slowfast中加GCNET注意力机制代码

以下是在SlowFast模型中使用GCNet注意力机制的示例代码：

首先，需要导入相关的库和模块：

import torch
import torch.nn as nn
import torch.nn.functional as F

然后，定义GCNet注意力模块：

class GCNet(nn.Module):
    def __init__(self, in_channels, reduction_ratio=16):
        super(GCNet, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(in_channels, in_channels // reduction_ratio),
            nn.ReLU(inplace=True),
            nn.Linear(in_channels // reduction_ratio, in_channels),
            nn.Sigmoid()
        )

    def forward(self, x):
        bs, ch, _, _ = x.size()
        y = self.avg_pool(x).view(bs, ch)
        y = self.fc(y).view(bs, ch, 1, 1)
        return x * y.expand_as(x)

接下来，在SlowFast网络中使用GCNet注意力模块：

class SlowFast(nn.Module):
    def __init__(self, num_classes):
        super(SlowFast, self).__init__()
        
        # Slow pathway
        self.slow_conv1 = nn.Conv3d(3, 64, kernel_size=(1, 7, 7), stride=(1, 2, 2), padding=(0, 3, 3))
        self.slow_gc1 = GCNet(64)
        
        # Fast pathway
        self.fast_conv1 = nn.Conv3d(3, 8, kernel_size=(5, 7, 7), stride=(1, 2, 2), padding=(2, 3, 3))
        self.fast_gc1 = GCNet(8)
        
        # Other layers...
        
        self.fc = nn.Linear(1024, num_classes)

    def forward(self, slow_input, fast_input):
        # Slow pathway
        slow_out = F.relu(self.slow_conv1(slow_input))
        slow_out = self.slow_gc1(slow_out)
        
        # Fast pathway
        fast_out = F.relu(self.fast_conv1(fast_input))
        fast_out = self.fast_gc1(fast_out)
        
        # Other layers...
        
        # Fusion
        out = torch.cat((slow_out, fast_out), dim=2)
        
        # Other layers...
        
        return out

以上是在SlowFast模型中使用GCNet注意力机制的示例代码。根据实际需求，你可能需要调整GCNet注意力模块的参数和SlowFast网络的结构。希望这能帮到你！