pytorch通道注意力 减少维度

PyTorch中的通道注意力可以通过以下步骤减少维度：

1.定义一个通道注意力层，其中包括一个全局池化层和一个线性层。

class ChannelAttention(nn.Module):
    def __init__(self, in_planes, ratio=16):
        super(ChannelAttention, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)

        self.fc1   = nn.Conv2d(in_planes, in_planes // 16, 1, bias=False)
        self.relu1 = nn.ReLU()
        self.fc2   = nn.Conv2d(in_planes // 16, in_planes, 1, bias=False)
        
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x))))
        max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))
        out = self.sigmoid(avg_out + max_out)
        return out

2.在模型中应用该层，对输入的张量进行通道注意力操作，从而减少维度。

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.ca1 = ChannelAttention(64)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(64)
        self.ca2 = ChannelAttention(64)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(128)
        self.ca3 = ChannelAttention(128)
        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn4 = nn.BatchNorm2d(128)
        self.ca4 = ChannelAttention(128)
        self.conv5 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn5 = nn.BatchNorm2d(256)
        self.ca5 = ChannelAttention(256)
        self.conv6 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn6 = nn.BatchNorm2d(256)
        self.ca6 = ChannelAttention(256)
        self.fc = nn.Linear(256, 10)

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = self.ca1(x) * x
        x = F.relu(self.bn2(self.conv2(x)))
        x = self.ca2(x) * x
        x = F.max_pool2d(x, 2)
        x = F.relu(self.bn3(self.conv3(x)))
        x = self.ca3(x) * x
        x = F.relu(self.bn4(self.conv4(x)))
        x = self.ca4(x) * x
        x = F.max_pool2d(x, 2)
        x = F.relu(self.bn5(self.conv5(x)))
        x = self.ca5(x) * x
        x = F.relu(self.bn6(self.conv6(x)))
        x = self.ca6(x) * x
        x = F.avg_pool2d(x, 8)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

在这个例子中，通道注意力层被应用到模型的每个卷积层输出上，从而减少了每个卷积层输出的通道维度。