在这寒冷的天气，家用多联机就要发挥作用了，但是当你打开空调，发现在线控器上怎么调都没有“制热模式”的 太阳 呢？只有制冷、送风和除湿，难道买的是单冷机吗？
月度归档： 2019 年 11 月
在ubuntu 18.04(GeForce GTX 760 4GB显存)使用MaskTextSpotter(CUDA-10.1)进行训练
参考 在ubuntu 18.04(GeForce GTX 760 4GB显存)编译/测试MaskTextSpotter(CUDA-10.1) 建立能运行的测试环境。
由于测试集使用的是 icdar2013 ,因此，务必保证已经可以在 icdar2013 数据集中进行测试。
接下来就是进行数据训练：
1. 修改训练脚本，默认情况下，训练脚本中使用了 8 张卡进行训练，我们只有一张卡，因此要调整训练参数
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$ cd MaskTextSpotter $ export ROOT_PATH=`pwd` $ sed -i 's/nproc_per_node=8/nproc_per_node=1/g' train.sh |
2. 下载训练集 MaskTextSpotter 默认使用的是 SynthText 数据集进行训练，需要先下载这个数据集，大约 40GB
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$ mkdir datasets $ cd datasets $ sudo apt-get install aria2 $ aria2c -c -j16 -s16 -x16 --follow-torrent=mem -o 'hyperai.torrent' 'https://hyper.ai/tracker/download?torrent=7783' # 也可下载种子文件 wget https://www.mobibrw.com/wp-content/uploads/2019/11/SynthText.zip |
3. 解压缩 SynthText 数据集到指定目录
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$ mkdir synthtext $ unzip SynthText/data/SynthText.zip -d synthtext # 目录改名 $ mv synthtext/SynthText synthtext/train_images |
4. 下载转换后的 SynthText 数据集索引文件，上面解压缩出来的索引是 .mat 扩展名的文件，我们需要转换成 MaskTextSpotter 需要的数据索引文件，作者提供了一份已经转换好的文件，我们直接下载并使用这个文件即可,这个文件大概要 1.6GB 的样子。
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$ aria2 -c https://1drv.ms/u/s!ArsnjfK83FbXgb5vgOOVPYywgCWuQw?e=UPuNTa # 解压缩到指定目录 $ tar -xvf SynthText_GT_E2E.tar.gz -C synthtext # 目录改名 $ mv synthtext/SynthText_GT_E2E synthtext/train_gts |
5. 生成训练文件 train_list.txt
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import os path = 'train_images' train_list = 'train_list.txt' tf = open(train_list, 'w') for root, dirs, files in os.walk(path): files = [f for f in files if not f[0] == '.'] dirs[:] = [d for d in dirs if not d[0] == '.'] # use files and dirs for file_name in files: fn = os.path.join(root, file_name) fn = fn.replace('./', '') fn = fn.replace(path + '/', '') ext = os.path.splitext(fn)[1] if '.jpg' == ext : tf.write(fn + '\n') tf.close() |
执行脚本，生成文件
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$ cd synthtext $ python gen_train.py |
执行测试
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$ cd $ROOT_PATH # 减少一次性加载图片数量，解决“OSError: [Errno 24] Too many open files” # 参数设置为 0 代表从主进程加载图片资源 $ sed -i "s/NUM_WORKERS: 4/NUM_WORKERS: 0/g" configs/pretrain.yaml # 调整训练参数，对于单个GPU来说，默认参数太大了，会导致GPU内存不足 # 解决 “RuntimeError: CUDA out of memory.” $ sed -i "s/IMS_PER_BATCH: 8/IMS_PER_BATCH: 1/g" configs/pretrain.yaml # 修正错误 “AttributeError: module 'torch' has no attribute 'bool'” # 从Pytorch 1.2开始，torch.uint8被修改为torch.bool,如果是低于 Pytorch 1.2的版本 # 需要修改为torch.uint8 $ sed -i "s/torch.bool/torch.uint8/g" maskrcnn_benchmark/modeling/rpn/inference.py $ sed -i "s/torch.bool/torch.uint8/g" maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py # 修改SOLVER设置上的GPU相关参数 # https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14 # 官方参考建议单个GPU的学习速率是0.0025但是实际运行中会报错，调整为0.0015可以正常运行 $ sed -i "s/BASE_LR: 0.01/BASE_LR: 0.0015/g" configs/pretrain.yaml # 4GB 显存设置为 8 ，8GB显存可以设置为64/128 $ sed -i "s/MASK_BATCH_SIZE_PER_IM: 512/MASK_BATCH_SIZE_PER_IM: 8/g" configs/pretrain.yaml # 目前在RTX 2070 Super 8GB显存版本上测试来看，使用 # “WEIGHT: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl” # 的配置情况下，BASE_LR可以设置为 0.0025 , MASK_BATCH_SIZE_PER_IM 可以设置为 128 # 进入运行环境 $ source activate MaskTextSpotter $ bash train.sh |
注意，我们在 configs/pretrain.yaml 加载的权重文件是 "WEIGHT: "./outputs/finetune/model_finetune.pth" ,这个权重文件是从 SynthText 训练得来的，那么这个"model_finetune.pth"是怎么生成的呢？
作者没有详细介绍，我们从 masktextspotter.caffe2 项目的配置文件中可以知道，这个文件其实是从 " WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl" 开始生成的。这个文件也可以从本站下载 R-50.pkl
R-50.pkl: converted copy of MSRA’s original ResNet-50 model
具体配置文件内容参考如下：
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MODEL: TYPE: generalized_rcnn CONV_BODY: FPN.add_fpn_ResNet50_conv5_body NUM_CLASSES: 2 FASTER_RCNN: True MASK_ON: True NAME: shrink++ NUM_GPUS: 1 SOLVER: WEIGHT_DECAY: 0.0001 LR_POLICY: steps_with_decay BASE_LR: 0.005 #synth GAMMA: 0.1 MAX_ITER: 200000 STEPS: [0, 120000] FPN: FPN_ON: True MULTILEVEL_ROIS: True MULTILEVEL_RPN: True USE_DEFORMABLE: False RPN_ASPECT_RATIOS: (0.5, 1, 2) FAST_RCNN: ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head ROI_XFORM_METHOD: RoIAlign ROI_XFORM_RESOLUTION: 7 ROI_XFORM_SAMPLING_RATIO: 2 MRCNN: ROI_MASK_HEAD: text_mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs RESOLUTION: 28 # (output mask resolution) default 14 RESOLUTION_H: 32 RESOLUTION_W: 128 ROI_XFORM_METHOD: RoIAlign ROI_XFORM_RESOLUTION: 14 # default 7 ROI_XFORM_RESOLUTION_H: 16 ROI_XFORM_RESOLUTION_W: 64 ROI_XFORM_SAMPLING_RATIO: 2 # default 0 DILATION: 1 # default 2 CONV_INIT: MSRAFill # default GaussianFill IS_E2E: True MASK_BATCH_SIZE_PER_IM: 16 WEIGHT_LOSS_MASK: 1.0 WEIGHT_LOSS_CHAR_BOX: 1.0 WEIGHT_WH: True ## default is false TRAIN: BATCH_SIZE_PER_IM: 512 RPN_PRE_NMS_TOP_N: 2000 # Per FPN level AUTO_RESUME: True SNAPSHOT_ITERS: 10000 ##################### pre-train on synth ########################## WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl DATASETS: ('synth_train', ) SCALES: (800,) MAX_SIZE: 1333 IMS_PER_BATCH: 2 ASPECT_GROUPING: True MIX_TRAIN: False USE_CHARANNS: [True] ###################### Fine tune ################################# # MIX_TRAIN: True # WEIGHTS: ./train/synth_train/pretrain_model/model_iter159999.pkl # DATASETS: ('totaltext_train', 'scut-eng-char_train', 'synth_train', 'icdar2013_train', 'icdar2015_train') # USE_CHARANNS: [False, True, True, True, False] # # the ratios of synth, icdar2013, icdar2015 is 2:1:1, defaultly # # MIX_RATIOS: [0.125, 0.125, 0.5, 0.125, 0.125] # MIX_RATIOS: [1.0 / 6, 1.0 / 6, 1.0 / 3, 1.0 / 6, 1.0 / 6] # SCALES: (600, 800, 1000) # MAX_SIZE: 1333 # # # SCALES: (800,) # # # MAX_SIZE: 1333 # IMS_PER_BATCH: 1 # ASPECT_GROUPING: False IMAGE: aug: False saturation_prob: 0.5 saturation_lower: 0.5 saturation_upper: 1.5 hue_prob: 0.5 hue_delta: 18 lighting_noise_prob: 0.5 contrast_prob: 0.5 contrast_lower: 0.5 contrast_upper: 1.5 brightness_prob: 0.5 brightness_delta: 32 rotate_prob: 0.5 rotate_delta: 15 TEST: OUTPUT_POLYGON: False # only set to True for totaltext WEIGHTS: ./train/shrink++_finetune/model_iter79999.pkl DATASETS: ('icdar2015_test',) SCALES: (1000,) MAX_SIZE: 3333 NMS: 0.5 RPN_PRE_NMS_TOP_N: 1000 # Per FPN level RPN_POST_NMS_TOP_N: 1000 VIS: False SCORE_THRESH: 0.2 BBOX_AUG: ENABLED: False SCORE_HEUR: UNION # AVG NOTE: cannot use AVG for e2e model COORD_HEUR: UNION # AVG NOTE: cannot use AVG for e2e model H_FLIP: False SCALES: (800,) MAX_SIZE: 2000 SCALE_H_FLIP: False SCALE_SIZE_DEP: False AREA_TH_LO: 2500 # 50^2 AREA_TH_HI: 32400 # 180^2 ASPECT_RATIOS: () ASPECT_RATIO_H_FLIP: False MASK_AUG: ENABLED: False HEUR: SOFT_AVG H_FLIP: False SCALES: (1600,) MAX_SIZE: 3333 SCALE_H_FLIP: False SCALE_SIZE_DEP: False AREA_TH: 32400 # 180^2 ASPECT_RATIOS: () ASPECT_RATIO_H_FLIP: False BBOX_VOTE: ENABLED: True VOTE_TH: 0.9 SOFT_NMS: ENABLED: False OUTPUT_DIR: . |
其实我们直接删除或者注释掉权重文件加载部分也是可以的。只是，如果想要复现原作者的测试成果的话，我们最好使用相同的配置信息。
对于 4GB 显存的机器来说，由于显存非常有限，导致非常可能在运行的途中出现 "RuntimeError: CUDA out of memory." ,目前测试来看，继续执行命令即可。
训练结果存储在 outputs/pretrain 目录下，训练结果会在训练到一定阶段之后，存储到这个目录下。
如果出现类似如下错误，请适当减少学习速率 BASE_LR
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Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed. ~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [29,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed. ~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [30,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed. ~/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:53: lambda [](int)->auto::operator()(int)->auto: block: [1,0,0], thread: [31,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed. Traceback (most recent call last): File "tools/train_net.py", line 173, in <module> main() File "tools/train_net.py", line 166, in main model = train(cfg, args.local_rank, args.distributed) File "tools/train_net.py", line 76, in train cfg, File "~/MaskTextSpotter/maskrcnn_benchmark/engine/trainer.py", line 66, in do_train loss_dict = model(images, targets) File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__ result = self.forward(*input, **kwargs) File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py", line 50, in forward proposals, proposal_losses = self.rpn(images, features, targets) File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__ result = self.forward(*input, **kwargs) File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/rpn.py", line 94, in forward return self._forward_train(anchors, objectness, rpn_box_regression, targets) File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/rpn.py", line 110, in _forward_train anchors, objectness, rpn_box_regression, targets File "~/.conda/envs/MaskTextSpotter/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__ result = self.forward(*input, **kwargs) File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/inference.py", line 138, in forward sampled_boxes.append(self.forward_for_single_feature_map(a, o, b)) File "~/MaskTextSpotter/maskrcnn_benchmark/modeling/rpn/inference.py", line 113, in forward_for_single_feature_map boxlist = remove_small_boxes(boxlist, self.min_size) File "~/MaskTextSpotter/maskrcnn_benchmark/structures/boxlist_ops.py", line 46, in remove_small_boxes (ws >= min_size) & (hs >= min_size) RuntimeError: copy_if failed to synchronize: cudaErrorAssert: device-side assert triggered |
参考链接
- SynthText 自然场景图像数据集
- 「CV」 SynthText 数据集
- Pytorch 1.2.0 来了！
- tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml
- RuntimeError: copy_if failed to synchronize: device-side assert triggered #658
- pytorch报RuntimeError: copy_if failed to synchronize: device-side assert triggered
- masktextspotter.caffe2/configs/text/mask_textspotter.yaml
- Caffe2 - Detectron 初步使用
HTML实现点击图片放大功能
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<html> <head> <style> .over {position: fixed; left:0; top:0; width:100%; z-index:100;} .tempContainer {position:fixed; width:100%; margin-right:0px; margin-left:0px; text-align:center; z-index:101;} </style> <script src="http://code.jquery.com/jquery-1.8.0.min.js"></script> </head> <body> <div class="over"></div><!--背景层--> <div class="amplifyImg"><!--注意：此处的amlifyImg不可少--> <img src="test.jpg"/><!-- 此处是引入图片的路径 --> </div> <script> $(document).ready(function () { var imgsObj = $('.amplifyImg img');//需要放大的图像 if(imgsObj){ $.each(imgsObj,function(){ $(this).click(function(){ var currImg = $(this); coverLayer(1); var tempContainer = $('<div class=tempContainer></div>');//图片容器 with(tempContainer){//width方法等同于$(this) appendTo("body"); var windowWidth=$(window).width(); var windowHeight=$(window).height(); //获取图片原始宽度、高度 var orignImg = new Image(); orignImg.src =currImg.attr("src") ; var currImgWidth= orignImg.width; var currImgHeight = orignImg.height; if(currImgWidth<windowWidth){//为了让图片不失真，当图片宽度较小的时候，保留原图 if(currImgHeight<windowHeight){ var topHeight=(windowHeight-currImgHeight)/2; if(topHeight>35){/*此处为了使图片高度上居中显示在整个手机屏幕中：因为在android,ios的微信中会有一个title导航，35为title导航的高度*/ topHeight=topHeight-35; css('top',topHeight); }else{ css('top',0); } html('<img border=0 src=' + currImg.attr('src') + '>'); }else{ css('top',0); html('<img border=0 src=' + currImg.attr('src') + ' height='+windowHeight+'>'); } }else{ var currImgChangeHeight=(currImgHeight*windowWidth)/currImgWidth; if(currImgChangeHeight<windowHeight){ var topHeight=(windowHeight-currImgChangeHeight)/2; if(topHeight>35){ topHeight=topHeight-35; css('top',topHeight); }else{ css('top',0); } html('<img border=0 src=' + currImg.attr('src') + ' width='+windowWidth+';>'); }else{ css('top',0); html('<img border=0 src=' + currImg.attr('src') + ' width='+windowWidth+'; height='+windowHeight+'>'); } } } tempContainer.click(function(){ $(this).remove(); coverLayer(0); }); }); }); } else{ return false; } //使用禁用蒙层效果 function coverLayer(tag){ with($('.over')){ if(tag==1){ css('height',$(document).height()); css('display','block'); css('opacity',1); css("background-color","#FFFFFF"); css("background-color","rgba(0,0,0,0.7)" ); //蒙层透明度 } else{ css('display','none'); } } } }); </script> </body> </html> |
参考链接
HTTPS建立连接的详细过程
简介
HTTPS是在HTTP的基础上和SSL/TLS证书结合起来的一种协议,保证了传输过程中的安全性,减少了被恶意劫持的可能.很好的解决了解决了HTTP的三个缺点（被监听、被篡改、被伪装）
TCP滑动窗口
TCP滑动窗口
TCP/IP TIME_WAIT状态原理
TIME_WAIT状态原理
----------------------------
通信双方建立TCP连接后，主动关闭连接的一方就会进入TIME_WAIT状态。
客户端主动关闭连接时，会发送最后一个ACK后，然后会进入TIME_WAIT状态，再停留2个MSL时间(后有MSL的解释)，进入CLOSED状态。
Python HttpServer服务器
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#!/usr/bin/python # -*- coding: UTF-8 -*- import sys import os #Python的标准库中的os模块包含普遍的操作系统功能 import re #引入正则表达式对象 import urllib #用于对URL进行编解码 if sys.version_info < (3, 0): from BaseHTTPServer import HTTPServer, BaseHTTPRequestHandler #导入HTTP处理相关的模块 else : from http.server import HTTPServer, BaseHTTPRequestHandler import cgi #文件上传处理 import base64 #base64编码 from io import BytesIO #本地文件读取 #自定义处理程序，用于处理HTTP请求 class EasyHTTPHandler(BaseHTTPRequestHandler): def do_RespHtml(self, html): self.protocal_version = 'HTTP/1.1' #设置协议版本 self.send_response(200) #设置响应状态码 self.send_header("Welcome", "Contect") #设置响应头 self.end_headers() if sys.version_info >= (3, 0): html = html.encode('utf-8') self.wfile.write(html) #输出响应内容 def do_ImageHtml(self, img): templateStr = ''' <html> <head> <title>EasyHttpServer</title> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> </head> <body> <form enctype="multipart/form-data" method="post"> 选择图片：<input type="file" name="uploadImage" id="uploadImage" accept="image/png, image/jpeg"> <input type="submit" value="识别"> </form> <div>{0}</div> </body> </html> '''.format(img) self.do_RespHtml(templateStr) def do_FileRecv(self): form = cgi.FieldStorage( fp=self.rfile, headers=self.headers, environ={'REQUEST_METHOD':'POST', 'CONTENT_TYPE':self.headers['Content-Type'], } ) imageHtml = "" for field in form.keys(): field_item = form[field] filename = field_item.filename filevalue = field_item.value filesize = len(filevalue)#文件大小(字节) #print len(filevalue) #print (filename) ext = os.path.splitext(filename)[1] if sys.version_info >= (3, 0): filename = filename.encode('utf-8') with open(filename, 'wb') as f: f.write(filevalue) #将图片保存到内存中 f = BytesIO() with open(filename, 'rb') as img: f.write(img.read()) #从内存中取出bytes类型的图片 data = f.getvalue() #将bytes转成base64 data = base64.b64encode(data).decode() imageHtml += "<p> <image width='200px' src='data:image/" + ext + ";base64," + data + "'/> </p>" self.do_ImageHtml(imageHtml) def doIndexHtml(self): #获取URL #print 'URL=',self.path #页面输出模板字符串 templateStr = ''' <html> <head> <title>EasyHttpServer</title> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> </head> <body> <form enctype="multipart/form-data" method="post"> 选择图片：<input type="file" name="uploadImage" id="uploadImage" accept="image/png, image/jpeg"> <input type="submit" value="上传"> </form> </body> </html> ''' self.do_RespHtml(templateStr) #处理GET请求 def do_GET(self): self.doIndexHtml() #处理POST请求 def do_POST(self): self.do_FileRecv() #启动服务函数 def start_server(port): http_server = HTTPServer(('', int(port)), EasyHTTPHandler) http_server.serve_forever() #设置一直监听并接收请求 #os.chdir('static') #改变工作目录到 static 目录 start_server(8000) #启动服务，监听8000端口 |
参考链接
Visual Studio Code调试时报错"raise RuntimeError('already started')"
Visual Studio Code远程调试pytorch模型训练时，报错如下：
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File "/home/xxxx/.vscode-server/extensions/ms-python.python-2019.5.18875/pythonFiles/lib/python/ptvsd/daemon.py", line 145, in start raise RuntimeError(‘already started’) RuntimeError: already started |
解决办法，在.py文件头添加如下语句：
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import multiprocessing multiprocessing.set_start_method('spawn',True) |
更新：
如果你的工程是基于pytorch的，那么检查自己的dataLoader，是否使用了num_workers参数。当使用该参数时，可能会报上述错误。解决办法是将num_workers设置为0
参考链接
Windows系统盘符错乱导致桌面无法加载
系统：Windows 7
问题：
在ubuntu 18.04 系统上，使用 gparted 调整 Windows 7 系统分区大小之后，开机登陆用户，桌面无法显示，屏幕黑屏或者蓝色空白。
屏幕右下角显示:
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Windows 7 内部版本7601 此Windows副本不是正版 |
按下 Ctrl+Shift+DEL 能打开任务管理器，但右击相关进程属性发现所有的系统应用显示为 E: 盘，可以确定是盘符错乱导致无法正常加载系统文件及和户配置。
解决方法：
用U盘PE启动,命令行中执行 regist32.exe，定位到 HKEY_LOCAL_MACHINE\SYSTEM\MountedDevices，复制 \DosDevices\C: 的值，这个值有长有短，但只要硬盘不变，分区不变，PE下得到的值和硬盘系统的值是一样的，可以直接复制过来用。
将光标定位在 HKEY_LOCAL_MACHINE 上，文件菜单或右键菜单上就可以有 加载配置单元 ，到硬盘系统中注册表保存位置，C:\Windows\System32\config 中，选择注册表文件 SYSTEM ，在弹出的框中输入一个 项名字(名字可以随意取)，如 TEST，然后 HKEY_LOCAL_MACHINE 下就会新增一个 TEST 节点，加载刚才选择的 SYSTEM 的配置。定位到 MountedDevices，找到对应的 \DosDevices\C:，把刚才复制的值粘贴过去。
重新启动系统。
参考链接
ubuntu 18.04上使用Openconnect代替Cisco Anyconnect
OpenConnect 是一个 Cisco Anyconnect 的替代品，具有开源、易获取、可靠等优点。而官方版本的 Cisco Anyconnect 配置较为繁琐，需要在管理界面同时部署多平台客户端才能支持多平台。相比之下 OpenConnect 在这点就具有优势，可以在官方版本无法跨平台时替代使用。
命令行模式：
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$ sudo apt-get install -y \ openconnect libopenconnect-dev \ network-manager-openconnect-gnome \ resolvconf |
安装完成之后，在网络管理界面上的 VPN 设置界面上可以配置 Cisco Anyconnect 相关的项目。
如果刚刚安装之后， VPN 设置界面上没有出现这个选项，则可能需要重启系统才行。