slowfast中加ECA注意力机制代码

以下是在SlowFast模型中添加ECA注意力机制的代码实现：

import torch.nn as nn

class ECALayer(nn.Module):
    def __init__(self, channel, gamma=2, b=1):
        super(ECALayer, self).__init__()
        t = int(abs(math.log(channel, 2) + b) / gamma)
        k = t if t % 2 else t - 1
        self.avg_pool = nn.AdaptiveAvgPool3d(1)
        self.conv = nn.Conv3d(channel, channel, kernel_size=(k+1, 1, 1), padding=(k//2, 0, 0), bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        y = self.avg_pool(x)
        y = self.conv(y)
        y = self.sigmoid(y)
        return x * y

在SlowFast模型中，可以在每个残差块的输出上应用ECALayer，以增强模型的表达能力和性能。具体实现方法可以参考以下代码：

class ResBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(ResBlock, self).__init__()
        self.conv1 = nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn1 = nn.BatchNorm3d(out_channels)
        self.conv2 = nn.Conv3d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm3d(out_channels)
        self.conv3 = nn.Conv3d(out_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn3 = nn.BatchNorm3d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
        self.eca = ECALayer(out_channels)

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)
        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)
        out = self.eca(out)

        return out