├── LICENSE.txt ├── Mods ├── HotPositionL2RA.HOT ├── dinput8.dll └── scripts │ ├── NFSMWExtraOptions.asi │ └── NFSMWExtraOptionsSettings.ini ├── README.md ├── createData.py ├── images ├── dataFlowchart.jpg ├── nvidia_arch.png └── trainFlowchart.jpg ├── model_architectures.py ├── play.py ├── play_util.py ├── processData.py ├── train_model.py └── trained_models └── defaultRaw.pth /LICENSE.txt: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . -------------------------------------------------------------------------------- /Mods/HotPositionL2RA.HOT: -------------------------------------------------------------------------------- 1 | // 2 | // HotPosition - TRACKS\HotPositionL2RA.HOT 3 | // 4 | 5 | HOTPOSITION: 1732.85, 2506.55, 148.39 Angle = 0x753d Speed = 10.98 6 | -------------------------------------------------------------------------------- /Mods/dinput8.dll: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fehimornek/DeepForSpeed/ddfefa5d87f5a2d2ebe7a6c7145b83c2330c23dd/Mods/dinput8.dll -------------------------------------------------------------------------------- /Mods/scripts/NFSMWExtraOptions.asi: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fehimornek/DeepForSpeed/ddfefa5d87f5a2d2ebe7a6c7145b83c2330c23dd/Mods/scripts/NFSMWExtraOptions.asi -------------------------------------------------------------------------------- /Mods/scripts/NFSMWExtraOptionsSettings.ini: -------------------------------------------------------------------------------- 1 | [Hotkeys] // Look at http://cherrytree.at/misc/vk.htm for key values (decimal) 2 | HeatLevelHack = 118 // Enables Ultimate Force Heat Level hack, Force-enables Heat Level Override and lets you select a heat level with the key below. (Default = 118 = F7 key) 3 | ChangeHeatLevel = 33 // Changes heat levels when the Ultimate Force Heat Level hack is enabled. (Default = 33 = PageUp key) 4 | ToggleCops = 145 // Toggles Cops on/off. Use with Ultimate Force Heat Level hack. (Default = 145 = Scroll Lock key) 5 | CopLights = 79 // Toggles Cop Lights on/off. Use a cop car for best results. (Default = 79 = O key) 6 | Headlights = 72 // Toggles Headlights on/off. (Default = 72 = H key) 7 | FreezeCar = 115 // Enables Freeze Car hack. (Default = 115 = F4 key) 8 | FreezeCamera = 19 // Freezes/unfreezes camera when pressed. (Default = 19 = Pause/Break key) 9 | UnlockAllThings = 116 // Unlocks everything in game and lets you play hidden tracks. (Default = 116 = F5 key) 10 | AutoDrive = 117 // Enables Auto Drive (Drunk Driver) hack. (Default = 117 = F6 key) 11 | EnableSaveLoadHotPos = 1 // Lets you save (LShift + 1/5) or load (LCtrl + 1/5) your position on the world. (0 = False (Default), 1 = True) 12 | 13 | [LapControllers] 14 | Minimum = 0 // Minimum number of laps can be selected at Quick Race options screen. (Default = 0) (0/127) 15 | Maximum = 127 // Maximum number of laps can be selected at Quick Race options screen. (Default = 127) (0/127) 16 | RandomRace = 127 // Maximum number of laps can be selected at Random Race screen. (Default = 127) (0/127) 17 | UnfreezeKO = 1 // Allows changing the laps count for KO. (0 = False, 1 = True (Default)) 18 | 19 | [OpponentControllers] 20 | Minimum = 0 // Minimum number of opponents can be selected at Quick Race options screen. (Default = 0) (0/15) 21 | Maximum = 6 // Maximum number of opponents can be selected at Quick Race options screen. (Default = 6) (0/15) 22 | RandomRace = 6 // Maximum number of opponents can be selected at Random Race screen. (Default = 6) (0/15) 23 | MaxUniqueOpponentCars = 3 // Maximum number of different car models per race that will be used by AI Opponents. (Default = 3) 24 | MoreCarsForOpponents = 0 // Allows opponents to use bonus or police cars. (0 = False (Default), 1 = True) 25 | CarbonStyleRandomCars = 0 // Removes random vinyls from AI opponents. (0 = False (Default), 1 = True) 26 | 27 | [TrafficControllers] 28 | Low = 10 // Traffic Density for option Minimum (Default = 10) (0/100) 29 | Medium = 30 // Traffic Density for option Moderate (Default = 30) (0/100) 30 | High = 50 // Traffic Density for option Maximum (Default = 50) (0/100) 31 | 32 | [Menu] 33 | ShowOnline = 1 // Shows Online option on Main Menu. (0 = False, 1 = True (Default)) 34 | ShowOnlineOpts = 0 // Shows Online Options on Options Menu. (0 = False (Default), 1 = True) 35 | ShowSubs = 0 // Shows Subtitles for English Language. (0 = False (Default), 1 = True) 36 | ShowMoreCarCategories = 0 // Shows Debug and Preset car categories on Car Select screen. (0 = False (Default), 1 = True) 37 | ShowSpecialVinyls = 1 // Adds Special vinyl category to vinyls menu. (0 = False, 1 = True (Default)) 38 | ShowDebugCarCustomize = 0 // Adds Debug Car Customize option to Main Menu. (0 = False (Default), 1 = True) 39 | ShowDebugEventID = 0 // Shows Event ID for races. (0 = False (Default), 1 = True) 40 | ShowTimeOfDay = 0 // Shows Time of Day from Xbox 360 release in Video Options menu. (0 = False (Default), 1 = True) 41 | ShowHiddenTracks = 0 // Shows Hidden Tracks using a different track listing method. (0 = False (Default), 1 = True) 42 | BetterRandomRaces = 0 // Makes more cars and tracks available in random races. (0 = False (Default), 1 = True) 43 | MaximumBlacklistRival = 15 // (ONLY FOR EXPERTS) Maximum Blacklist Rival. (Default = 15) (0/127) 44 | ReplayBlacklistRaces = 0 // Lets you replay Blacklist Boss Races in Career mode. (0 = False (Default), 1 = True) 45 | ChallengeSeriesBlacklist = 19 // (ONLY FOR EXPERTS) Bin (Blacklist) for Challenge Series events. (Default = 19) (0/127) 46 | RandomizeCount = 30 // Randomize count for the Quick Play menu. (Default = 30) (1/500) 47 | SplashScreenTimeLimit = 30 // Duration of Splash Screen. (Default = 30.0) 48 | CarSelectTireSteerAngle = 21.6723 // Sets the angle of front tires on menus. (Default = 21.6723) 49 | ShowcaseCamInfiniteZoom = 0 // Zoom hack for Showcase Camera mode. (0 = False (Default), 1 = True) 50 | ShowcaseCamInfiniteRotation = 0 // Rotation hack for Showcase Camera mode. (0 = False (Default), 1 = True) 51 | ShowcaseCamAlwaysEnable = 0 // Use Showcase Camera mode on every menu. (0 = False (Default), 1 = True) 52 | AllowLongerProfileNames = 1 // Lets you input up to 15 characters, use spaces and special characters in profile names. (0 = False, 1 = True (Default)) 53 | DisableTakeover = 0 // Removes "Extra Options by ExOpts Team" text from Copyright area. (0 = False (Default), 1 = True) 54 | 55 | [Gameplay] 56 | EnableHiddenCameraModes = 0 // Enables hidden camera modes and fixes the names. (0 = False (Default), 1 = True) 57 | EnableDebugCamera = 0 // Enables vanilla debug world camera function. Press Backspace to toggle. (0 = False (Default), 1 = True) 58 | ShowHUDOnLookBack = 0 // Prevents the HUD from disappearing when looked back, like in NFSU2. (0 = False (Default), 1 = True) 59 | GameSpeed = 1 // (FOR FUN) Speed modifier for races. (Default = 1.0) 60 | WorldAnimationSpeed = 45 // Speed modifier for animations (like waving trees) on the world. (Default = 45.0) 61 | DebugCamTurboSpeed = 3.06 // Speed modifier for Debug World Camera Turbo / Num- (Default = 3.06) 62 | DebugCamSuperTurboSpeed = 7.16 // Speed modifier for Debug World Camera Super Turbo / Num+ (Default = 7.16) 63 | CarScale = 1 // (FOR FUN) Resizes car model. (Default = 1.0) 64 | RemoveOldBridgeBarrier = 0 // Removes the barrier which closes the road to the Old Bridge. (0 = False (Default), 1 = True) 65 | RemoveNeonBarriers = 0 // Removes Neon Barriers which show the route or locked areas. (0 = False (Default), 1 = True) 66 | ShowPercentOn1LapRaces = 1 // Shows COMPLETED %X text instead of LAP 1/1 for 1-Lap races. (0 = False, 1 = True (Default)) 67 | StartingCash = 0 // Starts the game with specified amount of cash. 30000 from first 3 races are not included. (Default = 0) 68 | AwardedCash = 10000 // Awards (or penalties if you set a negative value) the player with specified amount of cash for having NFSU2 save of their PC. (Default = 10000) 69 | UnlockAllThings = 0 // Current status of UnlockAllThings hack. (0 = False (Default), 1 = True) 70 | ForceBlackEdition = 1 // Force enables Black Edition mode by skipping INGAMEC.BUN file check. (0 = False, 1 = True (Default)) 71 | UnlockBurgerKingChallenge = 0 // Unlocks Burger King challenge without using cheat code. (0 = False (Default), 1 = True) 72 | EnableMaxPerformanceOnShop = 1 // Lets you install maximum available parts to your car with one key on Career Performance Shop. (0 = False, 1 = True (Default)) 73 | ShowAllCarsInFE = 0 // (ONLY FOR EXPERTS) Makes any car available in Car Lot for NEW save games. It DOESN'T affect existing ones. Disable if you have too many add-on cars installed. (0 = False (Default), 1 = True) 74 | NoCatchUp = 0 // (EXPERIMENTAL) Attempts to remove the catch up. (0 = False (Default), 1 = True) 75 | NoRevLimiter = 0 // Removes rev limiter and lets you achieve better top speeds. (0 = False (Default), 1 = True) 76 | NosTrailRepeatCount = 8 // Sets trail repeat count for your taillights when you use NOS. (Default = 8) (High values may cause rendering issues.) 77 | SelectableMarkerCount = 2 // Lets you select more or less markers when you beat a Blacklist rival. (Default = 2) (1/6) 78 | PurchasedCarLimit = 10 // Allows you to increase/decrease the amount of cars you can purchase in the car lot. (Default = 10) (1/25) 79 | SBRechargeTime = 25 // Recharge time (in seconds) for Speedbreaker. (Default = 25.0) 80 | SBRechargeSpeedLimit = 100 // Speed limit (mph) for Speedbreaker to refill. (Default = 100.0) 81 | SBMassMultiplier = 2 // Car mass multipiler for Speedbreaker. (Default = 2.0) 82 | 83 | [Pursuit] 84 | HeatLevelOverride = 0 // Force minimum and maximum heat levels for every game event. (0 = False (Default), 1 = True) 85 | MinimumHeatLevel = 0 // Minimum heat level when HeatLevelOverride option is enabled. (Default = 1) 86 | MaximumHeatLevel = 0 // Maximum heat level when HeatLevelOverride option is enabled. (Default = 10) 87 | PursuitActionMode = 0 // Makes In-Race pursuits harder with Roadblocks and support units. (0 = False (Default), 1 = True) 88 | ShowPursuitCops = 1 // Shows Pursuit cop cars on your minimap. (0 = False, 1 = True (Default)) 89 | ShowNonPursuitCops = 0 // Shows Non-Pursuit cop cars on your minimap. (0 = False (Default), 1 = True) 90 | ShowBustedScreenFrame = 1 // Shows RPD Camera frame on Busted cutscene. (0 = False, 1 = True (Default)) 91 | UncensoredBustedScreen = 0 // Removes censor from Busted cutscene. (0 = False (Default), 1 = True) 92 | SpeedingLimit = 67 // Speed limit (in m/s) for Speeding Infraction. (Default = 67.0) 93 | ExcessiveSpeedingLimit = 80.5 // Speed limit (in m/s) for Excessive Speeding Infraction. (Default = 80.5) 94 | RecklessDrivingLimit = 89.4 // Speed limit (in m/s) for Reckless Driving Infraction. (Default = 89.4) 95 | 96 | [VisualTreatment] 97 | EnableVisualTreatmentOverride = 0 // Enables all visual treatment tweaks in this section. (0 = False (Default), 1 = True) 98 | Red = 0.88 // Red amount for Visual Treatment. (Default = 0.88) 99 | Green = 0.8 // Green amount for Visual Treatment. (Default = 0.8) 100 | Blue = 0.44 // Blue amount for Visual Treatment. (Default = 0.44) 101 | ColorBloomIntensity = 1.75 // Color Bloom amount for Visual Treatment. (Default = 1.75) 102 | Saturation = 0.5 // Saturation for Visual Treatment. (Default = 0.5) 103 | BlackBloomIntensity = 0.9 // Black Bloom amount for Visual Treatment. (Default = 0.9) 104 | 105 | [Weather] 106 | AlwaysRain = 0 // Forces rainy weather. (0 = False (Default), 1 = True) 107 | GeneralRainAmount = 0 // General multiplier for rain. (Default = 1) 108 | RoadReflectionAmount = 0 // Road Reflection for rain. (Default = 1) 109 | RainSize = 0 // Falling rain size. (Default = 0.01) 110 | RainIntensity = 0 // Rain intensity. (Default = 0.45) 111 | RainCrossing = 0 // Rain crossing amount. (Default = 0.02) 112 | RainSpeed = 0 // Rain falling speed. (Default = 0.03) 113 | RainGravity = 0 // Rain gravity. (Default = 0.35) 114 | 115 | [Fixes] 116 | DisappearingWheelsFix = 1 // Fixes disappearing wheels when lost focus. Useful for open-wheel cars. (0 = False, 1 = True (Default)) 117 | ExperimentalSplitScreenFix = 0 // (ONLY FOR EXPERTS) (EXPERIMENTAL) Fixes black screen and crash for Split Screen mode. Race behaviours are not fixed yet. (0 = False (Default), 1 = True) 118 | HelicopterBountyFix = 1 // Fixes very low bounty reward and lack of screen message for Helicopter. (0 = False, 1 = True (Default)) 119 | ZeroBountyFix = 1 // Fixes 0 bounty reward for x10 and higher heat levels. (0 = False, 1 = True (Default)) 120 | CarSkinFix = 0 // (EXPERIMENTAL) Fixes skinless ghost cars. (0 = False (Default), 1 = True) 121 | ImmobileCollisionsFix = 0 // (EXPERIMENTAL) Fixes infinite mass of some objects. (0 = False (Default), 1 = True) 122 | DDayFix = 1 // Fixes an issue when the user is forced to play DDay (prologue) events even if they have done so. (0 = False, 1 = True (Default)) 123 | BustedNISFix = 1 // Enables unused Busted NISs by fixing a bug in the chooser function. (0 = False, 1 = True (Default)) 124 | 125 | [Misc] 126 | WindowedMode = 2 // Runs the game in a window instead of full screen. (0 = False (Default), 1 = True, 2 = Bordered) 127 | SkipMovies = 0 // Skips cutscenes. (0 = False (Default), 1 = True) 128 | SkipNISs = 0 // Skips some NISs (non-interactive scenes). (0 = False (Default), 1 = True) 129 | EnableSound = 1 // Enables in-game sounds. (0 = False, 1 = True (Default)) 130 | EnableMusic = 1 // Enables in-game music. (0 = False, 1 = True (Default)) 131 | ShowMessage = 1 // Shows in-game messages when you use some features. (0 = False, 1 = True (Default)) 132 | SkipCareerIntro = 0 // (ONLY FOR EXPERTS) Skips DDay and Career Intro events. (0 = False (Default), 1 = True) 133 | AllowMultipleInstances = 0 // Allows running more than 1 instance of the game. (0 = False (Default), 1 = True) 134 | ExpandMemoryPools = 0 // Expands memory area of the game. (0 = False (Default), 1 = True) 135 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DeepForSpeed: Data Wanted 2 | ## A self-driving car in Need For Speed 3 | The inspiration comes from how Nvidia built a self-driving car with just a single convolutional 4 | neural network instead of many fancy algorithms combined. Here my goal is to replicate 5 | the amazing results they've gotten but inside a game. But i also tried to create it as a platform/interface 6 | in which different architectures can be tested relatively easily, so it can also be used as a benchmark. 7 | So it's like a fun driving simulator (of course not an accurate one) that you can test your own neural networks 8 | at and maybe conduct some experiments. 9 | 10 | ## Watch The Latest Version 11 | [![Watch the video](https://img.youtube.com/vi/t0iqfM36mRc/maxresdefault.jpg)](https://youtu.be/t0iqfM36mRc) 12 | note: this is a cherry picked example and many times model will not perform this well. Im hoping to change that in future versions. 13 | 14 | ## Things used 15 | > Python 3.9 16 | > 17 | > Pytorch 1.10 18 | > 19 | > Numpy 20 | > 21 | > OpenCV 22 | > 23 | > Matplotlib 24 | > 25 | > Need For Speed: Most Wanted 2005 26 | > 27 | > Base architecture 28 | > 29 | > Nvidia's architecture 30 | 31 | 32 | ## How to use it 33 | There is different ways to use it depending on what you want. Additional info can be found inside the scripts. 34 | 35 | 36 | Creating and processing data 37 | 38 | flowchart 39 | 40 | Using models 41 | 42 | flowchart 43 | 44 | 45 | ## Pull requests 46 | TLDR: Basically any improvements are really appreciated. 47 | 48 | - Other Neural Network architectures 49 | - Refinements in the code 50 | - Trained Models 51 | - Anything you can get done on future updates part 52 | 53 | 54 | 55 | ## For Future Updates: 56 | - Add tensorflow board 57 | - Only use np arrays instead of both lists and np arrays in data 58 | - RGB images instead of gray images 59 | - Train on more data 60 | - Increase data resolution 61 | - Controller or a steering wheel to get the input 62 | - Different activation functions 63 | - Try Weight Decay 64 | - Add merging data function for easing data creation 65 | - Save models whilst training 66 | 67 | ### References: 68 | paper by nvidia: https://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf 69 | 70 | Sentdex's PyGta5 playlist: https://www.youtube.com/watch?v=ks4MPfMq8aQ&list=PLQVvvaa0QuDeETZEOy4VdocT7TOjfSA8a 71 | 72 | NFS:MW mods are taken from: https://github.com/ExOptsTeam/NFSMWExOpts 73 | -------------------------------------------------------------------------------- /createData.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import time 3 | import mss 4 | import numpy as np 5 | import keyboard 6 | import os 7 | 8 | 9 | """ 10 | this script records road, minimap and speed-o-meter with the correct key presses associated with them. 11 | I can get around 20fps with this script with i5 9th generation processor. If your fps is less than this 12 | it may not create enough data or play the game right. To check your fps you can uncomment the lines that show fps. 13 | """ 14 | 15 | 16 | class CreateData: 17 | def __init__(self): 18 | # places of the screen that are relevant to us 19 | self.road = {'left': 120, 'top': 310, 'width': 570, 'height': 110} 20 | self.minimap = {'left': 60, 'top': 460, 'width': 125, 'height': 110} 21 | self.speed = {'left': 645, 'top': 535, 'width': 67, 'height': 30} 22 | # variable for seeing fps 23 | self.last_time = time.time() 24 | 25 | def key_press(self): 26 | onehotencoding = [0,0,0,0,0,0] # elements of the list on the left corresponds to [w, a, d, wa, wd, nothing] 27 | if keyboard.is_pressed("w"): 28 | if keyboard.is_pressed("a"): 29 | onehotencoding[3] = 1 30 | elif keyboard.is_pressed("d"): 31 | onehotencoding[4] = 1 32 | else: 33 | onehotencoding[0] = 1 34 | elif keyboard.is_pressed("a"): 35 | onehotencoding[1] = 1 36 | elif keyboard.is_pressed("d"): 37 | onehotencoding[2] = 1 38 | else: 39 | onehotencoding[5] = 1 40 | return onehotencoding 41 | 42 | def get_screen(self): 43 | with mss.mss() as sct: 44 | # take the screenshot of the relevant places on the screen 45 | road_arr = np.array(sct.grab(self.road)) 46 | minimap_arr = np.array(sct.grab(self.minimap)) 47 | speed_arr = np.array(sct.grab(self.speed)) 48 | 49 | road_gray = cv2.cvtColor(road_arr, cv2.COLOR_RGB2GRAY) 50 | minimap_gray = cv2.cvtColor(minimap_arr, cv2.COLOR_RGB2GRAY) 51 | speed_gray = cv2.cvtColor(speed_arr, cv2.COLOR_RGB2GRAY) 52 | 53 | # resize 54 | road_arr = list(cv2.resize(road_gray,(120,60))) 55 | minimap_arr = list(cv2.resize(minimap_gray, (50, 50))) 56 | speed_arr = list(cv2.resize(speed_gray, (15, 15))) 57 | 58 | # uncomment the next lines to test if road is visible if it is not make the needed adjustments 59 | #cv2.imshow("window", np.array(road_arr)) 60 | #if cv2.waitKey(25) & 0xFF == ord("q"): 61 | # cv2.destroyAllWindows() 62 | 63 | # return list of images as a list 64 | return [road_arr, minimap_arr, speed_arr] 65 | 66 | def main(self): 67 | name = input("enter the name for the training file: ") 68 | 69 | # get current directory and add the data to the training_data folder 70 | file = os.getcwd() + "\\training_data\\{}".format(name) 71 | 72 | if not os.path.exists(file): 73 | print("new data created!") 74 | os.mkdir(file) 75 | training_data_X = [] 76 | training_data_Y = [] 77 | 78 | else: 79 | print("data already exists! appending to the data.") 80 | training_data_X = list(np.load(file + f"\\{name}X.npy", allow_pickle=True)) 81 | training_data_Y = list(np.load(file + f"\\{name}y.npy", allow_pickle=True)) 82 | 83 | for i in range(3, 0, -1): 84 | print("data collection starts in {}".format(i)) 85 | time.sleep(1) 86 | 87 | print("started collecting data!") 88 | while True: 89 | key = self.key_press() # key is a one hot encoded array 90 | image = self.get_screen() # image is an array of three elements 91 | 92 | training_data_X.append(image) 93 | training_data_Y.append(key) 94 | 95 | # code snippet by sentdex <3 96 | if len(training_data_X) % 1500 == 0: 97 | print("training files saved! frame count: {}".format(len(training_data_X))) 98 | np.save(file + f"\\{name}X.npy", training_data_X) 99 | np.save(file + f"\\{name}Y.npy", training_data_Y) 100 | 101 | if keyboard.is_pressed("q"): # if q is pressed data collection ends 102 | print("stopping data collection!") 103 | break 104 | 105 | # you can test your fps through uncommenting these lines 106 | # print("fps:", round(1/(time.time() - self.last_time))) 107 | # self.last_time = time.time() 108 | 109 | if __name__ == "__main__": 110 | a = CreateData() 111 | a.main() -------------------------------------------------------------------------------- /images/dataFlowchart.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fehimornek/DeepForSpeed/ddfefa5d87f5a2d2ebe7a6c7145b83c2330c23dd/images/dataFlowchart.jpg -------------------------------------------------------------------------------- /images/nvidia_arch.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fehimornek/DeepForSpeed/ddfefa5d87f5a2d2ebe7a6c7145b83c2330c23dd/images/nvidia_arch.png -------------------------------------------------------------------------------- /images/trainFlowchart.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fehimornek/DeepForSpeed/ddfefa5d87f5a2d2ebe7a6c7145b83c2330c23dd/images/trainFlowchart.jpg -------------------------------------------------------------------------------- /model_architectures.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | # slightly modified nvidia architecture 5 | class nvidia_arch(nn.Module): 6 | """ 7 | 1- nvidia doesn't mention any activation function in their paper so i took the liberty to use ReLU. 8 | 2- nvidia uses 200x66 resolution image but to make things just a little faster and more memory. 9 | efficient we will be using images of size 120x60 but this might change in the future. 10 | 3- hyper parameters are also chosen by me because they are not mentioned. 11 | 4- network will also do additional convolutions to understand information from minimap and speedometer. 12 | """ 13 | 14 | def __str__(self): 15 | return "this is nvidia architecture" 16 | 17 | def __init__(self): 18 | super().__init__() 19 | # conv1 will look at the road, conv2 will look at the minimap and conv3 will look at the speedometer 20 | self.conv1 = nn.Sequential( 21 | nn.Conv2d(in_channels=1, out_channels=24, kernel_size=(5,5), stride=(2,2)), 22 | nn.ReLU(), 23 | nn.Conv2d(in_channels=24, out_channels=36, kernel_size=(5,5), stride=(2,2)), 24 | nn.ReLU(), 25 | nn.Conv2d(in_channels=36, out_channels=48, kernel_size=(5,5), stride=(2,2)), 26 | nn.ReLU(), 27 | nn.Conv2d(in_channels=48, out_channels=64, kernel_size=(3,3), stride=(1,1), padding=1), 28 | nn.ReLU(), 29 | nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(3,3), stride=(1,1)), 30 | nn.Flatten() 31 | ) 32 | self.conv2 = nn.Sequential( 33 | nn.Conv2d(in_channels=1, out_channels=6, kernel_size=(5,5), stride=(2,2)), 34 | nn.ReLU(), 35 | nn.Conv2d(in_channels=6, out_channels=12, kernel_size=(5, 5), stride=(2, 2)), 36 | nn.ReLU(), 37 | nn.Conv2d(in_channels=12, out_channels=18, kernel_size=(5, 5), stride=(2, 2)), 38 | nn.Flatten() 39 | ) 40 | self.conv3 = nn.Sequential( 41 | nn.Conv2d(in_channels=1, out_channels=6, kernel_size=(5, 5), stride=(2, 2)), 42 | nn.ReLU(), 43 | nn.Conv2d(in_channels=6, out_channels=12, kernel_size=(5, 5), stride=(2, 2)), 44 | nn.Flatten() 45 | ) 46 | 47 | # fully connected dense layers 48 | self.linear = nn.Sequential( 49 | # input to the fully connected layer will be a (17x1) image with 64 channels 50 | nn.Linear(in_features=1454, out_features=100, bias=False), 51 | nn.ReLU(), 52 | nn.Linear(in_features=100, out_features=50, bias=False), 53 | nn.ReLU(), 54 | nn.Linear(in_features=50, out_features=10, bias=False), 55 | nn.ReLU(), 56 | nn.Linear(in_features=10, out_features=6, bias=False) 57 | ) 58 | 59 | # x1 is road x2 is minimap and x3 is the speedometer 60 | def forward(self, x1, x2, x3): 61 | x1 = self.conv1(x1) 62 | x2 = self.conv2(x2) 63 | x3 = self.conv3(x3) 64 | x = torch.concat((x1,x2,x3), dim=1) 65 | x = self.linear(x) 66 | return x 67 | 68 | -------------------------------------------------------------------------------- /play.py: -------------------------------------------------------------------------------- 1 | from play_util import * 2 | from createData import CreateData 3 | import model_architectures 4 | import torch 5 | import os 6 | import keyboard 7 | import time 8 | import numpy as np 9 | 10 | def calcProb(tensor): 11 | return (np.array(tensor)*100) / sum(sum(np.array(tensor))) 12 | 13 | 14 | # function that acts as an api between game and the neural network 15 | def playGame(modelName, trainedModelName): 16 | # get the neuralnet from model architectures 17 | neural_net = getattr(model_architectures, modelName) 18 | neuralnet = neural_net() 19 | # check if trained model exists if it does load them else return from the function 20 | nn_location = os.getcwd() + f"\\trained_models\\{trainedModelName}.pth" 21 | if os.path.exists(nn_location): 22 | neuralnet.load_state_dict(torch.load(nn_location)) 23 | neuralnet.eval() 24 | else: 25 | print("that trained model does not exist") 26 | return 27 | 28 | # read the screen 29 | dataloader = CreateData() 30 | 31 | # wait a little before running the script to give time to open the game 32 | for i in range(5,0,-1): 33 | print(i) 34 | time.sleep(1) 35 | 36 | pause = False 37 | # main loop 38 | with torch.no_grad(): 39 | while True: 40 | while not pause: 41 | screen = dataloader.get_screen() 42 | # turn the screen to tensors 43 | road, minimap, speed = torch.tensor(screen[0]), torch.tensor(screen[1]), torch.tensor(screen[2]) 44 | # some dummy dimension for pytorch 45 | road = road[None, None] 46 | minimap = minimap[None, None] 47 | speed = speed[None, None] 48 | output = neuralnet.forward(road/255, minimap/255, speed/255) 49 | print(calcProb(output)) 50 | index = torch.argmax(output) 51 | """ 52 | directx scan codes http://www.gamespp.com/directx/directInputKeyboardScanCodes.html 53 | 0x11 = w 54 | 0x1E = A 55 | 0x20 = D 56 | """ 57 | if index == 0: # press w 58 | PressKey(0x11) 59 | time.sleep(0.3) 60 | ReleaseKey(0x11) 61 | print("forward") 62 | elif index == 1: # press a 63 | PressKey(0x1E) 64 | time.sleep(0.05) 65 | ReleaseKey(0x1E) 66 | print("left") 67 | elif index == 2: # press d 68 | PressKey(0x20) 69 | time.sleep(0.05) 70 | ReleaseKey(0x20) 71 | print("right") 72 | elif index == 3: # press wa 73 | PressKey(0x11) 74 | PressKey(0x1E) 75 | time.sleep(0.05) 76 | ReleaseKey(0x11) 77 | ReleaseKey(0x1E) 78 | print("forward left") 79 | elif index == 4: # press wd 80 | PressKey(0x11) 81 | PressKey(0x20) 82 | time.sleep(0.05) 83 | ReleaseKey(0x11) 84 | ReleaseKey(0x20) 85 | print("forward right") 86 | 87 | elif index == 5: # nothing 88 | time.sleep(0.05) 89 | print("do nothing") 90 | 91 | # if q is pressed quit 92 | if keyboard.is_pressed("q"): 93 | return 94 | 95 | # pause the game 96 | if keyboard.is_pressed("z"): 97 | pause = True 98 | 99 | if keyboard.is_pressed("z"): 100 | pause = False 101 | 102 | playGame("nvidia_arch", "defaultRaw" ) -------------------------------------------------------------------------------- /play_util.py: -------------------------------------------------------------------------------- 1 | """ 2 | inspired by: sentdex 3 | code taken from: https://stackoverflow.com/questions/14489013/simulate-python-keypresses-for-controlling-a-game 4 | """ 5 | 6 | import ctypes 7 | 8 | SendInput = ctypes.windll.user32.SendInput 9 | 10 | # C struct redefinitions 11 | PUL = ctypes.POINTER(ctypes.c_ulong) 12 | class KeyBdInput(ctypes.Structure): 13 | _fields_ = [("wVk", ctypes.c_ushort), 14 | ("wScan", ctypes.c_ushort), 15 | ("dwFlags", ctypes.c_ulong), 16 | ("time", ctypes.c_ulong), 17 | ("dwExtraInfo", PUL)] 18 | 19 | class HardwareInput(ctypes.Structure): 20 | _fields_ = [("uMsg", ctypes.c_ulong), 21 | ("wParamL", ctypes.c_short), 22 | ("wParamH", ctypes.c_ushort)] 23 | 24 | class MouseInput(ctypes.Structure): 25 | _fields_ = [("dx", ctypes.c_long), 26 | ("dy", ctypes.c_long), 27 | ("mouseData", ctypes.c_ulong), 28 | ("dwFlags", ctypes.c_ulong), 29 | ("time",ctypes.c_ulong), 30 | ("dwExtraInfo", PUL)] 31 | 32 | class Input_I(ctypes.Union): 33 | _fields_ = [("ki", KeyBdInput), 34 | ("mi", MouseInput), 35 | ("hi", HardwareInput)] 36 | 37 | class Input(ctypes.Structure): 38 | _fields_ = [("type", ctypes.c_ulong), 39 | ("ii", Input_I)] 40 | # Actuals Functions 41 | 42 | def PressKey(hexKeyCode): 43 | extra = ctypes.c_ulong(0) 44 | ii_ = Input_I() 45 | ii_.ki = KeyBdInput( 0, hexKeyCode, 0x0008, 0, ctypes.pointer(extra) ) 46 | x = Input( ctypes.c_ulong(1), ii_ ) 47 | ctypes.windll.user32.SendInput(1, ctypes.pointer(x), ctypes.sizeof(x)) 48 | 49 | def ReleaseKey(hexKeyCode): 50 | extra = ctypes.c_ulong(0) 51 | ii_ = Input_I() 52 | ii_.ki = KeyBdInput( 0, hexKeyCode, 0x0008 | 0x0002, 0, ctypes.pointer(extra) ) 53 | x = Input( ctypes.c_ulong(1), ii_ ) 54 | ctypes.windll.user32.SendInput(1, ctypes.pointer(x), ctypes.sizeof(x)) 55 | -------------------------------------------------------------------------------- /processData.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import random 4 | 5 | from PIL import Image, ImageEnhance 6 | 7 | """ 8 | The training data is quite imbalanced because most of the time we are just 9 | pressing the forward key and maybe only half of the time we are steering. If our convnet 10 | sees this data it will be super biased to just going forward all the time so we need to change that. 11 | Amount of do nothings is also quite big too this also needs to be taken care of. 12 | """ 13 | 14 | 15 | def preprocess(): 16 | # this code piece is added for safety and ensures that there always will be the folder 17 | check_processed_folder = os.getcwd() + "\\training_data\\balanced" 18 | if not os.path.exists(check_processed_folder): 19 | os.mkdir(check_processed_folder) 20 | 21 | data_name = input("which data do you want to preprocess: ") 22 | file = os.getcwd() + f"\\training_data\\raw\\{data_name}" 23 | 24 | if os.path.exists(file): 25 | print("loading data!") 26 | training_dataX = list(np.load(file + f"\\{data_name}X.npy", allow_pickle=True)) 27 | training_dataY = list(np.load(file + f"\\{data_name}Y.npy", allow_pickle=True)) 28 | else: 29 | print("data doesnt exist!") 30 | return 31 | 32 | os.mkdir(os.getcwd() + f"\\training_data\\processed\\{data_name}") 33 | 34 | forward, right, left, forward_right, forward_left, do_nothing = [], [], [], [], [], [] 35 | 36 | idx = 0 37 | 38 | print("separating data!") 39 | for data in training_dataX: 40 | # if data is a forward 41 | if training_dataY[idx][0] == 1: 42 | """ 43 | append the data and then remove it from training_dataX this is done because 44 | we will remove most of the forwards and then add them back to training_dataX 45 | if we dont remove them we will just have even more forwards which would be super bad. 46 | """ 47 | forward.append(data) 48 | 49 | elif training_dataY[idx][1] == 1: 50 | left.append(data) 51 | 52 | elif training_dataY[idx][2] == 1: 53 | right.append(data) 54 | 55 | elif training_dataY[idx][3] == 1: # forward lefts 56 | forward_left.append(data) 57 | 58 | elif training_dataY[idx][4] == 1: # forward rights 59 | forward_right.append(data) 60 | 61 | elif training_dataY[idx][5] == 1: # do nothing 62 | do_nothing.append(data) 63 | 64 | # used to move on to the next data 65 | idx += 1 66 | 67 | random.shuffle(forward),random.shuffle(forward_left),random.shuffle(forward_right) 68 | random.shuffle(do_nothing), random.shuffle(left),random.shuffle(right) 69 | 70 | lengths = [len(forward),len(forward_left),len(forward_right),len(do_nothing), len(left), len(right)] 71 | 72 | minimum_length = min(lengths) 73 | 74 | balanced_forward = [forward[i] for i in range(round(minimum_length))] 75 | balanced_forward_left = [forward_left[i] for i in range(round(minimum_length))] 76 | balanced_forward_right = [forward_right[i] for i in range(round(minimum_length))] 77 | balanced_do_nothing = [do_nothing[i] for i in range(round(minimum_length))] 78 | balanced_left = [left[i] for i in range(minimum_length)] 79 | balanced_right = [right[i] for i in range(minimum_length)] 80 | 81 | training_dataX = balanced_forward + balanced_left + balanced_right + \ 82 | balanced_forward_left + balanced_forward_right + balanced_do_nothing 83 | 84 | for i in range(minimum_length): 85 | training_dataY.append([1,0,0,0,0,0]) 86 | for i in range(minimum_length): 87 | training_dataY.append([0,1,0,0,0,0]) 88 | for i in range(minimum_length): 89 | training_dataY.append([0,0,1,0,0,0]) 90 | for i in range(minimum_length): 91 | training_dataY.append([0,0,0,1,0,0]) 92 | for i in range(minimum_length): 93 | training_dataY.append([0,0,0,0,1,0]) 94 | for i in range(minimum_length): 95 | training_dataY.append([0,0,0,0,0,1]) 96 | 97 | permutation = np.arange(len(training_dataX)) 98 | np.random.shuffle(permutation) 99 | training_dataY = np.array(training_dataY) 100 | training_dataX = np.array(training_dataX) 101 | dataX_shuffled = training_dataX[permutation] 102 | dataY_shuffled = training_dataY[permutation] 103 | 104 | np.save(os.getcwd() + f"\\training_data\\balanced\\{data_name}\\{data_name}X.npy", dataX_shuffled) 105 | np.save(os.getcwd() + f"\\training_data\\balanced\\{data_name}\\{data_name}Y.npy", dataY_shuffled) 106 | print("balanced data saved!") 107 | 108 | """ 109 | Augmentations 110 | 1-change brightness values 111 | 2-flip the images when going either to right or left to create more turning data 112 | might add rotation by a small degree 113 | """ 114 | 115 | def augmentData(): 116 | # this code piece is added for safety and ensures that there always will be the folder 117 | check_augment_folder = os.getcwd() + "\\training_data\\augmented" 118 | if not os.path.exists(check_augment_folder): 119 | os.mkdir(check_augment_folder) 120 | 121 | data_name = input("what data do you want to augment: ") 122 | data_location = input("will you augment raw data or processed data? (options: raw - augmented): ") 123 | img_path = os.getcwd() + f"\\training_data\\{data_location}\\{data_name}" 124 | 125 | # load data in a safe manner 126 | if os.path.exists(img_path): 127 | print("loading data!") 128 | imageData = np.load(img_path + f"\\{data_name}X.npy", allow_pickle=True) 129 | labels = np.load(img_path + f"\\{data_name}Y.npy", allow_pickle=True) 130 | else: 131 | print("data doesnt exist!") 132 | return 133 | 134 | new_images = [] 135 | new_labels = [] 136 | 137 | print("processing darker versions of images...") 138 | # for every image add that image that's %40 darker 139 | for i in range(len(imageData)): 140 | # get every individual image 141 | image = np.array(imageData[i][0]) 142 | image_normal = Image.fromarray(image, mode="L") 143 | # get a new image that has %60 of the originals brightness 144 | img = ImageEnhance.Brightness(image_normal) 145 | image_dark = img.enhance(0.6) 146 | # add the new image to the data list 147 | image_arr = np.array(image_dark) 148 | new_images.append([image_arr, imageData[i][1], imageData[i][2]]) 149 | new_labels.append(labels[i]) 150 | 151 | """ 152 | for images where label is either right, left, forward right or forward left we will flip the images. 153 | example: if the images label is left then when we flip it, the flipped image will have label right. 154 | """ 155 | print("processing flipped images...") 156 | for i in range(len(imageData)): 157 | label = labels[i] 158 | # if label is not forward and do nothing 159 | if label[0] == 0 and label[-1] == 0: 160 | # take road and minimap images and flip them 161 | road_image = np.array(imageData[i][0]) 162 | minimap_image = np.array(imageData[i][1]) 163 | road_pil = Image.fromarray(road_image, mode="L") 164 | minimap_pil = Image.fromarray(minimap_image, mode="L") 165 | road_flipped = road_pil.transpose(Image.FLIP_LEFT_RIGHT) 166 | minimap_flipped = minimap_pil.transpose(Image.FLIP_LEFT_RIGHT) 167 | # add the flipped images to the list 168 | new_images.append([np.array(road_flipped), np.array(minimap_flipped), imageData[i][2]]) 169 | # get the correct label for the flipped image (logic is explained above) 170 | if label[1] == 1: 171 | new_labels.append([0,0,1,0,0,0]) 172 | elif label[2] == 1: 173 | new_labels.append([0,1,0,0,0,0]) 174 | elif label[3] == 1: 175 | new_labels.append([0,0,0,0,1,0]) 176 | elif label[4] == 1: 177 | new_labels.append([0,0,0,1,0,0]) 178 | 179 | new_image_data = np.concatenate((imageData, new_images)) 180 | new_label_data = np.concatenate((labels, new_labels)) 181 | perm = np.arange((len(new_image_data))) 182 | np.random.shuffle(perm) 183 | image_shuffled = new_image_data[perm] 184 | label_shuffled = new_label_data[perm] 185 | print("operations complete! previous data amount was", len(imageData), "new data amount is", len(image_shuffled)) 186 | folder = input("what your data should be saved as: ") 187 | print("saving data!") 188 | os.mkdir(os.getcwd() + f"\\training_data\\augmented\\{folder}") 189 | np.save(os.getcwd() + f"\\training_data\\augmented\\{folder}\\{folder}X.npy", image_shuffled) 190 | np.save(os.getcwd() + f"\\training_data\\augmented\\{folder}\\{folder}Y.npy", label_shuffled) 191 | print("augmented data saved!") 192 | 193 | augmentData() -------------------------------------------------------------------------------- /train_model.py: -------------------------------------------------------------------------------- 1 | import time 2 | import model_architectures 3 | import torch.optim as optim 4 | import torch 5 | import os 6 | import numpy as np 7 | import matplotlib.pyplot as plt 8 | 9 | # training the neural network 10 | def training(neural_net_name, epochs, batches, learning_rate,neural_net_folder_name = "default"): 11 | """ 12 | - neural_net_name = is a string, used for choosing model from model_architectures.py 13 | - epochs = is an int, how many times neural network will see the whole data 14 | - batches = is an int, how many images will be used for training at every iteration 15 | - learning_rate = is a float, determines how much of an effect each gradient update has on the network 16 | - neural_net_folder_name = is a string, which is the saved models name. If you write a new name instead of default it will start 17 | training a new neural net. 18 | """ 19 | 20 | ## get the training data 21 | data_name = input("enter the name of the data: ") 22 | data_location = input("which folder do you want to get the data from? (options: raw - balanced - augmented): ") 23 | file = os.getcwd() + f"\\training_data\\{data_location}\\{data_name}" 24 | 25 | ## check if the training file exists, 26 | if os.path.exists(file): 27 | print("loading training data...") 28 | training_data_X = np.load(file + f"\\{data_name}X.npy", allow_pickle=True) 29 | training_data_Y = np.load(file + f"\\{data_name}Y.npy", allow_pickle=True) 30 | else: 31 | print("data doesnt exist at this path!") 32 | 33 | # get the model from model_architectures 34 | class_neuralnet = getattr(model_architectures, neural_net_name) 35 | neural_net = class_neuralnet() 36 | neural_net.train() 37 | device = "cuda" if torch.cuda.is_available() else "cpu" 38 | neural_net = neural_net.to(device) 39 | 40 | print("loaded neural network: ", neural_net) 41 | print("using: ", device) 42 | 43 | # get or set a new model weights 44 | nn_file = os.getcwd() + f"\\trained_models\\{neural_net_folder_name}.pth" 45 | if os.path.exists(nn_file): 46 | neural_net.load_state_dict(torch.load(os.getcwd() + f"\\trained_models\\{neural_net_folder_name}.pth")) 47 | print("loaded model weights of {}.pth".format(neural_net_folder_name)) 48 | else: 49 | print("model weights initialized!") 50 | 51 | optimizer = optim.Adam(neural_net.parameters(), lr=learning_rate) 52 | loss_func = torch.nn.CrossEntropyLoss() 53 | 54 | # separation of images in training_data_X 55 | road = [] 56 | minimap = [] 57 | speed = [] 58 | 59 | print("separating data...") 60 | for data in training_data_X: 61 | road.append(data[0]) 62 | minimap.append(data[1]) 63 | speed.append(data[2]) 64 | 65 | # turn the training data to tensor and load them to gpu with .cuda() 66 | road, minimap, speed, training_data_Y = torch.tensor(road).cuda(), torch.tensor(minimap).cuda(), torch.tensor(speed).cuda(), torch.tensor(training_data_Y).cuda() 67 | loss_arr = [] 68 | start_time = time.time() 69 | print("starting training!") 70 | # training starts here 71 | for epoch in range(epochs): 72 | # take random permutation of the data this will be used for mini batch training 73 | permutation = torch.randperm(len(road)) 74 | print("epoch: ", epoch + 1) 75 | for i in range(0, len(road), batches): 76 | # take indices using the random permutation 77 | indices = permutation[i:i + batches] 78 | # take batches of data with the indices and then turn them into tensors because numpy arrays can't be fed to the neural net 79 | road_batch, minimap_batch, speed_batch, y_batch = road[indices], minimap[indices], speed[indices], \ 80 | training_data_Y[indices].float() 81 | 82 | # add a dummy dimension because we dont have a channel value 83 | road_batch = road_batch[None, :] 84 | minimap_batch = minimap_batch[None, :] 85 | speed_batch = speed_batch[None, :] 86 | 87 | # turn the data from (channel, batches, width, height ) to (batches, channel, width, height ) 88 | road_batch = torch.permute(road_batch, (1, 0, 2, 3)) 89 | minimap_batch = torch.permute(minimap_batch, (1, 0, 2, 3)) 90 | speed_batch = torch.permute(speed_batch, (1, 0, 2, 3)) 91 | 92 | """ 93 | y_batch = [0,0,0,0,0,1] is sth like this and argmax turns it into y_batch = 6 this 94 | is needed because the loss function expects target as an int 95 | """ 96 | y_batch = torch.argmax(y_batch, dim=1) 97 | 98 | """ 99 | we have to turn data into float because pytorch expects data to be float type. This can be achieved by dividing by 255 which also 100 | regularizes the data. 101 | """ 102 | output = neural_net(road_batch / 255, minimap_batch / 255, speed_batch / 255) 103 | # calculate the error 104 | loss = loss_func(output, y_batch) 105 | loss_arr.append(loss.detach().cpu().numpy()) 106 | # backprop, thank god pytorch for that am i right? lol 107 | neural_net.zero_grad() 108 | loss.backward() 109 | optimizer.step() 110 | 111 | torch.save(neural_net.state_dict(), os.getcwd() + f"\\trained_models\\{neural_net_folder_name}.pth") 112 | print("saved neural network weights!") 113 | print("training took: ", (time.time() - start_time) / 60, "minutes") 114 | # show how loss changed 115 | plt.plot(loss_arr) 116 | plt.show() 117 | 118 | 119 | if __name__ == "__main__": 120 | training(learning_rate=0.001, epochs=200, neural_net_name="nvidia_arch", batches=32, neural_net_folder_name="defaultBetter") -------------------------------------------------------------------------------- /trained_models/defaultRaw.pth: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fehimornek/DeepForSpeed/ddfefa5d87f5a2d2ebe7a6c7145b83c2330c23dd/trained_models/defaultRaw.pth --------------------------------------------------------------------------------