neural network sprawny wraz z interfejsem

algorithms/bfs.py

@@ -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

algorithms/neural_network/neural_network.py

@@ -0,0 +1,22 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class NeuralNetwork(nn.Module):
+    def __init__(self, num_classes=4):
+        super(NeuralNetwork, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels=3, out_channels=10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+        self.pool = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))
+        self.conv2 = nn.Conv2d(in_channels=10, out_channels=20, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+        self.fc1 = nn.Linear(20*9*9, num_classes)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = self.pool(x)
+        x = F.relu(self.conv2(x))
+        x = self.pool(x)
+        x = x.reshape(x.shape[0], -1)
+        x = self.fc1(x)
+
+        return x

algorithms/neural_network/neural_network_interface.py

@@ -0,0 +1,91 @@
+import torch
+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
+
+CNN = NeuralNetwork().to(device)
+
+
+def train(model):
+    model.train()
+    trainset = WaterSandTreeGrass('./data/train_csv_file.csv', './data/train/all', transform=setup_photos)
+    train_loader = DataLoader(trainset, 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 epoch % 2 == 0:
+                print("epoch: %3d loss: %.4f" % (epoch, loss.item()))
+
+    print("FINISHED!")
+    print("Checking accuracy.")
+    check_accuracy(train_loader)
+
+    torch.save(model.state_dict(), "./learnedNetwork.pt")
+
+
+def check_accuracy(loader):
+    num_correct = 0
+    num_samples = 0
+    model = NeuralNetwork()
+
+    model.load_state_dict(torch.load("./learnedNetwork.pt"))
+    model = model.to(device)
+
+    with torch.no_grad():
+        model.eval()
+        for x, y in loader:
+            x = x.to(device=device)
+            y = y.to(device=device)
+
+            scores = model(x)
+
+            _, predictions = scores.max(1)
+            num_correct += (predictions == y).sum()
+            num_samples += predictions.size(0)
+
+        print(f"Got {num_correct}/{num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}")
+
+
+testset_loader = DataLoader(
+    WaterSandTreeGrass('./data/test_csv_file.csv', './data/test/all', transform=setup_photos),
+    batch_size=batch_size
+)
+
+
+def what_is_it(img_path):
+    image = read_image(img_path, mode=ImageReadMode.RGB)
+    image = setup_photos(image).unsqueeze(0)
+    model = NeuralNetwork()
+
+    model.load_state_dict(torch.load("./learnedNetwork.pt"))
+    model = model.to(device)
+    image = image.to(device)
+
+    with torch.no_grad():
+        model.eval()
+        idx = int(model(image).argmax(dim=1))
+        return id_to_class[idx]
+
+
+
+check_accuracy(testset_loader)
+
+print(what_is_it('./data/test/water/water.png'))

algorithms/neural_network/watersandtreegrass.py

@@ -0,0 +1,28 @@
+import torch
+from torch.utils.data import Dataset
+import pandas as pd
+from torchvision.io import read_image, ImageReadMode
+from common.helpers import createCSV
+import os
+
+
+class WaterSandTreeGrass(Dataset):
+    def __init__(self, annotations_file, img_dir, transform=None):
+        createCSV()
+        self.img_labels = pd.read_csv(annotations_file)
+        self.img_dir = img_dir
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.img_labels)
+
+    def __getitem__(self, idx):
+        img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])
+        image = read_image(img_path, mode=ImageReadMode.RGB)
+        label = torch.tensor(int(self.img_labels.iloc[idx, 1]))
+
+        if self.transform:
+            image = self.transform(image)
+
+        return image, label
+

common/constants.py

@@ -1,4 +1,6 @@
 from enum import Enum
+import torchvision.transforms as transforms
+import torch
 
 GAME_TITLE = 'WMICraft'
 WINDOW_HEIGHT = 800
@@ -67,3 +69,22 @@ ACTION = {
 BAR_ANIMATION_SPEED = 1
 BAR_WIDTH_MULTIPLIER = 0.9  # (0;1>
 BAR_HEIGHT_MULTIPLIER = 0.1
+
+#NEURAL_NETWORK
+learning_rate = 0.001
+batch_size = 7
+num_epochs = 10
+
+device = torch.device('cuda')
+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()}

common/helpers.py

@@ -1,5 +1,7 @@
 import pygame
-from common.constants import GRID_CELL_PADDING, GRID_CELL_SIZE, COLUMNS, ROWS
+from common.constants import GRID_CELL_PADDING, GRID_CELL_SIZE, COLUMNS, ROWS, classes, class_to_id
+import csv
+import os
 
 
 def draw_text(text, color, surface, x, y, text_size=30, is_bold=False):
@@ -12,6 +14,35 @@ def draw_text(text, color, surface, x, y, text_size=30, is_bold=False):
     textrect.topleft = (x, y)
     surface.blit(textobj, textrect)
 
+def createCSV():
+    train_csvfile = open('./data/train_csv_file.csv', 'w', newline="")
+    writer = csv.writer(train_csvfile)
+    writer.writerow(["filename", "type"])
+
+    train_data_path = './data/train'
+    test_data_path = './data/test'
+
+    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([filename, class_to_id[class_name]])
+
+    test_csvfile = open('./data/test_csv_file.csv', 'w', newline="")
+    writer = csv.writer(test_csvfile)
+    writer.writerow(["filename", "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([filename, class_to_id[class_name]])
+
+    test_csvfile.close()
+    train_csvfile.close()
+
 
 def print_numbers():
     display_surface = pygame.display.get_surface()

logic/health_bar.py

@@ -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):