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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms

# 定义超参数
BATCH_SIZE = 512
EPOCHS = 20
LEARNING_RATE = 1e-3
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 数据加载和预处理，在 data 目录下载 MNIST 数据集
train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('data', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.RandomRotation(10),  # 随机旋转，提高模型的泛化能力
                       transforms.RandomAffine(0, shear=10, scale=(0.8, 1.2)),  # 仿射变换，提高模型的泛化能力
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=BATCH_SIZE, shuffle=True)

test_loader = torch.utils.data.DataLoader(
    datasets.MNIST('data', train=False,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=BATCH_SIZE, shuffle=False)

# 定义模型的结构，这里是一个简单的卷积神经网络
class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3)
        self.conv2 = nn.Conv2d(32, 64, 3)
        self.dropout1 = nn.Dropout(0.25)
        self.fc1 = nn.Linear(64 * 5 * 5, 128)
        self.dropout2 = nn.Dropout(0.5)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = x.view(-1, 64 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = self.dropout2(x)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

# 初始化模型和优化器
model = ConvNet().to(DEVICE)
optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.5)  # 学习率衰减

# 训练函数
def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if (batch_idx + 1) % 30 == 0:
            print(f"Train Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)} "
                  f"({100. * batch_idx / len(train_loader):.0f}%)]\tLoss: {loss.item():.6f}")

# 测试函数
def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target, reduction='sum').item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()
    test_loss /= len(test_loader.dataset)
    accuracy = 100. * correct / len(test_loader.dataset)
    print(f"\nTest set: Average loss: {test_loss:.4f}, Accuracy: {correct}/{len(test_loader.dataset)} ({accuracy:.2f}%)\n")

# 训练和测试循环
for epoch in range(1, EPOCHS + 1):
    train(model, DEVICE, train_loader, optimizer, epoch)
    test(model, DEVICE, test_loader)
    scheduler.step()  # 更新学习率

# 保存模型
torch.save(model.state_dict(), "mnist_cnn.pth")
print("Model saved to mnist_cnn.pth")