Tomas G.
9 months ago
parent
78fe5fe47d
commit
74688f6ff3
@ -1 +1,10 @@
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Zadání projektu do předmětu pokročilé asemblery 2024
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# IPA projekt - Postprocessing dat z region proposal detekční neuronové sítě
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Zadání projektu do předmětu pokročilé asemblery pro akademický rok 2023/2024.
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# Struktura repozitáře
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* vs_linux - projekt pro VS Code pro OS linux
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* vs_win - projekt pro VS Code pro OS Windows. Nutné zkopírovat do kořenového adresáře šablony z druhého cvičení (https://www.fit.vutbr.cz/~igoldmann/courses/IPA/cv2_64bit_2021_win.zip).
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* zadani_x_x.pdf (1.0) - zadání projektu
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Realizaci můžete dělat s využitím intrinsic funkcí tak přímo v asembleru.
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@ -1,15 +0,0 @@
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#Not used for project
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[BITS 64]
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global f1:function
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section .data
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section .text
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f1:
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mov rax, 0
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ret
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@ -0,0 +1,21 @@
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{
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"configurations": [
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{
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"name": "Win32",
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"includePath": [
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"${workspaceFolder}/**",
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"${workspaceFolder}\\..\\include"
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],
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"defines": [
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"_DEBUG",
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"UNICODE",
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"_UNICODE"
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],
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"compilerPath": "${workspaceFolder}\\..\\mingw64\\bin\\g++.exe",
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"cStandard": "c11",
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"cppStandard": "c++17",
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"intelliSenseMode": "gcc-x64"
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}
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],
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"version": 4
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}
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@ -0,0 +1,40 @@
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{
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"version": "0.2.0",
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"configurations": [
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{
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"name": "(gdb) Launch",
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"type": "cppdbg",
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"request": "launch",
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"program": "${workspaceFolder}/retinanetpost.exe",
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"args": ["input/input.jpg", "input/input.txt"],
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"stopAtEntry": true,
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"cwd": "${workspaceFolder}",
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"environment": [{"name": "PATH", "value": "${env:PATH};${workspaceFolder}/runtime_lib/"}],
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"externalConsole": false,
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"preLaunchTask": "Build IPA_projekt_2024",
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"windows": {
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"MIMode": "gdb",
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"miDebuggerPath": "${workspaceFolder}/../mingw64/bin/gdb.exe",
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"setupCommands": [
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{
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"description": "Enable pretty-printing for gdb",
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"text": "-enable-pretty-printing",
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"ignoreFailures": true
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},
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{
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"description": "Reduce gdb verbosity",
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"text": "set print thread-events on",
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"ignoreFailures": true
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}
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],
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"logging": {
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"trace": false,
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"traceResponse": false,
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"engineLogging": false
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}
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}
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}
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]
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}
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@ -0,0 +1,5 @@
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{
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"files.associations": {
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"iostream": "cpp"
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}
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}
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@ -0,0 +1,84 @@
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{
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"version": "2.0.0",
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"windows": {
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},
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"tasks": [
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{
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"label": "Build IPA_projekt_2024",
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"type": "shell",
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"command": "g++",
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"args": [
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"-g",
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"-std=c++11",
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"-fpic",
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"-o",
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"retinanetpost.exe",
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"-DDEBUG",
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"retinanetpost_asm.o",
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"retinanetpost.cpp",
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"src/utils.cpp",
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"src/prior_boxes.cpp",
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"src/reader.cpp",
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"-Iinclude",
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"-L..\\lib",
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"-I..\\include",
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"-lopencv_world451"
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],
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"group": {
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"kind": "build",
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"isDefault": true
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},
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"problemMatcher": [],
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"dependsOn": [
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"ASM Part"
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],
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"isBackground": false
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},
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{
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"label": "ASM Part",
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"type": "shell",
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"command": "gcc",
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"args": [
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"-masm=intel",
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"-g",
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"-c",
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"retinanetpost_asm.s"
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],
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"group": {
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"kind": "build",
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"isDefault": true
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},
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"problemMatcher": [],
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"dependsOn": [
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],
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"isBackground": false
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},
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{
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"label": "Run",
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"type": "shell",
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"command": "retinanetpost.exe",
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"args": [
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"input/input.jpg",
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"input/input.txt"
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],
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"group": {
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"kind": "build",
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"isDefault": true
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},
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"options": {
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"env": {
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"PATH": "${env:PATH};${workspaceFolder}/runtime_lib/"
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}
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},
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"problemMatcher": [],
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"dependsOn": [
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"Build IPA_projekt_2024"
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],
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"isBackground": false
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}
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]
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}
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@ -0,0 +1,29 @@
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/*
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* Prior boxes for RetinaNet
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* Tomas Goldmann,2023
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*/
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#include <iostream>
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#include <vector>
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#include <cmath>
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class PriorBox {
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private:
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std::vector<std::vector<int>> feature_maps;
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std::vector<std::vector<float>> min_sizes;
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std::vector<int> steps;
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bool clip;
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std::vector<int> image_size;
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std::string name;
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public:
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PriorBox(std::vector<int> image_size = std::vector<int>(), std::string phase = "train");
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std::vector<std::vector<float>> forward();
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};
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std::vector<std::vector<float>> decode(const std::vector<std::vector<float>>& loc, const std::vector<std::vector<float>>& priors, const std::vector<float>& variances);
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@ -0,0 +1,18 @@
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/*
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* Functions for reading and processing txt file with floats
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* Tomas Goldmann,2024
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*/
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#pragma once
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#include <iostream>
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#include <fstream>
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#include <sstream>
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#include <vector>
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using namespace std;
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vector<vector<float>> splitFloats(const vector<float>& floats, int index) ;
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vector<float> readFloatsFromFile(const string& filename);
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/*
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* Non-maximum suppression
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* Tomas Goldmann,2024
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*/
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#include <iostream>
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#include <vector>
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#include <cmath>
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#include <algorithm>
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std::vector<std::vector<float>> nms(std::vector<std::vector<float>>& bboxes, float threshold);
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After Width: | Height: | Size: 284 KiB |
File diff suppressed because one or more lines are too long
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#include <stdio.h>
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#include <inttypes.h>
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#include <x86intrin.h>
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class InstructionCounter
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{
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private:
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unsigned long long counter;
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unsigned long long end_count;
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unsigned long long total_count;
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public:
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void start()
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{
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counter= __rdtsc();
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};
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void end()
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{
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end_count = __rdtsc();
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};
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void print()
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{
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printf("%I64lld \n", getCyclesCount());
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}
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unsigned long long getCyclesCount()
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{
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end();
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total_count = end_count - counter;
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return total_count;
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}
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};
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#include <cstring>
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#include <string>
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#include <iostream>
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#include <inttypes.h>
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#include "ipa_tool.h"
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#include <opencv2/highgui.hpp>
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#include <opencv2/core.hpp>
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#include <opencv2/imgproc.hpp>
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using namespace std;
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using namespace cv;
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#include "prior_boxes.hpp"
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#include "utils.hpp"
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#include "reader.hpp"
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//example: call extern function
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extern "C" { void f1(int a);}
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#define CONFIDENCE_THRESHOLD 0.999
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#define INPUT_WIDTH 640
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#define INPUT_HEIGHT 480
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#define ANCHORS_COUNT 12600
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int main(int argc, char** argv)
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{
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if (argc != 3)
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{
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std::cout << "Run program by: ./retinapost input/vector.txt input/image.png";
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}
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Mat image = imread(argv[1]);
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if (image.empty())
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{
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cout << "Could not open or find a image" << endl;
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return -1;
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}
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//All constants refer to the configuration used in prior_boxes.cpp and to the 640x480 resolution.
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std::vector<int> image_size = {INPUT_WIDTH, INPUT_HEIGHT};
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std::vector<float> variances = {0.1f, 0.2f};
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size_t total0_len = ANCHORS_COUNT*4;
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size_t total1_len = ANCHORS_COUNT*2;
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size_t num_anchors = total0_len / 4;
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PriorBox priorBox(image_size, "projekt");
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std::vector<std::vector<float>> priors = priorBox.forward();
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Scalar color(0, 255, 0); // Color of the rectangle (in BGR)
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int thickness = 2; // Thickness of the rectangle border
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InstructionCounter counter;
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counter.start();
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/*******************Part to optmize*********************/
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vector<float> floats = readFloatsFromFile(argv[2]);
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vector<vector<float>> split_vectors = splitFloats(floats, 12600*4);
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vector<float> floatarr = split_vectors[0];
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vector<float> floatarrscr = split_vectors[1];
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std::vector<std::vector<float>> loc;
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for (size_t i = 0; i < num_anchors; i++) {
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loc.push_back({floatarr[i * 4], floatarr[i * 4 + 1], floatarr[i * 4 + 2], floatarr[i * 4 + 3]});
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}
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std::vector<std::vector<float>> decoded_boxes = decode(loc, priors, variances);
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std::vector<float> scores;
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std::vector<int> inds;
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std::vector<float> det_scores;
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std::vector<std::vector<float>> det_boxes;
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for (size_t i = 0; i < total1_len/2; i++) {
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scores.push_back(floatarrscr[i*2+1]);
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if(floatarrscr[i*2+1] > CONFIDENCE_THRESHOLD)
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{
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inds.push_back(i);
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decoded_boxes[i].push_back(floatarrscr[i*2+1]);
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decoded_boxes[i][0]= decoded_boxes[i][0]* 640;
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decoded_boxes[i][1]= decoded_boxes[i][1]* 480;
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decoded_boxes[i][2]= decoded_boxes[i][2]* 640;
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decoded_boxes[i][3]= decoded_boxes[i][3]* 480;
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det_boxes.push_back(decoded_boxes[i]);
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det_scores.push_back(scores[i]);
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}
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}
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auto out = nms(det_boxes, 0.4);
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//Test
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//f1(10);
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counter.print();
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/************************************************/
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for (int i = 0; i < out.size(); i++)
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{
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#ifdef DEBUG
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printf("Box %f %f %f %f %f\n", out[i][0], out[i][1], out[i][2], out[i][3], out[i][4]);
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#endif
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cv::Rect roi((int)out[i][0], (int)out[i][1], (int)out[i][2]- (int)out[i][0], (int)out[i][3] - (int)out[i][1]);
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rectangle(image, roi, color, thickness);
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}
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imshow("Output", image);
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waitKey(0);
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return 0;
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}
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@ -0,0 +1,12 @@
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.intel_syntax noprefix
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.data
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.text
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.global f1
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f1:
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ret 0
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@ -0,0 +1,109 @@
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/*
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* Prior boxes for RetinaNet
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* Tomas Goldmann,2023
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*/
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#include <iostream>
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#include <vector>
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#include <cmath>
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#include <map>
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#include <algorithm>
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#include "prior_boxes.hpp"
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PriorBox::PriorBox(std::vector<int> image_size, std::string phase)
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{
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std::map<std::string, std::vector<std::vector<float>>> cfg;
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this->min_sizes = {{16.0f, 32.0f}, {64.0f, 128.0f}, {256.0f, 512.0f}};
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this->image_size = {image_size[1], image_size[0]};
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this->steps = {8, 16, 32};
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for (const float step : steps)
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{
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this->feature_maps.push_back({(int)std::ceil(image_size[0] / step), (int)std::ceil(image_size[1] / step)});
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}
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}
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std::vector<std::vector<float>> PriorBox::forward()
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{
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std::vector<float> anchors;
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for (size_t k = 0; k < this->feature_maps.size(); k++)
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{
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const auto &f = this->feature_maps[k];
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const auto &min_sizes = this->min_sizes[k];
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for (int i = 0; i < f[1]; i++)
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{
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for (int j = 0; j < f[0]; j++)
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{
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for (const auto &min_size : min_sizes)
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{
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float s_kx = min_size / this->image_size[1];
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float s_ky = min_size / this->image_size[0];
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std::vector<float> dense_cx = {static_cast<float>(j + 0.5) * this->steps[k] / this->image_size[1]};
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std::vector<float> dense_cy = {static_cast<float>(i + 0.5) * this->steps[k] / this->image_size[0]};
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for (const auto &cy : dense_cy)
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{
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for (const auto &cx : dense_cx)
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{
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anchors.push_back(cx);
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anchors.push_back(cy);
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anchors.push_back(s_kx);
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anchors.push_back(s_ky);
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}
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||||
}
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||||
}
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||||
}
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||||
}
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||||
}
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std::vector<std::vector<float>> output;
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size_t num_anchors = anchors.size() / 4;
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||||
for (size_t i = 0; i < num_anchors; i++)
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{
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||||
for (size_t j = 0; j < 4; j++)
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||||
{
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anchors[i * 4 + j] = std::min(std::max(anchors[i * 4 + j], 0.0f), 1.0f);
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}
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output.push_back({anchors[i * 4], anchors[i * 4 + 1], anchors[i * 4 + 2], anchors[i * 4 + 3]});
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}
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//std::cout << "len " << output.size() << std::endl;
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return output;
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||||
}
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||||
std::vector<std::vector<float>> decode(const std::vector<std::vector<float>> &loc, const std::vector<std::vector<float>> &priors, const std::vector<float> &variances)
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||||
{
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std::vector<std::vector<float>> boxes;
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||||
for (size_t i = 0; i < loc.size(); i++)
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||||
{
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||||
const auto &prior = priors[i];
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||||
const auto &loc_pred = loc[i];
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||||
float prior_x = prior[0];
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||||
float prior_y = prior[1];
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||||
float prior_w = prior[2];
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||||
float prior_h = prior[3];
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||||
float var_x = variances[0];
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||||
float var_y = variances[1];
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||||
float var_w = variances[2];
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||||
float var_h = variances[3];
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||||
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||||
float decoded_x = prior_x + loc_pred[0] * var_x * prior_w;
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float decoded_y = prior_y + loc_pred[1] * var_y * prior_h;
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||||
float decoded_w = prior_w * std::exp(loc_pred[2] * var_w);
|
||||
float decoded_h = prior_h * std::exp(loc_pred[3] * var_h);
|
||||
|
||||
float decoded_xmin = decoded_x - decoded_w / 2;
|
||||
float decoded_ymin = decoded_y - decoded_h / 2;
|
||||
float decoded_xmax = decoded_x + decoded_w / 2;
|
||||
float decoded_ymax = decoded_y + decoded_h / 2;
|
||||
|
||||
boxes.push_back({decoded_xmin, decoded_ymin, decoded_xmax, decoded_ymax});
|
||||
}
|
||||
|
||||
return boxes;
|
||||
}
|
||||
@ -0,0 +1,59 @@
|
||||
/*
|
||||
* Function to read floats from txt
|
||||
* Tomas Goldmann,2023
|
||||
*/
|
||||
|
||||
|
||||
#include "reader.hpp"
|
||||
|
||||
using namespace std;
|
||||
|
||||
vector<vector<float>> splitFloats(const vector<float>& floats, int index) {
|
||||
|
||||
vector<float> first_half;
|
||||
vector<float> second_half;
|
||||
|
||||
// Ensure the original vector has enough elements for splitting
|
||||
if (floats.size() < 6*12600) {
|
||||
cerr << "Error: The input must containt 6*12600 floats" << endl;
|
||||
return {first_half, second_half}; // Return empty vectors
|
||||
}
|
||||
|
||||
// Split the original vector into two separate vectors
|
||||
first_half.insert(first_half.end(), floats.begin(), floats.begin() + index);
|
||||
second_half.insert(second_half.end(), floats.begin() + index, floats.end() );
|
||||
|
||||
// Return a vector containing the two split vectors
|
||||
return {first_half, second_half};
|
||||
}
|
||||
|
||||
|
||||
vector<float> readFloatsFromFile(const string& filename) {
|
||||
vector<float> floats;
|
||||
ifstream file(filename);
|
||||
|
||||
// Check if the file is opened successfully
|
||||
if (!file.is_open()) {
|
||||
cerr << "Error opening the file" << endl;
|
||||
return floats; // Return an empty vector if the file cannot be opened
|
||||
}
|
||||
|
||||
string line;
|
||||
|
||||
// Read each line from the file
|
||||
while (getline(file, line)) {
|
||||
// Create a string stream from the line
|
||||
stringstream ss(line);
|
||||
string token;
|
||||
|
||||
while (getline(ss, token, ',')) {
|
||||
// Convert the string to a float and store it in the vector
|
||||
floats.push_back(stof(token));
|
||||
}
|
||||
}
|
||||
|
||||
// Close the file
|
||||
file.close();
|
||||
|
||||
return floats;
|
||||
}
|
||||
@ -0,0 +1,62 @@
|
||||
#include "utils.hpp"
|
||||
|
||||
|
||||
bool CompareBBox(const std::vector<float> & a, const std::vector<float> & b)
|
||||
{
|
||||
return a[4] > b[4];
|
||||
}
|
||||
|
||||
std::vector<std::vector<float>> nms(std::vector<std::vector<float>>& bboxes, float threshold)
|
||||
{
|
||||
std::vector<std::vector<float>> bboxes_nms;
|
||||
std::sort(bboxes.begin(), bboxes.end(), CompareBBox);
|
||||
|
||||
int32_t select_idx = 0;
|
||||
int32_t num_bbox = static_cast<int32_t>(bboxes.size());
|
||||
std::vector<int32_t> mask_merged(num_bbox, 0);
|
||||
bool all_merged = false;
|
||||
|
||||
while (!all_merged) {
|
||||
while (select_idx < num_bbox && mask_merged[select_idx] == 1)
|
||||
select_idx++;
|
||||
if (select_idx == num_bbox) {
|
||||
all_merged = true;
|
||||
continue;
|
||||
}
|
||||
|
||||
bboxes_nms.push_back(bboxes[select_idx]);
|
||||
mask_merged[select_idx] = 1;
|
||||
|
||||
std::vector<float> select_bbox = bboxes[select_idx];
|
||||
float area1 = static_cast<float>((select_bbox[2] - select_bbox[0] + 1) * (select_bbox[3] - select_bbox[1] + 1));
|
||||
float x1 = static_cast<float>(select_bbox[0]);
|
||||
float y1 = static_cast<float>(select_bbox[1]);
|
||||
float x2 = static_cast<float>(select_bbox[2]);
|
||||
float y2 = static_cast<float>(select_bbox[3]);
|
||||
|
||||
select_idx++;
|
||||
for (int32_t i = select_idx; i < num_bbox; i++) {
|
||||
if (mask_merged[i] == 1)
|
||||
continue;
|
||||
|
||||
std::vector<float>& bbox_i = bboxes[i];
|
||||
float x = std::max<float>(x1, static_cast<float>(bbox_i[0]));
|
||||
float y = std::max<float>(y1, static_cast<float>(bbox_i[1]));
|
||||
float w = std::min<float>(x2, static_cast<float>(bbox_i[2])) - x + 1;
|
||||
float h = std::min<float>(y2, static_cast<float>(bbox_i[3])) - y + 1;
|
||||
|
||||
if (w <= 0 || h <= 0)
|
||||
continue;
|
||||
|
||||
float area2 = static_cast<float>((bbox_i[2] - bbox_i[0] + 1) * (bbox_i[3] - bbox_i[1] + 1));
|
||||
float area_intersect = w * h;
|
||||
|
||||
|
||||
if (static_cast<float>(area_intersect) / (area1 + area2 - area_intersect) > threshold) {
|
||||
mask_merged[i] = 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return bboxes_nms;
|
||||
}
|
||||
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Reference in new issue