| /* |
| * Copyright (c) 2016-2023 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 __UTILS_UTILS_H__ |
| #define __UTILS_UTILS_H__ |
| |
| /** @dir . |
| * brief Boiler plate code used by examples. Various utilities to print types, load / store assets, etc. |
| */ |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Window.h" |
| #include "arm_compute/runtime/Tensor.h" |
| #pragma GCC diagnostic push |
| #pragma GCC diagnostic ignored "-Wunused-parameter" |
| #pragma GCC diagnostic ignored "-Wstrict-overflow" |
| #include "libnpy/npy.hpp" |
| #pragma GCC diagnostic pop |
| #include "support/StringSupport.h" |
| |
| #ifdef ARM_COMPUTE_CL |
| #include "arm_compute/core/CL/OpenCL.h" |
| #include "arm_compute/runtime/CL/CLTensor.h" |
| #endif /* ARM_COMPUTE_CL */ |
| |
| #include <cstdlib> |
| #include <cstring> |
| #include <fstream> |
| #include <iostream> |
| #include <memory> |
| #include <random> |
| #include <string> |
| #include <tuple> |
| #include <vector> |
| |
| namespace arm_compute |
| { |
| namespace utils |
| { |
| /** Supported image types */ |
| enum class ImageType |
| { |
| UNKNOWN, |
| PPM, |
| JPEG |
| }; |
| |
| /** Abstract Example class. |
| * |
| * All examples have to inherit from this class. |
| */ |
| class Example |
| { |
| public: |
| /** Setup the example. |
| * |
| * @param[in] argc Argument count. |
| * @param[in] argv Argument values. |
| * |
| * @return True in case of no errors in setup else false |
| */ |
| virtual bool do_setup(int argc, char **argv) |
| { |
| ARM_COMPUTE_UNUSED(argc, argv); |
| return true; |
| }; |
| /** Run the example. */ |
| virtual void do_run(){}; |
| /** Teardown the example. */ |
| virtual void do_teardown(){}; |
| |
| /** Default destructor. */ |
| virtual ~Example() = default; |
| }; |
| |
| /** Run an example and handle the potential exceptions it throws |
| * |
| * @param[in] argc Number of command line arguments |
| * @param[in] argv Command line arguments |
| * @param[in] example Example to run |
| */ |
| int run_example(int argc, char **argv, std::unique_ptr<Example> example); |
| |
| template <typename T> |
| int run_example(int argc, char **argv) |
| { |
| return run_example(argc, argv, std::make_unique<T>()); |
| } |
| |
| /** Draw a RGB rectangular window for the detected object |
| * |
| * @param[in, out] tensor Input tensor where the rectangle will be drawn on. Format supported: RGB888 |
| * @param[in] rect Geometry of the rectangular window |
| * @param[in] r Red colour to use |
| * @param[in] g Green colour to use |
| * @param[in] b Blue colour to use |
| */ |
| void draw_detection_rectangle( |
| arm_compute::ITensor *tensor, const arm_compute::DetectionWindow &rect, uint8_t r, uint8_t g, uint8_t b); |
| |
| /** Gets image type given a file |
| * |
| * @param[in] filename File to identify its image type |
| * |
| * @return Image type |
| */ |
| ImageType get_image_type_from_file(const std::string &filename); |
| |
| /** Parse the ppm header from an input file stream. At the end of the execution, |
| * the file position pointer will be located at the first pixel stored in the ppm file |
| * |
| * @param[in] fs Input file stream to parse |
| * |
| * @return The width, height and max value stored in the header of the PPM file |
| */ |
| std::tuple<unsigned int, unsigned int, int> parse_ppm_header(std::ifstream &fs); |
| |
| /** Parse the npy header from an input file stream. At the end of the execution, |
| * the file position pointer will be located at the first pixel stored in the npy file //TODO |
| * |
| * @param[in] fs Input file stream to parse |
| * |
| * @return The width and height stored in the header of the NPY file |
| */ |
| npy::header_t parse_npy_header(std::ifstream &fs); |
| |
| /** Obtain numpy type string from DataType. |
| * |
| * @param[in] data_type Data type. |
| * |
| * @return numpy type string. |
| */ |
| inline std::string get_typestring(DataType data_type) |
| { |
| // Check endianness |
| const unsigned int i = 1; |
| const char *c = reinterpret_cast<const char *>(&i); |
| std::string endianness; |
| if (*c == 1) |
| { |
| endianness = std::string("<"); |
| } |
| else |
| { |
| endianness = std::string(">"); |
| } |
| const std::string no_endianness("|"); |
| |
| switch (data_type) |
| { |
| case DataType::U8: |
| case DataType::QASYMM8: |
| return no_endianness + "u" + support::cpp11::to_string(sizeof(uint8_t)); |
| case DataType::S8: |
| case DataType::QSYMM8: |
| case DataType::QSYMM8_PER_CHANNEL: |
| return no_endianness + "i" + support::cpp11::to_string(sizeof(int8_t)); |
| case DataType::U16: |
| case DataType::QASYMM16: |
| return endianness + "u" + support::cpp11::to_string(sizeof(uint16_t)); |
| case DataType::S16: |
| case DataType::QSYMM16: |
| return endianness + "i" + support::cpp11::to_string(sizeof(int16_t)); |
| case DataType::U32: |
| return endianness + "u" + support::cpp11::to_string(sizeof(uint32_t)); |
| case DataType::S32: |
| return endianness + "i" + support::cpp11::to_string(sizeof(int32_t)); |
| case DataType::U64: |
| return endianness + "u" + support::cpp11::to_string(sizeof(uint64_t)); |
| case DataType::S64: |
| return endianness + "i" + support::cpp11::to_string(sizeof(int64_t)); |
| case DataType::F16: |
| return endianness + "f" + support::cpp11::to_string(sizeof(half)); |
| case DataType::F32: |
| return endianness + "f" + support::cpp11::to_string(sizeof(float)); |
| case DataType::F64: |
| return endianness + "f" + support::cpp11::to_string(sizeof(double)); |
| case DataType::SIZET: |
| return endianness + "u" + support::cpp11::to_string(sizeof(size_t)); |
| default: |
| ARM_COMPUTE_ERROR("Data type not supported"); |
| } |
| } |
| |
| /** Maps a tensor if needed |
| * |
| * @param[in] tensor Tensor to be mapped |
| * @param[in] blocking Specified if map is blocking or not |
| */ |
| template <typename T> |
| inline void map(T &tensor, bool blocking) |
| { |
| ARM_COMPUTE_UNUSED(tensor); |
| ARM_COMPUTE_UNUSED(blocking); |
| } |
| |
| /** Unmaps a tensor if needed |
| * |
| * @param tensor Tensor to be unmapped |
| */ |
| template <typename T> |
| inline void unmap(T &tensor) |
| { |
| ARM_COMPUTE_UNUSED(tensor); |
| } |
| |
| #ifdef ARM_COMPUTE_CL |
| /** Maps a tensor if needed |
| * |
| * @param[in] tensor Tensor to be mapped |
| * @param[in] blocking Specified if map is blocking or not |
| */ |
| inline void map(CLTensor &tensor, bool blocking) |
| { |
| tensor.map(blocking); |
| } |
| |
| /** Unmaps a tensor if needed |
| * |
| * @param tensor Tensor to be unmapped |
| */ |
| inline void unmap(CLTensor &tensor) |
| { |
| tensor.unmap(); |
| } |
| #endif /* ARM_COMPUTE_CL */ |
| |
| /** Specialized class to generate random non-zero FP16 values. |
| * uniform_real_distribution<half> generates values that get rounded off to zero, causing |
| * differences between ACL and reference implementation |
| */ |
| template <typename T> |
| class uniform_real_distribution_16bit |
| { |
| static_assert(std::is_same<T, half>::value || std::is_same<T, bfloat16>::value, |
| "Only half and bfloat16 data types supported"); |
| |
| public: |
| using result_type = T; |
| /** Constructor |
| * |
| * @param[in] min Minimum value of the distribution |
| * @param[in] max Maximum value of the distribution |
| */ |
| explicit uniform_real_distribution_16bit(float min = 0.f, float max = 1.0) : dist(min, max) |
| { |
| } |
| |
| /** () operator to generate next value |
| * |
| * @param[in] gen an uniform random bit generator object |
| */ |
| T operator()(std::mt19937 &gen) |
| { |
| return T(dist(gen)); |
| } |
| |
| private: |
| std::uniform_real_distribution<float> dist; |
| }; |
| |
| /** Numpy data loader */ |
| class NPYLoader |
| { |
| public: |
| /** Default constructor */ |
| NPYLoader() : _fs(), _shape(), _fortran_order(false), _typestring(), _file_layout(DataLayout::NCHW) |
| { |
| } |
| |
| /** Open a NPY file and reads its metadata |
| * |
| * @param[in] npy_filename File to open |
| * @param[in] file_layout (Optional) Layout in which the weights are stored in the file. |
| */ |
| void open(const std::string &npy_filename, DataLayout file_layout = DataLayout::NCHW) |
| { |
| ARM_COMPUTE_ERROR_ON(is_open()); |
| try |
| { |
| _fs.open(npy_filename, std::ios::in | std::ios::binary); |
| ARM_COMPUTE_EXIT_ON_MSG_VAR(!_fs.good(), "Failed to load binary data from %s", npy_filename.c_str()); |
| _fs.exceptions(std::ifstream::failbit | std::ifstream::badbit); |
| _file_layout = file_layout; |
| |
| npy::header_t header = parse_npy_header(_fs); |
| _shape = header.shape; |
| _fortran_order = header.fortran_order; |
| _typestring = header.dtype.str(); |
| } |
| catch (const std::ifstream::failure &e) |
| { |
| ARM_COMPUTE_ERROR_VAR("Accessing %s: %s", npy_filename.c_str(), e.what()); |
| } |
| } |
| /** Return true if a NPY file is currently open */ |
| bool is_open() |
| { |
| return _fs.is_open(); |
| } |
| |
| /** Return true if a NPY file is in fortran order */ |
| bool is_fortran() |
| { |
| return _fortran_order; |
| } |
| |
| /** Initialise the tensor's metadata with the dimensions of the NPY file currently open |
| * |
| * @param[out] tensor Tensor to initialise |
| * @param[in] dt Data type to use for the tensor |
| */ |
| template <typename T> |
| void init_tensor(T &tensor, arm_compute::DataType dt) |
| { |
| ARM_COMPUTE_ERROR_ON(!is_open()); |
| ARM_COMPUTE_ERROR_ON(dt != arm_compute::DataType::F32); |
| |
| // Use the size of the input NPY tensor |
| TensorShape shape; |
| shape.set_num_dimensions(_shape.size()); |
| for (size_t i = 0; i < _shape.size(); ++i) |
| { |
| size_t src = i; |
| if (_fortran_order) |
| { |
| src = _shape.size() - 1 - i; |
| } |
| shape.set(i, _shape.at(src)); |
| } |
| |
| arm_compute::TensorInfo tensor_info(shape, 1, dt); |
| tensor.allocator()->init(tensor_info); |
| } |
| |
| /** Fill a tensor with the content of the currently open NPY file. |
| * |
| * @note If the tensor is a CLTensor, the function maps and unmaps the tensor |
| * |
| * @param[in,out] tensor Tensor to fill (Must be allocated, and of matching dimensions with the opened NPY). |
| */ |
| template <typename T> |
| void fill_tensor(T &tensor) |
| { |
| ARM_COMPUTE_ERROR_ON(!is_open()); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_NOT_IN(&tensor, arm_compute::DataType::QASYMM8, arm_compute::DataType::S32, |
| arm_compute::DataType::F32, arm_compute::DataType::F16); |
| try |
| { |
| // Map buffer if creating a CLTensor |
| map(tensor, true); |
| |
| // Check if the file is large enough to fill the tensor |
| const size_t current_position = _fs.tellg(); |
| _fs.seekg(0, std::ios_base::end); |
| const size_t end_position = _fs.tellg(); |
| _fs.seekg(current_position, std::ios_base::beg); |
| |
| ARM_COMPUTE_ERROR_ON_MSG((end_position - current_position) < |
| tensor.info()->tensor_shape().total_size() * tensor.info()->element_size(), |
| "Not enough data in file"); |
| ARM_COMPUTE_UNUSED(end_position); |
| |
| // Check if the typestring matches the given one |
| std::string expect_typestr = get_typestring(tensor.info()->data_type()); |
| |
| bool enable_f32_to_f16_conversion = false; |
| if (_typestring != expect_typestr) |
| { |
| const std::string f32_typestring = "<f4"; |
| const std::string f16_typestring = "<f2"; |
| // if typestring does not match, check whether _typestring is F32 and can be downcasted to expect_typestr |
| if (_typestring == f32_typestring && expect_typestr == f16_typestring) |
| { |
| enable_f32_to_f16_conversion = true; |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR("Typestrings mismatch"); |
| } |
| } |
| |
| bool are_layouts_different = (_file_layout != tensor.info()->data_layout()); |
| // Correct dimensions (Needs to match TensorShape dimension corrections) |
| if (_shape.size() != tensor.info()->tensor_shape().num_dimensions()) |
| { |
| for (int i = static_cast<int>(_shape.size()) - 1; i > 0; --i) |
| { |
| if (_shape[i] == 1) |
| { |
| _shape.pop_back(); |
| } |
| else |
| { |
| break; |
| } |
| } |
| } |
| |
| TensorShape permuted_shape = tensor.info()->tensor_shape(); |
| arm_compute::PermutationVector perm; |
| if (are_layouts_different && tensor.info()->tensor_shape().num_dimensions() > 2) |
| { |
| perm = (tensor.info()->data_layout() == arm_compute::DataLayout::NHWC) |
| ? arm_compute::PermutationVector(2U, 0U, 1U) |
| : arm_compute::PermutationVector(1U, 2U, 0U); |
| arm_compute::PermutationVector perm_vec = |
| (tensor.info()->data_layout() == arm_compute::DataLayout::NCHW) |
| ? arm_compute::PermutationVector(2U, 0U, 1U) |
| : arm_compute::PermutationVector(1U, 2U, 0U); |
| |
| arm_compute::permute(permuted_shape, perm_vec); |
| } |
| |
| // Validate tensor shape |
| ARM_COMPUTE_ERROR_ON_MSG(_shape.size() != tensor.info()->tensor_shape().num_dimensions(), |
| "Tensor ranks mismatch"); |
| for (size_t i = 0; i < _shape.size(); ++i) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(permuted_shape[i] != _shape[i], "Tensor dimensions mismatch"); |
| } |
| |
| switch (tensor.info()->data_type()) |
| { |
| case arm_compute::DataType::QASYMM8: |
| case arm_compute::DataType::S32: |
| case arm_compute::DataType::F32: |
| case arm_compute::DataType::F16: |
| { |
| // Read data |
| if (!are_layouts_different && !_fortran_order && tensor.info()->padding().empty() && |
| !enable_f32_to_f16_conversion) |
| { |
| // If tensor has no padding read directly from stream. |
| _fs.read(reinterpret_cast<char *>(tensor.buffer()), tensor.info()->total_size()); |
| } |
| else |
| { |
| // If tensor has padding or is in fortran order accessing tensor elements through execution window. |
| Window window; |
| const unsigned int num_dims = _shape.size(); |
| if (_fortran_order) |
| { |
| for (unsigned int dim = 0; dim < num_dims; dim++) |
| { |
| permuted_shape.set(dim, _shape[num_dims - dim - 1]); |
| perm.set(dim, num_dims - dim - 1); |
| } |
| if (are_layouts_different) |
| { |
| // Permute only if num_dimensions greater than 2 |
| if (num_dims > 2) |
| { |
| if (_file_layout == DataLayout::NHWC) // i.e destination is NCHW --> permute(1,2,0) |
| { |
| arm_compute::permute(perm, arm_compute::PermutationVector(1U, 2U, 0U)); |
| } |
| else |
| { |
| arm_compute::permute(perm, arm_compute::PermutationVector(2U, 0U, 1U)); |
| } |
| } |
| } |
| } |
| window.use_tensor_dimensions(permuted_shape); |
| |
| execute_window_loop(window, |
| [&](const Coordinates &id) |
| { |
| Coordinates dst(id); |
| arm_compute::permute(dst, perm); |
| if (enable_f32_to_f16_conversion) |
| { |
| float f32_val = 0; |
| _fs.read(reinterpret_cast<char *>(&f32_val), 4u); |
| half f16_val = |
| half_float::half_cast<half, std::round_to_nearest>(f32_val); |
| *(reinterpret_cast<half *>(tensor.ptr_to_element(dst))) = f16_val; |
| } |
| else |
| { |
| _fs.read(reinterpret_cast<char *>(tensor.ptr_to_element(dst)), |
| tensor.info()->element_size()); |
| } |
| }); |
| } |
| |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Unsupported data type"); |
| } |
| |
| // Unmap buffer if creating a CLTensor |
| unmap(tensor); |
| } |
| catch (const std::ifstream::failure &e) |
| { |
| ARM_COMPUTE_ERROR_VAR("Loading NPY file: %s", e.what()); |
| } |
| } |
| |
| private: |
| std::ifstream _fs; |
| std::vector<unsigned long> _shape; |
| bool _fortran_order; |
| std::string _typestring; |
| DataLayout _file_layout; |
| }; |
| |
| /** Template helper function to save a tensor image to a PPM file. |
| * |
| * @note Only U8 and RGB888 formats supported. |
| * @note Only works with 2D tensors. |
| * @note If the input tensor is a CLTensor, the function maps and unmaps the image |
| * |
| * @param[in] tensor The tensor to save as PPM file |
| * @param[in] ppm_filename Filename of the file to create. |
| */ |
| template <typename T> |
| void save_to_ppm(T &tensor, const std::string &ppm_filename) |
| { |
| ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(&tensor, arm_compute::Format::RGB888, arm_compute::Format::U8); |
| ARM_COMPUTE_ERROR_ON(tensor.info()->num_dimensions() > 2); |
| |
| std::ofstream fs; |
| |
| try |
| { |
| fs.exceptions(std::ofstream::failbit | std::ofstream::badbit | std::ofstream::eofbit); |
| fs.open(ppm_filename, std::ios::out | std::ios::binary); |
| |
| const unsigned int width = tensor.info()->tensor_shape()[0]; |
| const unsigned int height = tensor.info()->tensor_shape()[1]; |
| |
| fs << "P6\n" << width << " " << height << " 255\n"; |
| |
| // Map buffer if creating a CLTensor |
| map(tensor, true); |
| |
| switch (tensor.info()->format()) |
| { |
| case arm_compute::Format::U8: |
| { |
| arm_compute::Window window; |
| window.set(arm_compute::Window::DimX, arm_compute::Window::Dimension(0, width, 1)); |
| window.set(arm_compute::Window::DimY, arm_compute::Window::Dimension(0, height, 1)); |
| |
| arm_compute::Iterator in(&tensor, window); |
| |
| arm_compute::execute_window_loop( |
| window, |
| [&](const arm_compute::Coordinates &) |
| { |
| const unsigned char value = *in.ptr(); |
| |
| fs << value << value << value; |
| }, |
| in); |
| |
| break; |
| } |
| case arm_compute::Format::RGB888: |
| { |
| arm_compute::Window window; |
| window.set(arm_compute::Window::DimX, arm_compute::Window::Dimension(0, width, width)); |
| window.set(arm_compute::Window::DimY, arm_compute::Window::Dimension(0, height, 1)); |
| |
| arm_compute::Iterator in(&tensor, window); |
| |
| arm_compute::execute_window_loop( |
| window, |
| [&](const arm_compute::Coordinates &) { |
| fs.write(reinterpret_cast<std::fstream::char_type *>(in.ptr()), |
| width * tensor.info()->element_size()); |
| }, |
| in); |
| |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Unsupported format"); |
| } |
| |
| // Unmap buffer if creating a CLTensor |
| unmap(tensor); |
| } |
| catch (const std::ofstream::failure &e) |
| { |
| ARM_COMPUTE_ERROR_VAR("Writing %s: (%s)", ppm_filename.c_str(), e.what()); |
| } |
| } |
| |
| /** Template helper function to save a tensor image to a NPY file. |
| * |
| * @note Only F32 data type supported. |
| * @note If the input tensor is a CLTensor, the function maps and unmaps the image |
| * |
| * @param[in] tensor The tensor to save as NPY file |
| * @param[in] npy_filename Filename of the file to create. |
| * @param[in] fortran_order If true, save matrix in fortran order. |
| */ |
| template <typename T, typename U = float> |
| void save_to_npy(T &tensor, const std::string &npy_filename, bool fortran_order) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_NOT_IN(&tensor, arm_compute::DataType::F32, arm_compute::DataType::QASYMM8); |
| |
| std::ofstream fs; |
| try |
| { |
| fs.exceptions(std::ofstream::failbit | std::ofstream::badbit | std::ofstream::eofbit); |
| fs.open(npy_filename, std::ios::out | std::ios::binary); |
| |
| std::vector<npy::ndarray_len_t> shape(tensor.info()->num_dimensions()); |
| |
| for (unsigned int i = 0, j = tensor.info()->num_dimensions() - 1; i < tensor.info()->num_dimensions(); ++i, --j) |
| { |
| shape[i] = tensor.info()->tensor_shape()[!fortran_order ? j : i]; |
| } |
| |
| // Map buffer if creating a CLTensor |
| map(tensor, true); |
| |
| using typestring_type = typename std::conditional<std::is_floating_point<U>::value, float, qasymm8_t>::type; |
| |
| std::vector<typestring_type> tmp; /* Used only to get the typestring */ |
| const npy::dtype_t dtype = npy::dtype_map.at(std::type_index(typeid(tmp))); |
| |
| std::ofstream stream(npy_filename, std::ofstream::binary); |
| npy::header_t header{dtype, fortran_order, shape}; |
| npy::write_header(stream, header); |
| |
| arm_compute::Window window; |
| window.use_tensor_dimensions(tensor.info()->tensor_shape()); |
| |
| arm_compute::Iterator in(&tensor, window); |
| |
| arm_compute::execute_window_loop( |
| window, |
| [&](const arm_compute::Coordinates &) |
| { stream.write(reinterpret_cast<const char *>(in.ptr()), sizeof(typestring_type)); }, |
| in); |
| |
| // Unmap buffer if creating a CLTensor |
| unmap(tensor); |
| } |
| catch (const std::ofstream::failure &e) |
| { |
| ARM_COMPUTE_ERROR_VAR("Writing %s: (%s)", npy_filename.c_str(), e.what()); |
| } |
| } |
| |
| /** Load the tensor with pre-trained data from a binary file |
| * |
| * @param[in] tensor The tensor to be filled. Data type supported: F32. |
| * @param[in] filename Filename of the binary file to load from. |
| */ |
| template <typename T> |
| void load_trained_data(T &tensor, const std::string &filename) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32); |
| |
| std::ifstream fs; |
| |
| try |
| { |
| fs.exceptions(std::ofstream::failbit | std::ofstream::badbit | std::ofstream::eofbit); |
| // Open file |
| fs.open(filename, std::ios::in | std::ios::binary); |
| |
| if (!fs.good()) |
| { |
| throw std::runtime_error("Could not load binary data: " + filename); |
| } |
| |
| // Map buffer if creating a CLTensor |
| map(tensor, true); |
| |
| Window window; |
| |
| window.set(arm_compute::Window::DimX, arm_compute::Window::Dimension(0, 1, 1)); |
| |
| for (unsigned int d = 1; d < tensor.info()->num_dimensions(); ++d) |
| { |
| window.set(d, Window::Dimension(0, tensor.info()->tensor_shape()[d], 1)); |
| } |
| |
| arm_compute::Iterator in(&tensor, window); |
| |
| execute_window_loop( |
| window, |
| [&](const Coordinates &) |
| { |
| fs.read(reinterpret_cast<std::fstream::char_type *>(in.ptr()), |
| tensor.info()->tensor_shape()[0] * tensor.info()->element_size()); |
| }, |
| in); |
| |
| // Unmap buffer if creating a CLTensor |
| unmap(tensor); |
| } |
| catch (const std::ofstream::failure &e) |
| { |
| ARM_COMPUTE_ERROR_VAR("Writing %s: (%s)", filename.c_str(), e.what()); |
| } |
| } |
| |
| template <typename T, typename TensorType> |
| void fill_tensor_value(TensorType &tensor, T value) |
| { |
| map(tensor, true); |
| |
| Window window; |
| window.use_tensor_dimensions(tensor.info()->tensor_shape()); |
| |
| Iterator it_tensor(&tensor, window); |
| execute_window_loop( |
| window, [&](const Coordinates &) { *reinterpret_cast<T *>(it_tensor.ptr()) = value; }, it_tensor); |
| |
| unmap(tensor); |
| } |
| |
| template <typename T, typename TensorType> |
| void fill_tensor_zero(TensorType &tensor) |
| { |
| fill_tensor_value(tensor, T(0)); |
| } |
| |
| template <typename T, typename TensorType> |
| void fill_tensor_vector(TensorType &tensor, std::vector<T> vec) |
| { |
| ARM_COMPUTE_ERROR_ON(tensor.info()->tensor_shape().total_size() != vec.size()); |
| |
| map(tensor, true); |
| |
| Window window; |
| window.use_tensor_dimensions(tensor.info()->tensor_shape()); |
| |
| int i = 0; |
| Iterator it_tensor(&tensor, window); |
| execute_window_loop( |
| window, [&](const Coordinates &) { *reinterpret_cast<T *>(it_tensor.ptr()) = vec.at(i++); }, it_tensor); |
| |
| unmap(tensor); |
| } |
| |
| template <typename T, typename TensorType> |
| void fill_random_tensor(TensorType &tensor, |
| std::random_device::result_type seed, |
| T lower_bound = std::numeric_limits<T>::lowest(), |
| T upper_bound = std::numeric_limits<T>::max()) |
| { |
| constexpr bool is_fp_16bit = std::is_same<T, half>::value || std::is_same<T, bfloat16>::value; |
| constexpr bool is_integral = std::is_integral<T>::value && !is_fp_16bit; |
| |
| using fp_dist_type = typename std::conditional<is_fp_16bit, arm_compute::utils::uniform_real_distribution_16bit<T>, |
| std::uniform_real_distribution<T>>::type; |
| using dist_type = typename std::conditional<is_integral, std::uniform_int_distribution<T>, fp_dist_type>::type; |
| |
| std::mt19937 gen(seed); |
| dist_type dist(lower_bound, upper_bound); |
| |
| map(tensor, true); |
| |
| Window window; |
| window.use_tensor_dimensions(tensor.info()->tensor_shape()); |
| |
| Iterator it(&tensor, window); |
| execute_window_loop( |
| window, [&](const Coordinates &) { *reinterpret_cast<T *>(it.ptr()) = dist(gen); }, it); |
| |
| unmap(tensor); |
| } |
| |
| template <typename T, typename TensorType> |
| void fill_random_tensor(TensorType &tensor, |
| T lower_bound = std::numeric_limits<T>::lowest(), |
| T upper_bound = std::numeric_limits<T>::max()) |
| { |
| std::random_device rd; |
| fill_random_tensor(tensor, rd(), lower_bound, upper_bound); |
| } |
| |
| template <typename T> |
| void init_sgemm_output(T &dst, T &src0, T &src1, arm_compute::DataType dt) |
| { |
| dst.allocator()->init(TensorInfo( |
| TensorShape(src1.info()->dimension(0), src0.info()->dimension(1), src0.info()->dimension(2)), 1, dt)); |
| } |
| /** This function returns the amount of memory free reading from /proc/meminfo |
| * |
| * @return The free memory in kB |
| */ |
| uint64_t get_mem_free_from_meminfo(); |
| |
| /** Compare two tensors |
| * |
| * @param[in] tensor1 First tensor to be compared. |
| * @param[in] tensor2 Second tensor to be compared. |
| * @param[in] tolerance Tolerance used for the comparison. |
| * |
| * @return The number of mismatches |
| */ |
| template <typename T> |
| int compare_tensor(ITensor &tensor1, ITensor &tensor2, T tolerance) |
| { |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(&tensor1, &tensor2); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(&tensor1, &tensor2); |
| |
| int num_mismatches = 0; |
| Window window; |
| window.use_tensor_dimensions(tensor1.info()->tensor_shape()); |
| |
| map(tensor1, true); |
| map(tensor2, true); |
| |
| Iterator itensor1(&tensor1, window); |
| Iterator itensor2(&tensor2, window); |
| |
| execute_window_loop( |
| window, |
| [&](const Coordinates &) |
| { |
| if (std::abs(*reinterpret_cast<T *>(itensor1.ptr()) - *reinterpret_cast<T *>(itensor2.ptr())) > tolerance) |
| { |
| ++num_mismatches; |
| } |
| }, |
| itensor1, itensor2); |
| |
| unmap(itensor1); |
| unmap(itensor2); |
| |
| return num_mismatches; |
| } |
| } // namespace utils |
| } // namespace arm_compute |
| #endif /* __UTILS_UTILS_H__*/ |