aff.py

import torch
import torch.nn as nn


class DAF(nn.Module):
    '''
    直接相加 DirectAddFuse
    '''

    def __init__(self):
        super(DAF, self).__init__()

    def forward(self, x, residual):
        return x + residual


class iAFF(nn.Module):
    '''
    多特征融合 iAFF
    '''

    def __init__(self, channels=64, r=4):
        super(iAFF, self).__init__()
        inter_channels = int(channels // r)

        # 本地注意力
        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),
        )

        # 全局注意力
        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),
        )

        # 第二次本地注意力
        self.local_att2 = 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),
        )
        # 第二次全局注意力
        self.global_att2 = 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, residual):
        xa = x + residual
        xl = self.local_att(xa)
        xg = self.global_att(xa)
        xlg = xl + xg
        wei = self.sigmoid(xlg)
        xi = x * wei + residual * (1 - wei)

        xl2 = self.local_att2(xi)
        xg2 = self.global_att(xi)
        xlg2 = xl2 + xg2
        wei2 = self.sigmoid(xlg2)
        xo = x * wei2 + residual * (1 - wei2)
        return xo


class AFF(nn.Module):
    '''
    多特征融合 AFF
    '''

    def __init__(self, channels=64, r=4):
        super(AFF, 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, residual):
        xa = x + residual
        xl = self.local_att(xa)
        xg = self.global_att(xa)
        xlg = xl + xg
        wei = self.sigmoid(xlg)

        xo = 2 * x * wei + 2 * residual * (1 - wei)
        return xo


class MS_CAM(nn.Module):
    '''
    单特征 进行通道加权,作用类似SE模块
    '''

    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 + xg
        wei = self.sigmoid(xlg)
        return x * wei



if __name__ == '__main__':
    import os
    os.environ['CUDA_VISIBLE_DEVICES'] = "0"
    device = torch.device("cuda:0")

    x, residual= torch.ones(2,128, 128, 128).to(device),torch.ones(2,256, 64, 64).to(device)
    residual_up = nn.Upsample(scale_factor=2, mode='nearest')(residual)
    de_layer = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=3, stride=1, padding=1).to(device)
    residual_de = de_layer(residual_up)
    channels=x.shape[1]

    model=AFF(channels=channels)
    model=model.to(device).train()
    output = model(x, residual_de)
    print(output.shape)