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PromptKing/
GTA5_PROCESS_LEARNING_AI

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graph-ml

gpl-3.0

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import contextlib import os from matplotlib import pyplot as plt import numpy as np import torch import torch.nn as nn import torch.optim as optim import requests from torchvision import datasets, transforms import psutil import time import subprocess import onnxruntime as ort import matplotlib.pyplot as plt import numpy as np import numexpr as ne

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("janpase97/codeformer-pretrained")

model = AutoModelForSeq2SeqLM.from_pretrained("janpase97/codeformer-pretrained")

def check_graphics_api(target_app_name): graphics_api = None

with contextlib.suppress(subprocess.CalledProcessError):
    output = subprocess.check_output(['tasklist', '/FI', f'imagename eq {target_app_name}', '/M']).decode('utf-8')
    if "opengl32.dll" in output:
        graphics_api = "OpenGL"
    elif "d3d11.dll" in output:
        graphics_api = "DirectX11"
    elif "d3d12.dll" in output:
        graphics_api = "DirectX12"
    elif "vulkan" in output:
        graphics_api = "VULKAN"       
return graphics_api

Get the target application's process object

def get_target_app_process(target_app_name): return next( ( process for process in psutil.process_iter(['name']) if process.info['name'] == target_app_name ), None, )

Attach the AI to the application's process by PID

def attach_ai_to_app_pid(target_app_process): if target_app_process is not None: print(f"AI is attached to the application's process with PID: {target_app_process.pid}") return True else: print("Could not find the target application's process to attach the AI.") return False

Check if the targeted application is running

def is_target_app_running(target_app_name): return any( process.info['name'] == target_app_name for process in psutil.process_iter(['name']) )

Create the directory if it doesn't exist

directory = r"G:\Epic Games\GTAV\GTA5_AI\trained_models" if not os.path.exists(directory): os.makedirs(directory)

Define the neural network model

class NanoCircuit(nn.Module): def init(self): super(NanoCircuit, self).init() self.fc1 = nn.Linear(784, 128) self.fc2 = nn.Linear(128, 10)

def forward(self, x):
    x = x.view(-1, 784)  # Reshape the input from (batch_size, 28, 28) to (batch_size, 784)
    x = torch.relu(self.fc1(x))
    x = self.fc2(x)
    return x

Set the device to GPU if available

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

Load the MNIST dataset

transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

Initialize the model and move it to the GPU

model = NanoCircuit().to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)

Train the model on the GPU with a data cap

def train_with_data_cap(model, data_loader, criterion, optimizer, device, data_cap_gb): data_processed = 0 data_cap_bytes = data_cap_gb * (1024 ** 3) epoch = 0

while data_processed < data_cap_bytes:
    running_loss = 0.0
    for i, data in enumerate(data_loader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        # Update the amount of data processed
        data_processed += inputs.nelement() * inputs.element_size()
        if data_processed >= data_cap_bytes:
            break

        optimizer.zero_grad()

        outputs = model(inputs.view(-1, 28 * 28))
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

    epoch += 1
    print(f"Epoch {epoch}, Loss: {running_loss / (i + 1)}")
    print(f"Data processed: {data_processed / (1024 ** 3):.2f} GB")

return model

Save the updated model as a .onnx file

def save_model(model, filepath): dummy_input = torch.randn(1, 1, 28, 28).to(device) torch.onnx.export(model, dummy_input, filepath, input_names=['input'], output_names=['output'], opset_version=11)

Train the model with a 1 GB data cap

trained_model = train_with_data_cap(model, train_loader, criterion, optimizer, device, data_cap_gb=50) save_model(trained_model, os.path.join(directory, 'GTA5_TRAINED.onnx'))

target_app_name = "GTA5_TRAINED.exe" save_interval_seconds = 5 * 60 application_was_running = False while True: if is_target_app_running(target_app_name): print("Target application is running. Training and updating the model...") trained_model = train_with_data_cap(model, train_loader, criterion, optimizer, device, data_cap_gb=.1) save_model(trained_model, os.path.join(directory, 'GTA5_TRAINED.onnx')) application_was_running = True elif application_was_running: print("Target application has exited. Saving the model...") save_model(trained_model, os.path.join(directory, 'GTA5_TRAINED.onnx')) print("Finished training and saved the model.") break else: print("Target application is not running. Waiting to start training and updating the model...")

time.sleep(save_interval_seconds)

def train_with_data_cap(model, data_loader, criterion, optimizer, device, data_cap_gb): data_processed = 0 data_cap_bytes = data_cap_gb * (1024 ** 3) epoch = 0

while data_processed < data_cap_bytes:
    running_loss = 0.0
    for i, data in enumerate(data_loader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        # Update the amount of data processed
        data_processed += inputs.nelement() * inputs.element_size()
        if data_processed >= data_cap_bytes:
            break

        optimizer.zero_grad()

        # Compute the outputs and loss using numexpr
        outputs = model(inputs.view(-1, 28 * 28))
        outputs = outputs.cpu().detach().numpy()
        labels = labels.cpu().detach().numpy()
        loss = ne.evaluate("sum(-log(outputs[arange(outputs.shape[0]), labels]))") / len(labels)

        # Backpropagate and update the model parameters
        ne.evaluate("loss", out=loss)
        grad_outputs = np.ones_like(outputs)
        grad_outputs[np.arange(grad_outputs.shape[0]), labels] = -1
        grad_outputs /= len(labels)
        grad_outputs = ne.evaluate("grad_outputs * loss_grad")
        grad_outputs = torch.from_numpy(grad_outputs).to(device)
        outputs = torch.from_numpy(outputs).to(device)
        loss.backward(grad_outputs)
        optimizer.step()

        running_loss += loss.item()

    epoch += 1
    print(f"Epoch {epoch}, Loss: {running_loss / (i + 1)}")
    print(f"Data processed: {data_processed / (1024 ** 3):.2f} GB")

return model

Train the model with a 10 GB data cap

trained_model = train_with_data_cap(model, train_loader, criterion, optimizer, os.device_encoding, data_cap_gb=10) save_model(trained_model, os.path.join(directory, 'GTA5_TRAINED.onnx'))

target_app_name = "GTA5.exe" save_interval_seconds = 5 * 60 application_was_running = False while True: if is_target_app_running(target_app_name): print("Target application is running. Training and updating the model...") trained_model = train_with_data_cap(model, train_loader, criterion, optimizer, os.device_encoding, data_cap_gb=10) save_model(trained_model, os.path.join(directory, 'GTA5_TRAINED.onnx')) application_was_running = True elif application_was_running: print("Target application has exited. Saving the model...") save_model(trained_model, os.path.join(directory, 'GTA5_TRAINED.onnx')) print("Finished training and saved the model.") break else: print("Target application is not running. Waiting to start training and updating the model...")

time.sleep(save_interval_seconds)

def train_with_data_cap(model, data_loader, criterion, optimizer, device, data_cap_gb): data_processed = 0 data_cap_bytes = data_cap_gb * (1024 ** 3) epoch = 0

while data_processed < data_cap_bytes:
    running_loss = 0.0
    for i, data in enumerate(data_loader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        # Update the amount of data processed
        data_processed += inputs.nelement() * inputs.element_size()
        if data_processed >= data_cap_bytes:
            break

        optimizer.zero_grad()

        # Compute the outputs and loss using numexpr
        outputs = model(inputs.view(-1, 28 * 28))
        outputs = outputs.cpu().detach().numpy()
        labels = labels.cpu().detach().numpy()
        loss = ne.evaluate("sum(-log(outputs[arange(outputs.shape[0]), labels]))") / len(labels)

        # Backpropagate and update the model parameters
        ne.evaluate("loss", out=loss)
        grad_outputs = np.ones_like(outputs)
        grad_outputs[np.arange(grad_outputs.shape[0]), labels] = -1
        grad_outputs /= len(labels)
        grad_outputs = ne.evaluate("grad_outputs * loss_grad")
        grad_outputs = torch.from_numpy(grad_outputs).to(device)
        outputs = torch.from_numpy(outputs).to(device)
        loss.backward(grad_outputs)
        optimizer.step()

        running_loss += loss.item()

    epoch += 1
    print(f"Epoch {epoch}, Loss: {running_loss / (i + 1)}")
    print(f"Data processed: {data_processed / (1024 ** 3):.2f} GB")

return model

target_app_name = "GTA5.exe" save_interval_seconds = 1 * 60 application_was_running = False

while True: if is_target_app_running(target_app_name): print("Target application is running. Training and updating the model...") trained_model = train_with_data_cap(model, train_loader, criterion, optimizer, device, data_cap_gb=10) save_model(trained_model, os.path.join(directory, 'GTA5_TRAINED.onnx')) application_was_running = True elif application_was_running: print("Target application has exited. Saving the model...") save_model(trained_model, os.path.join(directory, 'GTA5_TRAINED.onnx')) print("Finished training and saved the model.") break else: start_time = time.time() print("Target application is not running. Waiting to detect the graphics API...") while (time.time() - start_time) < 5: if is_target_app_running(target_app_name): if graphics_api := check_graphics_api(target_app_name): print(f"Detected {graphics_api} in the target application.") break else: print("Could not detect the graphics API used in the target application.") time.sleep(1)

if not is_target_app_running(target_app_name):
    print("Target application not detected in 5 seconds. Shutting down the AI.")
    break

while True: if is_target_app_running(target_app_name): if graphics_api := check_graphics_api(target_app_name): print(f"Detected {graphics_api} in the target application.") else: print("Could not detect the graphics API used in the target application.") else: start_time = time.time() print("Target application is not running. Waiting to start training and updating the model...") while (time.time() - start_time) < 5: if is_target_app_running(target_app_name): print(f"Detected {graphics_api} in the target application.") break time.sleep(1)

    if not is_target_app_running(target_app_name):
        print("Target application not detected in 5 seconds. Shutting down the AI.")
        break

#Generate some random data for the boxplots np.random.seed(0) original_data = np.random.normal(0, 1, 100) trained_data = np.random.normal(0.5, 1, 100)

    # Create a box plot of the original and trained data
    plt.figure()
    plt.boxplot([original_data, trained_data], labels=["Original Data", "Trained Data"])
    plt.title("Boxplot of Original and Trained Data")
    plt.ylabel("Values")
    plt.show()

    # Save the box plot as an image
    plt.savefig(r"G:\Epic Games\GTAV\GTA5_AI\Plot Box Comparison\boxplot_comparison.png")

    application_was_running = True
elif application_was_running:
    print("Target application has exited. Saving the model...")
    save_model(trained_model, os.path.join(directory, 'GTA5_TRAINED.onnx'))
    print("Finished training and saved the model.")
    break
else:
    start_time = time.time()
    print("Target application is not running. Waiting to detect the graphics API...")
    while (time.time() - start_time) < 5:
        if is_target_app_running(target_app_name):
            if graphics_api := check_graphics_api(target_app_name):
                print(f"Detected {graphics_api} in the target application.")
                break
            else:
                print("Could not detect the graphics API used in the target application.")
        time.sleep(1)

    if not is_target_app_running(target_app_name):
        print("Target application not detected in 5 seconds. Shutting down the AI.")
        break

PromptKing/GTA5_PROCESS_LEARNING_AI

Get the target application's process object

Attach the AI to the application's process by PID

Check if the targeted application is running

Create the directory if it doesn't exist

Define the neural network model

Set the device to GPU if available

Load the MNIST dataset

Initialize the model and move it to the GPU

Train the model on the GPU with a data cap

Save the updated model as a .onnx file

Train the model with a 1 GB data cap

Train the model with a 10 GB data cap

PromptKing/
GTA5_PROCESS_LEARNING_AI