每日Attention学习27——Patch-based Graph Reasoning

news/2025/3/20 23:22:14/

模块出处

[NC 25] [link] Graph-based context learning network for infrared small target detection

模块名称

Patch-based Graph Reasoning (PGR)

模块结构

在这里插入图片描述

模块特点

使用图结构更好的捕捉特征的全局上下文
将图结构与特征切片(Patching)相结合，从而促进全局/局部特征互补

模块代码

import torch
import torch.nn as nn
import torch.nn.functional as Fclass graph(nn.Module):def __init__(self, p2=4, nIn=64, N=16):super(graph, self).__init__()self.p2 = p2self.N = Nself.conv30 = nn.Sequential(nn.Conv2d(nIn, self.N, kernel_size=3, stride=1, padding=1, groups=1),nn.ReLU(inplace=True))self.conv10 = nn.Sequential(nn.Conv1d(nIn, nIn, kernel_size=1, stride=1, padding=0),nn.ReLU(inplace=True))self.conv11 = nn.Sequential(nn.Conv1d(self.N, self.N, kernel_size=1, stride=1, padding=0),nn.ReLU(inplace=True))self.adaptivemax = nn.AdaptiveAvgPool2d((8, 8))self.conv12 = nn.Sequential(nn.Conv1d(p2 ** 2, p2, kernel_size=1, stride=1, padding=0),nn.ReLU(inplace=True),nn.Conv1d(p2, p2, kernel_size=1, stride=1, padding=0),nn.ReLU(inplace=True),nn.Conv1d(p2, p2 ** 2, kernel_size=1, stride=1, padding=0),nn.Sigmoid())def ADP_weight(self, x):b, C, H, W = x.shapefg = self.adaptivemax(x)  fg1 = fg.view(b, C, self.p2 ** 2)  fg1 = torch.transpose(fg1, 1, 2)  fg2 = self.conv12(fg1)  fg3 = fg2.unsqueeze(-1).unsqueeze(-1)return fg3def graph_convolution(self, fs, x):b, C, H, W = x.shapeh, w = H // self.p2, W // self.p2L = h * wB = self.conv30(fs)  B1 = B.view(-1, self.N, L)  fs1 = fs.view(-1, C, L)  fs1 = torch.transpose(fs1, 1, 2) fs2 = torch.bmm(B1, fs1)  fs3 = self.conv11(fs2)  fs5 = self.conv10(torch.transpose(fs3, 1, 2))  fs6 = torch.bmm(torch.transpose(B1, 1, 2), torch.transpose(fs5, 1, 2))fs6 = torch.transpose(fs6, 1, 2) fs6 = fs6.view(b, self.p2 ** 2, C, h, w) return fs6def forward(self, fs, x):fs6 = self.graph_convolution(fs, x)weight = self.ADP_weight(x)out = weight * fs6return outclass PGR(nn.Module):def __init__(self, p2=4, nIn=32, nOut=32, add=True):super(PGR, self).__init__()self.p2 = p2self.N = nIn // 4self.add = addself.graph0 = graph(p2, nIn, self.N)self.conv31 = nn.Sequential(nn.Conv2d(nOut, nOut, kernel_size=1, stride=1),nn.BatchNorm2d(nOut),nn.ReLU(inplace=True))def forward(self, x):b, C, H, W = x.shapeh, w = H // self.p2, W // self.p2L = h * wfs = torch.zeros((b, self.p2 ** 2, C, h, w)).cuda()for i in range(1, self.p2 + 1):for j in range(1, self.p2 + 1):fs[:, i * j - 1, :, :, :] = x[:, :, (i - 1) * h: i * h, (j - 1) * w: j * w]fs = fs.view(b * self.p2 ** 2, C, h, w)fs6 = self.graph0(fs, x)out = torch.zeros_like(x)for i in range(1, self.p2 + 1):for j in range(1, self.p2 + 1):out[:, :, (i - 1) * h: i * h, (j - 1) * w: j * w] = fs6[:, i * j - 1, :, :, :]out = self.conv31(out)if self.add:out = out + xreturn outif __name__ == '__main__':x = torch.randn([1, 64, 44, 44]).cuda()pgr = PGR(p2=8, nIn=64, nOut=64).cuda()out = pgr(x)print(out.shape) # [1, 64, 44, 44]