FTP协议的TCP NAT扩展实现解析

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0 下载量 65 浏览量 更新于2024-10-19 收藏 2KB RAR 举报
资源摘要信息:"nf_nat_ftp.rar_ftp" 知识点： 1. 网络地址转换（NAT）: NAT是一种网络技术，用于将一个IP地址域映射到另一个IP地址域的技术。它的主要作用是使得私有网络中的多台计算机可以共享一个公网IP地址访问互联网，这种技术在家庭网络和企业网络中得到了广泛的应用。 2. NAT与FTP协议：FTP（文件传输协议）是一种用于在网络上进行文件传输的协议。然而，传统的FTP协议在NAT环境中存在一些问题，因为FTP协议会涉及到多个端口的连接（例如：控制连接和数据连接）。当这些连接通过NAT设备时，NAT设备需要对这些连接进行特殊处理，以保证数据的正确传输。 3. FTP扩展：这个压缩包文件名为"nf_nat_ftp.rar_ftp"，其中包含的文件名为"nf_nat_ftp.c"。从这个文件名我们可以推测，这可能是关于网络地址转换（NAT）对FTP协议扩展的一个实现。这个扩展可能是用来处理FTP在NAT环境下的各种问题，如端口的动态分配，数据的正确传输等。 4. TCP协议：在描述中提到"TCP NAT alteration"，TCP（传输控制协议）是一种面向连接的、可靠的、基于字节流的传输层通信协议。TCP协议保证了数据包的顺序传输和数据的完整性。NAT alteraion可能是指在NAT环境下对TCP连接进行的一些特殊处理，以保证数据的正确传输。 5. C语言：文件"nf_nat_ftp.c"表明这个扩展可能是用C语言编写的。C语言是一种广泛使用的高级编程语言，特别是在系统编程和嵌入式系统开发中。这个文件可能是Linux内核模块的一部分，用于处理NAT和FTP的交互。 总结：本压缩包"nf_nat_ftp.rar_ftp"可能包含了一个用于Linux内核的模块，该模块是对FTP协议在网络地址转换（NAT）环境下的扩展。这个模块可能是用C语言编写的，用于处理FTP在NAT环境下的连接问题，如端口的动态分配，数据的正确传输等。这对于理解和实现NAT环境下的FTP协议具有重要的意义。

nf_nat_ftp.rar_V2 _ftp

2022-09-23 上传
文件`nf_nat_ftp.c`是该模块的核心源代码，包含具体的实现逻辑。通过对源代码的分析，开发者可以更深入地理解NF_NAT_FTP的工作机制，以及如何根据需要对其进行定制和扩展。 总之，NF_NAT_FTP是Linux内核为了保证FTP...

nf_nat_ftp.rar_ftp_编译nf_nat_ftp_编译出nf_nat_ftp

2022-09-20 上传
Linux Network FTP extension for TCP NAT alteration driver .

select * from (select t1.[id] as t1_id,t1.[requestId] as t1_requestId,t1.[htqsrq] as t1_htqsrq,t1.[htjzrq] as t1_htjzrq,t1.[htbh] as t1_htbh,t1.[gf] as t1_gf,t1.[xf] as t1_xf,t1.[rq] as t1_rq,t1.[fkfs] as t1_fkfs,t1.[formmodeid] as t1_formmodeid,t1.[modedatacreater] as t1_modedatacreater,t1.[modedatacreatertype] as t1_modedatacreatertype,t1.[modedatacreatedate] as t1_modedatacreatedate,t1.[modedatacreatetime] as t1_modedatacreatetime,t1.[modedatamodifier] as t1_modedatamodifier,t1.[modedatamodifydatetime] as t1_modedatamodifydatetime,t1.[form_biz_id] as t1_form_biz_id,t1.[MODEUUID] as t1_MODEUUID,t1.[htfj] as t1_htfj,t1.[zje] as t1_zje,t1.[ds] as t1_ds,t1.[zjedx] as t1_zjedx,t1.[cspp] as t1_cspp,t1.[yfk] as t1_yfk,t1.[gxid] as t1_gxid,t1.[bz] as t1_bz,t1.[gfqymc] as t1_gfqymc,t1.[gfjc] as t1_gfjc,t1.[bh] as t1_bh,t1.[jylx] as t1_jylx,t1.[cght] as t1_cght,t1.[yf] as t1_yf,t1.[yfk1] as t1_yfk1,t1.[yf11] as t1_yf11,t1.[nf] as t1_nf,t1.[rksj] as t1_rksj,t1.[cclx] as t1_cclx,t1.[cgbt] as t1_cgbt,t1.[yfk2] as t1_yfk2,t1.[sywf] as t1_sywf,t1.[yfbl] as t1_yfbl,t1.[fhbl] as t1_fhbl,t1.[yfh] as t1_yfh,t1.[sykf] as t1_sykf,t1.[hzsdlqys] as t1_hzsdlqys,t1.[sys_workflowid] as t1_sys_workflowid,t1.[cgqzyz] as t1_cgqzyz,t1.[htwjpdf] as t1_htwjpdf,t1.[cghtlc] as t1_cghtlc,t1.[htzt] as t1_htzt,t1.[qzfs] as t1_qzfs,t1.[htwjtp] as t1_htwjtp,t1.[cgqzlc] as t1_cgqzlc,t1.[sjfk] as t1_sjfk,t1.[ydkds] as t1_ydkds,t1.[chpt] as t1_chpt,t1.[lxdhchr] as t1_lxdhchr,t1.[gxsjkx] as t1_gxsjkx,t1.[hkzt] as t1_hkzt,t1.[lcfkd] as t1_lcfkd,t1.[fkzlcid] as t1_fkzlcid,t1.[mode_top_4] as t1_mode_top_4,t1.[cgdj] as t1_cgdj,t1.[mode_top_22] as t1_mode_top_22,t2.[id] as t2_id,t2.[mainid] as t2_mainid,t2.[sld] as t2_sld,t2.[ppcj] as t2_ppcj,t2.[hsdj] as t2_hsdj,t2.[bz] as t2_bz,t2.[je] as t2_je,t2.[xhggyt] as t2_xhggyt,t2.[mxgxid] as t2_mxgxid,t2.[dqkckc] as t2_dqkckc,t2.[rkhkc] as t2_rkhkc,t2.[yf] as t2_yf,t2.[yldjbhyf] as t2_yldjbhyf,SELECT year(rksj) as 年 FROM uf_gfht as cus_年年 from uf_gfht t1 INNER join uf_gfht_dt1 t2 on t1.id = t2.mainid) tmp1错在哪里

2023-05-14 上传
t1.[bh] AS t1_bh, t1.[jylx] AS t1_jylx, t1.[cght] AS t1_cght, t1.[yf] AS t1_yf, t1.[yfk1] AS t1_yfk1, t1.[yf11] AS t1_yf11, t1.[nf] AS t1_nf, t1.[rksj] AS t1_rksj, t1.[cclx] AS t1_cclx, t1.[cgbt] AS ...

如何将self.conv1 = nn.Conv2d(4 * num_filters, num_filters, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(512, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, 512, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(512, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, 512, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) # 初始化为0.5 与torchvision.ops.deform_conv2d，加入到：class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d kw = 4 padw = int(np.ceil((kw-1)/2)) sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True) ] nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2n, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2n_layers, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] if use_sigmoid: sequence += [nn.Sigmoid()] self.model = nn.Sequential(*sequence) def forward(self, input): return self.model(input)中，请给出修改后的代码

2023-05-30 上传
super(NLayerDiscriminator, self).__init__() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer...

class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d kw = 4 padw = int(np.ceil((kw - 1) / 2)) sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True) ] nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2 ** n, 8) if n == 1: num_filters = ndf * nf_mult self.conv1 = nn.Conv2d(4 * num_filters, num_filters, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(512, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, 512, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) self.conv_mask1 = nn.Conv2d(512, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, 512, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) sequence += [ torchvision.ops.DeformConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2 ** n_layers, 8) sequence += [ torchvision.ops.DeformConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw) ] if use_sigmoid: sequence += [nn.Sigmoid()] self.model = nn.Sequential(*sequence) def forward(self, input): offset1 = self.conv_offset1(input) mask1 = self.conv_mask1(input) input = torch.cat([input, offset1, mask1], dim=1) return self.model(input)，运行上述代码出现错误：RuntimeError: Given groups=1, weight of size [18, 512, 3, 3], expected input[1, 3, 512, 512] to have 512 channels, but got 3 channels instead，如何修改，给出代码

2023-05-30 上传
super(NLayerDiscriminator, self).__init__() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer...

Running modprobe nf_nat failed with message: `modprobe: WARNING: Module nf_nat not found in directory /lib/modules/5.4.61+`, error: exit status 1

2023-06-06 上传
这个错误意味着 `nf_nat` 内核模块不存在或者未能正确加载。可以尝试使用以下命令安装 `nf_nat` 模块： 1. 首先更新软件包列表： ``` sudo apt-get update ``` 2. 安装 `nf_nat` 模块： ``` sudo apt-get ...

Defines the PatchGAN discriminator with the specified arguments. class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d self.conv1 = nn.Conv2d(input_nc, ndf, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(ndf, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, ndf, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(ndf, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, ndf, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) # 初始化为0.5 kw = 4 padw = int(np.ceil((kw-1)/2)) nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2n, 8) self.sequence2 = [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2n_layers, 8) self.sequence2 += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] self.sequence2 += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] if use_sigmoid: self.sequence2 += [nn.Sigmoid()] def forward(self, input): input = self.conv1(input) offset1 = self.conv_offset1(input) mask1 = torch.sigmoid(self.conv_mask1(input)) sequence1 = [ torchvision.ops.deform_conv2d(input=input, offset=offset1, weight=self.conv1.weight, mask=mask1, padding=(1, 1)) 上述代码中出现错误：RuntimeError: Expected weight_c.size(1) * n_weight_grps == input_c.size(1) to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)，请问如何解决，给出修改后的代码

2023-05-30 上传
super(NLayerDiscriminator, self).__init__() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer...

nf_conntrack_core.c文件中所有函数用法和意思

2023-07-11 上传
nf_conntrack_core.c 文件是 Linux 内核中网络连接跟踪子系统（Netfilter Connection Tracking subsystem）的核心文件。该文件包含了一些关键函数，用于实现网络连接跟踪的功能。以下是 nf_conntrack_core.c 文件中...

level=warning msg="Running modprobe nf_nat failed with message: `modprobe: ERROR: ../libkmod/libkmod.c:586 kmod_search_moddep

2023-06-09 上传
nf_nat 是一个内核模块，用于实现 Linux 上的网络地址转换 (NAT) 功能。您可以尝试以下解决方法： 1. 确认您的系统已经安装了 nf_nat 内核模块。可以使用以下命令来检查： ``` lsmod | grep nf_nat ``` 如果...

obj-$(CONFIG_NF_LOG_COMMON) += nf_log_common.o什么意思

2023-07-15 上传
`obj-$(CONFIG_NF_LOG_COMMON) += nf_log_common.o` 表示如果配置选项 CONFIG_NF_LOG_COMMON 被设置为 "y" 或者 "m"（即内核配置中启用了 NF_LOG_COMMON），则将 nf_log_common.o 添加到编译目标列表中。 在 Linux ...
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