每日Attention学习19------Convolutional Multi-Focal Attention
模块出处
ICLR 25 Submission\] [\[link\]](https://openreview.net/forum?id=BefqqrgdZ1) UltraLightUNet: Rethinking U-shaped Network with Multi-kernel Lightweight Convolutions for Medical Image Segmentation *** ** * ** *** ##### 模块名称 Convolutional Multi-Focal Attention (CMFA) *** ** * ** *** ##### 模块作用 轻量解码器 *** ** * ** *** ##### 模块结构  *** ** * ** *** ##### 模块特点 * 使用最大池化与平均池化构建通道注意力 * 使用Channel Max与Channel Average构建空间注意力 * 核心思想与CBAM较为类似，串联通道注意力与空间注意力 *** ** * ** *** ##### 模块代码 ```python import torch import torch.nn as nn import torch.nn.functional as F class SpatialAttention(nn.Module): def __init__(self, kernel_size=7): super(SpatialAttention, self).__init__() assert kernel_size in (3, 7, 11), 'kernel size must be 3 or 7 or 11' padding = kernel_size // 2 self.conv = 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.conv(x) return self.sigmoid(x) class ChannelAttention(nn.Module): def __init__(self, in_planes, out_planes=None, ratio=16): super(ChannelAttention, self).__init__() self.in_planes = in_planes self.out_planes = out_planes if self.in_planes < ratio: ratio = self.in_planes self.reduced_channels = self.in_planes // ratio if self.out_planes == None: self.out_planes = in_planes self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.activation = nn.ReLU(inplace=True) self.fc1 = nn.Conv2d(in_planes, self.reduced_channels, 1, bias=False) self.fc2 = nn.Conv2d(self.reduced_channels, self.out_planes, 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_pool_out = self.avg_pool(x) avg_out = self.fc2(self.activation(self.fc1(avg_pool_out))) max_pool_out= self.max_pool(x) max_out = self.fc2(self.activation(self.fc1(max_pool_out))) out = avg_out + max_out return self.sigmoid(out) class CMFA(nn.Module): def __init__(self, in_planes, out_planes=None,): super(CMFA, self).__init__() self.ca = ChannelAttention(in_planes=64, out_planes=64) self.sa = SpatialAttention() def forward(self, x): x = x*self.ca(x) x = x*self.sa(x) return x if __name__ == '__main__': x = torch.randn([1, 64, 44, 44]) cmfa = CMFA(in_planes=64, out_planes=64) out = cmfa(x) print(out.shape) # [1, 64, 44, 44] ``` *** ** * ** ***