cbam代码pytorch

以下是基于PyTorch实现的CBAM模块代码示例：

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

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 // ratio, 1, bias=False)
        self.relu1 = nn.ReLU()
        self.fc2 = nn.Conv2d(in_planes // ratio, 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 = avg_out + max_out
        return self.sigmoid(out)

class SpatialAttention(nn.Module):
    def __init__(self, kernel_size=7):
        super(SpatialAttention, self).__init__()

        assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
        padding = 3 if kernel_size == 7 else 1

        self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = torch.mean(x, dim=1, keepdim=True)
        max_out, _ = torch.max(x, dim=1, keepdim=True)
        x = torch.cat([avg_out, max_out], dim=1)
        x = self.conv1(x)
        return self.sigmoid(x)

class CBAM(nn.Module):
    def __init__(self, in_planes, ratio=16, kernel_size=7):
        super(CBAM, self).__init__()
        self.channel_att = ChannelAttention(in_planes, ratio)
        self.spatial_att = SpatialAttention(kernel_size)

    def forward(self, x):
        out = self.channel_att(x) * x
        out = self.spatial_att(out) * out
        return out

这是一个包含Channel Attention和Spatial Attention的CBAM模块的实现。其中，Channel Attention和Spatial Attention分别用于对通道和空间维度进行注意力加权。Channel Attention使用全局平均池化和全局最大池化来提取特征，并通过两个全连接层获得通道维度的权重，最后使用Sigmoid函数进行归一化。Spatial Attention则使用通道维度上的加权特征对空间维度进行注意力加权，其中包含了平均池化和最大池化操作，以及一个卷积层和Sigmoid函数。CBAM模块将Channel Attention和Spatial Attention结合起来，对输入特征图进行加权。