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create_model.py

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+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)