Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 1 | /* |
Anthony Barbier | 6db0ff5 | 2018-01-05 10:59:12 +0000 | [diff] [blame] | 2 | * Copyright (c) 2016, 2018 ARM Limited. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 3 | * |
| 4 | * SPDX-License-Identifier: MIT |
| 5 | * |
| 6 | * Permission is hereby granted, free of charge, to any person obtaining a copy |
| 7 | * of this software and associated documentation files (the "Software"), to |
| 8 | * deal in the Software without restriction, including without limitation the |
| 9 | * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| 10 | * sell copies of the Software, and to permit persons to whom the Software is |
| 11 | * furnished to do so, subject to the following conditions: |
| 12 | * |
| 13 | * The above copyright notice and this permission notice shall be included in all |
| 14 | * copies or substantial portions of the Software. |
| 15 | * |
| 16 | * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| 17 | * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| 18 | * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| 19 | * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| 20 | * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| 21 | * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| 22 | * SOFTWARE. |
| 23 | */ |
| 24 | |
| 25 | #include "arm_compute/runtime/NEON/NEFunctions.h" |
| 26 | |
| 27 | #include "arm_compute/core/Types.h" |
| 28 | #include "utils/Utils.h" |
| 29 | |
| 30 | #include <cstring> |
| 31 | #include <iostream> |
| 32 | |
| 33 | using namespace arm_compute; |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 34 | using namespace utils; |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 35 | |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 36 | class NEONCopyObjectsExample : public Example |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 37 | { |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 38 | public: |
| 39 | void do_setup(int argc, char **argv) override |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 40 | { |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 41 | ARM_COMPUTE_UNUSED(argc); |
| 42 | ARM_COMPUTE_UNUSED(argv); |
| 43 | |
| 44 | /** [Copy objects example] */ |
| 45 | constexpr unsigned int width = 4; |
| 46 | constexpr unsigned int height = 3; |
| 47 | constexpr unsigned int batch = 2; |
| 48 | |
| 49 | src_data = new float[width * height * batch]; |
| 50 | dst_data = new float[width * height * batch]; |
| 51 | |
| 52 | // Fill src_data with dummy values: |
| 53 | for(unsigned int b = 0; b < batch; b++) |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 54 | { |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 55 | for(unsigned int h = 0; h < height; h++) |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 56 | { |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 57 | for(unsigned int w = 0; w < width; w++) |
| 58 | { |
| 59 | src_data[b * (width * height) + h * width + w] = static_cast<float>(100 * b + 10 * h + w); |
| 60 | } |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 61 | } |
| 62 | } |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 63 | |
| 64 | // Initialize the tensors dimensions and type: |
| 65 | const TensorShape shape(width, height, batch); |
| 66 | input.allocator()->init(TensorInfo(shape, 1, DataType::F32)); |
| 67 | output.allocator()->init(TensorInfo(shape, 1, DataType::F32)); |
| 68 | |
| 69 | // Configure softmax: |
| 70 | softmax.configure(&input, &output); |
| 71 | |
| 72 | // Allocate the input / output tensors: |
| 73 | input.allocator()->allocate(); |
| 74 | output.allocator()->allocate(); |
| 75 | |
| 76 | // Fill the input tensor: |
| 77 | // Simplest way: create an iterator to iterate through each element of the input tensor: |
| 78 | Window input_window; |
| 79 | input_window.use_tensor_dimensions(input.info()->tensor_shape()); |
| 80 | std::cout << " Dimensions of the input's iterator:\n"; |
| 81 | std::cout << " X = [start=" << input_window.x().start() << ", end=" << input_window.x().end() << ", step=" << input_window.x().step() << "]\n"; |
| 82 | std::cout << " Y = [start=" << input_window.y().start() << ", end=" << input_window.y().end() << ", step=" << input_window.y().step() << "]\n"; |
| 83 | std::cout << " Z = [start=" << input_window.z().start() << ", end=" << input_window.z().end() << ", step=" << input_window.z().step() << "]\n"; |
| 84 | |
| 85 | // Create an iterator: |
| 86 | Iterator input_it(&input, input_window); |
| 87 | |
| 88 | // Iterate through the elements of src_data and copy them one by one to the input tensor: |
| 89 | // This is equivalent to: |
| 90 | // for( unsigned int z = 0; z < batch; ++z) |
| 91 | // { |
| 92 | // for( unsigned int y = 0; y < height; ++y) |
| 93 | // { |
| 94 | // for( unsigned int x = 0; x < width; ++x) |
| 95 | // { |
| 96 | // *reinterpret_cast<float*>( input.buffer() + input.info()->offset_element_in_bytes(Coordinates(x,y,z))) = src_data[ z * (width*height) + y * width + x]; |
| 97 | // } |
| 98 | // } |
| 99 | // } |
| 100 | // Except it works for an arbitrary number of dimensions |
| 101 | execute_window_loop(input_window, [&](const Coordinates & id) |
| 102 | { |
| 103 | std::cout << "Setting item [" << id.x() << "," << id.y() << "," << id.z() << "]\n"; |
| 104 | *reinterpret_cast<float *>(input_it.ptr()) = src_data[id.z() * (width * height) + id.y() * width + id.x()]; |
| 105 | }, |
| 106 | input_it); |
| 107 | |
| 108 | // More efficient way: create an iterator to iterate through each row (instead of each element) of the output tensor: |
| 109 | Window output_window; |
| 110 | output_window.use_tensor_dimensions(output.info()->tensor_shape(), /* first_dimension =*/Window::DimY); // Iterate through the rows (not each element) |
| 111 | std::cout << " Dimensions of the output's iterator:\n"; |
| 112 | std::cout << " X = [start=" << output_window.x().start() << ", end=" << output_window.x().end() << ", step=" << output_window.x().step() << "]\n"; |
| 113 | std::cout << " Y = [start=" << output_window.y().start() << ", end=" << output_window.y().end() << ", step=" << output_window.y().step() << "]\n"; |
| 114 | std::cout << " Z = [start=" << output_window.z().start() << ", end=" << output_window.z().end() << ", step=" << output_window.z().step() << "]\n"; |
| 115 | |
| 116 | // Create an iterator: |
| 117 | Iterator output_it(&output, output_window); |
| 118 | |
| 119 | // Iterate through the rows of the output tensor and copy them to dst_data: |
| 120 | // This is equivalent to: |
| 121 | // for( unsigned int z = 0; z < batch; ++z) |
| 122 | // { |
| 123 | // for( unsigned int y = 0; y < height; ++y) |
| 124 | // { |
| 125 | // memcpy( dst_data + z * (width*height) + y * width, input.buffer() + input.info()->offset_element_in_bytes(Coordinates(0,y,z)), width * sizeof(float)); |
| 126 | // } |
| 127 | // } |
| 128 | // Except it works for an arbitrary number of dimensions |
| 129 | execute_window_loop(output_window, [&](const Coordinates & id) |
| 130 | { |
| 131 | std::cout << "Copying one row starting from [" << id.x() << "," << id.y() << "," << id.z() << "]\n"; |
| 132 | // Copy one whole row: |
| 133 | memcpy(dst_data + id.z() * (width * height) + id.y() * width, output_it.ptr(), width * sizeof(float)); |
| 134 | }, |
| 135 | output_it); |
| 136 | |
| 137 | /** [Copy objects example] */ |
| 138 | } |
| 139 | void do_run() override |
| 140 | { |
| 141 | // Run NEON softmax: |
| 142 | softmax.run(); |
| 143 | } |
| 144 | void do_teardown() override |
| 145 | { |
| 146 | delete[] src_data; |
| 147 | delete[] dst_data; |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 148 | } |
| 149 | |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 150 | private: |
| 151 | Tensor input{}, output{}; |
| 152 | float *src_data{}; |
| 153 | float *dst_data{}; |
| 154 | NESoftmaxLayer softmax{}; |
| 155 | }; |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 156 | /** Main program for the copy objects test |
| 157 | * |
| 158 | * @param[in] argc Number of arguments |
| 159 | * @param[in] argv Arguments |
| 160 | */ |
Anthony Barbier | 6db0ff5 | 2018-01-05 10:59:12 +0000 | [diff] [blame] | 161 | int main(int argc, char **argv) |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 162 | { |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 163 | return utils::run_example<NEONCopyObjectsExample>(argc, argv); |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 164 | } |