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百度网站优化排行做响应式网站应该注意什么

news 2025/11/1 10:25:34

百度网站优化排行,做响应式网站应该注意什么,源码交易平台网站源码,厦门关键词推广优化模块出处 [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/…

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]