| /* |
| * Copyright (c) 2016, 2017 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__ |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| #include "arm_compute/runtime/Tensor.h" |
| #include "libnpy/npy.hpp" |
| #include "support/ToolchainSupport.h" |
| |
| #ifdef ARM_COMPUTE_CL |
| #include "arm_compute/core/CL/OpenCL.h" |
| #include "arm_compute/runtime/CL/CLDistribution1D.h" |
| #include "arm_compute/runtime/CL/CLTensor.h" |
| #endif /* ARM_COMPUTE_CL */ |
| #ifdef ARM_COMPUTE_GC |
| #include "arm_compute/runtime/GLES_COMPUTE/GCTensor.h" |
| #endif /* ARM_COMPUTE_GC */ |
| |
| #include <cstdlib> |
| #include <cstring> |
| #include <fstream> |
| #include <iostream> |
| #include <random> |
| #include <string> |
| #include <tuple> |
| #include <vector> |
| |
| namespace arm_compute |
| { |
| namespace utils |
| { |
| /** Signature of an example to run |
| * |
| * @param[in] argc Number of command line arguments |
| * @param[in] argv Command line arguments |
| */ |
| using example = void(int argc, const char **argv); |
| |
| /** 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] func Pointer to the function containing the code to run |
| */ |
| int run_example(int argc, const char **argv, example &func); |
| |
| /** 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); |
| |
| /** 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 |
| */ |
| std::tuple<std::vector<unsigned long>, bool, std::string> 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: |
| return no_endianness + "u" + support::cpp11::to_string(sizeof(uint8_t)); |
| case DataType::S8: |
| return no_endianness + "i" + support::cpp11::to_string(sizeof(int8_t)); |
| case DataType::U16: |
| return endianness + "u" + support::cpp11::to_string(sizeof(uint16_t)); |
| case DataType::S16: |
| return endianness + "i" + support::cpp11::to_string(sizeof(int16_t)); |
| case DataType::U32: |
| return endianness + "u" + support::cpp11::to_string(sizeof(uint32_t)); |
| case DataType::S32: |
| return endianness + "i" + support::cpp11::to_string(sizeof(int32_t)); |
| case DataType::U64: |
| return endianness + "u" + support::cpp11::to_string(sizeof(uint64_t)); |
| case DataType::S64: |
| return endianness + "i" + support::cpp11::to_string(sizeof(int64_t)); |
| case DataType::F32: |
| return endianness + "f" + support::cpp11::to_string(sizeof(float)); |
| case DataType::F64: |
| return endianness + "f" + support::cpp11::to_string(sizeof(double)); |
| case DataType::SIZET: |
| return endianness + "u" + support::cpp11::to_string(sizeof(size_t)); |
| default: |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| } |
| } |
| |
| /** 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(); |
| } |
| |
| /** Maps a distribution if needed |
| * |
| * @param[in] distribution Distribution to be mapped |
| * @param[in] blocking Specified if map is blocking or not |
| */ |
| inline void map(CLDistribution1D &distribution, bool blocking) |
| { |
| distribution.map(blocking); |
| } |
| |
| /** Unmaps a distribution if needed |
| * |
| * @param distribution Distribution to be unmapped |
| */ |
| inline void unmap(CLDistribution1D &distribution) |
| { |
| distribution.unmap(); |
| } |
| #endif /* ARM_COMPUTE_CL */ |
| |
| #ifdef ARM_COMPUTE_GC |
| /** 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(GCTensor &tensor, bool blocking) |
| { |
| tensor.map(blocking); |
| } |
| |
| /** Unmaps a tensor if needed |
| * |
| * @param tensor Tensor to be unmapped |
| */ |
| inline void unmap(GCTensor &tensor) |
| { |
| tensor.unmap(); |
| } |
| #endif /* ARM_COMPUTE_GC */ |
| |
| /** Class to load the content of a PPM file into an Image |
| */ |
| class PPMLoader |
| { |
| public: |
| PPMLoader() |
| : _fs(), _width(0), _height(0) |
| { |
| } |
| /** Open a PPM file and reads its metadata (Width, height) |
| * |
| * @param[in] ppm_filename File to open |
| */ |
| void open(const std::string &ppm_filename) |
| { |
| ARM_COMPUTE_ERROR_ON(is_open()); |
| try |
| { |
| _fs.exceptions(std::ifstream::failbit | std::ifstream::badbit); |
| _fs.open(ppm_filename, std::ios::in | std::ios::binary); |
| |
| unsigned int max_val = 0; |
| std::tie(_width, _height, max_val) = parse_ppm_header(_fs); |
| |
| ARM_COMPUTE_ERROR_ON_MSG(max_val >= 256, "2 bytes per colour channel not supported in file %s", ppm_filename.c_str()); |
| } |
| catch(const std::ifstream::failure &e) |
| { |
| ARM_COMPUTE_ERROR("Accessing %s: %s", ppm_filename.c_str(), e.what()); |
| } |
| } |
| /** Return true if a PPM file is currently open |
| */ |
| bool is_open() |
| { |
| return _fs.is_open(); |
| } |
| |
| /** Initialise an image's metadata with the dimensions of the PPM file currently open |
| * |
| * @param[out] image Image to initialise |
| * @param[in] format Format to use for the image (Must be RGB888 or U8) |
| */ |
| template <typename T> |
| void init_image(T &image, arm_compute::Format format) |
| { |
| ARM_COMPUTE_ERROR_ON(!is_open()); |
| ARM_COMPUTE_ERROR_ON(format != arm_compute::Format::RGB888 && format != arm_compute::Format::U8); |
| |
| // Use the size of the input PPM image |
| arm_compute::TensorInfo image_info(_width, _height, format); |
| image.allocator()->init(image_info); |
| } |
| |
| /** Fill an image with the content of the currently open PPM file. |
| * |
| * @note If the image is a CLImage, the function maps and unmaps the image |
| * |
| * @param[in,out] image Image to fill (Must be allocated, and of matching dimensions with the opened PPM). |
| */ |
| template <typename T> |
| void fill_image(T &image) |
| { |
| ARM_COMPUTE_ERROR_ON(!is_open()); |
| ARM_COMPUTE_ERROR_ON(image.info()->dimension(0) != _width || image.info()->dimension(1) != _height); |
| ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(&image, arm_compute::Format::U8, arm_compute::Format::RGB888); |
| try |
| { |
| // Map buffer if creating a CLTensor/GCTensor |
| map(image, true); |
| |
| // Check if the file is large enough to fill the image |
| 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) < image.info()->tensor_shape().total_size() * image.info()->element_size(), |
| "Not enough data in file"); |
| ARM_COMPUTE_UNUSED(end_position); |
| |
| switch(image.info()->format()) |
| { |
| case arm_compute::Format::U8: |
| { |
| // We need to convert the data from RGB to grayscale: |
| // Iterate through every pixel of the image |
| 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 out(&image, window); |
| |
| unsigned char red = 0; |
| unsigned char green = 0; |
| unsigned char blue = 0; |
| |
| arm_compute::execute_window_loop(window, [&](const arm_compute::Coordinates & id) |
| { |
| red = _fs.get(); |
| green = _fs.get(); |
| blue = _fs.get(); |
| |
| *out.ptr() = 0.2126f * red + 0.7152f * green + 0.0722f * blue; |
| }, |
| out); |
| |
| break; |
| } |
| case arm_compute::Format::RGB888: |
| { |
| // There is no format conversion needed: we can simply copy the content of the input file to the image one row at the time. |
| // Create a vertical window to iterate through the image's rows: |
| arm_compute::Window window; |
| window.set(arm_compute::Window::DimY, arm_compute::Window::Dimension(0, _height, 1)); |
| |
| arm_compute::Iterator out(&image, window); |
| |
| arm_compute::execute_window_loop(window, [&](const arm_compute::Coordinates & id) |
| { |
| // Copy one row from the input file to the current row of the image: |
| _fs.read(reinterpret_cast<std::fstream::char_type *>(out.ptr()), _width * image.info()->element_size()); |
| }, |
| out); |
| |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Unsupported format"); |
| } |
| |
| // Unmap buffer if creating a CLTensor/GCTensor |
| unmap(image); |
| } |
| catch(const std::ifstream::failure &e) |
| { |
| ARM_COMPUTE_ERROR("Loading PPM file: %s", e.what()); |
| } |
| } |
| |
| /** Fill a tensor with 3 planes (one for each channel) with the content of the currently open PPM file. |
| * |
| * @note If the image is a CLImage, the function maps and unmaps the image |
| * |
| * @param[in,out] tensor Tensor with 3 planes to fill (Must be allocated, and of matching dimensions with the opened PPM). Data types supported: U8/F32 |
| * @param[in] bgr (Optional) Fill the first plane with blue channel (default = false) |
| */ |
| template <typename T> |
| void fill_planar_tensor(T &tensor, bool bgr = false) |
| { |
| ARM_COMPUTE_ERROR_ON(!is_open()); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::U8, DataType::F32); |
| ARM_COMPUTE_ERROR_ON(tensor.info()->dimension(0) != _width || tensor.info()->dimension(1) != _height || tensor.info()->dimension(2) != 3); |
| |
| try |
| { |
| // Map buffer if creating a CLTensor |
| map(tensor, true); |
| |
| // Check if the file is large enough to fill the image |
| 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(), |
| "Not enough data in file"); |
| ARM_COMPUTE_UNUSED(end_position); |
| |
| // Iterate through every pixel of the image |
| 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)); |
| window.set(arm_compute::Window::DimZ, arm_compute::Window::Dimension(0, 1, 1)); |
| |
| arm_compute::Iterator out(&tensor, window); |
| |
| unsigned char red = 0; |
| unsigned char green = 0; |
| unsigned char blue = 0; |
| |
| size_t stride_z = tensor.info()->strides_in_bytes()[2]; |
| |
| arm_compute::execute_window_loop(window, [&](const arm_compute::Coordinates & id) |
| { |
| red = _fs.get(); |
| green = _fs.get(); |
| blue = _fs.get(); |
| |
| switch(tensor.info()->data_type()) |
| { |
| case arm_compute::DataType::U8: |
| { |
| *(out.ptr() + 0 * stride_z) = bgr ? blue : red; |
| *(out.ptr() + 1 * stride_z) = green; |
| *(out.ptr() + 2 * stride_z) = bgr ? red : blue; |
| break; |
| } |
| case arm_compute::DataType::F32: |
| { |
| *reinterpret_cast<float *>(out.ptr() + 0 * stride_z) = static_cast<float>(bgr ? blue : red); |
| *reinterpret_cast<float *>(out.ptr() + 1 * stride_z) = static_cast<float>(green); |
| *reinterpret_cast<float *>(out.ptr() + 2 * stride_z) = static_cast<float>(bgr ? red : blue); |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_ERROR("Unsupported data type"); |
| } |
| } |
| }, |
| out); |
| |
| // Unmap buffer if creating a CLTensor |
| unmap(tensor); |
| } |
| catch(const std::ifstream::failure &e) |
| { |
| ARM_COMPUTE_ERROR("Loading PPM file: %s", e.what()); |
| } |
| } |
| |
| /** Return the width of the currently open PPM file. |
| */ |
| unsigned int width() const |
| { |
| return _width; |
| } |
| |
| /** Return the height of the currently open PPM file. |
| */ |
| unsigned int height() const |
| { |
| return _height; |
| } |
| |
| private: |
| std::ifstream _fs; |
| unsigned int _width, _height; |
| }; |
| |
| class NPYLoader |
| { |
| public: |
| NPYLoader() |
| : _fs(), _shape(), _fortran_order(false), _typestring() |
| { |
| } |
| |
| /** Open a NPY file and reads its metadata |
| * |
| * @param[in] npy_filename File to open |
| */ |
| void open(const std::string &npy_filename) |
| { |
| ARM_COMPUTE_ERROR_ON(is_open()); |
| try |
| { |
| _fs.exceptions(std::ifstream::failbit | std::ifstream::badbit); |
| _fs.open(npy_filename, std::ios::in | std::ios::binary); |
| |
| std::tie(_shape, _fortran_order, _typestring) = parse_npy_header(_fs); |
| } |
| catch(const std::ifstream::failure &e) |
| { |
| ARM_COMPUTE_ERROR("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 an image's metadata with the dimensions of the NPY file currently open |
| * |
| * @param[out] tensor Tensor to initialise |
| * @param[in] format Format to use for the image |
| */ |
| template <typename T> |
| void init_tensor(T &tensor, arm_compute::Format format) |
| { |
| ARM_COMPUTE_ERROR_ON(!is_open()); |
| ARM_COMPUTE_ERROR_ON(format != arm_compute::Format::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) |
| { |
| shape.set(i, _shape.at(i)); |
| } |
| |
| arm_compute::TensorInfo tensor_info(shape, format); |
| 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_FORMAT_NOT_IN(&tensor, arm_compute::Format::F32); |
| 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()); |
| ARM_COMPUTE_ERROR_ON_MSG(_typestring != expect_typestr, "Typestrings mismatch"); |
| |
| // Validate tensor shape |
| ARM_COMPUTE_ERROR_ON_MSG(_shape.size() != tensor.shape().num_dimensions(), "Tensor ranks mismatch"); |
| if(_fortran_order) |
| { |
| for(size_t i = 0; i < _shape.size(); ++i) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(tensor.shape()[i] != _shape[i], "Tensor dimensions mismatch"); |
| } |
| } |
| else |
| { |
| for(size_t i = 0; i < _shape.size(); ++i) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(tensor.shape()[i] != _shape[_shape.size() - i - 1], "Tensor dimensions mismatch"); |
| } |
| } |
| |
| switch(tensor.info()->format()) |
| { |
| case arm_compute::Format::F32: |
| { |
| // Read data |
| if(tensor.info()->padding().empty()) |
| { |
| // 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 accessing tensor elements through execution window. |
| Window window; |
| window.use_tensor_dimensions(tensor.info()->tensor_shape()); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| _fs.read(reinterpret_cast<char *>(tensor.ptr_to_element(id)), tensor.info()->element_size()); |
| }); |
| } |
| |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Unsupported format"); |
| } |
| |
| // Unmap buffer if creating a CLTensor |
| unmap(tensor); |
| } |
| catch(const std::ifstream::failure &e) |
| { |
| ARM_COMPUTE_ERROR("Loading NPY file: %s", e.what()); |
| } |
| } |
| |
| private: |
| std::ifstream _fs; |
| std::vector<unsigned long> _shape; |
| bool _fortran_order; |
| std::string _typestring; |
| }; |
| |
| /** 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/GCTensor |
| 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 & id) |
| { |
| 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 & id) |
| { |
| 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/GCTensor |
| unmap(tensor); |
| } |
| catch(const std::ofstream::failure &e) |
| { |
| ARM_COMPUTE_ERROR("Writing %s: (%s)", ppm_filename.c_str(), e.what()); |
| } |
| } |
| |
| /** Template helper function to save a tensor image to a NPY file. |
| * |
| * @note Only F32 format 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 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> |
| void save_to_npy(T &tensor, const std::string &npy_filename, bool fortran_order) |
| { |
| ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(&tensor, arm_compute::Format::F32); |
| 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(npy_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]; |
| std::vector<npy::ndarray_len_t> shape(2); |
| |
| if(!fortran_order) |
| { |
| shape[0] = height, shape[1] = width; |
| } |
| else |
| { |
| shape[0] = width, shape[1] = height; |
| } |
| |
| // Map buffer if creating a CLTensor |
| map(tensor, true); |
| |
| switch(tensor.info()->format()) |
| { |
| case arm_compute::Format::F32: |
| { |
| std::vector<float> tmp; /* Used only to get the typestring */ |
| npy::Typestring typestring_o{ tmp }; |
| std::string typestring = typestring_o.str(); |
| |
| std::ofstream stream(npy_filename, std::ofstream::binary); |
| npy::write_header(stream, typestring, fortran_order, shape); |
| |
| 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 & id) |
| { |
| stream.write(reinterpret_cast<const char *>(in.ptr()), sizeof(float)); |
| }, |
| 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("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/GCTensor |
| 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 & id) |
| { |
| 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/GCTensor |
| unmap(tensor); |
| } |
| catch(const std::ofstream::failure &e) |
| { |
| ARM_COMPUTE_ERROR("Writing %s: (%s)", filename.c_str(), e.what()); |
| } |
| } |
| |
| template <typename T> |
| void fill_random_tensor(T &tensor, float lower_bound, float upper_bound) |
| { |
| std::random_device rd; |
| std::mt19937 gen(rd()); |
| |
| TensorShape shape(tensor.info()->dimension(0), tensor.info()->dimension(1)); |
| |
| Window window; |
| window.set(Window::DimX, Window::Dimension(0, shape.x(), 1)); |
| window.set(Window::DimY, Window::Dimension(0, shape.y(), 1)); |
| |
| map(tensor, true); |
| |
| Iterator it(&tensor, window); |
| |
| switch(tensor.info()->format()) |
| { |
| case arm_compute::Format::F32: |
| { |
| std::uniform_real_distribution<float> dist(lower_bound, upper_bound); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| *reinterpret_cast<float *>(it.ptr()) = dist(gen); |
| }, |
| it); |
| |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_ERROR("Unsupported format"); |
| } |
| } |
| |
| unmap(tensor); |
| } |
| |
| template <typename T> |
| void init_sgemm_output(T &dst, T &src0, T &src1, arm_compute::Format format) |
| { |
| dst.allocator()->init(TensorInfo(src1.info()->dimension(0), src0.info()->dimension(1), format)); |
| } |
| |
| } // namespace utils |
| } // namespace arm_compute |
| #endif /* __UTILS_UTILS_H__*/ |