Der Pytorch-Code implementiert den Aufmerksamkeitsmechanismus A2_Attention

A2 Aufmerksamkeitsmechanismus

Der Originaltext des A2-Aufmerksamkeitsmechanismus lautet „A2-Nets: Double Attention Networks“. Das Prinzip besteht darin, mithilfe von Aufmerksamkeitspooling zweiter Ordnung alle Schlüsselmerkmale des gesamten Bildes in einem Satz zu sammeln und anschließend einen anderen Aufmerksamkeitsmechanismus zu verwenden Kombinieren Sie diese Funktionen. Jeder Ort des Bildes separat.

Papieradresse: https://arxiv.org/pdf/1810.11579.pdf

Code wie folgt anzeigen:

import numpy as np
import torch
from torch import nn
from torch.nn import init
from torch.nn import functional as F



class DoubleAttention(nn.Module):

    def __init__(self, in_channels,c_m=128,c_n=128,reconstruct = True):
        super().__init__()
        self.in_channels=in_channels
        self.reconstruct = reconstruct
        self.c_m=c_m
        self.c_n=c_n
        self.convA=nn.Conv2d(in_channels,c_m,1)
        self.convB=nn.Conv2d(in_channels,c_n,1)
        self.convV=nn.Conv2d(in_channels,c_n,1)
        if self.reconstruct:
            self.conv_reconstruct = nn.Conv2d(c_m, in_channels, kernel_size = 1)
        self.init_weights()


    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                init.normal_(m.weight, std=0.001)
                if m.bias is not None:
                    init.constant_(m.bias, 0)

    def forward(self, x):
        b, c, h,w=x.shape
        assert c==self.in_channels
        A=self.convA(x) #b,c_m,h,w
        B=self.convB(x) #b,c_n,h,w
        V=self.convV(x) #b,c_n,h,w
        tmpA=A.view(b,self.c_m,-1)
        attention_maps=F.softmax(B.view(b,self.c_n,-1))
        attention_vectors=F.softmax(V.view(b,self.c_n,-1))
        # step 1: feature gating
        global_descriptors=torch.bmm(tmpA,attention_maps.permute(0,2,1)) #b.c_m,c_n
        # step 2: feature distribution
        tmpZ = global_descriptors.matmul(attention_vectors) #b,c_m,h*w
        tmpZ=tmpZ.view(b,self.c_m,h,w) #b,c_m,h,w
        if self.reconstruct:
            tmpZ=self.conv_reconstruct(tmpZ)

        return tmpZ 


if __name__ == '__main__':
    input=torch.randn(50,512,7,7)
    a2 = DoubleAttention(512)
    output=a2(input)
    print(output.shape)