work on what_is_it func;

algorithms/neural_network/neural_network.py

@@ -1,11 +1,9 @@
 import torch
 import pytorch_lightning as pl
 import torch.nn as nn
-from torch.optim import SGD, Adam, lr_scheduler
+from torch.optim import Adam
 import torch.nn.functional as F
-from torch.utils.data import DataLoader
+from common.constants import BATCH_SIZE, LEARNING_RATE
-from watersandtreegrass import WaterSandTreeGrass
-from common.constants import DEVICE, BATCH_SIZE, NUM_EPOCHS, LEARNING_RATE, SETUP_PHOTOS, ID_TO_CLASS
 
 
 class NeuralNetwork(pl.LightningModule):

algorithms/neural_network/neural_network_interface.py

@@ -1,9 +1,9 @@
 import torch
 import common.helpers
+from algorithms.neural_network.neural_network import NeuralNetwork
+from algorithms.neural_network.watersandtreegrass import WaterSandTreeGrass
 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
@@ -100,7 +100,7 @@ def what_is_it(img_path, show_img=False):
         plt.imshow(plt.imread(img_path))
         plt.show()
     image = SETUP_PHOTOS(image).unsqueeze(0)
-    model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_13/checkpoints/epoch=4-step=405.ckpt')
+    model = NeuralNetwork.load_from_checkpoint('D:/DEV/UAM/WMICraft/algorithms/neural_network/lightning_logs/version_3/checkpoints/epoch=8-step=810.ckpt')
 
     with torch.no_grad():
         model.eval()
@@ -108,18 +108,18 @@ def what_is_it(img_path, show_img=False):
         return ID_TO_CLASS[idx]
 
 
-CNN = NeuralNetwork()
+# CNN = NeuralNetwork()
-common.helpers.createCSV()
+# common.helpers.createCSV()
 
 #trainer = pl.Trainer(accelerator='gpu', devices=1, callbacks=[EarlyStopping('val_loss')], max_epochs=NUM_EPOCHS)
-trainer = pl.Trainer(accelerator='gpu', devices=1, auto_lr_find=True, max_epochs=NUM_EPOCHS)
+# trainer = pl.Trainer(accelerator='cpu', devices=1, auto_lr_find=True, max_epochs=NUM_EPOCHS)
-
+#
-trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
+# trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
-testset = WaterSandTreeGrass('./data/test_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)
+# train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
-test_loader = DataLoader(testset, batch_size=BATCH_SIZE)
+# test_loader = DataLoader(testset, batch_size=BATCH_SIZE)
-
+#
-trainer.fit(CNN, train_loader, test_loader)
+# 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))

common/constants.py

@@ -6,7 +6,7 @@ GAME_TITLE = 'WMICraft'
 WINDOW_HEIGHT = 800
 WINDOW_WIDTH = 1360
 FPS_COUNT = 60
-TURN_INTERVAL = 300
+TURN_INTERVAL = 500
 
 GRID_CELL_PADDING = 5
 GRID_CELL_SIZE = 36

logic/game.py

@@ -61,8 +61,8 @@ class Game:
                     if event.key == pygame.K_n:
                         print_numbers_flag = not print_numbers_flag
 
-                if event.type == NEXT_TURN:  # is called every 'TURN_INTERVAL' milliseconds
+                # if event.type == NEXT_TURN:  # is called every 'TURN_INTERVAL' milliseconds
-                    level.handle_turn()
+                #     level.handle_turn()
 
             stats.update()
             logs.draw()

logic/level.py

@@ -3,6 +3,7 @@ import random
 import pygame
 
 from algorithms.a_star import a_star, State, TURN_RIGHT, TURN_LEFT, FORWARD
+from algorithms.neural_network.neural_network_interface import what_is_it
 from common.constants import *
 from learning.decision_tree import DecisionTree
 from logic.knights_queue import KnightsQueue
@@ -31,10 +32,12 @@ class Level:
         self.knights_queue = None
 
     def create_map(self):
-        self.generate_map()
+        print("Create map")
-        self.setup_base_tiles()
+        print(what_is_it('D:/DEV/UAM/WMICraft/resources/textures/t2.jpg'))
-        self.setup_objects()
+        # self.generate_map()
-        self.knights_queue = KnightsQueue(self.list_knights_blue, self.list_knights_red)
+        # self.setup_base_tiles()
+        # self.setup_objects()
+        # self.knights_queue = KnightsQueue(self.list_knights_blue, self.list_knights_red)
 
     def generate_map(self):
         spawner = Spawner(self.map)