cohen裁剪算法Python代码

以下是使用PyTorch实现Cohen剪枝算法的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

def cohen_prune(model, prune_percent):
    """
    Perform Cohen's weight pruning on the model, removing prune_percent % of the weights with the smallest magnitude.
    """
    parameters_to_prune = []
    for module in model.modules():
        if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear):
            parameters_to_prune.append((module, 'weight'))
    prune.global_unstructured(
        parameters_to_prune,
        pruning_method=prune.L1Unstructured,
        amount=prune_percent/100
    )

# Define your model
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
        self.fc1 = nn.Linear(1600, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.relu(self.conv2(x))
        x = x.view(-1, 1600)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

model = Net()

# Train your model
optimizer = optim.Adam(model.parameters(), lr=0.01)
loss_fn = nn.CrossEntropyLoss()

for epoch in range(10):
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = loss_fn(output, target)
        loss.backward()
        optimizer.step()

# Prune the model
cohen_prune(model, 50)

# Evaluate the pruned model
correct = 0
total = 0
with torch.no_grad():
    for data, target in test_loader:
        output = model(data)
        _, predicted = torch.max(output.data, 1)
        total += target.size(0)
        correct += (predicted == target).sum().item()

print('Accuracy of the pruned model: %d %%' % (
    100 * correct / total))

这里我们定义了一个简单的模型，使用MNIST数据集进行训练和测试，然后使用`cohen_prune`函数进行剪枝。在这个例子中，我们将剪去50％的权重。