每日Attention学习19——Convolutional Multi-Focal Attention

每日Attention学习19——Convolutional Multi-Focal Attention

模块出处

[ICLR 25 Submission] [link] UltraLightUNet: Rethinking U-shaped Network with Multi-kernel Lightweight Convolutions for Medical Image Segmentation

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模块名称

Convolutional Multi-Focal Attention (CMFA)

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模块作用

轻量解码器

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模块结构

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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

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模块代码

import torch
import torch.nn as nn
import torch.nn.functional as Fclass 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 // 2self.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_planesself.out_planes = out_planesif self.in_planes < ratio:ratio = self.in_planesself.reduced_channels = self.in_planes // ratioif self.out_planes == None:self.out_planes = in_planesself.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_outreturn 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 xif __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]

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