每日Attention学习5——Multi-Scale Channel Attention Module

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模块出处

[link] [code] [WACV 21] Attentional Feature Fusion

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模块名称

Multi-Scale Channel Attention Module (MS-CAM)

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模块作用

通道注意力

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模块结构

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模块代码

import torch
import torch.nn as nnclass MS_CAM(nn.Module):def __init__(self, channels=64, r=4):super(MS_CAM, self).__init__()inter_channels = int(channels // r)self.local_att = nn.Sequential(nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),nn.BatchNorm2d(inter_channels),nn.ReLU(inplace=True),nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),nn.BatchNorm2d(channels),)self.global_att = nn.Sequential(nn.AdaptiveAvgPool2d(1),nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),nn.BatchNorm2d(inter_channels),nn.ReLU(inplace=True),nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),nn.BatchNorm2d(channels),)self.sigmoid = nn.Sigmoid()def forward(self, x):xl = self.local_att(x)xg = self.global_att(x)xlg = xl + xgwei = self.sigmoid(xlg)return x * weiif __name__ == '__main__':x = torch.randn([3, 256, 16, 16])ms_sam = MS_CAM(channels=256)out = ms_sam(x)print(out.shape)  # 3, 256, 16, 16

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原文表述

MS-CAM的核心思想在于，通过改变空间池化的大小，可以在多个尺度上实现通道注意力。为了尽可能保持轻量级，我们只是在注意力模块内将局部上下文添加到全局上下文中。我们选择点卷积（PointWise Conv）作为局部通道上下文融合器，它只利用每个空间位置的点级通道交互。

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