import os
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, 3)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(16, 32, 3)
        self.fc1 = nn.Linear(32 * 14 * 14, 128)
        self.fc2 = nn.Linear(128, 5) 

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = x.view(-1, 32 * 14 * 14)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

def main() -> None:
    transform = transforms.Compose([
        transforms.Resize((64, 64)),
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ])

    path = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
    path = os.path.join(path, 'garbage_photos/train_set')
    trainset = torchvision.datasets.ImageFolder(root=path, transform=transform)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True, num_workers=2)

    net = Net()

    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    net.to(device)

    for epoch in range(10):
        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            inputs, labels = data[0].to(device), data[1].to(device)
        
            optimizer.zero_grad()
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            running_loss += loss.item()
            if i % 200 == 199:  
                print(f'Epoch: {epoch + 1}, Batch: {i + 1}, Loss: {running_loss / 200:.3f}')
                running_loss = 0.0

    torch.save({
        'model_state_dict': net.state_dict(),
        'optimizer_state_dict': optimizer.state_dict(),
        'epoch': epoch,
        'loss': loss,
    }, 'model.pt')

    print("Uczenie zakończone.")

if __name__ == "__main__":
    main()