BAM注意力机制模块

BAM是基于注意力机制的一种网络模型，用于提高模型的性能。BAM模块应用于深度学习模型的瓶颈处，可以分别从通道和空间两个方面对输入进行注意力机制的处理。通道注意力机制可以学习不同通道之间的关系，而空间注意力机制可以学习图像不同位置之间的区别，从而提高模型的鲁棒性和泛化能力。BAM模块的应用可以有效地提高网络的性能，增强模型的特征表达能力，减少计算量，加快网络的收敛速度。

以下是BAM模块的实现示例代码[^1]：

import torch.nn as nn
import torch

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 = 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 BAM(nn.Module):
    def __init__(self, in_planes):
        super(BAM, self).__init__()
        self.ca = ChannelAttention(in_planes)
        self.sa = SpatialAttention()

    def forward(self, x):
        out = self.ca(x) * x
        out = self.sa(out) * out
        return out