Saravanan666/pypi-org huggingface dataset:
Package: dlds-client
Deep Learning DS ClientThe Deep Learning DS (DLDS) Client acts as an interface to the data spree vision platform and provides an command-line
tool that allows importing datasets of the following formats:DLDSKITTICOCOFurthermore, it is possible to export datasets from the platform.UsageGeneral UsageUsage: dlds [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  export
  importDataset ImportUsage: dlds import [OPTIONS]

Options:
  --format [dlds|kitti|coco]  Dataset format to import
  --dataset_name TEXT         Name of the newly created dataset.
  --dataset_id INTEGER        ID of the dataset to which new items should be
                              imported. If set to '-1', a new dataset will be
                              created
  --images PATH               Directory containing the images to import.
                              [required]
  --annotations PATH          Directory or file containing the annotations to
                              import.  [required]
  --http_retries INTEGER      Number of HTTP retries.  [default: 10]
  --username TEXT             Username for data spree vision platform.
  --password TEXT             Password for data spree vision platform.
  --url TEXT                  URL to the API of the platform.
  --help                      Show this message and exit.Dataset ExportUsage: dlds export [OPTIONS]

Options:
  -o, --output_dir DIRECTORY   Output directory.  [required]
  -i, --id INTEGER             ID of the dataset to download.
  -n, --n_items INTEGER        Number of items to download. Download all
                               items: '-1'  [default: -1]
  --http_retries INTEGER       Number of HTTP retries.  [default: 10]
  --parallel_requests INTEGER  Number of parallel requests.  [default: 16]
  --username TEXT              Username for data spree vision platform.
  --password TEXT              Password for data spree vision platform.
  --url TEXT                   URL to the API of the platform.
  --help                       Show this message and exit.LicenseApache License 2.0
Package: dlearn
dLearn is a Python package which contains machine learning modules.The project is not yet publicly available.InstallationDependenciesdLearn requires:NumPysklearnUser installationThe easiest way to install dLearn is usingpippip install -U dlearnOr if you want to install from sources you can execute in
thedLearndirectory (same one where you found this file after
cloning the git repo)python setup.py installSource codeYou can check the latest sources with the command:git clone https://github.com/de-eplearn/dlearn.gitUsageSome examples of how to start using the modules one can find
in the subfolder /examples/AuthorDirk ElsinghorstLicensedLearn is published under the MIT License.
Package: dle-encoder
DLE Encoder for PythonThis encoder provides a simple ASCII transport layer for serial data. It usesthe C0 and C1 ASCII control charactersfor this.
You can find a corresponding C++ implementationhere.
This encoder supports two modes:Escaped modeThe encoded stream starts with a STX marker and ends with an ETX marker.
STX and ETX occurrences in the stream are escaped and internally encoded as well so the
receiver side can simply check for STX and ETX markers as frame delimiters. When using a
strictly char based reception of packets encoded with DLE,
STX can be used to notify a reader that actual data will start to arrive
while ETX can be used to notify the reader that the data has ended.Example:[0, STX, DLE] -> [STX, 0, 0, DLE, STX + 0x40, DLE, DLE, ETX]Non-escaped modeThe encoded stream starts with DLE STX and ends with DLE ETX. All DLE occurrences in the stream
are escaped with DLE. If the receiver detects a DLE char, it needs to read the next char
to determine whether a start (STX) or end (ETX) of a frame has been detected.Example:[0, STX, DLE] -> [DLE, STX, 0, DLE, STX, DLE, DLE, DLE, ETX]InstallYou can install this package from PyPILinux:python3-mpipinstalldle-encoderWindows:py-mpipinstalldle-encoderExamplesimportdle_encoderencoder=dle_encoder.DleEncoder()test_array=bytearray([1,2,3])encoded=encoder.encode(test_array)retval,decoded,bytes_decoded=encoder.decode(encoded)print(test_array)print(encoded)print(decoded)The non-escaped mode can be used by passingescape_stx_etc=Falseto theDleEncoderconstructor.LicenseCopyright 2019-2021 Robin Mueller, KSat e.V. StuttgartLicensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: dlen
dlendlen for a world with less code.Check the length of the method (maximum 20 lines) and
the class (maximum 500 lines). To keep your code clean and clear
you should always try to avoid having too many lines of code.
Also recommended to use pep8, isort, pydocstyle pack.Getting StartedSupports Python 2.7+Installing` git clone git@github.com:Endika/dlen.git cd dlen python setup.py install `Or` pip install dlen `Using dlen` dlen my_script.py `Output` [ERROR] 'my_script.py' big_function function too long (25 > 20 lines) [WARN] 'my_script.py' my_function function too long (13 > 12 lines) `Or` dlen . dlen /path/project `AuthorsEndika Iglesias-https://github.com/Endika
Package: dlengine
Introductiondlengine is a trainning engine targeting to separating miscellaneous trainning things from modeling.With dlengine, you can focuse on building model. dlengine takes care of logging, model saving and restoring, very efficient multi-gpu training for you.dlengine contains features:very efficient multi-gpu training based onDistributedDataParallelautomatic loggingautomatic model saving and restoringUsagestep 1. inheriting fromDefaultTrainer，overwrite neccesary methodsbuild_model,build_optimizer,build_lr_scheduler,build_train_loader,build_test_loader,build_evaluator.fromdlengine.defaultsimportDefaultTrainerclassTrainer(DefaultTrainer):def__init__(self,args):super(Trainer,self).__init__(args,args.output_dir,"classifier",args.num_epoch,args.log_every_n_epoch)@classmethoddefbuild_model(cls,args):pass@classmethoddefbuild_optimizer(cls,args,model):pass@classmethoddefbuild_lr_scheduler(cls,args,end_iter,optimizer):pass@classmethoddefbuild_train_loader(cls,args):pass@classmethoddefbuild_test_loader(cls,args):pass@classmethoddefbuild_evaluator(cls,args):passstep 2. calllanuchfunction to start trainingfromdlengine.launchimportlaunchfromdlengine.defaultsimportdefault_setupdefmain(args):default_setup(args.output_dir,"classifier",args)trainer=Trainer(args)returntrainer.train()if__name__=="__main__":args=parser.parse_args()launch(main,num_gpus_per_machine=args.num_gpu,dist_url="tcp://127.0.0.1:28662",num_machines=1,machine_rank=0,args=(args,))Installationinstall package dependencies: pytorch and termcolor.pip install dlengineLICENSECopyright (c) 2021 The Python Packaging AuthorityPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: dlercloud
Python DlerCloud API这是什么这是一个可用于调用DlerCloudAPI 的非官方 Python 模块。你可以用它实现自动节点筛选和配置更新等操作。支持 Python 3.x，尚未验证 Python 2.x 中的兼容性。怎么安装使用 pip 安装:pip3install-Udlercloud如何使用DlerCloud 在其Telegram 频道中提供了 API 的简要说明。本 Python 模块按照该说明进行构建。APIURLDlerCloudAPI()登陆/api/v1/login.login(email, password)SS 节点列表/api/v1/nodes/ss.nodes.ss()V2ray 节点列表/api/v1/nodes/v2ray.nodes.v2ray()Clash SS 托管配置/api/v1/managed/clash_ss.managed.clash_ss()Clash V2ray 托管配置/api/v1/managed/clash_v2.managed.clash_v2()SS 订阅地址/api/v1/subscribe/ss.subscribe.ss()SSD 订阅地址/api/v1/subscribe/ssd.subscribe.ssd()SSR 订阅地址/api/v1/subscribe/ssr.subscribe.ssr()V2RayN 的订阅地址/api/v1/subscribe/av2.subscribe.av2()Quantumult V2ray 订阅地址/api/v1/subscribe/qv2.subscribe.qv2()使当前 access token 失效/api/v1/logout.logout()示例代码这些示例代码可以帮助你更好的理解：fromdlercloudimportDlerCloudAPIapi=DlerCloudAPI()# 使用邮箱和密码登录api.login('your@email.com','YoUr*PasSwoRD')# 若不想每次重复登陆，可将获得的 access_token 保存下来，# 并在下次创建 DlerCloudAPI 实例时作为初始化参数传入，如:# api = DlerCloudAPI('mYtOkEnAbCdEf0123456789')# 目前 access_token 的有效期为 24 小时# 例: 使用 SSNode 节点信息，创建一条 Surge 节点配置# 获得 ss 节点列表nodes=api.nodes.ss()# [<SSNode: 节点 1>, <SSNode: 节点 2>, <SSNode: 节点 3>, ...]node=nodes[0]# <SSNode: 节点 1>surge_node_conf='''{n.name}= ss,{n.server},{n.port}, encrypt-method={n.cipher}, password={n.password}{o}{u}'''.format(n=node,o=', obfs={}'.format(node.advanced['obfs'][12:])ifnode.advanced.get('obfs')notin('plain',None)else'',u=', udp-relay=true'ifnode.udpelse'').strip()print(surge_node_conf)# 节点 1 = ss, 1.2.3.4, 1234, encrypt-method=aes-256-gcm, password=abc123, obfs=tls, udp-relay=true免责声明本 Python 模块的作者并非 DlerCloud 网站或公司的人员，不参与 DlerCloud 的经营和建设，也并非其投资者。
Package: dlestxetx
DLE/STX/ETXis apacket framing algorithm,
used by some devices (such as Metlink LED passenger information displays)
to transmitdataaspacketsover a serial medium.
This algorithm delimits data usingDLE,STX, andETXcontrol codes.Packets begin with aDLE STXsequence,
follow with abyte-stuffeddata stream
(allDLEbytes in the data are conveyed asDLE DLE),
and end with aDLE ETXsequence.Thedlestxetxmodule provides functions
to encode data into packets:>>> from dlestxetx import encode
>>> encode(b'\x01\x10\x05')
b'\x10\x02\x01\x10\x10\x05\x10\x03'decode packets into data:>>> from dlestxetx import decode
>>> decode(b'\x10\x02\x01\x10\x10\x05\x10\x03')
b'\x01\x10\x05'and read packets directly fromfile objects:>>> from dlestxetx import read
>>> packets = BytesIO(encode(b'\x04\x05\x06') + encode(b'\x07\x08\x09'))
>>> read(packets)
b'\x04\x05\x06'
>>> read(packets)
b'\x07\x08\x09'InstallationInstall this module fromPyPIusingpip:pip install dlestxetxChanges1.0.1Add support for Python 3.5.1.0.0Initial release.ContributeSource code:https://github.com/Lx/python-dlestxetxSupportIssue tracker:https://github.com/Lx/python-dlestxetx/issuesLicenseThis project is licensed under theMIT License.
Package: dlex
[!] This code is under development and mainly for my personal use. This project is for fast prototyping of deep learning and machine learning model with minimal code. Some parts of the code may not be well-commented or lack of citation.dlex is an open source framework for machine learning scientific experiment.FeaturesConfiguration-based experiment setup. Less code for more efficiency and reproducibilityPytorch or Tensorflow 2.0 or scikit-learn as backend with similar training flowConvenient "environment" for training similar models or tuning hyperparameterInstallTo install the current releasepip install dlexTry your first dlex programfromdleximportyaml_configs,Configsfromdlex.torchimportPytorchBackend@yaml_configs("""backend: pytorchmodel:name: dlex.torch.models.DNNlayers: [200, 100]dataset:name: dlex.datasets.MNISTnum_train: 100num_test: 10num_classes: 5train:num_epochs: 10batch_size: 128optimizer:name: adamlr: 0.01test:metrics: [acc]""")deftrain(configs:Configs):params=configs.get_default_params()report=PytorchBackend(params).run_train()print(report.results)if__name__=="__main__":train()ResourcesDocumentationGetting StartedVarious model implementationsImplementations of machine learning algorithms for graphLicenseContributingContributions are more than welcome! Please get in touch if you would like to help out.
Package: dlfast
DLFast: Simplifying Deep Learning with Pythondlfastis a Python deep learning library that's revolutionizing the way we build neural networks. Designed for both beginners and experienced data scientists, dlfast empowers users to create intricate deep learning models effortlessly. With its intuitive API and high-level abstractions, you can construct complex networks with just a few lines of code. The library offers modularity, efficiency, and extensive documentation, making it a powerful tool for anyone looking to harness the potential of deep learning.ANNTheANNfunction is a versatile tool for building and training neural networks with ease. It provides options for various tasks, data preprocessing, model architecture, optimization, and more. Whether you're working on binary classification, multiclass classification, or regression,ANNsimplifies the process and offers flexibility in customization.ParametersX(numpy.ndarray): The feature data.y(numpy.ndarray): The target data.task(str, optional): Specifies the task type. Options are'binary'(default),'multiclass', or'regression'.scaler(str, optional): Specifies the data scaling method. Options are'standard'(default),'robust', or'minmax'.epochs(int, optional): The number of training epochs (default: 10).optimizer(str, optional): Specifies the optimizer. Options are'Adam'(default) or'SGD'.activation(str, optional): Specifies the activation functions for input and hidden layers. Format:'input_activation/hidden_activation'(default:'relu').layers(tuple, optional): Defines the neural network architecture as a tuple of layer sizes (default:(100, 50, 3)).early_stop(bool, optional): Enables or disables early stopping (default:True).dropout(float, optional): Dropout rate for regularization (default:None).save(bool, optional): Specifies whether to save the model as an h5 file (default:False).Returnsmodel(tensorflow.python.keras.engine.sequential.Sequential): The trained neural network model.evaluation_results(float): The evaluation results (e.g., accuracy for classification, mean squared error for regression).Example UsagefromdlfastimportANN# Assuming you have your X and y data loaded from your datasetX,y=load_data()Binary Classification Example:model,results=ANN(X,y,task='binary',scaler='standard',epochs=20,optimizer='Adam',activation='relu/sigmoid',early_stop=True,dropout=0.2,save=True)Multiclass Classification Example:model,results=ANN(X,y,task='multiclass',scaler='standard',epochs=20,optimizer='Adam',activation='relu/softmax',early_stop=True,dropout=0.2,save=True)Regression Example:model,results=ANN(X,y,task='regression',scaler='standard',epochs=20,optimizer='Adam',activation='relu/linear',early_stop=True,dropout=0.2,save=True)
Package: dlffmpeg
Simple python module/standalone script to download and install the latest (static binary) release of FFmpeg from different sources. You can find the links to these sources on the official homepage of FFmpeg.Supported systems are Linux, OS X and Windows. Currently armv7 and armv8 architectures are only available for Linux systems.System (architecture)SourceLinux (64-/32bit, armv7, armv8)https://johnvansickle.com/ffmpegOS X (64-/32bit, PPC)http://evermeet.cx/ffmpegandhttp://www.ffmpegmac.netWindows (64-/32bit)https://ffmpeg.zeranoe.comQuick run!In a hurry or feeling lazy? Run one of these oneliners to download and execute the latest binary release. No python needed.Linux and OS X:# Using cURL
curl https://git.io/vDdvo | sudo bash

# Using wget (not installed by default on OSX)
wget -O - https://git.io/vDdvo | sudo bashWindows:At the moment windows users are stuck with downloading and runningthe latest binary filemanually (as admin). But not for long!InstallationUsing pip:sudo pip install-Udlffmpegor pip with git:sudo pip installgit+https://github.com/iwconfig/dlffmpeg.gitFrom source code:git clone https://github.com/iwconfig/dlffmpeg.git
cd dlffmpeg
sudo python setup.py installUsagedlffmpeg._run()installs into default path if no argument, else
string as path.getFFmpeg()contains all options, e.g.:from dlffmpeg import getFFmpeg
dl = getFFmpeg()
dl.path = '/path/to/dir'
dl.silent = True
dl.pretty = True
dl.verbose = FalseExecute withdl.run()Standalone takes one argument for custom path or no argument for default.~/ $ dlffmpeg --help
usage: dlffmpeg.py [-h] [-s] [-lv] [-p] [--version] [path]

specify installation path. no path equals to default path.

positional arguments:
  path

optional arguments:
  -h, --help           show this help message and exit
  -s, --silent
  -lv, --less-verbose
  -p, --pretty
  --version            show program's version number and exitTodoUse logging module insteadAbility to compile and install from source, cross-platformBetter module supportContributeI am certain my code needs better perspective and from what this script
represent i’m currently not capable of comprehending better principles.
Please feel free to fork and improve. :)LicenseThis project is licensed under the terms ofthe MIT
license.
Package: dlg-casacore-components
dlg-casacore-componentsAwesome dlg_casacore_components created by ICRARInstallationThere are multiple options for the installation, depending on how you are intending to run the DALiuGE engine, directly in a virtual environment (host) or inside a docker container. You can also install it either from PyPI (latest released version).Install it from PyPIEngine in virtual environmentpipinstalldlg_casacore_componentsEngine in Docker containerdockerexec-tdaliuge-enginebash-c'pip install --prefix=$DLG_ROOT/code dlg_casacore_components'UsageFor example the MyComponent component will be available to the engine when you specifyfromdlg_casacore_componentsimportMSReadAppMSReadApp('a','a')in the AppClass field of a Python Branch component. The EAGLE palette associated with these components are also generated and can be loaded directly into EAGLE. In that case all the fields are correctly populated for the respective components.Linting and TestingTesting and linting is performed per branch push. Additionally this can be performed locally with docker using the following command:dockerbuild-tdlg-casacore-components.-fdebian.Containerfile&&dockerrun-it--rm--shm-size1Gdlg-casacore-components
Package: dlg-example-cmpts
DALiuGE example componentsThis repository contains dlg_example_cmpts created by ICRAR. These are mostly meant to demonstrate how to implement some of the more advancedDALiuGEfeatures in a component. Please refer to the mainDALiuGE documentationfor more information.InstallationThere are multiple options for the installation, depending on how you are intending to run the DALiuGE engine, directly in a virtual environment (host) or inside a docker container. You can also install it either from PyPI (latest released version).Install it from PyPIEngine in virtual environmentpipinstalldlg_example_cmptsEngine in Docker containerdockerexec-tdaliuge-enginebash-c'pip install --prefix=$DLG_ROOT/code dlg_example_cmpts'NOTE: If you had this package installed already you will need to re-start the engine after that.UsageFor example the MyBranch component will be available to the engine when you specifydlg_example_cmpts.apps.MyBranchin the AppClass field of a Python Branch component. The EAGLE palette associated with these components are also generated and can be loaded directly into EAGLE. In that case all the fields are correctly populated for the respective components.
Package: dlg-home-content
dlg-home-contentsetup environmentconda env create -f environment.ymlinstall detectron2 from sourcecpu version>condainstallpytorchtorchvisioncpuonly-cpytorch
>python-mpipinstalldetectron2-f\https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.6/index.html
>updateenvironment`condaenvupdate--fileenvironment.yml`for other gpu versions, usethisCLI commands availableconvert labelme2cocolabelme2coco--labelme_json_location'data/processed_tv_annotations_v1/'--labels_loc"assets/keypoints.yml"--save_json"data/keypoints/"--train_ratio0.9--seed50train using custom datasetWe need to define three config filesbase cfg file name available on detectron. checkdetectron/configsfor examples.cfg file which contains modified params . checkconfigsfolder for specific examplesdata_cfg which has dataset and keypoints related params. For exampleassets/datasets.yml# normal instance segmentationcustom_train--base_cfg'COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml'--cfg'configs/mask_only_exp1.yml'--data_cfg"assets/datasets.yml"# instance segmentation with keypointscustom_train--base_cfg'COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml'--cfg'configs/keypoint_mask_on_exp1.yml'--data_cfg"assets/datasets.yml"InferenceLOGO DetectionDownload latest inference file fromherefromdlg_home_content.tv_detectionimportInferLogoconfig='../assets/e2e_infer.ymlmodel=InferLogo(config)model.predict(img_loc,visualize=True)Inference for Keypoint DetetionDownload weight files and config files from [here] (https://fractalanalytic-my.sharepoint.com/:u:/g/personal/sindhura_k_fractal_ai/EXCaFSHWv3hMo99lvfP4zKIBLBO8dlnWzY7iUAFWYiXHKA?e=23XheZ)#for inner keyoint detectionfromdlg_home_content.inference_pipelineimportKeypointInferenceconfig='../assets/e2e_infer.yml'#kp_type in ['kp_inner_edge','kp_outer_edge','kp_sticky_note']model_inner=KeypointInference(config,kp_type='kp_inner_edge')predicted_keyoints=model_inner.predict_keypoints(img_loc,visualize=True)End-to-End Inference pipelinefromdlg_home_content.e2e_inferenceimportE2EInferenceconfig='../assets/e2e_infer.yml'final_pipeline=E2EInference(config)result=final_pipeline.infer(img_loc,8,8,True)
Package: dlg-lowpass-components
dlg_lowpass_componentsAwesome dlg_lowpass_components created by pritchardnInstallationThere are multiple options for the installation, depending on how you are intending to run the DALiuGE engine, directly in a virtual environment (host) or inside a docker container. You can also install it either from PyPI (latest released version).Install it from PyPIEngine in virtual environmentpipinstalldlg_lowpass_componentsThis will only work after releasing the project to PyPi.Engine in Docker containerdockerexec-tdaliuge-enginebash-c'pip install --prefix=$DLG_ROOT/code dlg_lowpass_components'UsageFor example the MyComponent component will be available to the engine when you specifydlg_lowpass_components.LPSignalGeneratorin the AppClass field of a Python Branch component. The EAGLE palette associated with these components are also generated and can be loaded directly into EAGLE. In that case all the fields are correctly populated for the respective components.