编写用于slowfast的剪枝算法的代码，Slow 分支使用 L1 算法，Fast 分支使用 AAoR 算法的这种混合剪枝

以下是用于slowfast的混合剪枝算法的代码，其中Slow分支使用L1算法，Fast分支使用AAoR算法：

import torch
import torch.nn as nn
import torch.nn.utils.prune as prune

# 定义SlowFast模型
class SlowFast(nn.Module):
    def __init__(self):
        super(SlowFast, self).__init__()
        # 定义Slow分支
        self.slow_conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.slow_bn1 = nn.BatchNorm2d(64)
        self.slow_relu1 = nn.ReLU(inplace=True)
        self.slow_maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        # ... 其他Slow分支的层
        
        # 定义Fast分支
        self.fast_conv1 = nn.Conv2d(3, 8, kernel_size=5, stride=1, padding=2)
        self.fast_bn1 = nn.BatchNorm2d(8)
        self.fast_relu1 = nn.ReLU(inplace=True)
        self.fast_maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        # ... 其他Fast分支的层
        
        # 定义全局平均池化和最终分类层
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(192, 10)

    def forward(self, x):
        # Slow分支的前向传播
        slow_x = self.slow_conv1(x)
        slow_x = self.slow_bn1(slow_x)
        slow_x = self.slow_relu1(slow_x)
        slow_x = self.slow_maxpool1(slow_x)
        # ... 其他Slow分支的层
        
        # Fast分支的前向传播
        fast_x = self.fast_conv1(x)
        fast_x = self.fast_bn1(fast_x)
        fast_x = self.fast_relu1(fast_x)
        fast_x = self.fast_maxpool1(fast_x)
        # ... 其他Fast分支的层
        
        # 将Slow分支和Fast分支的结果合并
        x = torch.cat((slow_x, fast_x), dim=1)
        
        # 全局平均池化和最终分类层的前向传播
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

# 定义Slow分支的剪枝函数，使用L1算法
def prune_slow(model, prune_ratio):
    for name, module in model.named_modules():
        if isinstance(module, nn.Conv2d):
            prune.l1_unstructured(module, name='weight', amount=prune_ratio)
            
# 定义Fast分支的剪枝函数，使用AAoR算法
def prune_fast(model, prune_ratio):
    for name, module in model.named_modules():
        if isinstance(module, nn.Conv2d):
            prune.custom_from_mask(module, name='weight', mask=torch.ones_like(module.weight), 
                                   importance=module.weight.abs().sum(dim=(1,2,3)), amount=prune_ratio)

# 定义混合剪枝函数，将Slow和Fast分支按照不同的剪枝比例分别进行剪枝
def hybrid_pruning(model, slow_prune_ratio, fast_prune_ratio):
    prune_slow(model.slow_pathway, slow_prune_ratio)
    prune_fast(model.fast_pathway, fast_prune_ratio)

使用方法：

model = SlowFast()
# 对Slow分支剪枝，剪枝比例为0.2
prune_slow(model.slow_pathway, 0.2)
# 对Fast分支剪枝，剪枝比例为0.3
prune_fast(model.fast_pathway, 0.3)
# 进行混合剪枝，Slow分支剪枝比例为0.4，Fast分支剪枝比例为0.5
hybrid_pruning(model, 0.4, 0.5)