import os
import torch
import torch.nn as nn
import torch.optim as optim
import warnings
from tqdm import tqdm
from torchvision import datasets, transforms
from torch.utils.data import DataLoader, random_split
from model import SimpleCNN, CustomCNN


warnings.filterwarnings("ignore", category=UserWarning)


# 数据预处理
transform = transforms.Compose([
    transforms.Resize((1024, 800)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载数据集
data_dir = './datasets'
full_dataset = datasets.ImageFolder(data_dir, transform=transform)

# 划分训练集和测试集
train_size = int(0.8 * len(full_dataset))
test_size = len(full_dataset) - train_size
train_dataset, test_dataset = random_split(full_dataset, [train_size, test_size])

# 创建数据加载器
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False)

# 初始化模型
num_classes = len(full_dataset.classes)
chosen_model = CustomCNN
model = chosen_model(num_classes)

# 初始化损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


if __name__ == '__main__':
    # 训练模型
    num_epochs = 16
    model.to(device)
    for epoch in range(num_epochs):
        model.train()
        running_loss = 0.0
        train_loader_tqdm = tqdm(train_loader, desc=f'Epoch {epoch + 1}/{num_epochs}', unit='batch')
        for i, (images, labels) in enumerate(train_loader_tqdm):
            images, labels = images.to(device), labels.to(device)

            optimizer.zero_grad()
            outputs = model(images)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            running_loss += loss.item()
            train_loader_tqdm.set_postfix(loss=running_loss / (i + 1))

        print(f'Epoch {epoch + 1}/{num_epochs}, Loss: {running_loss / len(train_loader)}')

    # 保存模型
    model_path = 'rdp_model.pth'
    torch.save(model.state_dict(), model_path)

    # 测试模型
    load_saved_model = True
    if load_saved_model and os.path.exists(model_path):
        model = chosen_model(num_classes)
        model.load_state_dict(torch.load(model_path, weights_only=True))
        model.to(device)

    model.eval()
    correct = 0
    total = 0

    test_loader_tqdm = tqdm(test_loader, desc='Testing', unit='batch')
    with torch.no_grad():
        for images, labels in test_loader_tqdm:
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
            test_loader_tqdm.set_postfix(accuracy=100 * correct / total)

    print(f'Accuracy on test set: {100 * correct / total}%')