astar_search.py

@@ -0,0 +1,115 @@
+class Node:
+    def __init__(self, state, parent='', action='', distance=0):
+        self.state = state
+        self.parent = parent
+        self.action = action
+        self.distance = distance
+
+class Search:
+    def __init__(self, cell_size, cell_number):
+        self.cell_size = cell_size
+        self.cell_number = cell_number
+
+    def succ(self, state):
+        x = state[0]
+        y = state[1]
+        angle = state[2]
+        match(angle):
+            case 'UP':
+                possible = [['left', x, y, 'LEFT'], ['right', x, y, 'RIGHT']]
+                if y != 0: possible.append(['move', x, y - self.cell_size, 'UP'])
+                return possible
+            case 'RIGHT':
+                possible = [['left', x, y, 'UP'], ['right', x, y, 'DOWN']]
+                if x != self.cell_size*(self.cell_number-1): possible.append(['move', x + self.cell_size, y, 'RIGHT'])
+                return possible
+            case 'DOWN':
+                possible = [['left', x, y, 'RIGHT'], ['right', x, y, 'LEFT']]
+                if y != self.cell_size*(self.cell_number-1): possible.append(['move', x, y + self.cell_size, 'DOWN'])
+                return possible
+            case 'LEFT':
+                possible = [['left', x, y, 'DOWN'], ['right', x, y, 'UP']]
+                if x != 0: possible.append(['move', x - self.cell_size, y, 'LEFT'])
+                return possible
+
+    def cost(self, node, stones, goal, flowers):
+        # cost = node.distance
+        cost = 0
+        # cost += 10 if stones[node.state[0], node.state[1]] == 1 else 1
+        cost += 1000 if (node.state[0], node.state[1]) in stones else 1
+        cost += 300 if ((node.state[0]), (node.state[1])) in flowers else 1
+
+        if node.parent:
+            node = node.parent
+            cost += node.distance  # should return only elem.action in prod
+        return cost
+
+    def heuristic(self, node, goal):
+        return abs(node.state[0] - goal[0]) + abs(node.state[1] - goal[1])
+
+    #bandaid to know about stones
+    def astarsearch(self, istate, goaltest, cStones, cFlowers):
+        
+        #to be expanded
+        def cost_old(x, y):
+            if (x, y) in stones:
+                return 10
+            else:
+                return 1
+
+        
+        x = istate[0]
+        y = istate[1]
+        angle = istate[2]
+
+        stones = [(x*50, y*50) for (x, y) in cStones]
+        flowers = [(x*50, y*50) for (x, y) in cFlowers]
+
+        print(stones)
+
+        # fringe = [(Node([x, y, angle]), cost_old(x, y))]  # queue (moves/states to check)
+        fringe = [(Node([x, y, angle]))]  # queue (moves/states to check)
+        fringe[0].distance = self.cost(fringe[0], stones, goaltest, flowers)
+        fringe.append((Node([x, y, angle]), self.cost(fringe[0], stones, goaltest, flowers)))
+        fringe.pop(0)
+
+        explored = []
+
+        while True:
+            if len(fringe) == 0:
+                return False
+
+            fringe.sort(key=lambda x: x[1])
+            elem = fringe.pop(0)[0]
+
+            # if goal_test(elem.state):
+            #     return
+            # print(elem.state[0], elem.state[1], elem.state[2])
+            if elem.state[0] == goaltest[0] and elem.state[1] == goaltest[1]:  # checks if we reached the given point
+                steps = []
+                while elem.parent: 
+                    steps.append([elem.action, elem.state[0], elem.state[1]])  # should return only elem.action in prod
+                    elem = elem.parent
+
+                steps.reverse()
+                print(steps)  # only for dev
+                return steps
+
+            explored.append(elem.state)
+
+            for (action, state_x, state_y, state_angle) in self.succ(elem.state):
+                x = Node([state_x, state_y, state_angle], elem, action)
+                x.parent = elem
+
+                priority = self.cost(elem, stones, goaltest, flowers) + self.heuristic(elem, goaltest)
+                elem.distance = priority
+                # priority = cost_old(x, y) + self.heuristic(elem, goaltest)
+                fringe_states = [node.state for (node, p) in fringe]
+
+                if x.state not in fringe_states and x.state not in explored:
+                    fringe.append((x, priority))
+                elif x.state in fringe_states:
+                    for i in range(len(fringe)):
+                        if fringe[i][0].state == x.state:
+                            if fringe[i][1] > priority:
+                                fringe[i] = (x, priority)

blocks.py

@@ -5,6 +5,7 @@ import soil
 
 
 class Blocks:
+
     def __init__(self, parent_screen,cell_size):
         self.parent_screen = parent_screen
         self.flower_image = pygame.image.load(r'resources/flower.png').convert_alpha()
@@ -25,9 +26,12 @@ class Blocks:
         self.fawn_wheat_image = pygame.image.load(r'resources/fawn_wheat.png').convert_alpha()
         self.fawn_wheat_image = pygame.transform.scale(self.fawn_wheat_image, (cell_size, cell_size))
 
+        self.red_image = pygame.image.load(r'resources/redBush.png').convert_alpha()
+        self.red_image = pygame.transform.scale(self.red_image, (cell_size, cell_size))
 
         self.soil = soil.Soil()
 
+
     def locate_blocks(self, blocks_number, cell_number, body):
         for i in range(blocks_number):
             self.x = random.randint(0, cell_number-1)
@@ -53,6 +57,8 @@ class Blocks:
                 self.parent_screen.blit(self.fawn_seed_image, (x, y))
             if color == 'fawn_wheat':
                 self.parent_screen.blit(self.fawn_wheat_image, (x, y))
+            if color == 'red':
+                self.parent_screen.blit(self.red_image, (x, y))
 
 
 

graph_search.py

@@ -6,22 +6,31 @@ class Node:
 
 
 class Search:
-    def __init__(self, cell_size):
+    def __init__(self, cell_size, cell_number):
         self.cell_size = cell_size
+        self.cell_number = cell_number
 
-    # WARNING! IT EXCEEDS THE PLANE!!!
-    def succ(self, state):  # successor function
+    def succ(self, state):
         x = state[0]
         y = state[1]
         angle = state[2]
-        if angle == 0:
-            return [['move', x, y - self.cell_size, 0], ['left', x, y, 270], ['right', x, y, 90]]
-        if angle == 90:
-            return [['move', x + self.cell_size, y, 90], ['left', x, y, 0], ['right', x, y, 180]]
-        if angle == 180:
-            return [['move', x, y + self.cell_size, 180], ['left', x, y, 90], ['right', x, y, 270]]
-        if angle == 270:
-            return [['move', x - self.cell_size, y, 270], ['left', x, y, 180], ['right', x, y, 0]]
+        match(angle):
+            case 'UP':
+                possible = [['left', x, y, 'LEFT'], ['right', x, y, 'RIGHT']]
+                if y != 0: possible.append(['move', x, y - self.cell_size, 'UP'])
+                return possible
+            case 'RIGHT':
+                possible = [['left', x, y, 'UP'], ['right', x, y, 'DOWN']]
+                if x != self.cell_size*(self.cell_number-1): possible.append(['move', x + self.cell_size, y, 'RIGHT'])
+                return possible
+            case 'DOWN':
+                possible = [['left', x, y, 'RIGHT'], ['right', x, y, 'LEFT']]
+                if y != self.cell_size*(self.cell_number-1): possible.append(['move', x, y + self.cell_size, 'DOWN'])
+                return possible
+            case 'LEFT':
+                possible = [['left', x, y, 'DOWN'], ['right', x, y, 'UP']]
+                if x != 0: possible.append(['move', x - self.cell_size, y, 'LEFT'])
+                return possible
 
     def graphsearch(self, istate, goaltest):
         x = istate[0]
@@ -44,7 +53,7 @@ class Search:
             # print(elem.state[0], elem.state[1], elem.state[2])
             if elem.state[0] == goaltest[0] and elem.state[1] == goaltest[1]:  # checks if we reached the given point
                 steps = []
-                while elem.parent != '':
+                while elem.parent: 
                     steps.append([elem.action, elem.state[0], elem.state[1]])  # should return only elem.action in prod
                     elem = elem.parent
 
@@ -55,8 +64,6 @@ class Search:
             explored.append(elem.state)
 
             for (action, state_x, state_y, state_angle) in self.succ(elem.state):
-                if state_x < 0 or state_y < 0:  # check if any of the values are negative
-                    continue
                 if [state_x, state_y, state_angle] not in fringe_state and \
                         [state_x, state_y, state_angle] not in explored:
                     x = Node([state_x, state_y, state_angle])
@@ -64,7 +71,3 @@ class Search:
                     x.action = action
                     fringe.append(x)
                     fringe_state.append(x.state)
-
-
-se = Search(50)
-se.graphsearch(istate=[50, 50, 0], goaltest=[150, 250])

learn_tree.py

@@ -0,0 +1,51 @@
+from collections import Counter
+
+def tree_learn(examples, attributes, default_class):
+    if len(examples) == 0:
+        return default_class
+    
+    if all(examples[0][-1] == example[-1] for example in examples):
+        return examples[0][-1]
+    
+    if len(attributes) == 0:
+        class_counts = Counter(example[-1] for example in examples)
+        majority_class = class_counts.most_common(1)[0][0]
+        return majority_class
+    
+    # Choose the attribute A as the root of the decision tree
+    A = select_attribute(attributes, examples)
+    
+    tree = {A: {}}
+    new_attributes = [attr for attr in attributes if attr != A]
+    new_default_class = Counter(example[-1] for example in examples).most_common(1)[0][0]
+    
+    for value in get_attribute_values(A):
+        new_examples = [example for example in examples if example[attributes.index(A)] == value]
+        subtree = tree_learn(new_examples, new_attributes, new_default_class)
+        tree[A][value] = subtree
+    
+    return tree
+
+# Helper function: Select the best attribute based on a certain criterion (e.g., information gain)
+def select_attribute(attributes, examples):
+    # Implement your attribute selection criterion here
+    pass
+
+# Helper function: Get the possible values of an attribute from the examples
+def get_attribute_values(attribute):
+    # Implement your code to retrieve the attribute values from the examples here
+    pass
+
+# Example usage with coordinates
+examples = [
+    [1, 2, 'A'],
+    [3, 4, 'A'],
+    [5, 6, 'B'],
+    [7, 8, 'B']
+]
+
+attributes = ['x', 'y']
+default_class = 'unknown'
+
+decision_tree = tree_learn(examples, attributes, default_class)
+print(decision_tree)

main.py

@@ -1,18 +1,18 @@
 import os
-
 import pygame
 import random
 import land
 import tractor
 import blocks
+import astar_search
+import neural_network.inference
 from pygame.locals import *
-from datetime import datetime
 
 
 class Game:
     cell_size = 50
     cell_number = 15  # horizontally
-    blocks_number = 15
+    blocks_number = 20
     
     def __init__(self):
 
@@ -22,6 +22,7 @@ class Game:
         self.flower_body = []
         self.dead_grass_body = []
         self.grass_body = []
+        self.red_block = [] #aim block
 
         self.fawn_seed_body = []
         self.fawn_wheat_body = []
@@ -54,6 +55,8 @@ class Game:
         self.blocks.locate_blocks(self.blocks_number, self.cell_number, self.stone_body)
         self.blocks.locate_blocks(self.blocks_number, self.cell_number, self.flower_body)
 
+        #self.blocks.locate_blocks(1, self.cell_number, self.red_block)
+
         # self.potato = blocks.Blocks(self.surface, self.cell_size)
         # self.potato.locate_soil('black earth', 6, 1, [])
 
@@ -66,12 +69,17 @@ class Game:
         # print(self.potato.get_soil_info().get_irrigation())
         running = True
         clock = pygame.time.Clock()
-        # last_time = datetime.now()
+
+        move_tractor_event = pygame.USEREVENT + 1
+        pygame.time.set_timer(move_tractor_event, 500)  # tractor moves every 1000 ms
+        tractor_next_moves = []
+        astar_search_object = astar_search.Search(self.cell_size, self.cell_number)
+
+        veggies = dict()
+        veggies_debug = dict()
 
         while running:
             clock.tick(60)  # manual fps control not to overwork the computer
-            # time_now = datetime.now()
-
             for event in pygame.event.get():
                 if event.type == KEYDOWN:
                     if pygame.key.get_pressed()[K_ESCAPE]:
@@ -92,29 +100,57 @@ class Game:
                     if pygame.key.get_pressed()[K_q]:
                         self.tractor.harvest(self.fawn_seed_body, self.fawn_wheat_body, self.cell_size)
                         self.tractor.put_seed(self.fawn_soil_body, self.fawn_seed_body, self.cell_size)
+                if event.type == move_tractor_event:
+                    if len(tractor_next_moves) == 0:
+                        random_x = random.randrange(0, self.cell_number * self.cell_size, 50)
+                        random_y = random.randrange(0, self.cell_number * self.cell_size, 50)
+                        print("Generated target: ",random_x, random_y)
+                        if self.red_block:
+                            self.red_block.pop()
+                        self.red_block.append([random_x/50, random_y/50])
+                        # below line should be later moved into tractor.py
+                        angles = {0: 'UP', 90: 'RIGHT', 270: 'LEFT', 180: 'DOWN'}
+                        #bandaid to know about stones
+                        tractor_next_moves = astar_search_object.astarsearch(
+                            [self.tractor.x, self.tractor.y, angles[self.tractor.angle]], [random_x, random_y], self.stone_body, self.flower_body)
+                        current_veggie = next(os.walk('./neural_network/images/test'))[1][random.randint(0, len(next(os.walk('./neural_network/images/test'))[1])-1)]
+                        if(current_veggie in veggies_debug):
+                            veggies_debug[current_veggie]+=1
+                        else:
+                            veggies_debug[current_veggie] = 1
 
+                        current_veggie_example = next(os.walk(f'./neural_network/images/test/{current_veggie}'))[2][random.randint(0, len(next(os.walk(f'./neural_network/images/test/{current_veggie}'))[2])-1)]
+                        predicted_veggie = neural_network.inference.main(f"./neural_network/images/test/{current_veggie}/{current_veggie_example}")
+                        if predicted_veggie in veggies:
+                            veggies[predicted_veggie]+=1
+                        else:
+                            veggies[predicted_veggie] = 1
+                        print("Debug veggies: ", veggies_debug, "Predicted veggies: ", veggies)
 
+                    else:
+                        self.tractor.move(tractor_next_moves.pop(0)[0], self.cell_size, self.cell_number)
                 elif event.type == QUIT:
                     running = False
 
                 self.surface.fill((123, 56, 51))  # background color
-
                 self.grass.set_and_place_block_of_grass('good')
                 self.black_earth.place_soil(self.black_earth_body, 'black_earth')
                 self.green_earth.place_soil(self.green_earth_body, 'green_earth')
                 self.fawn_soil.place_soil(self.fawn_soil_body, 'fawn_soil')
                 self.fen_soil.place_soil(self.fen_soil_body, 'fen_soil')
 
-                #plants examples
+                # plants examples
                 self.blocks.place_blocks(self.surface, self.cell_size, self.dead_leaf_body, 'leaf')
                 self.blocks.place_blocks(self.surface, self.cell_size, self.green_leaf_body, 'alive')
                 self.blocks.place_blocks(self.surface, self.cell_size, self.stone_body, 'stone')
                 self.blocks.place_blocks(self.surface, self.cell_size, self.flower_body, 'flower')
 
-                #seeds
+                self.blocks.place_blocks(self.surface, self.cell_size, self.red_block, 'red')
+
+                # seeds
                 self.blocks.place_blocks(self.surface, self.cell_size, self.fawn_seed_body, 'fawn_seed')
 
-                #wheat
+                # wheat
                 self.blocks.place_blocks(self.surface, self.cell_size, self.fawn_wheat_body, 'fawn_wheat')
 
                 self.tractor.draw()

neural_network/datasets.py

@@ -0,0 +1,42 @@
+import torchvision
+import torch
+import torchvision.transforms as transforms
+
+from torch.utils.data import DataLoader
+
+BATCH_SIZE = 64
+
+
+train_transform = transforms.Compose([
+    transforms.Resize((224, 224)), #validate that all images are 224x244
+    transforms.RandomHorizontalFlip(p=0.5),
+    transforms.RandomVerticalFlip(p=0.5),
+    transforms.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5)),
+    transforms.RandomRotation(degrees=(30, 70)), #random effects are applied to prevent overfitting
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.5, 0.5, 0.5],
+        std=[0.5, 0.5, 0.5]
+    )
+])
+
+valid_transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.5, 0.5, 0.5],
+        std=[0.5, 0.5, 0.5]
+    )
+])
+
+train_dataset = torchvision.datasets.ImageFolder(root='./images/train', transform=train_transform)
+
+validation_dataset = torchvision.datasets.ImageFolder(root='./images/validation', transform=valid_transform)
+
+train_loader = DataLoader(
+    train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=0, pin_memory=True
+)
+
+valid_loader = DataLoader(
+    validation_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=0, pin_memory=True
+)

neural_network/inference.py

@@ -0,0 +1,59 @@
+import torch
+import cv2
+import torchvision.transforms as transforms
+import argparse
+from neural_network.model import CNNModel
+# construct the argument parser
+parser = argparse.ArgumentParser()
+parser.add_argument('-i', '--input', 
+    default='',
+    help='path to the input image')
+args = vars(parser.parse_args())
+
+def main(path):
+    # the computation device
+    device = ('cuda' if torch.cuda.is_available() else 'cpu')
+    # list containing all the class labels
+    labels = [
+        'bean', 'bitter gourd', 'bottle gourd', 'brinjal', 'broccoli',
+        'cabbage', 'capsicum', 'carrot', 'cauliflower', 'cucumber',
+        'papaya', 'potato', 'pumpkin', 'radish', 'tomato'
+        ]
+
+    # initialize the model and load the trained weights
+    model = CNNModel().to(device)
+    checkpoint = torch.load('./neural_network/outputs/model.pth', map_location=device)
+    model.load_state_dict(checkpoint['model_state_dict'])
+    model.eval()
+
+    # define preprocess transforms
+    transform = transforms.Compose([
+        transforms.ToPILImage(),
+        transforms.Resize(224),
+        transforms.ToTensor(),
+        transforms.Normalize(
+            mean=[0.5, 0.5, 0.5],
+            std=[0.5, 0.5, 0.5]
+        )
+    ])  
+
+
+    # read and preprocess the image
+    image = cv2.imread(path)
+    # get the ground truth class
+    gt_class = path.split('/')[-2]
+    orig_image = image.copy()
+    # convert to RGB format
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    image = transform(image)
+    # add batch dimension
+    image = torch.unsqueeze(image, 0)
+    with torch.no_grad():
+        outputs = model(image.to(device))
+    output_label = torch.topk(outputs, 1)
+    pred_class = labels[int(output_label.indices)]
+    
+    return pred_class
+
+if __name__ == "__main__":
+    main(args['input'])

neural_network/model.py

@@ -0,0 +1,24 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+class CNNModel(nn.Module): #model of the CNN type
+    def __init__(self):
+        super(CNNModel, self).__init__()
+        self.conv1 = nn.Conv2d(3, 32, 5)
+        self.conv2 = nn.Conv2d(32, 64, 5)
+        self.conv3 = nn.Conv2d(64, 128, 3)
+        self.conv4 = nn.Conv2d(128, 256, 5)
+        
+        self.fc1 = nn.Linear(256, 50)
+        
+        self.pool = nn.MaxPool2d(2, 2)
+        
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = self.pool(F.relu(self.conv3(x)))
+        x = self.pool(F.relu(self.conv4(x)))
+        bs, _, _, _ = x.shape
+        x = F.adaptive_avg_pool2d(x, 1).reshape(bs, -1)
+        x = self.fc1(x)
+        return x

neural_network/train.py

@@ -0,0 +1,119 @@
+import torch
+import argparse
+import torch.nn as nn
+import torch.optim as optim
+import time
+from tqdm.auto import tqdm
+from neural_network.model import CNNModel
+from neural_network.datasets import train_loader, valid_loader
+from neural_network.utils import save_model, save_plots
+
+# construct the argument parser
+parser = argparse.ArgumentParser()
+parser.add_argument('-e', '--epochs', type=int, default=20,
+    help='number of epochs to train our network for')
+args = vars(parser.parse_args())
+
+
+lr = 1e-3
+epochs = args['epochs']
+device = ('cuda' if torch.cuda.is_available() else 'cpu')
+print(f"Computation device: {device}\n")
+
+model = CNNModel().to(device)
+print(model)
+
+total_params = sum(p.numel() for p in model.parameters())
+print(f"{total_params:,} total parameters.")
+total_trainable_params = sum(
+    p.numel() for p in model.parameters() if p.requires_grad)
+print(f"{total_trainable_params:,} training parameters.")
+# optimizer
+optimizer = optim.Adam(model.parameters(), lr=lr)
+# loss function
+criterion = nn.CrossEntropyLoss()
+
+
+# training
+def train(model, trainloader, optimizer, criterion):
+    model.train()
+    print('Training')
+    train_running_loss = 0.0
+    train_running_correct = 0
+    counter = 0
+    for i, data in tqdm(enumerate(trainloader), total=len(trainloader)):
+        counter += 1
+        image, labels = data
+        image = image.to(device)
+        labels = labels.to(device)
+        optimizer.zero_grad()
+        # forward pass
+        outputs = model(image)
+        # calculate the loss
+        loss = criterion(outputs, labels)
+        train_running_loss += loss.item()
+        # calculate the accuracy
+        _, preds = torch.max(outputs.data, 1)
+        train_running_correct += (preds == labels).sum().item()
+        # backpropagation
+        loss.backward()
+        # update the optimizer parameters
+        optimizer.step()
+    
+    # loss and accuracy for the complete epoch
+    epoch_loss = train_running_loss / counter
+    epoch_acc = 100. * (train_running_correct / len(trainloader.dataset))
+    return epoch_loss, epoch_acc
+
+# validation
+def validate(model, testloader, criterion):
+    model.eval()
+    print('Validation')
+    valid_running_loss = 0.0
+    valid_running_correct = 0
+    counter = 0
+    with torch.no_grad():
+        for i, data in tqdm(enumerate(testloader), total=len(testloader)):
+            counter += 1
+            
+            image, labels = data
+            image = image.to(device)
+            labels = labels.to(device)
+            # forward pass
+            outputs = model(image)
+            # calculate the loss
+            loss = criterion(outputs, labels)
+            valid_running_loss += loss.item()
+            # calculate the accuracy
+            _, preds = torch.max(outputs.data, 1)
+            valid_running_correct += (preds == labels).sum().item()
+        
+    # loss and accuracy for the complete epoch
+    epoch_loss = valid_running_loss / counter
+    epoch_acc = 100. * (valid_running_correct / len(testloader.dataset))
+    return epoch_loss, epoch_acc
+
+# lists to keep track of losses and accuracies
+train_loss, valid_loss = [], []
+train_acc, valid_acc = [], []
+# start the training
+for epoch in range(epochs):
+    print(f"[INFO]: Epoch {epoch+1} of {epochs}")
+    train_epoch_loss, train_epoch_acc = train(model, train_loader, 
+                                              optimizer, criterion)
+    valid_epoch_loss, valid_epoch_acc = validate(model, valid_loader,  
+                                                 criterion)
+    train_loss.append(train_epoch_loss)
+    valid_loss.append(valid_epoch_loss)
+    train_acc.append(train_epoch_acc)
+    valid_acc.append(valid_epoch_acc)
+    print(f"Training loss: {train_epoch_loss:.3f}, training acc: {train_epoch_acc:.3f}")
+    print(f"Validation loss: {valid_epoch_loss:.3f}, validation acc: {valid_epoch_acc:.3f}")
+    print('-'*50)
+    time.sleep(5)
+    
+# save the trained model weights
+save_model(epochs, model, optimizer, criterion)
+# save the loss and accuracy plots
+save_plots(train_acc, valid_acc, train_loss, valid_loss)
+print('TRAINING COMPLETE')

neural_network/utils.py

@@ -0,0 +1,49 @@
+import torch
+import matplotlib
+import matplotlib.pyplot as plt
+matplotlib.style.use('ggplot')
+
+def save_model(epochs, model, optimizer, criterion):
+    """
+    Function to save the trained model to disk.
+    """
+    torch.save({
+                'epoch': epochs,
+                'model_state_dict': model.state_dict(),
+                'optimizer_state_dict': optimizer.state_dict(),
+                'loss': criterion,
+                }, 'outputs/model.pth')
+    
+def save_plots(train_acc, valid_acc, train_loss, valid_loss):
+    """
+    Function to save the loss and accuracy plots to disk.
+    """
+    # accuracy plots
+    plt.figure(figsize=(10, 7))
+    plt.plot(
+        train_acc, color='green', linestyle='-', 
+        label='train accuracy'
+    )
+    plt.plot(
+        valid_acc, color='blue', linestyle='-', 
+        label='validataion accuracy'
+    )
+    plt.xlabel('Epochs')
+    plt.ylabel('Accuracy')
+    plt.legend()
+    plt.savefig('outputs/accuracy.png')
+    
+    # loss plots
+    plt.figure(figsize=(10, 7))
+    plt.plot(
+        train_loss, color='orange', linestyle='-', 
+        label='train loss'
+    )
+    plt.plot(
+        valid_loss, color='red', linestyle='-', 
+        label='validataion loss'
+    )
+    plt.xlabel('Epochs')
+    plt.ylabel('Loss')
+    plt.legend()
+    plt.savefig('outputs/loss.png')

tractor.py

@@ -31,22 +31,6 @@ class Tractor:
 
 
     def move(self, direction, cell_size, cell_number):
-        # if direction == 'up':
-        #     if self.y != 0:
-        #         self.y -= cell_size
-        #     self.image = self.up
-        # if direction == 'down':
-        #     if self.y != (cell_number-1)*cell_size:
-        #         self.y += cell_size
-        #     self.image = self.down
-        # if direction == 'left':
-        #     if self.x != 0:
-        #         self.x -= cell_size
-        #     self.image = self.left
-        # if direction == 'right':
-        #     if self.x != (cell_number-1)*cell_size:
-        #         self.x += cell_size
-        #     self.image = self.right
         if direction == 'move':
             if self.angle == 0 and self.y != 0:
                 self.y -= cell_size