import torch
import torch.nn as nn
import torch.nn.utils.prune as prune
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

# 1. 定义模型
class SimpleCNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
        self.fc1 = nn.Linear(32 * 13 * 13, 10)  # 假设输入为28x28，经过池化后为13x13

    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        return x

# 2. 加载数据
transform = transforms.Compose([transforms.ToTensor()])
train_data = datasets.MNIST('./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_data, batch_size=64, shuffle=True)

# 3. 初始化模型
model = SimpleCNN()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

# 4. 训练原始模型（可选）
def train(model, epochs=5):
    model.train()
    for epoch in range(epochs):
        for data, target in train_loader:
            optimizer.zero_grad()
            output = model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()

train(model)  # 可省略，直接使用预训练权重

# 5. 剪枝
# 对conv1层按L1范数剪枝50%
prune.l1_unstructured(module=model.conv1, name='weight', amount=0.5)
# 对fc1层剪枝30%
prune.l1_unstructured(module=model.fc1, name='weight', amount=0.3)

# 6. 移除剪枝掩码（永久剪枝）
def remove_pruning(model):
    for name, module in model.named_modules():
        if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear):
            prune.remove(module, 'weight')

remove_pruning(model)

# 7. 微调剪枝后模型
train(model, epochs=3)

# 8. 评估模型
def evaluate(model):
    model.eval()
    test_data = datasets.MNIST('./data', train=False, transform=transform)
    test_loader = DataLoader(test_data, batch_size=64)
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            output = model(data)
            pred = output.argmax(dim=1)
            correct += pred.eq(target).sum().item()
    print(f"Accuracy: {correct / len(test_data):.2f}")

evaluate(model)