@ -51,7 +51,7 @@ def graphsearch(initial_state: State, map, goal_list, fringe: List[Node] = None,
|
||||
explored_states = set()
|
||||
fringe_states = set()
|
||||
|
||||
# root Node
|
||||
# train Node
|
||||
fringe.append(Node(initial_state))
|
||||
fringe_states.add((initial_state.row, initial_state.column, initial_state.direction))
|
||||
|
||||
@ -71,7 +71,7 @@ def graphsearch(initial_state: State, map, goal_list, fringe: List[Node] = None,
|
||||
parent = element.parent
|
||||
|
||||
while parent is not None:
|
||||
# root's action will be None, don't add it
|
||||
# train's action will be None, don't add it
|
||||
if parent.action is not None:
|
||||
actions_sequence.append(parent.action)
|
||||
parent = parent.parent
|
||||
|
BIN
algorithms/neural_network/data/test/grass/grass1.png
Normal file
After Width: | Height: | Size: 814 B |
BIN
algorithms/neural_network/data/test/grass/grass2.png
Normal file
After Width: | Height: | Size: 820 B |
BIN
algorithms/neural_network/data/test/grass/grass3.png
Normal file
After Width: | Height: | Size: 789 B |
BIN
algorithms/neural_network/data/test/grass/grass4.png
Normal file
After Width: | Height: | Size: 1.0 KiB |
BIN
algorithms/neural_network/data/test/sand/sand.png
Normal file
After Width: | Height: | Size: 760 B |
BIN
algorithms/neural_network/data/test/tree/grass_with_tree.jpg
Normal file
After Width: | Height: | Size: 2.2 KiB |
BIN
algorithms/neural_network/data/test/water/water.png
Normal file
After Width: | Height: | Size: 725 B |
@ -0,0 +1 @@
|
||||
{}
|
@ -0,0 +1 @@
|
||||
{}
|
@ -0,0 +1 @@
|
||||
{}
|
@ -0,0 +1 @@
|
||||
{}
|
48
algorithms/neural_network/neural_network.py
Normal file
@ -0,0 +1,48 @@
|
||||
import torch
|
||||
import pytorch_lightning as pl
|
||||
import torch.nn as nn
|
||||
from torch.optim import SGD, Adam, lr_scheduler
|
||||
import torch.nn.functional as F
|
||||
from torch.utils.data import DataLoader
|
||||
from watersandtreegrass import WaterSandTreeGrass
|
||||
from common.constants import DEVICE, BATCH_SIZE, NUM_EPOCHS, LEARNING_RATE, SETUP_PHOTOS, ID_TO_CLASS
|
||||
|
||||
|
||||
class NeuralNetwork(pl.LightningModule):
|
||||
def __init__(self, numChannels=3, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, num_classes=4):
|
||||
super().__init__()
|
||||
self.layer = nn.Sequential(
|
||||
nn.Linear(36*36*3, 300),
|
||||
nn.ReLU(),
|
||||
nn.Linear(300, 4),
|
||||
nn.LogSoftmax(dim=-1)
|
||||
)
|
||||
self.batch_size = batch_size
|
||||
self.learning_rate = learning_rate
|
||||
|
||||
def forward(self, x):
|
||||
x = x.reshape(x.shape[0], -1)
|
||||
x = self.layer(x)
|
||||
return x
|
||||
|
||||
def configure_optimizers(self):
|
||||
optimizer = SGD(self.parameters(), lr=self.learning_rate)
|
||||
return optimizer
|
||||
|
||||
def training_step(self, batch, batch_idx):
|
||||
x, y = batch
|
||||
scores = self(x)
|
||||
loss = F.nll_loss(scores, y)
|
||||
return loss
|
||||
|
||||
def validation_step(self, batch, batch_idx):
|
||||
x, y = batch
|
||||
scores = self(x)
|
||||
val_loss = F.nll_loss(scores, y)
|
||||
self.log("val_loss", val_loss, on_step=True, on_epoch=True, sync_dist=True)
|
||||
|
||||
def test_step(self, batch, batch_idx):
|
||||
x, y = batch
|
||||
scores = self(x)
|
||||
test_loss = F.nll_loss(scores, y)
|
||||
self.log("test_loss", test_loss, on_step=True, on_epoch=True, sync_dist=True)
|
125
algorithms/neural_network/neural_network_interface.py
Normal file
@ -0,0 +1,125 @@
|
||||
import torch
|
||||
import common.helpers
|
||||
from common.constants import DEVICE, BATCH_SIZE, NUM_EPOCHS, LEARNING_RATE, SETUP_PHOTOS, ID_TO_CLASS
|
||||
from watersandtreegrass import WaterSandTreeGrass
|
||||
from torch.utils.data import DataLoader
|
||||
from neural_network import NeuralNetwork
|
||||
from torchvision.io import read_image, ImageReadMode
|
||||
import torch.nn as nn
|
||||
from torch.optim import Adam
|
||||
import matplotlib.pyplot as plt
|
||||
import pytorch_lightning as pl
|
||||
from pytorch_lightning.callbacks import EarlyStopping
|
||||
|
||||
|
||||
def train(model):
|
||||
model = model.to(DEVICE)
|
||||
model.train()
|
||||
trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
|
||||
testset = WaterSandTreeGrass('./data/test_csv_file.csv', transform=SETUP_PHOTOS)
|
||||
train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
|
||||
test_loader = DataLoader(testset, batch_size=BATCH_SIZE, shuffle=True)
|
||||
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = Adam(model.parameters(), lr=LEARNING_RATE)
|
||||
|
||||
for epoch in range(NUM_EPOCHS):
|
||||
for batch_idx, (data, targets) in enumerate(train_loader):
|
||||
data = data.to(device=DEVICE)
|
||||
targets = targets.to(device=DEVICE)
|
||||
|
||||
scores = model(data)
|
||||
loss = criterion(scores, targets)
|
||||
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
|
||||
optimizer.step()
|
||||
|
||||
if batch_idx % 4 == 0:
|
||||
print("epoch: %d loss: %.4f" % (epoch, loss.item()))
|
||||
|
||||
print("FINISHED TRAINING!")
|
||||
torch.save(model.state_dict(), "./learnednetwork.pth")
|
||||
|
||||
print("Checking accuracy for the train set.")
|
||||
check_accuracy(train_loader)
|
||||
print("Checking accuracy for the test set.")
|
||||
check_accuracy(test_loader)
|
||||
print("Checking accuracy for the tiles.")
|
||||
check_accuracy_tiles()
|
||||
|
||||
|
||||
def check_accuracy_tiles():
|
||||
answer = 0
|
||||
for i in range(100):
|
||||
if what_is_it('../../resources/textures/grass_with_tree.jpg') == 'tree':
|
||||
answer = answer + 1
|
||||
print("Accuracy(%) grass_with_tree.jpg", answer)
|
||||
|
||||
answer = 0
|
||||
for i in range(100):
|
||||
if what_is_it('../../resources/textures/grass2.png') == 'grass':
|
||||
answer = answer + 1
|
||||
print("Accuracy(%) grass2.png", answer)
|
||||
|
||||
answer = 0
|
||||
for i in range(100):
|
||||
if what_is_it('../../resources/textures/grass3.png') == 'grass':
|
||||
answer = answer + 1
|
||||
print("Accuracy(%) grass3.png", answer)
|
||||
|
||||
answer = 0
|
||||
for i in range(100):
|
||||
if what_is_it('../../resources/textures/grass4.png') == 'grass':
|
||||
answer = answer + 1
|
||||
print("Accuracy(%) grass4.png", answer)
|
||||
|
||||
answer = 0
|
||||
for i in range(100):
|
||||
if what_is_it('../../resources/textures/grass1.png') == 'grass':
|
||||
answer = answer + 1
|
||||
print("Accuracy(%) grass1.png", answer)
|
||||
|
||||
answer = 0
|
||||
for i in range(100):
|
||||
if what_is_it('../../resources/textures/water.png') == 'water':
|
||||
answer = answer + 1
|
||||
print("Accuracy(%) water.png", answer)
|
||||
|
||||
answer = 0
|
||||
for i in range(100):
|
||||
if what_is_it('../../resources/textures/sand.png') == 'sand':
|
||||
answer = answer + 1
|
||||
print("Accuracy(%) sand.png", answer)
|
||||
|
||||
|
||||
def what_is_it(img_path, show_img=False):
|
||||
image = read_image(img_path, mode=ImageReadMode.RGB)
|
||||
if show_img:
|
||||
plt.imshow(plt.imread(img_path))
|
||||
plt.show()
|
||||
image = SETUP_PHOTOS(image).unsqueeze(0)
|
||||
model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_3/checkpoints/epoch=8-step=810.ckpt')
|
||||
|
||||
with torch.no_grad():
|
||||
model.eval()
|
||||
idx = int(model(image).argmax(dim=1))
|
||||
return ID_TO_CLASS[idx]
|
||||
|
||||
|
||||
CNN = NeuralNetwork()
|
||||
|
||||
|
||||
trainer = pl.Trainer(accelerator='gpu', devices=1, auto_scale_batch_size=True, callbacks=[EarlyStopping('val_loss')], max_epochs=NUM_EPOCHS)
|
||||
#trainer = pl.Trainer(accelerator='gpu', devices=1, auto_lr_find=True, max_epochs=NUM_EPOCHS)
|
||||
|
||||
trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
|
||||
testset = WaterSandTreeGrass('./data/test_csv_file.csv', transform=SETUP_PHOTOS)
|
||||
train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
|
||||
test_loader = DataLoader(testset, batch_size=BATCH_SIZE)
|
||||
|
||||
#trainer.fit(CNN, train_loader, test_loader)
|
||||
#trainer.tune(CNN, train_loader, test_loader)
|
||||
check_accuracy_tiles()
|
||||
print(what_is_it('../../resources/textures/sand.png', True))
|
25
algorithms/neural_network/watersandtreegrass.py
Normal file
@ -0,0 +1,25 @@
|
||||
import torch
|
||||
from torch.utils.data import Dataset
|
||||
import pandas as pd
|
||||
from torchvision.io import read_image, ImageReadMode
|
||||
from common.helpers import createCSV
|
||||
|
||||
|
||||
class WaterSandTreeGrass(Dataset):
|
||||
def __init__(self, annotations_file, transform=None):
|
||||
createCSV()
|
||||
self.img_labels = pd.read_csv(annotations_file)
|
||||
self.transform = transform
|
||||
|
||||
def __len__(self):
|
||||
return len(self.img_labels)
|
||||
|
||||
def __getitem__(self, idx):
|
||||
image = read_image(self.img_labels.iloc[idx, 0], mode=ImageReadMode.RGB)
|
||||
label = torch.tensor(int(self.img_labels.iloc[idx, 1]))
|
||||
|
||||
if self.transform:
|
||||
image = self.transform(image)
|
||||
|
||||
return image, label
|
||||
|
@ -1,4 +1,6 @@
|
||||
from enum import Enum
|
||||
import torchvision.transforms as transforms
|
||||
import torch
|
||||
|
||||
GAME_TITLE = 'WMICraft'
|
||||
WINDOW_HEIGHT = 800
|
||||
@ -63,12 +65,34 @@ ACTION = {
|
||||
"go": 0,
|
||||
}
|
||||
|
||||
LEFT = 'LEFT'
|
||||
RIGHT = 'RIGHT'
|
||||
UP = 'UP'
|
||||
DOWN = 'DOWN'
|
||||
|
||||
# HEALTH_BAR
|
||||
BAR_ANIMATION_SPEED = 1
|
||||
BAR_WIDTH_MULTIPLIER = 0.9 # (0;1>
|
||||
BAR_HEIGHT_MULTIPLIER = 0.1
|
||||
|
||||
LEFT = 'LEFT'
|
||||
RIGHT = 'RIGHT'
|
||||
UP = 'UP'
|
||||
DOWN = 'DOWN'
|
||||
|
||||
#NEURAL_NETWORK
|
||||
LEARNING_RATE = 0.13182567385564073
|
||||
BATCH_SIZE = 64
|
||||
NUM_EPOCHS = 50
|
||||
|
||||
DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
|
||||
print("Using ", DEVICE)
|
||||
CLASSES = ['grass', 'sand', 'tree', 'water']
|
||||
|
||||
SETUP_PHOTOS = transforms.Compose([
|
||||
transforms.Resize(36),
|
||||
transforms.CenterCrop(36),
|
||||
transforms.ToPILImage(),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
|
||||
])
|
||||
|
||||
ID_TO_CLASS = {i: j for i, j in enumerate(CLASSES)}
|
||||
CLASS_TO_ID = {value: key for key, value in ID_TO_CLASS.items()}
|
||||
|
||||
|
@ -1,6 +1,9 @@
|
||||
from typing import Tuple, List
|
||||
|
||||
import pygame
|
||||
from common.constants import GRID_CELL_PADDING, GRID_CELL_SIZE, COLUMNS, ROWS, CLASSES, CLASS_TO_ID
|
||||
import csv
|
||||
import os
|
||||
|
||||
from common.constants import GRID_CELL_PADDING, GRID_CELL_SIZE
|
||||
from common.constants import ROWS, COLUMNS, LEFT, RIGHT, UP, DOWN
|
||||
@ -24,6 +27,44 @@ def draw_text(text, color, surface, x, y, text_size=30, is_bold=False):
|
||||
surface.blit(textobj, textrect)
|
||||
|
||||
|
||||
def createCSV():
|
||||
train_data_path = './data/train'
|
||||
test_data_path = './data/test'
|
||||
|
||||
if os.path.exists(train_data_path):
|
||||
train_csvfile = open('./data/train_csv_file.csv', 'w', newline="")
|
||||
writer = csv.writer(train_csvfile)
|
||||
writer.writerow(["filepath", "type"])
|
||||
|
||||
for class_name in CLASSES:
|
||||
class_dir = train_data_path + "/" + class_name
|
||||
for filename in os.listdir(class_dir):
|
||||
f = os.path.join(class_dir, filename)
|
||||
if os.path.isfile(f):
|
||||
writer.writerow([f, CLASS_TO_ID[class_name]])
|
||||
|
||||
train_csvfile.close()
|
||||
|
||||
else:
|
||||
print("Brak plików do uczenia")
|
||||
|
||||
if os.path.exists(test_data_path):
|
||||
test_csvfile = open('./data/test_csv_file.csv', 'w', newline="")
|
||||
writer = csv.writer(test_csvfile)
|
||||
writer.writerow(["filepath", "type"])
|
||||
|
||||
for class_name in CLASSES:
|
||||
class_dir = test_data_path + "/" + class_name
|
||||
for filename in os.listdir(class_dir):
|
||||
f = os.path.join(class_dir, filename)
|
||||
if os.path.isfile(f):
|
||||
writer.writerow([f, CLASS_TO_ID[class_name]])
|
||||
|
||||
test_csvfile.close()
|
||||
else:
|
||||
print("Brak plików do testowania")
|
||||
|
||||
|
||||
def print_numbers():
|
||||
display_surface = pygame.display.get_surface()
|
||||
font = pygame.font.SysFont('Arial', 16)
|
||||
|
@ -46,7 +46,7 @@ class HealthBar:
|
||||
def heal(self, amount):
|
||||
if self.current_hp + amount < self.max_hp:
|
||||
self.current_hp += amount
|
||||
elif self.current_hp + amount > self.max_hp:
|
||||
elif self.current_hp + amount >= self.max_hp:
|
||||
self.current_hp = self.max_hp
|
||||
|
||||
def show(self):
|
||||
|
@ -155,19 +155,6 @@ class Level:
|
||||
self.logs.enqueue_log(f'AI {current_knight.team}: Ruch w lewo.')
|
||||
self.map[knight_pos_y][knight_pos_x - 1] = current_knight.team_alias()
|
||||
|
||||
def update_health_bars(self):
|
||||
for knight in self.list_knights_blue:
|
||||
knight.health_bar.update()
|
||||
|
||||
for knight in self.list_knights_red:
|
||||
knight.health_bar.update()
|
||||
|
||||
for monster in self.list_monsters:
|
||||
monster.health_bar.update()
|
||||
|
||||
for castle in self.list_castles:
|
||||
castle.health_bar.update()
|
||||
|
||||
def update(self):
|
||||
bg_width = (GRID_CELL_PADDING + GRID_CELL_SIZE) * COLUMNS + BORDER_WIDTH
|
||||
bg_height = (GRID_CELL_PADDING + GRID_CELL_SIZE) * ROWS + BORDER_WIDTH
|
||||
@ -175,4 +162,4 @@ class Level:
|
||||
|
||||
# update and draw the game
|
||||
self.sprites.draw(self.screen)
|
||||
self.update_health_bars() # has to be called last
|
||||
self.sprites.update()
|
||||
|
@ -18,3 +18,6 @@ class Castle(pygame.sprite.Sprite):
|
||||
self.max_hp = 80
|
||||
self.current_hp = random.randint(1, self.max_hp)
|
||||
self.health_bar = HealthBar(screen, self.rect, current_hp=self.current_hp, max_hp=self.max_hp, calculate_xy=True, calculate_size=True)
|
||||
|
||||
def update(self):
|
||||
self.health_bar.update()
|
||||
|
@ -43,6 +43,9 @@ class Knight(pygame.sprite.Sprite):
|
||||
self.direction = self.direction.left()
|
||||
self.image = self.states[self.direction.value]
|
||||
|
||||
def update(self):
|
||||
self.health_bar.update()
|
||||
|
||||
def rotate_right(self):
|
||||
self.direction = self.direction.right()
|
||||
self.image = self.states[self.direction.value]
|
||||
|
@ -43,3 +43,6 @@ class Monster(pygame.sprite.Sprite):
|
||||
self.max_hp = 7
|
||||
self.attack = 2
|
||||
self.points = 2
|
||||
|
||||
def update(self):
|
||||
self.health_bar.update()
|
||||
|