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cnn_modifi
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6aee7bb207 | ||
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36f20d8895 |
@ -1,11 +1,9 @@
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import torch
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import pytorch_lightning as pl
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import torch.nn as nn
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from torch.optim import SGD, Adam, lr_scheduler
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from torch.optim import Adam
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import torch.nn.functional as F
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from torch.utils.data import DataLoader
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from watersandtreegrass import WaterSandTreeGrass
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from common.constants import DEVICE, BATCH_SIZE, NUM_EPOCHS, LEARNING_RATE, SETUP_PHOTOS, ID_TO_CLASS
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from common.constants import BATCH_SIZE, LEARNING_RATE
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class NeuralNetwork(pl.LightningModule):
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@ -1,9 +1,9 @@
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import torch
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import common.helpers
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from algorithms.neural_network.neural_network import NeuralNetwork
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from algorithms.neural_network.watersandtreegrass import WaterSandTreeGrass
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from common.constants import DEVICE, BATCH_SIZE, NUM_EPOCHS, LEARNING_RATE, SETUP_PHOTOS, ID_TO_CLASS
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from watersandtreegrass import WaterSandTreeGrass
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from torch.utils.data import DataLoader
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from neural_network import NeuralNetwork
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from torchvision.io import read_image, ImageReadMode
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import torch.nn as nn
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from torch.optim import Adam
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@ -100,7 +100,7 @@ def what_is_it(img_path, show_img=False):
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plt.imshow(plt.imread(img_path))
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plt.show()
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image = SETUP_PHOTOS(image).unsqueeze(0)
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model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_13/checkpoints/epoch=4-step=405.ckpt')
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model = NeuralNetwork.load_from_checkpoint('D:/DEV/UAM/WMICraft/algorithms/neural_network/lightning_logs/version_3/checkpoints/epoch=8-step=810.ckpt')
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with torch.no_grad():
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model.eval()
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@ -108,18 +108,18 @@ def what_is_it(img_path, show_img=False):
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return ID_TO_CLASS[idx]
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CNN = NeuralNetwork()
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common.helpers.createCSV()
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# CNN = NeuralNetwork()
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# common.helpers.createCSV()
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#trainer = pl.Trainer(accelerator='gpu', devices=1, callbacks=[EarlyStopping('val_loss')], max_epochs=NUM_EPOCHS)
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trainer = pl.Trainer(accelerator='gpu', devices=1, auto_lr_find=True, max_epochs=NUM_EPOCHS)
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trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
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testset = WaterSandTreeGrass('./data/test_csv_file.csv', transform=SETUP_PHOTOS)
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train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
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test_loader = DataLoader(testset, batch_size=BATCH_SIZE)
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trainer.fit(CNN, train_loader, test_loader)
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# trainer = pl.Trainer(accelerator='cpu', devices=1, auto_lr_find=True, max_epochs=NUM_EPOCHS)
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#
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# trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
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# testset = WaterSandTreeGrass('./data/test_csv_file.csv', transform=SETUP_PHOTOS)
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# train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
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# test_loader = DataLoader(testset, batch_size=BATCH_SIZE)
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#
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# trainer.fit(CNN, train_loader, test_loader)
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#trainer.tune(CNN, train_loader, test_loader)
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#check_accuracy_tiles()
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#print(what_is_it('../../resources/textures/sand.png', True))
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@ -6,7 +6,7 @@ GAME_TITLE = 'WMICraft'
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WINDOW_HEIGHT = 800
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WINDOW_WIDTH = 1360
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FPS_COUNT = 60
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TURN_INTERVAL = 300
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TURN_INTERVAL = 500
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GRID_CELL_PADDING = 5
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GRID_CELL_SIZE = 36
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@ -61,8 +61,8 @@ class Game:
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if event.key == pygame.K_n:
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print_numbers_flag = not print_numbers_flag
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if event.type == NEXT_TURN: # is called every 'TURN_INTERVAL' milliseconds
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level.handle_turn()
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# if event.type == NEXT_TURN: # is called every 'TURN_INTERVAL' milliseconds
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# level.handle_turn()
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stats.update()
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logs.draw()
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@ -3,6 +3,7 @@ import random
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import pygame
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from algorithms.a_star import a_star, State, TURN_RIGHT, TURN_LEFT, FORWARD
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from algorithms.neural_network.neural_network_interface import what_is_it
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from common.constants import *
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from learning.decision_tree import DecisionTree
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from logic.knights_queue import KnightsQueue
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@ -31,10 +32,12 @@ class Level:
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self.knights_queue = None
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def create_map(self):
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self.generate_map()
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self.setup_base_tiles()
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self.setup_objects()
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self.knights_queue = KnightsQueue(self.list_knights_blue, self.list_knights_red)
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print("Create map")
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print(what_is_it('D:/DEV/UAM/WMICraft/resources/textures/t2.jpg'))
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# self.generate_map()
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# self.setup_base_tiles()
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# self.setup_objects()
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# self.knights_queue = KnightsQueue(self.list_knights_blue, self.list_knights_red)
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def generate_map(self):
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spawner = Spawner(self.map)
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requirements.txt
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requirements.txt
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