Saravanan666/pypi-org huggingface dataset:
Package: deepfinder
🔍 DeepfinderSearch attributes easily using dot paths. Within structures of type dictionary, list and embedded substructures with simple format 'dict.users.0.name'.Getting StartedInstallationpipinstalldeepfinderUsageBasic samplefromdeepfinderimportdeep_finduser:dict={'name':'ash','links':{'pokehub':'@ash'},}print(deep_find(user,'links.pokehub'))# output: '@ash'List samplefromdeepfinderimportdeep_finduser:dict={'name':'ash','pokemons':[{'name':'pikachu','type':'electric'},{'name':'charmander','type':'fire'}]}print(deep_find(user,'pokemons.0.name'))# output: 'pikachu'List all result samplefromdeepfinderimportdeep_finduser:dict={'name':'ash','pokemons':[{'name':'pikachu','type':'electric'},{'name':'charmander','type':'fire'}]}print(deep_find(user,'pokemons.*.name'))# output: ['pikachu', 'charmander']Find the first non-null resultfromdeepfinderimportdeep_finduser:dict={'name':'ash','pokemons':[{'name':'pikachu',},{'name':'charmander','ball':'superball'}]}print(deep_find(user,'pokemons.?.ball'))# output: 'superball'Find all non-null resultsfromdeepfinderimportdeep_finduser:dict={'name':'ash','pokemons':[{'name':'pikachu',},{'name':'charmander','ball':'superball'},{'name':'lucario','ball':'ultraball'}]}print(deep_find(user,'pokemons.*?.ball'))# output: ['superball', 'ultraball']Use custom dict and listfromdeepfinder.entityimportDeepFinderDictuser:dict=DeepFinderDict({'name':'ash','pokemons':[{'name':'pikachu'},{'name':'charmander','ball':'superball'}]})print(user.deep_find('pokemons.?.ball'))# output: 'superball'fromdeepfinder.entityimportDeepFinderListusers:list=DeepFinderList([{'name':'ash','pokemons':[{'name':'pikachu'},{'name':'charmander','ball':'superball'}]}])print(users.deep_find('0.pokemons.?.ball'))# output: 'superball'
Package: deepfitvlife
DeepFitDeepFit is an open source package for creating novel methods that help all the stake holders better manage Patient Engagement. It leverages research technology like Data Shapley, Multi-Accuracy and cPCA from Stanford Artificial Intelligence Labs (SAIL)Getting StartedProgramming GuidevLifeLicenseGetting StartedPlease Install DeepFit package using!pip install deepfitvlifeProgramming GuideComing SoonLicenseComing Soon
Package: deepflow-by-zuo
ABOUTThis is a mini deep learning framework create by zuoFeaturesDefine Dynamic Compuing GraphDefine Neural Network OperatorDefine Linear,Sigmoid,L2_lossAuto-Diff ComputingAuto-FeedForward and BackwardRemainClassificationCross-EntropyCNN,RNNConnectionemail:zzuuoo666@sina.com
Package: deep-folder
DEEP FOLDER[create]If it is a path where a folder can be created, the folder is created up to the parent-path.[remove]If it is a path where a folder can be remove, the folder is remove down to the sub-pathINSTALLYou can usepipor just clone and copy the code.INSTALL FROM PIPpipinstall--upgradedeep-folderINSTALL FROM LOCALpipinstall--upgradesetuptoolswheeltwine
pythonsetup.pysdistbdist_wheel
pipinstall./dist/deep-folder-*.tar.gzLICENSEMITYou may modify or take away the code, but you cannot claim responsibility from me.- by sotaneum
Package: deepforecast
DeepForecastAn Easy-to-use Deep Model for Time Series ForecastMethodsSTTFSeq2SeqHow to installpip install deepforecastHow to useUse STTF as an exapmle:fromtensorflow.keras.utilsimportplot_modelfromdeepforecast.featuresimportSparseColumn,SequenceColumnfromdeepforecast.modelsimportSTTFattr_feats=["age","user","platform"]sequence_feats=["history","future"]attr_columns=[]forfeatinattr_feats:col=SparseColumn(name=feat,vocab_size=10,embed_dim=8)attr_columns.append(col)sequence_columns=[]hist_col=SequenceColumn(name="history",num_seq=5,seq_steps=28,dim=1)sequence_columns.append(hist_col)fut_col=SequenceColumn(name="future",num_seq=4,seq_steps=7,dim=1)sequence_columns.append(fut_col)model=STTF(attr_columns,sequence_columns,attr_attention_embed_dim=12)model.summary()plot_model(model,show_shapes=True)model.compile(optimizer="rmsprop",loss=["mse","mse"],loss_weights=[0.2,0.8],metrics=["mse"])
Package: deepforest
DeepforestFull documentationhttp://deepforest.readthedocs.io/en/latest/InstallationCompiled wheels have been made for linux, osx and windows#Install DeepForest
pip install deepforestGet in touchSee theGitHub Repoto see the
source code or submit issues and feature requests.CitationIf you use this software in your research please cite it as:Geographic Generalization in Airborne RGB Deep Learning Tree Detection
Ben. G. Weinstein, Sergio Marconi, Stephanie A. Bohlman, Alina Zare, Ethan P. White
bioRxiv 790071; doi:https://doi.org/10.1101/790071AcknowledgmentsDevelopment of this software was funded bythe Gordon and Betty Moore Foundation's Data-Driven Discovery InitiativethroughGrant GBMF4563to Ethan P. White.
Package: deep-forest
DF21is an implementation ofDeep Forest2021.2.1. It is designed to have the following advantages:Powerful: Better accuracy than existing tree-based ensemble methods.Easy to Use: Less efforts on tunning parameters.Efficient: Fast training speed and high efficiency.Scalable: Capable of handling large-scale data.DF21 offers an effective & powerful option to the tree-based machine learning algorithms such as Random Forest or GBDT.For a quick start, please refer toHow to Get Started. For a detailed guidance on parameter tunning, please refer toParameters Tunning.DF21 is optimized for what a tree-based ensemble excels at (i.e., tabular data), if you want to use the multi-grained scanning part to better handle structured data like images, please refer to theorigin implementationfor details.InstallationDF21 can be installed using pip viaPyPIwhich is the package installer for Python. You can use pip to install packages from the Python Package Index and other indexes. Referthisfor the documentation of pip. Use this command to download DF21 :pipinstalldeep-forestQuickstartClassificationfromsklearn.datasetsimportload_digitsfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportaccuracy_scorefromdeepforestimportCascadeForestClassifierX,y=load_digits(return_X_y=True)X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=1)model=CascadeForestClassifier(random_state=1)model.fit(X_train,y_train)y_pred=model.predict(X_test)acc=accuracy_score(y_test,y_pred)*100print("\nTesting Accuracy:{:.3f}%".format(acc))>>>TestingAccuracy:98.667%Regressionfromsklearn.datasetsimportload_bostonfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportmean_squared_errorfromdeepforestimportCascadeForestRegressorX,y=load_boston(return_X_y=True)X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=1)model=CascadeForestRegressor(random_state=1)model.fit(X_train,y_train)y_pred=model.predict(X_test)mse=mean_squared_error(y_test,y_pred)print("\nTesting MSE:{:.3f}".format(mse))>>>TestingMSE:8.068ResourcesDocumentationDeep Forest:[Conference]|[Journal]Keynote at AISTATS 2019:[Slides]Reference@article{zhou2019deep,
    title={Deep forest},
    author={Zhi-Hua Zhou and Ji Feng},
    journal={National Science Review},
    volume={6},
    number={1},
    pages={74--86},
    year={2019}}@inproceedings{zhou2017deep,
    title ={{Deep Forest:}Towards an alternative to deep neural networks},
    author ={Zhi-Hua Zhou and Ji Feng},
    booktitle ={IJCAI},
    pages ={3553--3559},
    year ={2017}}Thanks to all our contributors
Package: deepforest-pytorch
DeepforestFull documentationhttp://deepforest-pytorch.readthedocs.io/en/latest/InstallationCompiled wheels have been made for linux, osx and windows#Install DeepForest-pytorch
pip install deepforest-pytorchGet in touchSee theGitHub Repoto see the
source code or submit issues and feature requests.CitationIf you use this software in your research please cite it as:Geographic Generalization in Airborne RGB Deep Learning Tree Detection
Ben. G. Weinstein, Sergio Marconi, Stephanie A. Bohlman, Alina Zare, Ethan P. White
bioRxiv 790071; doi:https://doi.org/10.1101/790071AcknowledgmentsDevelopment of this software was funded bythe Gordon and Betty Moore Foundation's Data-Driven Discovery InitiativethroughGrant GBMF4563to Ethan P. White.
Package: deepfos-celery
Version:1.1.9Web:http://celeryproject.org/Download:https://pypi.org/project/deepfos-celery/Source:https://gitee.com/python-development-team/deepfos-celery/Keywords:task, queue, job, async, rabbitmq, amqp, redis,
python, distributed, actorsWhat is this?本项目基于celery-5.0.5开发，主要功能：添加asyncio支持，更容易集成到异步web框架支持redis集群注：项目是 ``celery`` 的超集，但并非所有功能都支持 ``asyncio`` ，并且目前仅完成了 ``redis`` 作为 ``broker`` 和 ``backend`` 的支持。Get Started创建任务和官方celery一样:fromceleryimportCeleryapp=Celery('hello',broker='redis://:@localhost/')@app.task(aio_variant=True)defhello():return'hello world'但增加了aio_variant这一参数，相较于普通的任务发送和获取结果的方式：task=hello.apply_async()result=task.get()可以使用:task=awaithello.aio.apply_async()result=awaittask.get()注意这不只是一个简单的语法糖，而是确实使用aioredis替换了原有redis-py的同步网络请求。LicenseThis software is licensed under theNew BSD License. See theLICENSEfile in the top distribution directory for the full license text.
Package: deepfos-ipc
介绍本项目提供Python进程间通信的基础功能，适用于多进程编程中一主多从的编程模型。项目中的Server可在主进程启动，Client在（多个）子进程中启动，客户端发送的消息能够被服务端接收，
并且对于不同的消息类型，可以注册不同的回调逻辑。
项目预置了一些常用通信协议，同时也提供简便的自定义通信协议的接口。进程间通信基于unix socket实现，因此不能在windows系统中运行。Quick Start启动服务端fromdipimportServerimportasynciodefcb(req,mtype,data):print(data['hello'])server=Server('/tmp/run.sock',callback=cb)asyncio.run(server.serve_forever())使用客户端发送消息fromdipimportClientcli=Client('/tmp/run.sock')cli.send_json({'hello':'world'})服务端将会输出>>>'world'自定义通信协议如果需要自定义进程间通信时传输字节流的格式，可以使用自定义ProtocolimporttypingfromdipimportProtocolclassNewProtocol(Protocol,mtype=b'N'):@classmethoddefdecode_body(cls,buf:bytes)->typing.Any:pass@classmethoddefencode_body(cls,data:typing.Any)->bytes:pass自定义通信协议需要使用一个byte作为标识（mtype），并且必须实现decode_body和encode_body两个方法。其中:encode_body用于将python对象序列化为字节流decode_body用于将字节流反序列化为python对象两者一般互为逆操作，即对于python对象obj：obj==Protocol.decode_body(Protocol.encode_body(obj))注: 这并不是硬性规定，只是一种通常做法下述是一个非常粗糙的固定字符串压缩协议的实现fromdipimportProtocolfromdipimporterrorsclassStringMappingProto(Protocol,mtype=b'S'):MAP={'Hello World':b'h',}MAP_REV={v:kfork,vinMAP.items()}@classmethoddefdecode_body(cls,buf:memoryview)->str:data=cls.MAP_REV.get(buf.tobytes())ifdataisnotNone:returndataelse:raiseerrors.ProtoDecodeError(f'Cannot decode byte:{buf!r}')@classmethoddefencode_body(cls,data:str)->bytes:compressed=cls.MAP.get(data)ifcompressedisnotNone:returncompressedelse:raiseerrors.ProtoEncodeError(f'Cannot encode string:{data!r}')使用客户端发送消息:cli.send_msg('S','Hello World')将会在服务端接收到消息Hello World
Package: deepfos-ipc-protocol
介绍本项目提供Python进程间通信的基础功能，适用于多进程编程中一主多从的编程模型。项目中的Server可在主进程启动，Client在（多个）子进程中启动，客户端发送的消息能够被服务端接收，
并且对于不同的消息类型，可以注册不同的回调逻辑。
项目预置了一些常用通信协议，同时也提供简便的自定义通信协议的接口。进程间通信基于unix socket实现，因此不能在windows系统中运行。Quick Start启动服务端fromdipimportServerimportasynciodefcb(req,mtype,data):print(data['hello'])server=Server('/tmp/run.sock',callback=cb)asyncio.run(server.serve_forever())使用客户端发送消息fromdipimportClientcli=Client('/tmp/run.sock')cli.send_json({'hello':'world'})服务端将会输出>>>'world'自定义通信协议如果需要自定义进程间通信时传输字节流的格式，可以使用自定义ProtocolimporttypingfromdipimportProtocolclassNewProtocol(Protocol,mtype=b'N'):@classmethoddef_decode(cls,buf:bytes)->typing.Any:pass@classmethoddef_encode(cls,data:typing.Any)->bytes:pass自定义通信协议需要使用一个byte作为标识（mtype），并且必须实现_decode和_encode两个方法。其中:_encode用于将python对象序列化为字节流_decode用于将字节流反序列化为python对象两者一般互为逆操作，即对于python对象obj：obj==Protocol.decode(Protocol.encode(obj))注: 这并不是硬性规定，只是一种通常做法下述是一个非常粗糙的固定字符串压缩协议的实现fromdipimportProtocolfromdipimporterrorsclassStringMappingProto(Protocol,mtype=b'S'):MAP={'Hello World':b'h',}MAP_REV={v:kfork,vinMAP.items()}@classmethoddef_decode(cls,buf:bytes)->str:data=cls.MAP_REV.get(buf)ifdataisnotNone:returndataelse:raiseerrors.ProtoDecodeError(f'Cannot decode byte:{buf!r}')@classmethoddef_encode(cls,data:str)->bytes:compressed=cls.MAP.get(data)ifcompressedisnotNone:returncompressedelse:raiseerrors.ProtoEncodeError(f'Cannot encode string:{data!r}')使用客户端发送消息:cli.send_msg('S','Hello World')将会在服务端接收到消息Hello World
Package: deepfree
deepfreeKeras-style deep network package for classification and prediction.installpipinstall--upgradenumpyh5pypipinstall--upgradedeepfreefeaturefast learningThe main framework of the program relies onModelincore._modelandLayerincore._layer, which can import directly through'from deepfree import Model, Layer'. You can quickly build and train the model by using them flexibly. In addition, the constructedDBNandSAEcan be employed directly, which are inherited fromModel.stacking blocksBy callingModel.add_layer(['a Layer of a list of Layer']), you can build the model like stack the blocks. There are a set ofLayercan be selected, such asphvariable,maxpooling2d,flatten,concatenate,Dense,Conv2D.flexible settingYou can set the model's parameters listed inbase._attributewhen first building model (DBN(para=...),SAE(para=...),Model(para=...)) or training it (Model.training(para=...)). If you do not set a value, the default value inbase._attributewill be applied.results display'loss & test accuracy - epoch'curve and'prediction - epoch'curve will be generated automatically. Furthermore,real label -> predicted labelcount result andt-SNE visualizationimage can be obtained by callingModel.plot_label_cntandModel.plot_tSNE, respectively.exampleA simple DNN can be constructed and trained as:fromdeepfreeimportModelfromdeepfreeimportphvariable,Densemodel=Model()model.struct=[784,100,10]model.input=phvariable(model.struct[0])('input')model.label=phvariable(model.struct[-1])('label')foriinrange(len(model.struct)-2):model.add_layer(Dense(model.struct[i+1],activation=model.next_hidden_activation(),is_dropout=True))model.add_layer(Dense(model.struct[-1],activation=model.output_func))model.training(dataset=...,data_path=...)plotThe running result can be find in'result'folder.loss & test accuracy - epoch curve:prediction - epoch curve:real label -> predicted label count result:t-SNE visualization:blogGithub,zhihu,CSDN,PyPIQQ Group:640571839
Package: deepgen
DeepGenA collection of modern Deep Generative Models.Installpip3 install deepgenUsageToy example:importosimporttorchfromdeepgen.gan.ganimportGANfromtorchvisionimportdatasetsimporttorchvision.transformsastransformsimg_size=(1,28,28)batch_size=64if__name__=='__main__':data_loader=torch.utils.data.DataLoader(datasets.MNIST('../data/',train=True,download=True,transform=transforms.Compose([transforms.Resize(28),transforms.ToTensor(),transforms.Normalize([0.5],[0.5])]),),batch_size=batch_size,shuffle=True,)os.makedirs('images',exist_ok=True)model=GAN()print(model)his=model.train(data_loader=data_loader,n_epoch=5,sample_interval=10)print(his)TODO:UpdatingGenerative Adversarial Network (GANs)Vanilla GANBoundary Seeking GANAuxiliary Classifier GANBEGANBicycleGANBoundary-Seeking GANConditional GANContext-Conditional GANContext EncoderCoupled GANCycleGANDeep Convolutional GANDiscoGANDRAGANDualGANEnergy-Based GANEnhanced Super-Resolution GANGANInfoGANLeast Squares GANMUNITPix2PixPixelDARelativistic GANSemi-Supervised GANSoftmax GANStarGANSuper-Resolution GANUNITWasserstein GANWasserstein GAN GPWasserstein GAN DIVVariational Autoencoder (VAEs)Vanilla VAEConditional VAEDenoising VAEAdversarial Variational BayesAdversarial Autoencoder
Package: deepgeo
Deep GeoEasy Deep LearningCopyright (c) 2019InfoLab(Donggun LEE)PIP :https://pypi.org/project/deepgeo/DemodeepGeo.ioDeepGeo.ioHow to installpipinstalldeepgeoother version# 0.0.1
pip install deepgeo==0.0.1

# 0.0.13
pip install deepgeo==0.0.13

# 0.1.0 : Support Yolo (Requirement OpenCV, Only Detect)
pip install deepgeo==0.1.0

# 0.2.0 : Support Extendsion Library
pip install deepgeo==0.2.0

# 0.2.1 : Support Font Management
pip install deepgeo==0.2.1requirementPython 3.6pipinstallexifreadpiexifpillowmatplotlibscikit-imageHow to usefont ManagementfromdeepgeoimportFont# registerFont.register("D:/User/Downloads/font.ttf","font")# deleteFont.delete("font")detectfromdeepgeoimportImage,Enginepath="D:"engine=Engine()engine.add_model('maskrcnn_mscoco','maskrcnn',path+'/default_config.json')image=Image("image.jpg",path)image=engine.detect('maskrcnn_mscoco',image)[0]image.draw_annotations(image.get_annotation())image.save(path+"/","test","PNG")trainimportsys,os,jsonfromdeepgeoimportEngine,Imagedefinit_loading_bar(max):max_=str(max)sys.stdout.write("%s/0"%max_)sys.stdout.flush()sys.stdout.write("\b")defupdate_loading_bar(num):sys.stdout.write("%s"%str(num))sys.stdout.flush()sys.stdout.write("\b"*len(str(num)))deffinal_loading_bar():sys.stdout.write("\n")deffjson_to_imgs(engine,dataset_name,path,image_path):file_list=os.listdir(path)file_list.sort()init_loading_bar(len(file_list))count=0foriteminfile_list:count+=1update_loading_bar(count)ifitem.find('.json')isnot-1:data=Nonewithopen(path+item)asdata_file:data=json.load(data_file)engine.add_data('mscoco',dataset_name,Image(data['uri'],image_path,data['annotations']))deldatafinal_loading_bar()delcountdelfile_listengine=Engine()engine.add_model('mscoco','maskrcnn','D:/test/config.json')engine.add_dataset('mscoco','train','maskrcnn')engine.add_dataset('mscoco','val','maskrcnn')fjson_to_imgs(engine,'train','D:/test/val2017/json/','D:/test/val2017/images/')fjson_to_imgs(engine,'val','D:/test/val2017/json/','D:/test/val2017/images/')## Training...print(" > STEP3 : Fine tune all layers")engine.set_config('mscoco',{"EPOCHS":160,"LAYERS":'all',"LEARNING_RATE":engine.get_config('mscoco',"LEARNING_RATE")/10})engine.train('mscoco',"train","val",None)...etc...
Package: deepgeo-ext-maskrcnn
DeepGeo Extension :: Mask R-CNNEasy Deep LearningCopyright (c) 2019InfoLab(Donggun LEE)How to installpipinstalldeepgeo_ext_maskrcnnother version# 0.0.1
pip install deepgeo_ext_maskrcnn==0.0.1requirementPython 3.6pipinstalldeepgeoHow to useimportdeepgeoengine=deepgeo.Engine()engine.add_model('maskrcnn_mscoco','maskrcnn','D:/default_config.json')image=deepgeo.Image.Image("image.jpg","D:/Project")image=engine.detect('maskrcnn_mscoco',image)image.draw_annotations(image.get_annotation())image.save("D:/","test","PNG")default_config.json{"BACKBONE":"resnet101","BACKBONE_STRIDES":[4,8,16,32,64],"BATCH_SIZE":1,"BBOX_STD_DEV":[0.1,0.1,0.2,0.2],"CATEGORY":["bg","person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic_light","fire_hydrant","stop_sign","parking_meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports_ball","kite","baseball_bat","baseball_glove","skateboard","surfboard","tennis_racket","bottle","wine_glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot_dog","pizza","donut","cake","chair","couch","potted_plant","bed","dining_table","toilet","tv","laptop","mouse","remote","keyboard","cell_phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy_bear","hair_drier","toothbrush"],"COMPUTE_BACKBONE_SHAPE":null,"DETECTION_MAX_INSTANCES":100,"DETECTION_MIN_CONFIDENCE":0.7,"DETECTION_NMS_THRESHOLD":0.3,"EPOCHS":1,"FPN_CLASSIF_FC_LAYERS_SIZE":1024,"GPU_COUNT":1,"GRADIENT_CLIP_NORM":5.0,"IMAGES_PER_GPU":1,"IMAGE_CHANNEL_COUNT":3,"IMAGE_MAX_DIM":1024,"IMAGE_META_SIZE":14,"IMAGE_MIN_DIM":800,"IMAGE_MIN_SCALE":0,"IMAGE_PATH":"image","IMAGE_RESIZE_MODE":"square","IMAGE_SHAPE":null,"LAYERS":"all","LEARNING_MOMENTUM":0.9,"LEARNING_RATE":0.001,"LOSS_WEIGHTS":{"mrcnn_bbox_loss":1.0,"mrcnn_class_loss":1.0,"mrcnn_mask_loss":1.0,"rpn_bbox_loss":1.0,"rpn_class_loss":1.0},"MASK_POOL_SIZE":14,"MASK_SHAPE":[28,28],"MAX_GT_INSTANCES":100,"MEAN_PIXEL":[123.7,116.8,103.9],"MEMO":"","MINI_MASK_SHAPE":[56,56],"MODEL_FILE_NAME":"mask_rcnn_coco.h5","MODEL_PATH":"model","MODEL_URI":"","NAME":"MASK_RCNN","NUM_CLASSES":80,"POOL_SIZE":7,"POST_NMS_ROIS_INFERENCE":1000,"POST_NMS_ROIS_TRAINING":2000,"PRE_NMS_LIMIT":6000,"RESULT_TEST_NUM":100,"ROI_POSITIVE_RATIO":0.33,"RPN_ANCHOR_RATIOS":[0.5,1,2],"RPN_ANCHOR_SCALES":[32,64,128,256,512],"RPN_ANCHOR_STRIDE":1,"RPN_BBOX_STD_DEV":[0.1,0.1,0.2,0.2],"RPN_NMS_THRESHOLD":0.7,"RPN_TRAIN_ANCHORS_PER_IMAGE":256,"STEPS_PER_EPOCH":1000,"TOP_DOWN_PYRAMID_SIZE":256,"TRAIN_BN":false,"TRAIN_ROIS_PER_IMAGE":200,"USE_MINI_MASK":true,"USE_RPN_ROIS":true,"VALIDATION_STEPS":50,"VERSION":"","WEIGHT_DECAY":0.0001}
Package: deepgeo-ext-yolo
DeepGeo Extension :: YOLOEasy Deep LearningCopyright (c) 2019InfoLab(Donggun LEE)How to installpipinstalldeepgeo_ext_yoloother version# 0.0.1
pip install deepgeo_ext_yolo==0.0.1requirementPython 3.6pipinstalldeepgeoopencv-pythonnumpyHow to useimportdeepgeoengine=deepgeo.Engine()engine.add_model('maskrcnn_yolo','yolo','D:/default_config.json')image=deepgeo.Image("image.jpg","D:/Project")image=engine.detect('maskrcnn_yolo',image)[0]image.draw_annotations(image.get_annotation())image.save("D:/","test","PNG")default_config.json{"VERSION"="""MODEL_FILE_NAME":"road-damage.weights","MODEL_PATH":"model","CLASSES":"road-damage-class.txt","CONFIG":"road-damage.cfg","MEMO":"road damage detect"}
Package: deep-geometry
deep-geometryA python library for preprocessing geospatial vector geometries for use in deep learningRationaleDeep learning can use geospatial vector polygons directly (rather than a feature-extracted pre-processd version), but it requires vectorization and normalisation first, like any data source.Installationpip install deep-geometryUsageGeometry vectorizationMake a numerical vector from a geometry:>>> from deep_geometry import vectorizer as gv

>>> geoms = [
...     'POINT(0 0)',
...     'POINT(1 1)',
...     'POINT(2 2)',
...     'POINT(3 3)',
...     'POINT(4 4)',
...     'POINT(5 5)',
...     'POLYGON((0 0, 1 0, 1 1, 0 1, 0 0))',
... ]

>>> gv.vectorize_wkt(geoms[0])
array([[ 0.,  0.,  0.,  0.,  0.,  0.,  1.]])

>>> gv.vectorize_wkt(geoms[6])
array([[ 0.,  0.,  0., 1., 1.,  0.,  0.],
       [ 1.,  0.,  0., 1., 1.,  0.,  0.],
       [ 1.,  1.,  0., 1., 1.,  0.,  0.],
       [ 0.,  1.,  0., 1., 1.,  0.,  0.],
       [ 0.,  0.,  0., 1., 0.,  0.,  1.]])Collect the max length from a set of geometries:>>> max_len = gv.get_max_points(geoms)
>>> print('Maximum geometry node size in set:', max_len)
Maximum geometry node size in set: 7Numerical data normalizationGeometries regularly are in some kind of earth projection that is far from the origin of the coordinate system. In order for machine learning models to learn, data needs to be normalized. A usual way to go about this is to mean-center the instances and to divide by the dataset standard deviation.The library provides a convenience class for normalization, modeled after the scalers fromscikit-learnwith a .fit() and a .transform() method:>>> from deep_geometry import GeomScaler
>>> import numpy
>>> gs = GeomScaler()  # simply initialize
>>> geom6 = gv.vectorize_wkt(geoms[6])
>>> dataset = numpy.repeat([geom6], 4, axis=0)
>>> gs.fit(dataset)
>>> gs.scale_factor
0.5
>>> normalized_data = gs.transform(dataset)
>>> normalized_data[0]  # see: zero-mean and scaled to standard deviation
array([[-1., -1.,  0.,  1.,  1.,  0.,  0.],
       [ 1., -1.,  0.,  1.,  1.,  0.,  0.],
       [ 1.,  1.,  0.,  1.,  1.,  0.,  0.],
       [-1.,  1.,  0.,  1.,  1.,  0.,  0.],
       [-1., -1.,  0.,  1.,  0.,  0.,  1.]])
Package: deepgnn-ge
DeepGNN OverviewDeepGNN is a framework for training machine learning models on large scale graph data. DeepGNN contains all the necessary features including:Distributed GNN training and inferencing on both CPU and GPU.Custom graph neural network design.Online Sampling: Graph Engine (GE) will load all graph data, each training worker will call GE to get node/edge/neighbor features and labels.Automatic graph partitioning.Highly performant and scalable.Project is in alpha version, there might be breaking changes in the future and they will be documented in the changelog.UsageInstall pip package:python-mpipinstalldeepgnnIf you want to build package from source, see instructions inCONTRIBUTING.md.Train and evaluate a graphsage model with pytorch on cora dataset:cdexamples/pytorch
pythonsage.pyMigrating ScriptsWe provide a python module to help you upgrade your scripts to new deepgnn versions.pipinstallgoogle-pasta
python-mdeepgnn.migrate.0_1_56--script_dirdirectory_to_migrateSeeCHANGELOG.mdfor full change details.