/* * Copyright (c) 2016-2019 ARM Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #include "arm_compute/runtime/NEON/NEFunctions.h" #include "arm_compute/core/Types.h" #include "utils/ImageLoader.h" #include "utils/Utils.h" using namespace arm_compute; using namespace utils; /** Gaussian 3x3 matrix */ const std::array gaussian3x3 = { 1, 2, 1, 2, 4, 2, 1, 2, 1 }; /** Gaussian 5x5 matrix */ const std::array gaussian5x5 = { 1, 4, 6, 4, 1, 4, 16, 24, 16, 4, 6, 24, 36, 24, 6, 4, 16, 24, 16, 4, 1, 4, 6, 4, 1 }; class NEONConvolutionExample : public Example { public: bool do_setup(int argc, char **argv) override { /** [Accurate padding] **/ PPMLoader ppm; if(argc < 2) { // Print help std::cout << "Usage: ./build/neon_convolution [input_image.ppm]\n\n"; std::cout << "No input_image provided, creating a dummy 640x480 image\n"; // Initialize just the dimensions and format of your buffers: src.allocator()->init(TensorInfo(640, 480, Format::U8)); } else { ppm.open(argv[1]); // Initialize just the dimensions and format of your buffers: ppm.init_image(src, Format::U8); } // Initialize just the dimensions and format of the temporary and destination images: tmp.allocator()->init(*src.info()); dst.allocator()->init(*src.info()); // Apply a Gaussian 3x3 filter to the source image followed by a Gaussian 5x5: // The function will automatically update the padding information inside input and output to match its requirements conv3x3.configure(&src, &tmp, gaussian3x3.data(), 0 /* Let arm_compute calculate the scale */, BorderMode::UNDEFINED); conv5x5.configure(&tmp, &dst, gaussian5x5.data(), 0 /* Let arm_compute calculate the scale */, BorderMode::UNDEFINED); // Now that the padding requirements are known we can allocate the images: src.allocator()->allocate(); tmp.allocator()->allocate(); dst.allocator()->allocate(); // Fill the input image with the content of the PPM image if a filename was provided: if(ppm.is_open()) { ppm.fill_image(src); output_filename = std::string(argv[1]) + "_out.ppm"; } /** [Accurate padding] **/ return true; } void do_run() override { //Execute the functions: conv3x3.run(); conv5x5.run(); } void do_teardown() override { // Save the result to file: if(!output_filename.empty()) { save_to_ppm(dst, output_filename); // save_to_ppm maps and unmaps the image to store as PPM } } private: Image src{}, tmp{}, dst{}; NEConvolution3x3 conv3x3{}; NEConvolution5x5 conv5x5{}; std::string output_filename{}; }; /** Main program for convolution test * * @param[in] argc Number of arguments * @param[in] argv Arguments ( [optional] Path to PPM image to process ) */ int main(int argc, char **argv) { return utils::run_example(argc, argv); }