seed = 0); /** Allows instances to move constructed */ RandomAccessor(RandomAccessor &&) = default; // Inherited methods overriden: bool access_tensor(ITensor &tensor) override; private: template void fill(ITensor &tensor, D &&distribution); PixelValue _lower; PixelValue _upper; std::random_device::result_type _seed; }; /** Numpy Binary loader class*/ class NumPyBinLoader final : public graph::ITensorAccessor { public: /** Default Constructor * * @param filename Binary file name */ NumPyBinLoader(std::string filename); /** Allows instances to move constructed */ NumPyBinLoader(NumPyBinLoader &&) = default; // Inherited methods overriden: bool access_tensor(ITensor &tensor) override; private: const std::string _filename; }; /** Generates appropriate random accessor * * @param[in] lower Lower random values bound * @param[in] upper Upper random values bound * @param[in] seed Random generator seed * * @return A ramdom accessor */ inline std::unique_ptr get_random_accessor(PixelValue lower, PixelValue upper, const std::random_device::result_type seed = 0) { return arm_compute::support::cpp14::make_unique(lower, upper, seed); } /** Generates appropriate weights accessor according to the specified path * * @note If path is empty will generate a DummyAccessor else will generate a NumPyBinLoader * * @param[in] path Path to the data files * @param[in] data_file Relative path to the data files from path * * @return An appropriate tensor accessor */ inline std::unique_ptr get_weights_accessor(const std::string &path, const std::string &data_file) { if(path.empty()) { return arm_compute::support::cpp14::make_unique(); } else { return arm_compute::support::cpp14::make_unique(path + data_file); } } /** Generates appropriate input accessor according to the specified ppm_path * * @note If ppm_path is empty will generate a DummyAccessor else will generate a PPMAccessor * * @param[in] ppm_path Path to PPM file * @param[in] preprocessor Preproccessor object * @param[in] bgr (Optional) Fill the first plane with blue channel (default = true) * * @return An appropriate tensor accessor */ inline std::unique_ptr get_input_accessor(const std::string &ppm_path, std::unique_ptr preprocessor = nullptr, bool bgr = true) { if(ppm_path.empty()) { return arm_compute::support::cpp14::make_unique(); } else { return arm_compute::support::cpp14::make_unique(ppm_path, bgr, std::move(preprocessor)); } } /** Utility function to return the TargetHint * * @param[in] target Integer value which expresses the selected target. Must be 0 for NEON, 1 for OpenCL or 2 for OpenCL with Tuner * * @return the TargetHint */ inline graph::TargetHint set_target_hint(int target) { ARM_COMPUTE_ERROR_ON_MSG(target > 2, "Invalid target. Target must be 0 (NEON), 1 (OpenCL) or 2 (OpenCL with Tuner)"); if((target == 1 || target == 2) && graph::Graph::opencl_is_available()) { // If type of target is OpenCL, check if OpenCL is available and initialize the scheduler return graph::TargetHint::OPENCL; } else { return graph::TargetHint::NEON; } } /** Generates appropriate output accessor according to the specified labels_path * * @note If labels_path is empty will generate a DummyAccessor else will generate a TopNPredictionsAccessor * * @param[in] labels_path Path to labels text file * @param[in] top_n (Optional) Number of output classes to print * @param[out] output_stream (Optional) Output stream * * @return An appropriate tensor accessor */ inline std::unique_ptr get_output_accessor(const std::string &labels_path, size_t top_n = 5, std::ostream &output_stream = std::cout) { if(labels_path.empty()) { return arm_compute::support::cpp14::make_unique(0); } else { return arm_compute::support::cpp14::make_unique(labels_path, top_n, output_stream); } } /** Utility function to return the TargetHint * * @param[in] target Integer value which expresses the selected target. Must be 0 for NEON or 1 for OpenCL or 2 (OpenCL with Tuner) * * @return the TargetHint */ inline graph2::Target set_target_hint2(int target) { ARM_COMPUTE_ERROR_ON_MSG(target > 2, "Invalid target. Target must be 0 (NEON) or 1 (OpenCL)"); if((target == 1 || target == 2) && arm_compute::opencl_is_available()) { // If type of target is OpenCL, check if OpenCL is available and initialize the scheduler return graph2::Target::CL; } else { return graph2::Target::NEON; } } } // namespace graph_utils } // namespace arm_compute #endif /* __ARM_COMPUTE_GRAPH_UTILS_H__ */