OpenFlow v1.0实验：Segment Routing配置与Mininet实践

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APNIC SDN Workshop Lab

©APNIC

Created: 24 June 2014

Updated: 24 June 2014

Lab Exercise

Part 1. Installation of Oracle VirtualBox

VirtualBox is supported on:

• Windows

• OS X

• Linux

Installation steps:

1. Download the appropriate install package for your operating system from:

! https://www.virtualbox.org/wiki/Downloads

2. Use the supplied installer to install using default parameters.

Part 2. Installation of Wireshark

Wireshark is supported on:

• Windows

• OS X

• Linux

Installation steps:

1. Download the appropriate install package for your operating system from:

! https://www.wireshark.org/download.html

2. Use the supplied installer to install using default parameters.

Part 3. Install Mininet VM on VirtualBox

Installation and configuration steps:

1. Download Mininet VM

The Mininet VM images are available at:

https://github.com/mininet/mininet/wiki/Mininet-VM-Images

In order to save time, your instructor will provide you instructions to download a mininet OVA

virtual appliance locally to create an Ubuntu virtual machine on VirtualBox.

Simply double-click the file mininet.ova and import it into VirtualBox. Ensure that you select

the option to re-initialise all MAC addresses.

剩余14页未读，继续阅读

create or replace package pk_hcp_salary is ------------- 結算員工當月薪資 -------------------------------------------------------- -- micro 2006/05/16 加入廠區起迄 procedure p_cal_salary(p_segment_no varchar2 /---公司ID -/, p_master_id number /---計薪期間ID -/, p_detail_id number /---期間每月ID -/, p_seq number /---月結序號 -/, p_pay_type number /---給付類別ID -/, p_yn varchar2 /---結算/取消結算 Y:結算 N:取消結算 -/, p_depbegin varchar2 /---部門起始ID -/, p_depend varchar2 /---部門迄止ID -/, p_idbegin varchar2 /---員工起始ID -/, p_idend varchar2 /---員工迄止ID -/, p_indate_b date /---員工到職日起 -/, p_indate_e date /---員工到職日迄 -/, p_outdate_b date /---員工离職日期起 -/, --added by hunk at 20140612 for mantis0008541 p_outdate_e date /---員工离職日期迄 -/, --added by hunk at 20140612 for mantis0008541 p_zone_b varchar2 default null /* 員工廠區起 add by micro 2006/05/16 /, p_zone_e varchar2 default null / 員工廠區迄 add by micro 2006/05/16 */); end; 这样是什么意思

pk_hcp_salary.p_cal_salary('公司ID', 1, 2, 3, 4, 'Y', '部门起始ID', '部门迄止ID', '员工起始ID', '员工迄止ID', to_date('2022-09-01', 'YYYY-MM-DD'), to_date('2022-09-30', 'YYYY-MM-DD'), to_date('2022-...

STM32 按键控制数码管

File "/app/PythonProjects/segment-anything-main/Import_test/down/test01.py", line 6, in <module> from ...segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor ImportError: attempted relative import with no known parent package

1. 将当前模块移动到正确的包目录中，确保包结构正确，或者在该模块中使用绝对导入语句而不是相对导入语句。 2. 使用绝对导入语句来代替相对导入语句。例如，如果你要导入 `segment_anything` 模块中的 `sam_model_...

拒绝访问。: 'D:\\yjs\\segment\\ritm_interactive_segmentation-master\\ritm_interactive_segmentation-master\\first_label\\004s_json'

1. 权限不足：你可能没有足够的权限来访问该路径下的文件或文件夹。你可以尝试使用管理员权限运行程序或者修改文件或文件夹的权限来解决这个问题。 2. 文件或文件夹被锁定：该路径下的文件或文件夹可能正在被其他...

使用ffmpeg将视频"1.mp4"，同时多分辨率切片，请提供参数

ffmpeg -i 1.mp4 -c:v h264 -c:a aac -hls_list_size 0 -hls_time 10 -hls_segment_type fmp4 -master_pl_name master.m3u8 -var_stream_map "v:0,480p,650k,25fps v:1,720p,1500k,25fps v:2,1080p,3500k,25fps" -f ...

优化以下Oracle语句： SELECT SUBSTR(msn.serial_number, 1, 10) genset_sn, msi2.segment1 Genset_BOM_NUM, msi2.inventory_item_id, msi.segment1 key_component, mut1.serial_number component_sn, msi.description component_desc, wdj.date_completed, (SELECT MAX(aps.vendor_name) FROM ap_suppliers aps, bom_resources bor, mtl_unit_transactions mut, po_headers_all poh, po_lines_all pol, wip_osp_resources_val_v wor WHERE aps.vendor_id = poh.vendor_id AND bor.resource_id = wor.resource_id AND poh.po_header_id = pol.po_header_id AND pol.item_id = bor.purchase_item_id AND wor.wip_entity_id = mut.transaction_source_id AND mut.serial_number = mut1.serial_number AND mut.inventory_item_id = mut1.inventory_item_id AND mut.organization_id = mut1.organization_id AND mut.receipt_issue_type = 2 AND mut.transaction_source_type_id = 5 ) supplier FROM mtl_material_transactions mmt1, mtl_material_transactions mmt2, mtl_parameters mpa, mtl_serial_numbers msn, mtl_system_items msi, mtl_system_items msi2, mtl_transaction_types mtt1, mtl_transaction_types mtt2, mtl_unit_transactions mut1, mtl_unit_transactions mut2, wip_discrete_jobs_v wdj WHERE mmt1.inventory_item_id = mut1.inventory_item_id AND mmt1.organization_id = mut1.organization_id AND WDJ.PRIMARY_ITEM_ID = msi2.INVENTORY_ITEM_ID AND mmt1.transaction_id = mut1.transaction_id AND mmt1.transaction_source_id = wdj.wip_entity_id AND mmt1.transaction_type_id = mtt1.transaction_type_id AND mtt1.transaction_type_name = 'WIP Issue' AND NOT EXISTS (SELECT 'WIP Negative Issue or WIP Return' FROM mtl_material_transactions mmt3, mtl_transaction_types mtt3, mtl_unit_transactions mut3 WHERE mmt3.transaction_id = mut3.transaction_id AND mmt3.transaction_type_id = mtt3.transaction_type_id AND mmt3.transaction_date > mmt1.transaction_date AND mtt3.transaction_type_name IN ('WIP Negative Issue', 'WIP Return') AND mut3.serial_number = mut1.serial_number AND mut3.inventory_item_id = mut1.inventory_item_id) AND mmt2.transaction_id = mut2.transaction_id AND mmt2.transaction_source_id = wdj.wip_entity_id AND mmt2.transaction_type_id = mtt2.transaction_type_id AND mtt2.transaction_type_name = 'WIP Completion' AND mpa.organization_code = 'WHP' AND msn.current_organization_id = mpa.organization_id AND LENGTH(msn.serial_number) >= 10 AND msi.inventory_item_id = mmt1.inventory_item_id AND msi.organization_id = mmt1.organization_id AND (msi.planning_make_buy_code = 2 OR msi.segment1 LIKE 'SO%') AND mut2.serial_number = msn.serial_number AND mut2.inventory_item_id = msn.inventory_item_id AND mut2.organization_id = mpa.organization_id AND msi2.ORGANIZATION_ID = '323'

1. 在以下表中创建索引：mtl_material_transactions、mtl_unit_transactions、mtl_system_items、mtl_parameters、mtl_transaction_types、mtl_serial_numbers、wip_discrete_jobs_v 和 bom_resources。 - 在 mtl_...

write6的scsi命令，ucd_req_ptr->header.dword_2怎么配

对于 `write6` SCSI 命令，需要将 `ucd_req_ptr->header.dword_2` 的值设置为 `0x0`。这是因为 `write6` 命令是一个简单的写入命令，不需要设置传输类型、数据方向、命令类型等参数。下面是一个示例代码，展示了...

for i in `seq 2 15` do python3 image_crop.py --image_file ${dataroot}/$i/Ottawa-$i.tif --save_path ${save_folder}/train_image python3 image_crop.py --image_file ${dataroot}/$i/segmentation.png --save_path ${save_folder}/train_segment python3 image_crop.py --image_file ${dataroot}/$i/edge.png --save_path ${save_folder}/train_edge python3 image_crop.py --image_file ${dataroot}/$i/centerline.png --save_path ${save_folder}/train_centerline done

这是一个带有循环的 Bash 脚本，它的作用是对 `$dataroot` 目录下的 2-15 号 Ottawa-$i.tif 图像及其对应的 segmentation.png、edge.png 和 centerline.png 进行裁剪，将裁剪后的图像、分割图、边缘图和中心线图分别...

import tensorflow as tf import tensorflow_hub as hub from tensorflow.keras import layers import bert import numpy as np from transformers import BertTokenizer, BertModel # 设置BERT模型的路径和参数 bert_path = "E:\\AAA\\523\\BERT-pytorch-master\\bert1.ckpt" max_seq_length = 128 train_batch_size = 32 learning_rate = 2e-5 num_train_epochs = 3 # 加载BERT模型 def create_model(): input_word_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="input_word_ids") input_mask = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="input_mask") segment_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="segment_ids") bert_layer = hub.KerasLayer(bert_path, trainable=True) pooled_output, sequence_output = bert_layer([input_word_ids, input_mask, segment_ids]) output = layers.Dense(1, activation='sigmoid')(pooled_output) model = tf.keras.models.Model(inputs=[input_word_ids, input_mask, segment_ids], outputs=output) return model # 准备数据 def create_input_data(sentences, labels): tokenizer = bert.tokenization.FullTokenizer(vocab_file=bert_path + "trainer/vocab.small", do_lower_case=True) # tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') input_ids = [] input_masks = [] segment_ids = [] for sentence in sentences: tokens = tokenizer.tokenize(sentence) tokens = ["[CLS]"] + tokens + ["[SEP]"] input_id = tokenizer.convert_tokens_to_ids(tokens) input_mask = [1] * len(input_id) segment_id = [0] * len(input_id) padding_length = max_seq_length - len(input_id) input_id += [0] * padding_length input_mask += [0] * padding_length segment_id += [0] * padding_length input_ids.append(input_id) input_masks.append(input_mask) segment_ids.append(segment_id) return np.array(input_ids), np.array(input_masks), np.array(segment_ids), np.array(labels) # 加载训练数据 train_sentences = ["Example sentence 1", "Example sentence 2", ...] train_labels = [0, 1, ...] train_input_ids, train_input_masks, train_segment_ids, train_labels = create_input_data(train_sentences, train_labels) # 构建模型 model = create_model() model.compile(optimizer=tf.keras.optimizers.Adam(lr=learning_rate), loss='binary_crossentropy', metrics=['accuracy']) # 开始微调 model.fit([train_input_ids, train_input_masks, train_segment_ids], train_labels, batch_size=train_batch_size, epochs=num_train_epochs)这段代码有什么问题吗？

因此，需要修改函数 `return` 语句为：`return np.array(input_ids), np.array(input_masks), np.array(segment_ids), np.array(labels).reshape(-1)`。 6. 在 `model.fit` 中，需要指定验证集的参数 `validation_...

ffmpeg 如何对文件进行分片

ffmpeg -i output_file -c copy -map 0 -f segment -segment_time [segment_time] -segment_format [segment_format] output_%03d.[segment_format] ``` 其中，`[segment_time]` 是每个分片的时间长度，单位为秒...

library(jiebaR) > library(tm) > cm1 <- readxl::read_excel("cm1.xlsx") > # 合并所有评论 > comments <- paste(cm1$comments, collapse = "") > # 重新读取停用词表，确保每个词语都在单独的一行中 > stopwords <- readLines("my_stopwords.txt", encoding = "UTF-8", warn = FALSE) > # 确认 stopwords 变量是长度为1的字符向量 > stopwords <- paste(stopwords, collapse = "\n") > # 使用 jiebaR 包进行分词 > worker <- worker(bylines = FALSE) > words <- segment(comments,worker) > # 删除停用词 > words <- removeWords(words, stopwords) Error in gsub(sprintf("(*UCP)\\b(%s)\\b", paste(sort(words, decreasing = TRUE), : 'pattern' is invalid UTF-8

这个错误可能是由于停用词表文件的编码格式与你的 R 会话不兼容导致的。你可以尝试在读取停用词表时指定编码格式，例如： ``` stopwords <- readLines("my_stopwords.txt", encoding = "UTF-8-BOM", warn = FALSE) ...

plsql get table file size

v_segment_type VARCHAR2(30); BEGIN -- Replace 'YOUR_TABLE_NAME' with the actual name of your table DBMS_SPACE.OBJECT_SPACE_USAGE( segment_owner => USER, segment_name => 'YOUR_TABLE_NAME', ...

MES-SAP 生产报工接口代码

LS_IDOC_DATA-SEGMENTNUM = '000001'. LS_IDOC_DATA-PARENT = '000000'. LS_IDOC_DATA-HLEVEL = '002'. LS_IDOC_DATA-MES_PROD_REPORT = LS_MES_PROD_REPORT. "发送IDOC数据包到SAP系统 CALL FUNCTION 'MASTER_IDOC...

--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[11], line 1 ----> 1 predictor.set_image("bingbao.jpg") File /workspace/segment-anything/segment_anything/predictor.py:56, in SamPredictor.set_image(self, image, image_format) 53 image = image[..., ::-1] 55 # Transform the image to the form expected by the model ---> 56 input_image = self.transform.apply_image(image) 57 input_image_torch = torch.as_tensor(input_image, device=self.device) 58 input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :] File /workspace/segment-anything/segment_anything/utils/transforms.py:30, in ResizeLongestSide.apply_image(self, image) 26 def apply_image(self, image: np.ndarray) -> np.ndarray: 27 """ 28 Expects a numpy array with shape HxWxC in uint8 format. 29 """ ---> 30 target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length) 31 return np.array(resize(to_pil_image(image), target_size)) AttributeError: 'str' object has no attribute 'shape'

这是一个Python的AttributeError错误，错误信息显示在predictor.py的第56行，因为传递给set_image方法的参数是一个字符串而不是numpy数组，因此在apply_image方法中调用image.shape时会出错。解决方法是在调用set_...

帮我翻译下： parser.add_argument( "--input", type=str, required=False, default=r'D:\DATA\AllData\QAdata\JPEGImages', help="Path to either a single input image or folder of images.", ) parser.add_argument( "--output", type=str, required=False, default=r'D:\DATA\AllData\QAdata\JPEGImages\result', help=( "Path to the directory where masks will be output. Output will be either a folder " "of PNGs per image or a single json with COCO-style masks." ), ) parser.add_argument( "--model-type", type=str, required=False, default='vit_h', help="The type of model to load, in ['default', 'vit_h', 'vit_l', 'vit_b']", ) parser.add_argument( "--checkpoint", type=str, required=False, default=r'D:\PROJECT\SAM\segment-anything-main\sam_vit_h_4b8939.pth', help="The path to the SAM checkpoint to use for mask generation.", )

这段代码是 Python 中 argparse 模块的使用，主要是定义了四个命令行参数。下面是参数的解释： 1. "--input"：输入图像或图像文件夹的路径...默认值为 "D:\PROJECT\SAM\segment-anything-main\sam_vit_h_4b8939.pth"。

[segment(i).min_mc,segment(i).bestseg,segment(i).t_seg1mc,segment(i).t_seg2mc] = topDownSeg(data,segment(i),q)

这行代码是调用了一个名为 topDownSeg 的函数，并将函数的返回值赋值给了 segment(i).min_mc、segment(i).bestseg、segment(i).t_seg1mc 和 segment(i).t_seg2mc 四个变量。具体来说，该函数的输入参数为 data、...

import jieba import torch from sklearn.metrics.pairwise import cosine_similarity from transformers import BertTokenizer, BertModel seed_words = ['姓名'] # 加载微博文本数据 text_data = [] with open("output/weibo1.txt", "r", encoding="utf-8") as f: for line in f: text_data.append(line.strip()) # 加载BERT模型和分词器 tokenizer = BertTokenizer.from_pretrained('bert-base-chinese') model = BertModel.from_pretrained('bert-base-chinese') seed_tokens = ["[CLS]"] + seed_words + ["[SEP]"] seed_token_ids = tokenizer.convert_tokens_to_ids(seed_tokens) seed_segment_ids = [0] * len(seed_token_ids) # 转换为张量，调用BERT模型进行编码 seed_token_tensor = torch.tensor([seed_token_ids]) seed_segment_tensor = torch.tensor([seed_segment_ids]) with torch.no_grad(): seed_outputs = model(seed_token_tensor, seed_segment_tensor) seed_encoded_layers = seed_outputs[0] jieba.load_userdict('data/userdict.txt') # 构建隐私词库 privacy_words = set() for text in text_data: words = jieba.lcut(text.strip()) tokens = ["[CLS]"] + words + ["[SEP]"] token_ids = tokenizer.convert_tokens_to_ids(tokens) segment_ids = [0] * len(token_ids) # 转换为张量，调用BERT模型进行编码 token_tensor = torch.tensor([token_ids]) segment_tensor = torch.tensor([segment_ids]) with torch.no_grad(): outputs = model(token_tensor, segment_tensor) encoded_layers = outputs[0] # 对于每个词，计算它与种子词的相似度 for i in range(1, len(tokens)-1): word = tokens[i] if word in seed_words: continue word_tensor = encoded_layers[0][i].reshape(1, -1) seed_tensors =seed_encoded_layers[0][i].reshape(1, -1) # 计算当前微博词汇与种子词的相似度 sim = cosine_similarity(word_tensor, seed_tensors, dense_output=False)[0].max() print(sim, word) if sim > 0.5 and len(word) > 1: privacy_words.add(word) print(privacy_words) 上述代码运行之后有错误，报错信息为：Traceback (most recent call last): File "E:/PyCharm Community Edition 2020.2.2/Project/WordDict/newsim.py", line 397, in <module> seed_tensors =seed_encoded_layers[0][i].reshape(1, -1) IndexError: index 3 is out of bounds for dimension 0 with size 3. 请帮我修改

seed_tensors = seed_encoded_layers[0][0].reshape(1, -1) # 修改此处，取第一个元素 # 计算当前微博词汇与种子词的相似度 sim = cosine_similarity(word_tensor, seed_tensors, dense_output=False)[0].max()...

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OpenFlow v1.0实验：Segment Routing配置与Mininet实践

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