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chiutaiyin/Iterative-feature-transformation-for-style-transfer | ['style transfer'] | ['Iterative Feature Transformation for Fast and Versatile Universal Style Transfer'] | utils/core.py utils/seg_utils.py utils/vgg_layerwise_SC.py utils/model.py utils/core_seg.py stylize_ot stylize_iter stylize_zca stylize_adain stylize_ot stylize_line stylize_iter whiten_line whiten_zca stylize_zca stylize_adain whiten_in EncDec DecoderBlock ins_norm Decoder change_seg compute_label_info read_segmentations VggEncBlock pad_reflect VggEnc vgg_from_t7 inv_sqrt_cov multiply reshape reduce_sum matmul reduce_mean inv_sqrt_cov multiply reshape reduce_sum matmul reduce_mean multiply reshape reduce_sum sqrt reduce_mean sum range matmul boolean_mask where range tensor_scatter_nd_update len boolean_mask where range tensor_scatter_nd_update len boolean_mask where range tensor_scatter_nd_update len boolean_mask where append tensor_scatter_nd_update len reduce_mean inv_sqrt_cov matmul sqrt reduce_mean cubic_solver reshape transpose concat reduce_max matmul reduce_mean trace eye eta_selection range zeros_like cubic_solver transpose concat boolean_mask reduce_max matmul where reduce_mean trace append tensor_scatter_nd_update eta_selection range len sqrt reduce_mean sum asarray zeros abs range size where unique zeros max change_seg open load nOutputPlane Lambda astype float32 Conv2D MaxPooling2D modules append Activation enumerate kH | # Iterative Feature Transformation for Fast and Versatile Universal Style Transfer Code for our paper "[Iterative Feature Transformation for Fast and Versatile Universal Style Transfer](https://github.com/chiutaiyin/Iterative-feature-transformation-for-style-transfer/blob/master/paper.pdf)" ([Supplementary Material](https://github.com/chiutaiyin/Iterative-feature-transformation-for-style-transfer/blob/master/supplementary%20material.pdf)) in ECCV 2020. ## Requirements ## - tensorflow v2.2.0 - torchfile v0.1.0 - file vgg_normalised.t7: download [here](https://s3.amazonaws.com/xunhuang-public/adain/vgg_normalised.t7) and save it to the folder utils ## Usage ## See [demo.ipynb](https://github.com/chiutaiyin/Iterative-feature-transformation-for-style-transfer/blob/master/demo.ipynb) ## Citation ## If you find this repo useful, please cite our paper **Iterative Feature Transformation for Fast and Versatile Universal Style Transfer** published in ECCV 2020. | 1,700 |
chizhizhen/DNT | ['visual tracking'] | ['Dual Deep Network for Visual Tracking'] | caffe/python/caffe/classifier.py caffe/python/caffe/__init__.py caffe/examples/web_demo/app.py caffe/python/detect.py caffe/tools/extra/resize_and_crop_images.py caffe/examples/web_demo/exifutil.py caffe/python/classify.py caffe/python/caffe/detector.py caffe/python/draw_net.py caffe/src/caffe/test/test_data/generate_sample_data.py caffe/examples/finetune_flickr_style/assemble_data.py caffe/python/caffe/draw.py caffe/scripts/cpp_lint.py caffe/scripts/copy_notebook.py caffe/tools/extra/extract_seconds.py caffe/python/caffe/pycaffe.py caffe/python/caffe/io.py caffe/scripts/download_model_binary.py download_image start_tornado start_from_terminal embed_image_html classify_upload index allowed_file ImagenetClassifier classify_url open_oriented_im apply_orientation main main main Classifier Detector draw_net_to_file draw_net get_pydot_graph get_enum_name_by_value blobproto_to_array datum_to_array array_to_blobproto arraylist_to_blobprotovecor_str resize_image array_to_datum blobprotovector_str_to_arraylist load_image oversample _Net_blobs _Net_forward_all _Net_deprocess _Net_set_input_arrays _Net_set_channel_swap _Net_backward _Net_preprocess _Net_params _Net_set_raw_scale _Net_set_mean _Net_forward _Net_forward_backward_all _Net_set_input_scale _Net_batch ParseNolintSuppressions CheckVlogArguments CheckSectionSpacing FindNextMultiLineCommentEnd ReplaceAll CheckForFunctionLengths _SetOutputFormat _IsTestFilename _VerboseLevel CheckBraces RemoveMultiLineComments ResetNolintSuppressions CheckForNonStandardConstructs _SetVerboseLevel PrintUsage _NestingState CheckIncludeLine CheckAccess _CppLintState Search CheckInvalidIncrement RemoveMultiLineCommentsFromRange CleansedLines CheckForBadCharacters UpdateIncludeState FindPreviousMatchingAngleBracket CheckEmptyBlockBody FindNextMultiLineCommentStart Match _NamespaceInfo CheckMakePairUsesDeduction CheckCheck IsBlankLine _SetFilters ProcessLine _FunctionState CheckPosixThreading GetLineWidth GetHeaderGuardCPPVariable IsCppString _IncludeState CheckSpacing _ClassInfo CheckForCopyright IsErrorSuppressedByNolint ProcessFileData CheckForMultilineCommentsAndStrings CloseExpression _PreprocessorInfo _OutputFormat CheckForIncludeWhatYouUse CheckSpacingForFunctionCall FindEndOfExpressionInLine FindNextMatchingAngleBracket _SetCountingStyle ProcessFile _IncludeError CleanseRawStrings CheckAltTokens CheckForNewlineAtEOF ParseArguments CheckForNonConstReference PrintCategories _Filters main FilesBelongToSameModule CheckCStyleCast FileInfo _BlockInfo CheckForHeaderGuard CheckCaffeDataLayerSetUp ReverseCloseExpression CleanseComments _DropCommonSuffixes _ClassifyInclude CheckStyle CheckCaffeAlternatives FindStartOfExpressionInLine _ShouldPrintError CheckComment Error _GetTextInside CheckLanguage CheckCaffeRandom GetPreviousNonBlankLine reporthook parse_readme_frontmatter model_checks_out valid_dirname extract_seconds extract_datetime_from_line ResizeCropImagesMapper PILResizeCrop OpenCVResizeCrop urlretrieve get read info load_image classify_image StringIO join replace info secure_filename save filename open_oriented_im classify_image fromarray replace astype save resize StringIO items list listen HTTPServer format print start WSGIContainer update start_tornado add_option OptionParser debug port parse_args ImagenetClassifier run hasattr _getexif astype float32 tile apply_orientation open transpose model_def endswith ArgumentParser save mean_file channel_swap output_file dirname expanduser parse_args input_file predict Classifier load time isdir print add_argument pretrained_model gpu len DataFrame Detector format to_hdf detect_selective_search set_index to_csv detect_windows read_csv read NetParameter basename Merge draw_net_to_file items list DESCRIPTOR add_edge list Dot layers get_enum_name_by_value name values Edge Node bottom append add_node top astype float32 tile resize tuple array zoom concatenate shape tile empty array shape BlobProto extend flat extend BlobProtoVector ParseFromString BlobProtoVector extend tostring shape Datum flat data len items list layers index set outputs _forward len items list _backward layers inputs index set len items list asarray extend copy next _batch iter forward values len items list asarray backward extend next _batch zip_longest zip iter forward values len shape transpose reshape get transpose astype float32 resize_image get squeeze transpose ascontiguousarray list concatenate iter num zeros next range values len error search add group clear compile compile compile SetOutputFormat SetCountingStyle SetFilters _Filters startswith IsErrorSuppressedByNolint _ShouldPrintError write IncrementErrorCount replace append Match group find startswith endswith range error FindNextMultiLineCommentEnd RemoveMultiLineCommentsFromRange FindNextMultiLineCommentStart rstrip find range len FindEndOfExpressionInLine range len FindStartOfExpressionInLine error min search I range len FileInfo RepositoryName sep sub ParseNolintSuppressions error startswith split GetHeaderGuardCPPVariable enumerate error enumerate error len error replace count error find error find error find error find error Search error match InnermostClass replace error escape Match Search error group Search Check error lines Count End group Begin NumLines Match raw_lines range Search error match group error Match group pop group append Search pop group append Search elided replace CheckSpacingForFunctionCall rfind error len group min CloseExpression NumLines sub find CheckComment Match range Search lines_without_raw_strings error group starting_linenum Match range Search error rfind len group ReverseCloseExpression Search Match CloseExpression find error Match CloseExpression find elided error strip group FindEndOfExpressionInLine find Match range CloseExpression len error Match finditer normalize isinstance GetLineWidth int InnermostClass CheckCheck error CheckAltTokens CheckBraces CheckSpacing CheckSectionSpacing CheckEmptyBlockBody CheckAccess GetHeaderGuardCPPVariable lines_without_raw_strings _DropCommonSuffixes RepositoryName match split CheckNextIncludeOrder CanonicalizeAlphabeticalOrder FileInfo error search group SetLastHeader match _ClassifyInclude Match pop end search set append values M rstrip replace CheckCStyleCast error _GetTextInside CheckIncludeLine search group lstrip startswith Match ResetSection Search split rfind error group ReverseCloseExpression lstrip findall Match range Search ReplaceAll error Match Search endswith replace setdefault group search CleanseComments open list FilesBelongToSameModule error search copy sub NumLines FullName keys range error search CheckPosixThreading ParseNolintSuppressions CheckVlogArguments CheckMakePairUsesDeduction CheckCaffeDataLayerSetUp CheckLanguage CheckInvalidIncrement CheckCaffeRandom CheckForNonConstReference check_fn Update CheckForNonStandardConstructs CheckStyle raw_lines CheckForMultilineCommentsAndStrings CheckCaffeAlternatives CheckForFunctionLengths CleansedLines _NestingState CheckForBadCharacters CheckForNewlineAtEOF _IncludeState RemoveMultiLineComments CheckForCopyright ResetNolintSuppressions CheckForHeaderGuard NumLines CheckCompletedBlocks CheckForIncludeWhatYouUse range ProcessLine _FunctionState Error rstrip endswith len write ProcessFileData _SetVerboseLevel range split write exit join write exit _VerboseLevel int getopt _SetOutputFormat set _SetVerboseLevel PrintCategories _SetFilters _OutputFormat PrintUsage _SetCountingStyle split getreader ParseArguments ResetErrorCounts stderr exit verbose_level PrintErrorCounts StreamReaderWriter ProcessFile getwriter int time write flush load join index int rfind datetime split total_seconds getctime strip write close extract_datetime_from_line year open | # Dual Deep Network for Visual Tracking ## Introduction DNT repository for **Dual Deep Network for Visual Tracking** is published in IEEE Transaction on Image Processing [[IEEE Xplore]](http://ieeexplore.ieee.org/document/7857085/) [[arXiv]](https://arxiv.org/abs/1612.06053). This package contains the source code to reproduce the experimental results of DNT paper. The source code is mainly written in MATLAB. There a tracking benchmark tracking [repo](https://github.com/foolwood/benchmark_results). Check them out! ## Usage + Supported OS: the source code was tested on 64-bit Arch Linux OS, and it should also be executable in other linux distributions. + Dependencies: + Deep learning framework [caffe](http://caffe.berkeleyvision.org/) and all its dependencies. + Cuda-enabled GPUs. + Installation: | 1,701 |
chloechsu/ldseig | ['time series', 'time series clustering'] | ['Linear Dynamics: Clustering without identification'] | plot_eig_error.py agg_stats_to_table.py experiments.py lds.py experiment_learn_eig.py arma.py plot_dist_correlation.py clustering.py iterated_regression.py get_eig_from_arparams fit_ar fit_arma_mle fit_arma_iter _fit_arma_iter kshape pad_seqs_to_matrix dtw_kmedoids ar_kmeans pca_kmeans lds_em_kmeans arma_mle_kmeans arma_iter_kmeans get_results generate_cluster_centers generate_lds_clusters main get_results plot_results get_results_seq_len plot_results get_eig_from_arparams main create_learning_fns fit_arparams_iter SequenceGenerator LinearDynamicalSystemSequence LinearDynamicalSystem generate_linear_dynamical_system fit_lds_pylds eig_dist zeros ARMA fit fit_regularized zeros_like concatenate reshape matmul add_constant max range lagmat zeros fit_ar ARMA fit roots output_dim zeros max range enumerate to_time_series_dataset max max get_clusters kmedoids choice zeros process distance_matrix_fast range enumerate len stack stack append outputs flatten fit_arma_mle stack PCA fit_transform stack inf generate_linear_dynamical_system info append eig_dist range str randn LinearDynamicalSystem hidden_state_dim sqrt average get_spectrum info append randint eig_dist range transition_matrix zeros len items list default_timer metric_fn OrderedDict fn info append randint array values seed str SequenceGenerator join update close tqdm linspace output_dir generate_cluster_centers range generate_lds_clusters join columns savefig figure pointplot join load_results plot_results get_results mkdir output_dir OrderedDict warn linspace seed list default_timer append range update SequenceGenerator message close sqrt generate_seq create_learning_fns items norm category tqdm generate_linear_dynamical_system get_spectrum reset_index set_xlim map apply lineplot get_results_seq_len fit_regularized zeros_like concatenate reshape matmul add_constant max range lagmat abs max rand inf A DefaultLDS add_data resample_model eigvals range | ## Code for paper ["Linear Dynamics: Clustering without identification"](https://arxiv.org/abs/1908.01039) ### Installation The dependencies are documented in environment.yml and can be installed via conda. <pre><code> conda env create -f environment.yml conda activate lds </code></pre> For more details, see [conda documentation for creating an environment from yml file](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file). If there is an error due to pybasicbayes and scipy version incompatibility, try installing the latest version of pybasicbayes from GitHub as the pip version might be outdated. ### Simulated experiments for eigenvalue estimation | 1,702 |
choiwb/Computer_Vision_for_Smart_Factory | ['multiple object tracking'] | ['Simple Online and Realtime Tracking'] | object_tracker_v2.0.py utils/utils.py utils/datasets.py object_tracker_v5.0.py utils/parse_config.py models.py pylive.py object_tracker_v3.0.py object_tracke_v1.0.py sort.py object_tracker_v4.0.py YOLOLayer create_modules Darknet EmptyLayer ccw detect_image intersect ccw detect_image intersect ccw make_line detect_image intersect detect_image intersect save_csv ccw intersect_count make_line intersect_point ccw detect_image intersect live_plotter live_plotter_xy KalmanBoxTracker iou Sort convert_bbox_to_z associate_detections_to_trackers convert_x_to_bbox parse_args ImageFolder ListDataset parse_data_config parse_model_config compute_ap build_targets bbox_iou_numpy to_categorical weights_init_normal load_classes bbox_iou non_max_suppression pop int YOLOLayer Sequential ZeroPad2d MaxPool2d add_module Conv2d ModuleList EmptyLayer Upsample append BatchNorm2d LeakyReLU sum enumerate unsqueeze_ Variable Compose min type float round print intersect FONT_HERSHEY_DUPLEX putText line str append DataFrame to_csv show format plot pause add_subplot ylabel title ylim figure ion set_ydata show format plot pause min add_subplot ylabel xlim title set_data ylim figure ion max minimum maximum float sqrt linear_assignment iou concatenate reshape append zeros empty enumerate add_argument ArgumentParser rstrip strip open startswith append split dict strip split open data normal_ __name__ constant_ concatenate size maximum sum range clamp min max minimum eps expand_dims maximum data sort new squeeze size shape unsqueeze cuda unique bbox_iou append max is_cuda cat enumerate int fill_ FloatTensor ones concatenate size range unsqueeze bbox_iou zeros argmax log | # pytorch_objectdetecttrack Object detection in images, and tracking across video frames Full story at: https://towardsdatascience.com/object-detection-and-tracking-in-pytorch-b3cf1a696a98 References: 1. YOLOv3: https://pjreddie.com/darknet/yolo/ 2. Minimal PyTorch implementation of YOLOv3: https://github.com/eriklindernoren/PyTorch-YOLOv3 3. YOLOv3 paper: https://pjreddie.com/media/files/papers/YOLOv3.pdf 4. SORT paper: https://arxiv.org/pdf/1602.00763.pdf 5. Alex Bewley's SORT implementation: https://github.com/abewley/sort | 1,703 |
choltz95/MTGP-NN | ['gaussian processes', 'time series'] | ['Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier'] | experiment.py dataset.py models.py Dataset Experiment move_to_cuda GPCNNLSTM LSTM CRNN MTGPInterpolationModel append cuda | # MTGP-NN Early sepsis detection via multitask gaussian process cnn-rnn We follow the general framework of [1] with several extensions. A Multitask Gaussian Process [2] is applied to normalize the time-scale of irregularly sampled clinical data (vital signs). Any prediction algorithm can be employed on top of this framework, but we process this data with a cnn-based encoder and perform temporal classification with a GRU-LSTM augmented with auxilliary features (patient demographics & coursened lab data statistics). Multitask learning is induced via prediction of hospital stay duration, etc. An implementation of gradnorm [3] is included to learn task-loss weights and the whole framework is optimized end-to-end with backprop. GPytorch [3] is used for MTGP interpolation & Pytorch [4] is used for the neural network. Results to be included. [1] Joseph Futoma, Sanjay Hariharan, Katherine Heller, Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier, ICML'17 - https://arxiv.org/abs/1706.04152 [2] Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, Andrew Rabinovich, GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks, ICML 2018 - https://arxiv.org/abs/1711.02257 [3] https://gpytorch.ai/ [4] https://arxiv.org/abs/1711.02257 | 1,704 |
chomd90/extreme_sparse | ['network pruning'] | ['ESPN: Extremely Sparse Pruned Networks'] | train.py train_imagenet_finetune.py prune_espn_finetune.py archs/init_utils.py prune_espn_imagenet_finetune.py archs/cifar_resnet.py train_imagenet_rewind.py train_utils.py architectures.py prune_espn_rewind.py datasets.py utils.py prune_espn_imagenet_rewind.py get_architecture _mnist10 _imagenet _fashion_mnist10 get_num_classes get_dataset _cifar10 _tinyimagenet _cifar100 main main main main main validate AverageMeter accuracy save_checkpoint ProgressMeter adjust_learning_rate main_worker main train validate AverageMeter accuracy save_checkpoint ProgressMeter adjust_learning_rate main_worker main train accuracy_list init_logfile AverageMeter accuracy get_logger log makedirs model_inference reset_forward_linear apply_prune_mask test mask_forward_conv2d mask_forward_linear mask_train train model_inference_imagenet reset_forward_conv2d resnet20 wide_resnet vgg19 WideBasicBlock WideResNet lenet5 resnet32 _weights_init BasicBlock LeNet300 LeNet_5_Caffe VGG fcn NetworkBlock LeNet5 ResNet LambdaLayer resnet50 lenet_5_caffe lenet300 conv3x3 resnet FCN weights_init to join Compose join Compose data zeros_like keep_ratio outdir SGD where create_supervised_trainer DataLoader weight modules device arch dataset get_architecture abs log create_supervised_evaluator mask_train Parameter run topk round StepLR str model_inference ones set_device get_dataset load_state_dict append to range cat weight_mask ones_like format logname apply_prune_mask ProgressBar eval lr mkdir alpha zip float attach Linear load join deepcopy savedir int NLLLoss isinstance print MethodType Conv2d parameters zeros epochs gpu len save_model init_logfile zero_grad ImageFolder save cuda data_val step state_dict Compose data_train Normalize net enumerate criterion backward keep_mask train epochs_warmup MultiStepLR time weight_decay seed finetune world_size manual_seed spawn discription multiprocessing_distributed warn device_count main_worker parse_args workers validate save_model batch_size multiprocessing_distributed SGD pretrained DataParallel DistributedDataParallel ImageFolder DataLoader adjust_learning_rate arch save_checkpoint features cuda max data_val set_device DistributedSampler rank load_state_dict to range format init_process_group apply_prune_mask Compose data_train start_epoch distributed lr resume Normalize keep_masks load join int savedir evaluate print set_epoch parameters isfile train epochs gpu update time criterion model backward display size AverageMeter zero_grad accuracy ProgressMeter item step cuda gpu enumerate len len eval AverageMeter ProgressMeter copyfile join save outdir param_groups lr close write open close write open dirname FileHandler getLogger addHandler strftime StreamHandler info setLevel INFO makedirs format to print eval time AverageMeter time format print AverageMeter eval avg time format print AverageMeter eval avg register_hook hook_factory zip update criterion model backward train AverageMeter size zero_grad modules item to step enumerate weight kaiming_normal_ __name__ fill_ isinstance Conv2d bias normal_ kaiming_normal_ zero_ BatchNorm2d weight constant_ Linear | ## ESPN: Extremely Sparse Pruned Networks This is the code to reproduce the results from the paper. <p> <img src="figures/figure1.png" height="300"> </p> ### Setup To setup the environment, use the requirements.txt file. Basic requirements: 1. Pytorch == 1.5.0 2. Torchvision == 0.6.0 | 1,705 |
chong-z/tree-ensemble-attack | ['adversarial attack'] | ['An Efficient Adversarial Attack for Tree Ensembles'] | baselines/xgbKantchelianAttack.py baselines/OPT_attack_lf.py baselines/models_cpu.py baselines/Sign_OPT_cpu.py random_forest/train_random_forest.py baselines/cube_attack.py baselines/HSJA.py baselines/test_attack_cpu.py cube_attack exact_attack_stumps binary_search_attack sampling_attack Cube coord_descent_attack_trees HSJA XGBoostModel CPUModel XGBoostTestLoader mulvt OPT_attack_lf OPT_attack_sign_SGD_cpu quad_solver sign run_attack node_wrapper sigmoid xgbMultiClassKantchelianAttack main xgbKantchelianAttack xgboost_wrapper calculate_accuracy reorder_trees train_rf ones argmin shape uniform fmargin zeros range clip minimum maximum choice shape fmargin zeros clip format ones print copy flatten shape any attack fmargin zeros max range minimum maximum argsort shape trees fmargin zeros clip cumsum max clip list argmin shape append sum range predict format inf w_r b full float keys print sort min argsort zeros array size expand unsqueeze range len argmax maximum range zeros abs diag seed norm inf print attack range arange load_svmlight_file Booster xgbKantchelianAttack xgbMultiClassKantchelianAttack abs max seed load_model set_printoptions attack sum predict format inf hstack astype power xgboost_wrapper enumerate time toarray print pow len get_label astype DMatrix sum predict append len range enumerate save_model dump_model replace reorder_trees print calculate_accuracy train DMatrix | # An Efficient Adversarial Attack for Tree Ensembles We study the problem of efficient adversarial attacks on tree based ensembles such as gradient boosting decision trees (GBDTs) and random forests (RFs). Since these models are non-continuous step functions and gradient does not exist, most existing efficient adversarial attacks are not applicable. In our work, we transform the attack problem into a discrete search problem specially designed for tree ensembles, where the goal is to find a valid "leaf tuple" that leads to mis-classification while having the shortest distance to the original input. With this formulation, we show that a simple yet effective greedy algorithm can be applied to iteratively optimize the adversarial example by moving the leaf tuple to its neighborhood within hamming distance 1. More details can be found in our paper: _Chong Zhang, Huan Zhang, Cho-Jui Hsieh_, "An Efficient Adversarial Attack for Tree Ensembles", NeurIPS 2020 [[poster session]](https://neurips.cc/virtual/2020/protected/poster_ba3e9b6a519cfddc560b5d53210df1bd.html) <img src="https://github.com/chong-z/tree-ensemble-attack/raw/main/img/paper-image-large.png" alt="Thumbnail of the paper" width="500px"> ## LT-Attack Setup ### Installation on Ubuntu 20.04 Our code requires `libboost>=1.66` for `thread_pool`: ``` sudo apt install libboost-all-dev ``` | 1,706 |
choosehappy/QuickAnnotator | ['whole slide images'] | ['Quick Annotator: an open-source digital pathology based rapid image annotation tool'] | QA_utils.py make_patches_for_embed.py make_embed.py train_model.py train_ae.py QA_pool.py unet.py QA_api.py cli/import_annotations_cli.py make_superpixel.py QA_db.py QA_config.py cli/rest_workflow_example_cli.py QA_html.py cli/extract_tiles_from_wsi_openslide.py cli/qa_cli_tester.py QA.py QA_worker.py make_output_unet_cmd.py make_superpixel_dl.py Dataset divide_batch LayerActivations divide_batch add_project check_existing_project delete_project prevnext_image get_superpixels_callback retrain_dl_callback get_annotation_stats get_prediction get_roi delete_image post_roimask get_latest_log get_image_thumb retrain_dl train_autoencoder_callback train_autoencoder add_roi_to_traintest get_embed_csv make_patches_callback get_number_of_objects get_image get_roimask get_superpixels make_patches make_embed_callback get_embed upload_image populate_training_files get_superpixels_boundary get_traintest_images make_embed get_mask remove_image_from_traintest getconfig get_model get_database_uri set_job_status Image Job JobidBase Project Roi clear_stale_jobs get_latest_modelid get_imagetable get_project_id plotembed annotation_main rendered_project_image index annotation renderprojectjob display_sample_images get_imagelist annotation_tool annotation_utils embed_main images_main favicon pool_get_image getUnfinishedJob worker_default_callback update_completed_job_status pool_run_script error_default_callback add_async_job has_cuda get_torch_device get_file_tail run_script timeSince LayerActivations Dataset asMinutes timeSince Dataset asMinutes UNetUpBlock UNetConvBlock UNet random_subset range join mkdir join name filter_by delete rmtree delete_image first first getint commit first info info create_engine update_completed_job_status get_database_uri execute dispose write close warn rois images info testingROI open get populate_training_files warn getboolean getint getfloat get_latest_modelid info append first scalar replace create_engine update_completed_job_status loads get_database_uri execute first dispose get commit glob make_patches_time warn getint path images info append first len replace info create_engine update_completed_job_status loads get_database_uri execute first dispose get commit exists warn getint get_latest_modelid info first replace create_engine update_completed_job_status loads get_database_uri execute first dispose get get_latest_modelid join get_imagetable savetxt first info commit first str commit id info first info get int make_response imencode resize tobytes imread commit remove replace exists glob filter_by delete path first get commit remove isfile replace info Image size thumbnail new add save filename splitext first open int make_response imencode search group tobytes imread commit imwrite COLOR_RGB2BGR imdecode frombuffer count_nonzero COLOR_BGR2RGB add shape imread get_number_of_objects get group Roi info IMREAD_UNCHANGED first int b64decode error zeros cvtColor send_from_directory send_from_directory replace get replace print warn getint get_latest_modelid info first get superpixel_modelid remove replace warn getint getfloat get_latest_modelid info first replace create_engine update_completed_job_status loads get_database_uri execute first dispose send_from_directory replace split first info items list dict sections append get error get_latest_modelid send_from_directory first split label relationship Text DateTime Column Integer relationship ForeignKey Text DateTime Column Integer ForeignKey Text DateTime Column Integer ForeignKey Text DateTime Column Integer Text Integer Column delete info create_engine get_database_uri execute dispose int iteration first all all get_imagetable first first get_traintest_images first get error get_latest_modelid info first first get first getint getint first jobs all first get_project_id first info set_job_status info update_completed_job_status info error commit create tometadata engine Job apply_async prepare add automap_base metadata create_api info get_project_id getUnfinishedJob send_from_directory split info is_available print get_device_capability current_device getint device join read seek count tell reversed append open set_job_status get_ident decode readline dispose info create_engine communicate debug error close iter get_database_uri execute Popen poll floor time randint len | # QuickAnnotator --- Quick Annotator is an open-source digital pathology annotation tool.  # Purpose --- Machine learning approaches for segmentation of histologic primitives (e.g., cell nuclei) in digital pathology (DP) Whole Slide Images (WSI) require large numbers of exemplars. Unfortunately, annotating each object is laborious and often intractable even in moderately sized cohorts. The purpose of the quick annotator is to rapidly bootstrap annotation creation for digital | 1,707 |
chreul/mptv | ['optical character recognition', 'active learning'] | ['Improving OCR Accuracy on Early Printed Books by utilizing Cross Fold Training and Voting', 'Calamari - A High-Performance Tensorflow-based Deep Learning Package for Optical Character Recognition', 'Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning'] | ocrolib/psegutils.py ocrolib/__init__.py ocrolib/ligatures.py ocrolib/lstm.py ocrolib/nutils.py ocrolib/lang.py ocrolib/voting.py ocrolib/utils.py ocrolib/native.py ocrolib/chars.py ocrolib/extras/fgen.py ocrolib/extras/cairoextras.py ocrolib/sl.py ocrolib/lineest.py ocrolib/toplevel.py ocrolib/morph.py ocrolib/hocr.py ocrolib/default.py ocrolib/edist.py setup.py ocrolib/exceptions.py ocrolib/common.py requote_fancy requote chist allsplitext midrange normalize_text ustrg2unicode warn write_image_gray RegionExtractor norm_max array2pil load_object binarize_range parallel_map save_object check_valid_class_label isbytearray plotgrid MovingStats caller quick_check_line_components iulib_page_iterator remove_noise base gt_explode read_line_segmentation read_image_gray project_text expand_args finddir rgb2int make_seg_black write_text_simple showrgb pil2array isintarray write_line_segmentation findfile int2rgb number_of_processors read_image_binary fvariant showgrid Record glob_all write_text unpickle_find_global set_params ocropus_find_file quick_check_page_components isintegerarray pad_by gt_implode read_text testset read_page_segmentation write_page_segmentation make_seg_white die warn_once obinfo isfloatarray write_image_binary getlocal xlevenshtein levenshtein Unimplemented FileNotFound BadClassLabel Warning Internal summary BadImage RecognitionError BadInput OcropusException footer header size_category common_ligatures LigatureTable scale_to_h CenterNormalizer hprime RangeError randu log_mul Softmax gfunc log_add ffunc Parallel rownorm make_target ctc_align_targets MLP1 check_nan ascii_codec translate_back Network Stacked forwardbackward SeqRecognizer MLP getstates_for_display translate_back_locations_extended BIDILSTM sumouter Codec Logreg add_training_info prepare_line hfunc forward_py normalize_nfkc gprime ocropus_codec sigmoid fprime forward_algorithm Reversed LSTM translate_back0 LSTM1 backward_py rg_closing select_regions keep_marked remove_marked r_erosion renumber_labels_ordered rg_erosion renumber_labels rg_dilation rb_closing find_objects check_binary renumber_by_xcenter r_opening propagate_labels r_dilation r_closing propagate_labels_simple rb_dilation pyargsort showlabels label all_neighbors correspondences rb_erosion rb_opening ordered_by_xcenter rg_opening spread_labels compile_and_load compile_and_find CompileError lockfile sumprod sumouter test extract reading_order estimate_scale B read_gray record compute_lines read_binary rgbshow show_lines topsort blackout_images binary_objects extract_masked compute_boxmap pad_image find dims volume area yoverlap mbox dim1 extend_to is_slices xoverlaps center0 yoverlaps center_in aspect pad center1 width cut union dim center height intersect raster_FIXME bounds start dim0 box empty xoverlap_rel math ycenter shift raster xoverlap stop yoverlap_rel xcenter DATASET failfunc makeargcheck WHITESEG checks DARK inttuple PAGE SEGMENTATION ANONNEG ABINARY DATASET_VRANK PAGEEXTRA uintpair BOOL ARANGE uinttuple checktype RECTANGLE AINT disabled RANGE CheckWarning ABYTE TDATASET ANY DATASET_SIZE NUMBER BLACKSEG DATASET_VSIZE DATASET_VRANGE ARANK AFLOAT tracing CHANNELS LINE PATCH ALL trace1 unchanged method LIGHT deprecated strc replacedby CheckError sumprod sumouter Voter select_voters synchronize count_sequences perform_vote add_sequence clean_text process_text text_to_voters process_files Sequence PycairoContext create_cairo_font_face_for_file pango_render_gray pango_render_string pango_families gauss_distort cairo_render_string gauss_degrade cairo_render_gray cairo_render_at str sub str sub replacements str normalize sub upper normalize_text sub normalize_text normalize_text tobytes fromstring mean pil2array isfloatarray open print array2pil array save isfloatarray clip pil2array amax open print array2pil array save zeros list shape copy copy rgb2int make_seg_black pil2array open array2pil make_seg_white int2rgb save rgb2int make_seg_black pil2array open array2pil make_seg_white int2rgb save read_image_gray dtype zeros shape exec print ocropus_find_file get imap_unordered Pool fun search ocropus_find_file getlocal split search glob sorted join curdir get_config_var getenv dirname normpath getfile append currentframe exists pardir allsplitext items list hasattr copy setattr _getframe getframeinfo caller write exit caller write caller write length at chr range str hasattr amin amax subplot ginput reshape min gray imshow clf ion range len imshow transpose minimum int subplot str yticks xlabel ylabel gray sqrt imshow title xticks range len enumerate split append minimum list label sum range list min range join arange minimum_filter array split append empty full range len int shape affine_transform eye array T vstack amax any zip sigmoid tanh tanh hfunc gfunc ffunc dot range hprime list hfunc sumouter gprime reversed dot fprime sumprod range Logreg Stacked Logreg LSTM Softmax Stacked Softmax Parallel Reversed LSTM Stacked zeros enumerate find_alternatives arange reshape len outputs tile append label maximum_position amax enumerate append argmax range amax len arange reshape tile label maximum_position amax log_mul arange log_add copy append range len forward_algorithm subplot T exp amax ginput maximum dot imshow clf figure log forwardbackward set isinstance check_binary r_erosion check_binary r_dilation zeros uniform_filter shape zeros uniform_filter shape rb_erosion rb_dilation r_erosion r_dilation imshow where reshape distance_transform_edt label ravel in1d unique keep_marked array unique correspondences T label zeros amax correspondences T label zeros amax find_objects argsort label zeros len array unique roll sorted arange unique zeros ravel amax len find_objects argsort zeros array amax enumerate len find_objects range array O_CREAT O_RDWR O_EXCL open update md5 print mkdir hexdigest compile_and_find len sumprod sumouter randn bytearray print pad_by length label_components copy unpack_rgb textImageProbabilities at bounding_boxes rectarray range fill_rect intarray label find_objects median sorted area shape binary_objects zeros zeros sorted binary_objects shape record find_objects append enumerate ones shape array shape affine_transform shift eye extract mask where maximum_filter pad_image amax center plot ginput print imshow title clf x_overlaps zeros above enumerate left_of visit zeros range len ravel nonzero center plot bounds len add_patch imshow shape clf ylim Rectangle append xlim range cla mean imread mean imread clip print transpose shape imshow zeros abs array range list slice start stop range len list dtype intersect bounds transpose shift dims start pad empty isinstance __name__ type_ callable isinstance isinstance isinstance sum GRAYSCALE1 unique zeros zeros Sequence text len range add_sequence list compute_median sort count_sequences compute_distance values enumerate recursive_sync init len winner place_vote start set_start enumerate select_voters synchronize text_to_voters get_font_face CDLL ctx FORMAT_A8 cairo_ft_font_face_create_for_ft_face c_void_p cairo_set_font_face ImageSurface Context FORMAT_ARGB32 set_source_rgb create_cairo_font_face_for_file get_data select_font_face max show_text move_to range Context bytearray set_font_face fill set_font_size ImageSurface int rectangle array len CairoContext FORMAT_ARGB32 get_context ImageSurface create_layout Context set_font_description FORMAT_ARGB32 set_text set_source_rgb get_data set_size max CairoContext rotate SCALE move_to create_layout range Context bytearray zoom show_layout FontDescription fill get_pixel_extents ImageSurface int rectangle set_markup array len int gaussian_filter distance_transform_edt min binary_erosion mean shape prod sum max binary_dilation list randn transpose shape meshgrid array range gaussian_filter FONT_SLANT_NORMAL FORMAT_ARGB32 FONT_WEIGHT_BOLD set_source_rgb create_cairo_font_face_for_file get_data select_font_face show_text move_to Context bytearray set_font_face fill set_font_size ImageSurface FONT_SLANT_OBLIQUE rectangle FONT_SLANT_ITALIC FONT_WEIGHT_NORMAL array | # mptv Tools necessary to perform a multi-fold pretrained voting approach utlizing OCRopus in order to significantly improve the achievable OCR error rate. Please note that this code was only used to provide a proof of concept for the publications listed below including the output of several (unnecessary) intermediate results. In order to efficiently implement the concepts into an OCR workflow it is **highly** recommend to use Christoph Wick's (@chwick) [Calamari](https://github.com/Calamari-OCR/calamari) (see [4]) which is under active development and natively supports cross fold training, confidence voting and pretraining. ## Installing #### Clone Repository `git clone https://github.com/chreul/mptv.git` #### Setup and Activate Virtual Enviroment `python -m pip install --user virtualenv` `python -m virtualenv path/to/venv` | 1,708 |
chrisbahnsen/aau-rainsnow-eval | ['rain removal', 'instance segmentation', 'semantic segmentation'] | ['Rain Removal in Traffic Surveillance: Does it Matter?'] | Segmentation/cdNet/processFolderOwnDataset.py InstanceSegmentation/summarizeParsedResults.py InstanceSegmentation/parseResults.py Segmentation/statAggregator/statAggregator.py InstanceSegmentation/summarizeParsedResults-sequenceAverage.py FeatureTrackingAccuracy/code/processFeatureTrackingOwnDataset.py Segmentation/cdNet/processFolder.py Segmentation/statAggregator/statParser.py FeatureTrackingAccuracy/code/processFeatureTrackingCDNet.py InstanceSegmentation/dataset_catalog.py InstanceSegmentation/copyJsonForRainSnow.py FeatureTrackingAccuracy/featureStatAggregator.py Segmentation/cdNet/processFolderOriginal.py Segmentation/cdNet/Stats.py FeatureTrackingAccuracy/code/featureStatAggregator.py InstanceSegmentation/boxPlotFromResults.py FeatureTrackingAccuracy/statParser.py InstanceSegmentation/cocoTools.py Segmentation/evaluateObjectSize/evaluateObjectSize.py Segmentation/evaluateProcessingSpeed/evaluateProcessingSpeed.py readBasicStatFile statAggregator readDetailedStatFile writeStatTables RepresentsFloat getStatsFromSubfolder convertCommaInFile formatSignificantDigits highlightBestResults combineTwoFiles computeSceneTotals toTex evaluateProcentualDifference convertCommaInFile readStatFile statAggregator getStats writeStatTables RepresentsFloat getStatsFromSubfolder convertCommaInFile getDirectories isValidRootFolder isValidFrameFolder deleteIfExists computeFeatureTrackingAccuracy isValidVideoFolder main processFolder getDirectories isValidRootFolder isValidFrameFolder deleteIfExists computeFeatureTrackingAccuracy isValidVideoFolder main processFolder boxPlotFromResults generateSplitSequenceTranslator removeInstancesInsideDontCare removeIgnoredCategories constrainImageRange maskInsideMask bboxInsideMask splitJsonSequences getDirectories isValidRootFolder readCMFile compareWithGroungtruth isValidResultsFolder deleteIfExists isValidVideoFolder main processFolder getDirectories isValidRootFolder readCMFile compareWithGroungtruth deleteIfExists isValidVideoFolder main processFolder getDirectories isValidRootFolder readCMFile compareWithGroungtruth isValidResultsFolder deleteIfExists isValidVideoFolder main processFolder sumListVectors cmToText Stats getStats mean addVectors writeComment computeObjectAppearance evaluateObjectsPerFrame getObjectsPerFrameFromSubFolder isGroundTruth computeObjectSize getStats writeStatTables evaluateObjectSize getObjectSizesFromSubFolder convertCommaInFile evaluateProcessingSpeed getProcessingSpeedsFromSubFolder isRainRemovalAlgorithm writeStatTables RepresentsFloat computeProcessingSpeed readStatFile statAggregator getStats writeStatTables RepresentsFloat getStatsFromSubfolder convertCommaInFile formatSignificantDigits toTex evaluateProcentualDifferenceSceneAverage evaluateProcentualDifference convertCommaInFile float load dump writeStatTables getStatsFromSubfolder open readBasicStatFile join format readDetailedStatFile isdir print listdir int TrackingParams len open float range split int list TrackingParams zip open float append split writer items list str deepcopy trackingDuration isinstance sort writerow close append maxPixelError method open replace open replace open replace open readStatFile exists basename dirname normpath open split print getStats split float convertCommaInFile print processFolder getDirectories join isValidFrameFolder deleteIfExists computeFeatureTrackingAccuracy isValidVideoFolder call getDirectories set remove exists subplots grid axis column_stack str list len savefig setp append range replace plot set_xticklabels boxplot Polygon set_axisbelow print text add_patch dict set_ylabel split dict list range isinstance append int str isinstance print dict append list basename isinstance dict append range join list basename items decode isinstance str replace dict COCO IMREAD_GRAYSCALE append imread annToMask zeros rectangle shape float sum update isValidResultsFolder compareWithGroungtruth addCategories Stats writeCategoryResult writeOverallResults call join deleteIfExists print join listdir len write load writeStatTables open load writeStatTables open join format isdir isGroundTruth print computeObjectSize listdir computeObjectAppearance join format isdir isGroundTruth print listdir join COLOR_BGR2GRAY CHAIN_APPROX_SIMPLE print findContours RETR_LIST countNonZero isfile imread listdir inRange cvtColor len join COLOR_BGR2GRAY CHAIN_APPROX_SIMPLE findContours RETR_LIST isfile imread listdir inRange cvtColor len load writeStatTables open join format isdir print isRainRemovalAlgorithm computeProcessingSpeed listdir dirname join getmtime sort isfile listdir | ## Rain Removal in Traffic Surveillance: Does it Matter? This repository contains the evaluation code and scripts for the article *Rain Removal in Traffic Surveillance: Does it Matter?* The article evaluates the impact of rain removal algorithms on subsequent segmentation, instance segmentation, and feature tracking algorithms. The evaluation scripts of these algorithms are placed in their respective folders. As this is a collection of research code, one might find some occational rough edges. We have tried to clean up the code to a decent level but if you encounter a bug or a regular mistake, please report it in our issue tracker. ### Rain removal algorithms Our implementation of the rain removal algorithm proposed by Garg and Nayar is found at [our sister repository](https://bitbucket.org/aauvap/rainremoval/src/master/) at Bitbucket. Here, you will also find references to other rain removal algorithms that we have evaluated in our survey paper. ### The AAU RainSnow dataset The evaluation code is built around the AAU RainSnow dataset which is published on [Kaggle](https://www.kaggle.com/aalborguniversity/aau-rainsnow/). ### Acknowledgements Please cite the following paper if you use our evaluation code: | 1,709 |
chrisbahnsen/aau-virada | ['rain removal'] | ['Is it Raining Outside? Detection of Rainfall using General-Purpose Surveillance Cameras'] | Analysis/analyseDataset.py Analysis/evaluate3DCNN.py Bossu/analyseParameterSweep.py Bossu/BossuVideoAnalysis.py 3DCNN/Read_tfEvents.py Analysis/metrics.py 3DCNN/models/C3D.py Analysis/evaluateBossu.py 3DCNN/plotCNNData.py Bossu/gofAndSurfaceSweep.py Analysis/RainGauges.py Analysis/generate_videoCSV.py Analysis/generate_labels.py Bossu/BossuCSVAnalysis.py 3DCNN/dataloading/dataloaders.py 3DCNN/main.py Analysis/utils.py 3DCNN/models/__init__.py validate load_model AverageMeter accuracy test save_checkpoint systemInfo main train plotCNNData plotGraph read_tfEvents RandomVidSampler RandomSubsetVidSampler NVVL get_file_info get_label_file SequentialVidSampler get_label_minutes get_loader load_labels c3d C3D analyseDataset evaluate3DCNN analyze_3DCNN_predictions evaluateBossu analyze_Bossu_predictions generateLabels saveJSON getGaugeValues Crossing2Files generate_csv Crossing1Files calculate_informedness calculate_rates calculate_markedness calculate_predictive_value calculate_MCC calculate_accuracy calculate_classification_metrics calculate_type_errors calculate_precision_recall calculate_AUC calculate_classification_metrics_full_dataset calculate_F_score RainGauges Recording toRadians RainMeasurement Location monthToInt get_frame_label plot_graph load_labels read_yaml make_metrics_plots analyseBossuParameterSweep plot_GOF_certainty plot_avg_rain_intensity plot_rain_detection plot_raw_rain_intensity analyseBossuCSVData plot_distribution_params videoAnalysis gofAndSurfaceSweep gofAndSurfaceSweepFile validate test_batchsize batchsize SGD frames get_loader save_checkpoint arch cuda max seed str step_size StepLR load_model add_text set_device strftime epochs device_count load_state_dict sleep range is_cropped format SummaryWriter test files start_epoch lr mkdir resume manual_seed root vars gamma listdir isoformat load join time add_scalar print now output parameters systemInfo isfile val_batchsize train step gpu len model zero_grad cuda view len update val format size item enumerate time criterion backward Variable print AverageMeter accuracy step add_scalar eval AverageMeter eval AverageMeter copyfile join save view print str format sum format print call device_count __version__ version current_device arange plot print xlabel grid ylabel tight_layout title clf savefig figure legend len plotGraph value print len simple_value tag append summary_iterator basename int min floor ceil len print list format keys join NVVL print test label_json root load_labels len C3D load_labels iterrows asarray get_frame_label astype calculate_classification_metrics set mean calculate_AUC append bool join format basename load_labels zeros makedirs asarray get_frame_label astype calculate_classification_metrics set mean calculate_AUC append bool range len join format basename load_labels zeros makedirs RainGauges int getNearestRainData list asarray second strptime timedelta ceil Location keys perSecond values VideoCapture CAP_PROP_FPS CAP_PROP_FRAME_COUNT saveJSON iterrows basename ones tolist get format astype isfinite int join print makedirs getGaugeValues read_csv len format int isdigit format split lower logical_not len calculate_informedness calculate_rates calculate_markedness calculate_predictive_value calculate_MCC calculate_accuracy calculate_type_errors calculate_precision_recall append calculate_F_score calculate_informedness calculate_rates calculate_markedness calculate_predictive_value calculate_MCC calculate_accuracy calculate_precision_recall calculate_AUC append enumerate join plot xlabel grid close ylabel title clf ylim figure legend savefig xlim range asarray plot_graph int min floor ceil len print join listdir makedirs subplot format suptitle plot xlabel min grid title clf array figure legend savefig append xlim max values subplot list str xlabel grid min title clf scatter figure legend savefig xlim sum max values len list plot xlabel grid min clf savefig figure legend xlim max range values subplots grid clf set_major_formatter xticks max list set_major_locator savefig legend range MaxNLocator plot set_xlim xlim FuncFormatter int total_seconds axes xlabel min set_ylabel figure max plot xlabel grid min clf savefig figure legend xlim array values RainGauges getNearestRainData abspath plot_raw_rain_intensity perSecond read_yaml values list plot_GOF_certainty plot_avg_rain_intensity dirname append format asarray strptime size mean timedelta Location keys plot_distribution_params int join print reshape plot_rain_detection read_csv makedirs append str dirname abspath CV_64F join KalmanFilter format eye array read_yaml join time format print range gofAndSurfaceSweepFile listdir read_csv makedirs | ## Is it Raining Outside? Detection of Rainfall using General-Purpose Surveillance Cameras This repository contains the code and scripts for the paper *Is it Raining Outside? Detection of Rainfall using General-Purpose Surveillance Cameras* The paper investigates rain detection with general-purpose surveillance cameras. The previous state-of-the-art method is compared with a 3D Convolutional Neural Network, on data from two different traffic crossings recorded in Aalborg, Denmark. As this is a collection of research code, one might find some occasional rough edges. We have tried to clean up the code to a decent level but if you encounter a bug or a regular mistake, please report it in our issue tracker. ### The AAU Visual Rain Dataset (VIRADA) The evaluation code is built around the AAU VIRADA dataset which is published at [Zenodo](https://zenodo.org/record/4715681). ### Code references The [NVidia Video Loader (NVVL)](https://github.com/NVIDIA/nvvl) framework was used for loading video snippets efficiently. The [Video Super-Resolution](https://github.com/NVIDIA/nvvl/tree/master/examples/pytorch_superres) example was used as the basis for the provided docker file The pytorch implementation of the basis C3D network was derived from [David Abati](https://github.com/DavideA/c3d-pytorch)'s implementation. The Bossu implementation utilizes OpenCV v. 3.1.0 | 1,710 |
chrisdxie/uois | ['instance segmentation', 'semantic segmentation'] | ['Unseen Object Instance Segmentation for Robotic Environments'] | src/data_loader.py src/networks.py src/losses.py src/util/utilities.py src/util/munkres.py src/data_augmentation.py src/train.py src/segmentation.py src/util/flowlib.py src/cluster.py src/evaluation.py MeanShift gaussian_kernel euclidean_distances GaussianMeanShift standardize_image dropout_random_ellipses add_noise_to_depth random_add rotate translate random_morphological_transform array_to_tensor add_noise_to_xyz random_ellipses random_horizontal_flip random_translation unstandardize_image random_rotation random_cut random_color_warp Tabletop_Object_Dataset get_Synth_RGBO_train_dataloader worker_init_fn Synthetic_RGB_Objects_Dataset get_TOD_train_dataloader get_TOD_test_dataloader get_RGBO_train_dataloader RGB_Objects_Dataset multilabel_metrics boundary_overlap ClusterLossWeighted CELossWeighted CELossWeightedMasked WeightedLoss SmoothL1LossWeighted create_M_GT BCEWithLogitsLossWeighted maxpool2x2 Conv2d_GN_ReLU ESPModule Upsample_Concat_Conv2d_GN_ReLU_Multi_Branch UNet_Decoder UNetESP_Encoder Conv2d_GN_ReLUx2 UNetESP_Decoder UNet_Encoder Upsample_Concat_Conv2d_GN_ReLU DepthSeedingNetwork DSNWrapper NetworkWrapper UOISNet3D RRNWrapper RegionRefinementNetwork hill_climb_one_iter Trainer DSNTrainer smart_random_sample_indices RRNTrainer make_color_wheel disp_to_flowfile read_flow read_disp_png read_image scale_image flow_error segment_flow evaluate_flow warp_image flow_to_image write_flow compute_color evaluate_flow_file show_flow visualize_flow read_flow_png munkres_match Munkres make_cost_matrix print_matrix visualize_segmentation mask_to_tight_box_pytorch get_color_mask largest_connected_component compute_xyz imwrite_indexed subplotter AverageMeter torch_to_numpy mask_to_tight_box_numpy imread_indexed seg2bmap concatenate_spatial_coordinates mask_to_tight_box build_matrix_of_indices float array getRotationMatrix2D gamma copy normal astype copy shape uniform resize randint T ellipse zeros_like tuple astype copy choice randint gamma range poisson range astype float32 array uint8 COLOR_RGB2HLS zeros_like random astype float32 shape COLOR_HLS2RGB cvtColor split copy int print getStructuringElement mean MORPH_ELLIPSE erode beta dilate randint mask_to_tight_box round zeros_like tuple where mask_to_tight_box max build_matrix_of_indices shape range poisson astype copy mean gamma T ellipse multivariate_normal print cov randint int print translate beta randint mask_to_tight_box round max print where rotate mean shape uniform build_matrix_of_indices int print copy shape uniform mask_to_tight_box round int print copy choice shape uniform logical_or mask_to_tight_box round seed Tabletop_Object_Dataset copy Tabletop_Object_Dataset copy RGB_Objects_Dataset Synthetic_RGB_Objects_Dataset uint8 disk astype logical_and seg2bmap dilate count_nonzero compute Munkres T tuple logical_and copy isnan int64 unique zeros sum max boundary_overlap enumerate unique zeros_like int randperm unique ceil zeros cat sum gaussian_kernel mm flow_to_image imshow show read_flow show arctan2 hsv_to_rgb pi sqrt imshow flow_to_image zeros max print close float32 int32 resize fromfile open list Reader zeros asDirect range len tofile array close open zeros mean sqrt eps min sqrt repeat compute_color max flow_error read_flow flow_error list Reader zeros asDirect range len tofile close dstack zeros array open array open minimum griddata uint8 concatenate reshape astype maximum imshow logical_or zeros range min astype float32 array max uint8 arctan2 size astype pi logical_not isnan shape sqrt floor zeros make_color_wheel range zeros transpose floor arange staticmethod append int print write log10 max compute Munkres astype zeros sum range uint8 astype array unique get_cmap max shape cat tile device to build_matrix_of_indices addWeighted uint8 drawContours findContours astype copy shape array unique zeros get_cmap CHAIN_APPROX_NONE max range RETR_CCOMP array open fromarray reshape putpalette save ravel transpose nonzero nonzero radians tan stack flipud build_matrix_of_indices argmax uint8 drawContours T zeros_like RETR_EXTERNAL findContours astype ascontiguousarray CHAIN_APPROX_NONE connectedComponents count_nonzero uint8 astype range transpose numpy range subplot imshow title figure range len | # Unseen Object Instance Segmentation for Robotic Environments <img src="gifs/pipeline.gif" height="200" /> This is a PyTorch-based implementation of our network, UOIS-Net-3D, for unseen object instance segmentation. Our instance segmentation algorithm utilizes a two-stage method to explicitly leverage the strengths of depth and RGB separately for stronger instance segmentation. Surprisingly, our framework is able to learn from synthetic RGB-D data where the RGB is non-photorealistic. Details of the algorithm can be found in our arXiv paper: [Unseen Object Instance Segmentation for Robotic Environments](https://arxiv.org/abs/2007.08073)<br/> [Christopher Xie](https://chrisdxie.github.io), [Yu Xiang](https://yuxng.github.io), [Arsalan Mousavian](https://cs.gmu.edu/~amousavi/), [Dieter Fox](https://homes.cs.washington.edu/~fox/) <br/> IEEE Transactions on Robotics (T-RO), 2021. ## Installation We highly recommend setting up a virtual environment using [Anaconda](https://www.anaconda.com/distribution/). Here is an example setup using these tools: ```bash git clone https://github.com/chrisdxie/uois.git | 1,711 |
chrisjbryant/errant | ['grammatical error correction'] | ['Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction'] | errant/commands/parallel_to_m2.py errant/commands/m2_to_m2.py errant/alignment.py errant/en/classifier.py setup.py errant/annotator.py errant/__init__.py errant/en/merger.py errant/commands/compare_m2.py errant/en/lancaster.py errant/edit.py Alignment Annotator Edit load get_cor_and_edits noop_edit main simplify_edits parse_args main noop_edit parse_args preceded_by_aux load_pos_map only_orth_change get_edit_info exact_reordering classify load_word_list get_two_sided_type get_one_sided_type LancasterStemmer get_rule_edits is_punct process_seq char_cost merge_edits import_module print parse_args load add_argument add_mutually_exclusive_group ArgumentParser append int split append sorted len split o_toks c_toks get_two_sided_type get_one_sided_type append dep_ get_edit_info issubset preceded_by_aux isalpha only_orth_change issubset distance get_edit_info exact_reordering lower_ max len join sorted children head startswith process_seq list Edit orig groupby align_seq cor append combinations list join sort set sub range len | # ERRANT v2.3.3 This repository contains the grammatical ERRor ANnotation Toolkit (ERRANT) described in: > Christopher Bryant, Mariano Felice, and Ted Briscoe. 2017. [**Automatic annotation and evaluation of error types for grammatical error correction**](https://www.aclweb.org/anthology/P17-1074/). In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vancouver, Canada. > Mariano Felice, Christopher Bryant, and Ted Briscoe. 2016. [**Automatic extraction of learner errors in ESL sentences using linguistically enhanced alignments**](https://www.aclweb.org/anthology/C16-1079/). In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. Osaka, Japan. If you make use of this code, please cite the above papers. More information about ERRANT can be found [here](https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-938.html). In particular, see Chapter 5 for definitions of error types. # Overview The main aim of ERRANT is to automatically annotate parallel English sentences with error type information. Specifically, given an original and corrected sentence pair, ERRANT will extract the edits that transform the former to the latter and classify them according to a rule-based error type framework. This can be used to standardise parallel datasets or facilitate detailed error type evaluation. Annotated output files are in M2 format and an evaluation script is provided. ### Example: **Original**: This are gramamtical sentence . **Corrected**: This is a grammatical sentence . | 1,712 |
christeefy/Novel-Techniques-for-PTR-FD | ['time series'] | ['An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery'] | packages/eccm/__init__.py packages/utils.py packages/eccm/models/__init__.py packages/granger_net/private/utils.py packages/causality_viz.py packages/eccm/models/ccm/__init__.py packages/granger_causality/__init__.py packages/granger_net/private/gpu/utils.py packages/metrics/metrics.py packages/granger_causality/granger_causality.py packages/data_generation.py packages/metrics/viz.py packages/metrics/utils.py packages/eccm/models/ccm/utils.py packages/eccm/models/eccm/__init__.py packages/granger_net/__init__.py packages/eccm/viz.py packages/eccm/graph.py packages/eccm/private/utils.py packages/granger_net/core/analysis.py packages/granger_net/private/regularizers.py packages/load_utils.py packages/eccm/models/eccm/utils.py packages/eccm/models/eccm/eccm.py packages/__init__.py packages/metrics/__init__.py packages/simulation_runner.py packages/eccm/models/ccm/ccm.py packages/granger_net/models/granger_net.py packages/granger_net/core/__init__.py causal_heatmap _has_autocorrelation causal_graph generate_ex3 rossler generate_ex2 predator_prey_4_species generate_ex1 nonisothermal_CSTR eastman load_results save_results _single_pass_eval evaluate_simulations is_interactive curry in_ipynb Graph Node visualise_predictions ccm parse_arguments _predict_target _causality_index ccm_one_way _kNN _euclidean_dist eccm _eccm_base _parse_arguments _URC_cross_map_lags _calc_significance _peak_causality_coordinates generate_delayed_df _create_dataset_vector_output granger_causality _calc_RSS _parse_arguments granger_net _parse_arguments _define_vars _build_tower build_graph hierarchical_L2_regularizer L2_regularizer L1_regularizer group_L2_regularizer hierarchical_L1_regularizer extract_weights_tf normalize_in_place create_dataset extract_weights generate_batch_size_scheduler average_gradients create_vars_on_CPU get_truncation_idx get_num_gpus fault_detected PRF MSE AUCPR metrics_list bounds ravel_without_diag _thresholds PR_curve clim where tick_top xticks yticks show list ylabel imshow shape savefig gca range format set_label_position mkdir zip _has_autocorrelation norm T xlabel print rc figure len norm node edge zipper Digraph where render max norm seed int normal uniform zeros DataFrame range seed int normal DataFrame range uniform randint abs zeros odeint DataFrame seed list dict uniform zip range read_csv mkdir savez list tqdm_func isinstance DataFrame to_csv dict Path mkdir zip append _single_pass_eval range norm list fault_detected is_fault datagen MSE AUCPR causality_func values get_ipython update iplot append_trace Scattergl make_subplots Layout append range parse_args add_argument ArgumentParser generate_delayed_df ccm_one_way expand_dims _calculate_weights _kNN _predict_target values parse_args add_argument ArgumentParser list generate_delayed_df reversed ccm_one_way append range list columns permutations set_index display prune print _peak_causality_coordinates dropna Graph adj_mat _eccm_base DataFrame range enumerate len append columns concat idxmin items _URC_cross_map_lags columns list DataFrame Series _calc_significance round append idxmax len items list from_tuples reset_index concat append DataFrame range len vstack array _calc_RSS slice delete shape _create_dataset_vector_output expand_dims range zeros len norm columns arange tqdm_func get_num_gpus now strftime normalize_in_place get_truncation_idx array create_dataset reset_default_graph max enumerate len _define_vars AdamOptimizer get_variable as_list reshape reduce_sum abs range as_list reshape square reduce_sum range arange len shuffle stack vstack array values drop insert reshape reshape transpose norm mean columns std append concat reduce_mean zip norm ravel_without_diag norm sum where ravel_without_diag metrics_list bounds size log sum range len append norm len list min max arange metrics_list bounds size Scatter Layout Figure sum array | # Novel Techniques for Process Topology Reconstruction and Fault Diagnosis (PTR-FD) This repository contains the codebases for the three PTR-FD algorithms investigated in my MASc thesis. The three algorithms include: 1. Granger Causality [[1](https://www.jstor.org/stable/1912791)] 2. Granger Net [[2](https://arxiv.org/abs/1711.08160)] 3. Extended Convergent Cross-Mapping [[3](http://science.sciencemag.org/content/338/6106/496), [4](https://www.nature.com/articles/srep14750)] ## Setup To use this repo, first clone it. Next, install external module dependencies: ``` pip install -r requirements.txt | 1,713 |
chshin10/epinet | ['depth estimation', 'data augmentation'] | ['EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images'] | epinet_fun/util.py epinet_fun/rotation_augmentation.py epinet_fun/func_makeinput.py EPINET_train.py epinet_fun/func_generate_traindata.py epinet_fun/func_pfm.py epinet_fun/func_savedata.py EPINET_plusX_9conv22_save.py epinet_fun/func_epinetmodel.py threadsafe_generator myGenerator threadsafe_iter layer1_multistream layer3_last define_epinet layer2_merged generate_traindata512 data_augmentation_for_train generate_traindata_for_train make_multiinput make_epiinput make_epiinput_lytro write_pfm read_pfm display_current_output rotation_augmentation load_LFdata read_pfm data_augmentation_for_train generate_traindata_for_train int Reshape Sequential add Conv2D range Activation BatchNormalization Sequential add Conv2D range Activation BatchNormalization Sequential add Conv2D Activation range concatenate RMSprop Model summary Input compile int abs squeeze rand astype randint float32 choice zeros sum array range transpose rand squeeze copy pow randint rot90 range minimum int squeeze astype float32 maximum zeros array range len zeros imread float32 zeros imread float32 int list make_epiinput_lytro make_epiinput len asarray byteorder print flatten flipud int uint8 reshape squeeze size transpose zeros abs imsave len transpose squeeze copy randint rot90 range uint8 read_pfm print float32 zeros imread range | # EPINET: A Fully-Convolutional Neural Network using Epipolar Geometry for Depth from Light Field Images EPINET: A Fully-Convolutional Neural Network using Epipolar Geometry for Depth from Light Field Images Changha Shin, Hae-Gon Jeon, Youngjin Yoon, In So Kweon and Seon Joo Kim IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2018 https://arxiv.org/pdf/1804.02379.pdf Contact: [email protected] # Environments - Python3.5.2, Anaconda 4.2.0 (64-bit), Tensorflow 1.6.0 - 1.12.0 - `pip install imageio` # Train the EPINET | 1,714 |
chu-data-lab/GOGGLES | ['few shot learning'] | ['GOGGLES: Automatic Image Labeling with Affinity Coding'] | goggles/affinity_matrix_construction/image_AF/neural_network_AFs.py goggles/affinity_matrix_construction/construct.py goggles/affinity_matrix_construction/image_AF/pretrained_models/vgg.py goggles/inference_models/hierarchical_model.py goggles/test/test.py goggles/utils/dataset.py goggles/utils/constants.py setup.py goggles/inference_models/semi_supervised_models.py goggles/__init__.py goggles/theory/theory.py goggles/inference_models/cluster_class_mapping.py construct_image_affinity_matrices _get_most_activated_patch_idxs_from_channels _get_score_matrix_for_image nn_AFs _get_most_activated_channels _get_patches Context Vgg16 solve_mapping construct_D infer_labels set_prob_dev_values pmf_bernoulli update_prob_using_mapping SemiGMM ConvergenceMeter SemiBMM DevSetTheory n_given_sum generate_d_matrix GogglesDataset nn_AFs extend normalize size max sorted list numpy zip list get_model_output _get_most_activated_patch_idxs_from_channels size numpy zip _get_most_activated_channels dataset _get_patches range append len join Vgg16 T _get_score_matrix_for_image savez _make_cuda print squeeze min len Context append trange dataset range makedirs T set zeros sum range len DevSetTheory linear_sum_assignment print construct_D seed SemiGMM hstack set tqdm SemiBMM append fit_predict len argmax solve_mapping int print squeeze multinomial trace append sum array range | # GOGGLES GOGGLES is a system for automatically generating probabilistic labels for image datasets based on the affinity coding paradigm. The paper can be found at https://arxiv.org/abs/1903.04552  ## Installation ```bash git clone https://github.com/chu-data-lab/GOGGLES.git cd GOGGLES pip3 install . ``` ## Example Usage | 1,715 |
chunfengc/ASNet | ['adversarial attack'] | ['Active Subspace of Neural Networks: Structural Analysis and Universal Attacks'] | datasets/dataloader_mnist_fashion.py model/ResNet/Train_ResNet110_CIFAR100.py ASNet/ASNet.py experiment/CIFAR100_oneclass_attack.py ASNet/ASModel.py experiment/CIFAR100_allclass_attack.py experiment/CIFAR10_Resnet110.py ASNet/AS_Attack.py experiment/CIFAR10_VGG19_bn.py ASNet/UAP.py datasets/dataloader_cifar.py experiment/MNIST_allclass_attack.py experiment/CIFAR10_cross_train_samples_attack.py model/model_MNIST.py model/vgg.py model/resnet.py experiment/CIFAR100_VGG19_bn.py model/MNIST/model_MNIST.py ASNet/utils.py experiment/CIFAR10_allclass_attack.py experiment/MNIST_oneclass_attack.py experiment/CIFAR100_Resnet110.py experiment/CIFAR10_oneclass_attack.py ASNet/PCEModel.py experiment/CIFAR10_cross_model_attack_one_class.py ASNet/FineTuning.py compute_grad_matrix FrequentDirections ASModel get_AS_transform_input_smalldataset randomized_svd streamASEmbedding get_ASModel_FD ASNET BasisLayer AS_attack_input_small_dataset_sz proj_v AS_attack_update_sz train_l1 train_kd soft_threshold compute_loss train PCEModel indexset universal_perturbation proj_lp compute_grad_matrix2 deepfool PossibleCutIdx Total_param compute_Z_AS_space get_seq_model_resnet get_seq_model_resnet_nomodule Vectorize Total_flops_sparse Total_flops get_seq_model Total_param_sparse active_eigs trainloader_cifar_vgg19 testloader_cifar_vgg19 dataset_index testloader_resnet UnNormalize trainloader_resnet load_mnist_fashion_test test train_and_save train load_mnist_fashion_train load_model validate_error Net train test resnet110 resnet20 ResNet LambdaLayer resnet44 test resnet1202 resnet56 resnet32 _weights_init BasicBlock vgg19 VGG vgg16_bn vgg19_bn vgg11_bn make_layers vgg11 vgg13 vgg13_bn vgg16 Net validate AverageMeter accuracy save_checkpoint main train children compute_grad_matrix hasattr view model ASModel t requires_grad_ eval repeat randomized_svd to loss device get list defaultdict print ASModel forward_backward t streamASEmbedding keys data backward contiguous zero_ zeros range zero_gradients int topk view clone shape zeros abs model proj_v repeat nonzero to range len norm format print clone get_AS_transform_input_smalldataset repeat AS_attack_update_sz to range format print dataset eval to sum max enumerate len abs relu backward model param_groups len zero_grad dataset to step cross_entropy enumerate data named_modules backward model param_groups len zero_grad dataset soft_threshold to step cross_entropy enumerate backward param_groups log_softmax train step zero_grad len dataset teacher eval softmax to student cross_entropy enumerate ones range cat data backward zero_ zeros range zero_gradients int norm inf abs f clone grads argsort shape requires_grad_ to argmax range norm format print proj_lp min clone to double range deepfool norm min sign device to abs named_modules m to named_modules m to named_modules children list Sequential bn1 AvgPool2d linear Sequential Vectorize ReLU conv1 bn1 AvgPool2d linear Sequential Vectorize ReLU conv1 append int named_modules zeros to cat enumerate sum range len Compose DataLoader Normalize dataset_index CIFAR10 CIFAR100 DataLoader CIFAR100 dataset_index CIFAR10 print DataLoader dataset_index CIFAR10 CIFAR100 print DataLoader dataset_index CIFAR10 CIFAR100 FashionMNIST FashionMNIST test SGD parameters save train range state_dict format print item format print eval dataset len to enumerate resnet20 testloader_cifar_vgg19 device trainloader_resnet resnet110 list OrderedDict vgg19_bn load_state_dict to format resnet44 eval resnet56 is_available load items trainloader_cifar_vgg19 testloader_resnet weight kaiming_normal_ __name__ Conv2d make_layers VGG make_layers VGG make_layers VGG make_layers VGG make_layers VGG make_layers VGG make_layers VGG make_layers VGG validate SGD MultiStepLR DataLoader save_checkpoint save_dir max resnet110 half epochs load_state_dict parse_args to CIFAR100 range format param_groups start_epoch resume Normalize lr load evaluate print parameters isfile train step makedirs update time criterion float AverageMeter size half update time format criterion model Variable float size AverageMeter print half eval item to enumerate len save topk size t eq mul_ expand_as append to sum max | # ASNet Code for Paper Chunfeng Cui, Kaiqi Zhang, Talgat Daulbaev, Julia Gusak, Ivan Oseledets, and Zheng Zhang. "Active Subspace of Neural Networks: Structural Analysis and Universal Attacks". accepted by SIAM Journal on Mathematics of Data Science (SIMODS) Dependence: -Torch -Numpy | 1,716 |
chungkwong/mathocr-tap | ['optical character recognition'] | ['Stroke extraction for offline handwritten mathematical expression recognition'] | work/src/crohmelib/bin/evalSymbIsole.py work/src/lgeval/src/sumDiff.py work/src/lgeval/src/compareTools.py work/src/lm.py work/src/lgeval/src/testNewSeg.py work/src/lgeval/src_py2/mergeLG.py work/src/lgeval/src_py2/statlgdb.py work/src/grammar.py work/src/lgeval/src_py2/smallGraph.py work/src/lgeval/src_py2/lgio.py work/src/lgeval/src/confHists.py work/src/optimizers.py work/src/lgeval/src/evallg.py work/src/lgeval/src_py2/sumDiff.py work/src/compute-wer.py work/src/lgeval/src_py2/lg2NE.py work/src/crohmelib/bin/removeGT.py work/src/gen_voc.py work/src/lgeval/src/lg2NE.py work/src/lgeval/src/metricDist.py work/src/fix_tex.py work/src/lgeval/src/lg2OR.py work/src/lgeval/src_py2/lg.py work/src/convert2symLG/process_mml.py work/src/lgeval/src_py2/testNewSeg.py work/src/lgeval/src/mergeLG.py work/src/train_nmt.py work/src/crohmelib/bin/segGenerator.py work/src/lgeval/src/lgfilter.py work/src/lgeval/src_py2/sumMetric.py work/src/lgeval/src_py2/compileLabels.py work/src/lgeval/src_py2/evallg.py work/src/train_nmt_weightnoise.py work/src/lgeval/src_py2/lg2OR.py work/src/lgeval/src_py2/SmGrConfMatrix.py work/src/convert2symLG/update_nodeTags.py work/src/lgeval/src/sumMetric.py work/src/lgeval/src_py2/testlg.py work/src/lgeval/src/lg2txt.py work/src/nmt_weightnoise.py work/src/lgeval/src/compileLabels.py work/src/lgeval/src/lgio.py work/src/lgeval/src_py2/lgfilter.py work/src/lgeval/src/statlgdb.py work/src/lgeval/src/lg.py work/src/lgeval/src/SmGrConfMatrix.py work/src/lgeval/src_py2/compareTools.py work/src/lgeval/src_py2/lg2txt.py work/src/lgeval/src_py2/metricDist.py work/src/crohmelib/bin/inkml.py work/src/nmt.py work/src/translate.py work/src/data_iterator.py work/src/lgeval/src_py2/confHists.py work/src/lgeval/src/lg2dot.py work/src/lgeval/src/smallGraph.py work/src/lgeval/src/testlg.py work/src/lgeval/src_py2/lg2dot.py cmp_result process loadFeature fopen loadAlign dataIterator normalize dataIterator_valid fix_tex gen_voc findStart parse isParseFinished load_dict isParseFailed verify compileGrammar loadGrammar parseNext parseStart findFollow test load_data FormulaSequence play load_language_model train predict build_sampler conv_norm_weight pred_probs gru_cond_layer param_init_gru param_init_gru_cond fflayer param_init_fflayer load_params init_params gen_sample zipp concatenate build_model gru_layer ortho_weight load_dict itemlist dropout_layer get_layer _p tanh norm_weight init_tparams unzip linear prepare_data train build_sampler conv_norm_weight pred_probs gru_cond_layer param_init_gru param_init_gru_cond fflayer param_init_fflayer load_params init_params gen_sample zipp concatenate build_model gru_layer ortho_weight load_dict itemlist dropout_layer get_layer _p apply_adative_noise tanh norm_weight init_tparams unzip linear prepare_data train itemlist_name sgd rmsprop itemlist adam adadelta_weightnoise adadelta main main main gen_sample normalizeSymbol build_seg_unit add_ID check_tags write_mml remove_unknown_tags norm_relTag write_lg update_LG_node_grouping writeCSS mergeSymbMat affRejectMat itResult affMatHTML main affRecoRatesInAccepted addOneError affMat affTopNHTML readGT affTopNMat sumDiag Inkml Segment main main generateRightSeg generateWrongSeg filteredMetric generateListErr intersectMetric synonymMetric defaultMetric main main runBatch mergeMaps mergeLabelLists getEdgesToNeighbours getEdgesBetweenThem Lg lgdot lgsegdot lgDag createRelPrimitivesLabel createLabelList lgPrimitiveDot dagSegmentRelString createSegPrimitivesLabel dagSegmentString main bipartiteNodeString getFirstElements primitiveNodeString main main translateStructure translate readMapFile main translateRelation main writeDiff writeMetrics writeCSVTuple fileListToLgs main main SmallGraph test SmDict ConfMatrixObject Counter ConfMatrix main getObjStruct writeCSS affMatHTML addOneError affMat main printTable meanStdDev printHist weightedMeanStdDev reportCoupleCSV intMetric histogramm main reportWMeanStdDev reportMeanStdDev fmeasure floatMetric testInvalidFiles testSummingGraphs testSubGraphCounting testEmpty testTreeEdges labelComparison testSegments testGenAllBG testshortCuts testStructCompare testInvertValues testLabelComparisons loadFiles main testMaxLabel testInput main filteredMetric generateListErr intersectMetric synonymMetric defaultMetric main main runBatch mergeMaps mergeLabelLists getEdgesToNeighbours getEdgesBetweenThem Lg main main translateStructure translate readMapFile main translateRelation main writeDiff writeMetrics writeCSVTuple fileListToLgs main main SmallGraph test main getObjStruct testInvalidFiles testSummingGraphs testSubGraphCounting testEmpty testTreeEdges labelComparison testSegments testGenAllBG testshortCuts testStructCompare testInvertValues testLabelComparisons loadFiles main testMaxLabel testInput main list min zeros range len format cmp_result write close float open hstack min sqrt vstack hypot max join print loadtxt strip close normalize open join print loadtxt strip close zeros open join list items sorted loadFeature print readlines close exit len loadAlign split append open join list items sorted loadFeature print readlines close exit len split append open append split set int print readlines close open split print readlines close open append split items list print add len items list print reversed set add union items findStart list addEntry print reversed reverse findFollow list len parseNext parseStart parse isParseFinished print readlines close strip open split append readlines close open save_model print load_dict fit_generator Model summary load_data compileGrammar loadGrammar Input compile len load_model print load_dict summary evaluate_generator load_data compileGrammar loadGrammar reshape array len load_model print f_next Model get_layer input_dim summary get_weights Input array set_weights readline load_model print load_dict predict items list set_value OrderedDict items list get_value binomial switch shape OrderedDict shared list items load items list warn svd randn rand ortho_weight randn set_subtensor ndim zeros sum range astype zip append max enumerate len norm_weight astype norm_weight astype ortho_weight concatenate dot scan alloc norm_weight conv_norm_weight concatenate astype ortho_weight dot scan _step alloc OrderedDict norm_weight range len zeros_like flatten max log RandomStreams set_subtensor dimshuffle tensor3 shape cast shared sum range categorical_crossentropy concatenate astype dropout_layer softmax matrix reshape float32 dict len vector switch function concatenate print reshape tensor3 mean shape cast softmax alloc matrix argmax max range len zip astype f_next log flatten copy enumerate f_init tile ceil argmax array range append len f_log_probs print prepare_data isnan mean append build_sampler function search dataIterator_valid open list sorted set_value f_grad_shared append shared load_params init_params f_update gen_sample range switch dump format zipp build_model readlines grad copy shuffle dataIterator mean sqrt close group pformat float items time join init_tparams savez unzip prepare_data write float32 system isnan zeros array scalar items list exp function astype get_value float32 OrderedDict itemlist cast zip append shared sum prod f_update_sigma f_apply_noise_to_weight f_update_miu f_copy_weight apply_adative_noise list function sqr get_value float32 sqrt zip append shared values function function function function print train isParseFinished mode parseNext build_sampler search compileGrammar loadGrammar dataIterator_valid open list RandomStreams append load_params init_params gen_sample range readlines close load_dict group load_language_model float flush items time init_tparams write system argsort zeros array len print findChildren name select decompose append str normalizeSymbol text findChild findAll enumerate normalizeSymbol close close print open startswith append write_lg norm_relTag split str sorted write str sorted write str format write range len str sorted format write keys max range len items list items list defaultdict copy remove mergeSymbMat strRate write set add sum keys values write write str defaultdict reader len write exit strip open str list reader strip write exit map open len mul affRejectMat affMat values str defaultdict mergeSymbMat exit map add affMatHTML addOneError affTopNHTML sum union sumDiag format writeCSS itResult copy set readGT affTopNMat keys stdout join min strip getInkMLwithoutGT Inkml str list write close strId getInkML Inkml label UI values open str list strokes write close sample open getInkML Inkml append UI Segment range len int generateWrongSeg generateRightSeg append len max set list map set max len set set sorted reader nlabels Lg elabels writeDiff incr strip ConfMatrix compareSegmentsStruct open str list nodes add ConfMatrixObject Lg edges compare close compareSubStruct set toHTML reader print write writeMetrics writeDiff labelMissingEdges filteredMetric intersectMetric runBatch compare writeMetrics items union combfn set items list deepcopy mergeLabelLists set union add set add set list sorted set add list set add list set sorted createLabelList list nlabels str sorted list createLabelList nlabels list bipartiteNodeString sorted segmentGraph list sorted createLabelList add set getFirstElements elabels sorted list createLabelList bipartiteNodeString getFirstElements elabels createSegPrimitivesLabel createLabelList sorted list createLabelList list createRelPrimitivesLabel separateTreeEdges dagSegmentRelString dagSegmentString segmentGraph lgdot hideUnlabeledEdges lgDag lgPrimitiveDot lgsegdot csv csvObject tuple sorted reader open list sorted tuple range len str list replace tuple write range len items list sorted replace translateStructure write translateRelation segmentGraph translate readMapFile separateTreeEdges str list write range len str write range len write writeCSVTuple range len reader strip close Lg open addWeightedLabelValues SmallGraph str fromStr toSVG iso printLG items list segmentGraph set Lg incr SmDict subStructIterator strftime toHTML difference float sum len float sum range len meanStdDev printTable printTable weightedMeanStdDev str write print format range len list print append printTable range printHist histogramm printTable intMetric reportCoupleCSV meanStdDev weightedMeanStdDev print csv Lg print loadFiles print loadFiles print str compare Lg str list print dict Lg compare print labelComparison str print error labelComparison Lg print str Lg segmentGraph print separateTreeEdges Lg str print addWeightedLabelValues Lg csv addWeightedLabelValues print Lg csv selectMaxLabels str print Lg csv BestBG range afficheDP print invertValues csv Lg str print subStructIterator compareSubStruct Lg incr SmDict subStructIterator print write compareSubStruct close ConfMatrixObject Lg ConfMatrix toHTML open compareSegmentsStruct testshortCuts testLabelComparisons testInput Bg keys keys keys keys keys | # Offline handwritten mathematical expression recognition via stroke extraction and TAP The purpose of this repository is to provide a trainable [online handwritten mathematical expression recognition system](https://github.com/JianshuZhang/TAP), which can be used with a [stroke extractor](https://github.com/chungkwong/mathocr-myscript) to do offline handwritten mathematical expression recognition. ## Usage 1. Ensure that Perl, Java, pandoc, [Theano](https://github.com/Theano/Theano) and [libgpuarray](https://github.com/Theano/libgpuarray) are installed 2. Clone this repository: `git clone 'https://github.com/chungkwong/mathocr-tap.git'` 3. Change directory: `cd mathocr-tap/work/src` 4. Train a model(optional, a pretrained model is included): `./train.sh && ./train_weightnoise.sh` 5. Test: `./test.sh` 6. Recognize your images: `./recognize.sh IMAGE_FILE IMAGE_FILE...` ## Structure | 1,717 |
chwilms/AttentionMask | ['object proposal generation'] | ['AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects'] | alchemy/utils/progress_bar.py caffe/python/caffe/classifier.py caffe/python/caffe/test/test_net.py evalCOCO.py caffe/examples/pycaffe/layers/pascal_multilabel_datalayers.py caffe/tools/extra/resize_and_crop_images.py caffe/examples/pycaffe/caffenet.py python_layers/data/boxSelectionLayerMP.py alchemy/datasets/coco.py alchemy/utils/image.py caffe/src/caffe/test/test_data/generate_sample_data.py alchemy/utils/timer.py alchemy/test_all.py alchemy/utils/load_config.py caffe/python/caffe/coord_map.py caffe/python/detect.py caffe/tools/extra/summarize.py caffe/python/caffe/detector.py caffe/python/draw_net.py caffe/examples/finetune_flickr_style/assemble_data.py spiders/coco_ssm_spider.py caffe/tools/extra/extract_seconds.py utils.py alchemy/tests/utils/mask.py alchemy/items/base_item.py caffe/python/caffe/io.py caffe/python/caffe/test/test_layer_type_list.py python_layers/data/layers.py caffe/python/caffe/__init__.py caffe/examples/pycaffe/layers/pyloss.py caffe/examples/web_demo/app.py alchemy/datasets/base_dataset.py trainAttentionMask.py alchemy/spiders/dataset_spider.py alchemy/utils/__init__.py caffe/python/classify.py alchemy/items/coco.py caffe/python/caffe/draw.py caffe/examples/pycaffe/tools.py alchemy/tests/utils/type_assert.py alchemy/engines/caffe_python_layers.py alchemy/spiders/base_spider.py caffe/python/caffe/test/test_python_layer_with_param_str.py testAttentionMask.py demo.py alchemy/tests/utils/image.py caffe/scripts/download_model_binary.py caffe/tools/extra/parse_log.py caffe/python/caffe/net_spec.py spiders/base_coco_ssm_spider.py caffe/examples/web_demo/exifutil.py caffe/python/caffe/test/test_python_layer.py caffe/python/caffe/test/test_solver.py alchemy/utils/mask.py caffe/scripts/cpp_lint.py alchemy/utils/type_assert.py config.py alchemy/tests/utils/load_config.py caffe/python/caffe/test/test_io.py caffe/scripts/copy_notebook.py caffe/python/caffe/pycaffe.py caffe/python/caffe/test/test_coord_map.py caffe/python/caffe/test/test_net_spec.py parse_args parse_args parse_args parse_args upsample_filt expand_score transplant gen_masks_new interp test_all BaseDataset Dummy COCO_DS AlchemyDataLayer BaseItem Field COCOItem BaseSpider DatasetSpider DatasetSpiderTest TestImage TestImage TestMask TestMasksAssert TestRLEAssert TestBoxesAssert resize_blob visualize_bbs sub_mean load_image image_to_data visualize_masks draw_attention load_config decode iou _masks_as_fortran_order bbs_in_bbs polygon_resize pts_in_bbs area crop _masks_as_c_order encode toBbox equal printProgress Timer is_RLE is_RLEs is_boxes is_masks is_box is_mask download_image make_net max_pool caffenet conv_relu fc_relu CaffeSolver SimpleTransformer print_info check_params PascalMultilabelDataLayerSync load_pascal_annotation BatchLoader EuclideanLossLayer start_tornado start_from_terminal embed_image_html classify_upload index allowed_file ImagenetClassifier classify_url open_oriented_im apply_orientation main main main parse_args Classifier coord_map UndefinedMapException conv_params coord_map_from_to AxisMismatchException inverse crop_params compose crop Detector get_edge_label draw_net get_layer_label get_pydot_graph choose_color_by_layertype get_pooling_types_dict draw_net_to_file Transformer blobproto_to_array datum_to_array array_to_blobproto array_to_datum resize_image arraylist_to_blobprotovector_str blobprotovector_str_to_arraylist load_image oversample Layers Function Parameters Top NetSpec assign_proto param_name_dict to_proto _Net_blobs _Net_forward_all _Net_set_input_arrays _Net_backward _Net_params _Net_forward _Net_outputs _Net_forward_backward_all _Net_blob_loss_weights _Net_batch _Net_get_id_name _Net_inputs TestCoordMap coord_net_spec TestBlobProtoToArray TestArrayToDatum TestLayerTypeList TestLevels TestStages simple_net_file TestNet TestAllInOne lenet TestNetSpec silent_net anon_lenet exception_net_file parameter_net_file SimpleLayer phase_net_file TestPythonLayer ParameterLayer PhaseLayer python_net_file ExceptionLayer SimpleParamLayer TestLayerWithParam python_param_net_file TestSolver ParseNolintSuppressions CheckVlogArguments CheckSectionSpacing FindNextMultiLineCommentEnd ReplaceAll CheckForFunctionLengths _SetOutputFormat _IsTestFilename _VerboseLevel CheckBraces RemoveMultiLineComments ResetNolintSuppressions CheckForNonStandardConstructs _SetVerboseLevel PrintUsage _NestingState CheckIncludeLine CheckAccess _CppLintState Search CheckInvalidIncrement RemoveMultiLineCommentsFromRange CleansedLines CheckForBadCharacters UpdateIncludeState FindPreviousMatchingAngleBracket CheckEmptyBlockBody FindNextMultiLineCommentStart Match _NamespaceInfo CheckMakePairUsesDeduction CheckCheck IsBlankLine _SetFilters ProcessLine _FunctionState CheckPosixThreading GetLineWidth GetHeaderGuardCPPVariable IsCppString _IncludeState CheckSpacing _ClassInfo CheckForCopyright IsErrorSuppressedByNolint ProcessFileData CheckForMultilineCommentsAndStrings CloseExpression _PreprocessorInfo _OutputFormat CheckForIncludeWhatYouUse CheckSpacingForFunctionCall FindEndOfExpressionInLine FindNextMatchingAngleBracket _SetCountingStyle ProcessFile _IncludeError CleanseRawStrings CheckAltTokens CheckForNewlineAtEOF ParseArguments CheckForNonConstReference PrintCategories _Filters main FilesBelongToSameModule CheckCStyleCast FileInfo _BlockInfo CheckForHeaderGuard CheckCaffeDataLayerSetUp ReverseCloseExpression CleanseComments _DropCommonSuffixes _ClassifyInclude CheckStyle CheckCaffeAlternatives FindStartOfExpressionInLine _ShouldPrintError CheckComment Error _GetTextInside CheckLanguage CheckCaffeRandom GetPreviousNonBlankLine reporthook parse_readme_frontmatter model_checks_out valid_dirname get_start_time extract_seconds extract_datetime_from_line get_log_created_year write_csv parse_log fix_initial_nan_learning_rate save_csv_files main parse_args parse_line_for_net_output ResizeCropImagesMapper PILResizeCrop OpenCVResizeCrop print_table printed_len summarize_net main read_net format_param NoLabelException dataSupply BoxSelectionLayer COCOSSMSpiderAttentionMask8_128 COCOSSMSpiderAttentionMask16_192 COCOSSMSpiderAttentionMask8_192 NoLabelException BaseCOCOSSMSpiderAttentionMask BaseCOCOSSMSpiderAttSizeTest COCOSSMSpiderAttentionMask8_128 COCOSSMSpiderAttentionMask16_192 COCOSSMDemoSpider COCOSSMSpiderAttentionMask8_192 add_argument ArgumentParser print params range flat len data num print shape upsample_filt data TEST_RFs int resize_blob hasattr reshape min RFs append zeros float forward max len join exists endswith listdir Field zeros imread astype float transpose RGB_MEAN copy fromarray uint8 cmap astype delete append get_cmap resize transpose INTER_LINEAR show imshow figure zeros range draw_attention len show subplot imshow figure draw_attention items list transpose asfortranarray uint8 astype transpose ascontiguousarray asarray array _masks_as_fortran_order is_RLEs is_RLE _masks_as_c_order is_RLEs is_RLE is_RLE is_RLE is_RLEs is_masks encode is_mask asarray str int format write float round flush imread urlretrieve Convolution InnerProduct Data SoftmaxWithLoss LRN Accuracy max_pool InnerProduct conv_relu fc_relu Dropout join list getElementsByTagName get_data_from_tag csr_matrix dict zip zeros float range enumerate len print format get read info load_image classify_image StringIO join replace info secure_filename save filename open_oriented_im classify_image fromarray replace astype save resize StringIO items list listen HTTPServer format print start WSGIContainer update start_tornado add_option OptionParser debug port parse_args ImagenetClassifier forward run hasattr _getexif astype float32 tile apply_orientation open transpose model_def endswith ArgumentParser save mean_file channel_swap output_file dirname expanduser parse_args input_file predict Classifier set_mode_cpu load time isdir print add_argument set_mode_gpu pretrained_model gpu len DataFrame Detector format to_hdf detect_selective_search mean set_index to_csv detect_windows read_csv read NetParameter output_image_file rankdir Merge TRAIN draw_net_to_file TEST get params array get params array crop_params conv_params pop collect_bottoms add fn coord_map compose coord_map_from_to items list DESCRIPTOR batch_size str num_output get_pooling_types_dict add_edge get_edge_label list Dot exclude get_layer_label add_node values choose_color_by_layertype Edge Node bottom append type layer include top data array diff shape BlobProto extend flat extend BlobProtoVector ParseFromString BlobProtoVector extend tostring shape Datum flat data len float32 tile zoom tuple resize fill empty array concatenate shape tile empty array LayerParameter list NetParameter _to_proto extend Counter OrderedDict values iteritems hasattr isinstance extend add getattr setattr list OrderedDict _blobs _blob_names zip list _blob_loss_weights OrderedDict _blob_names zip OrderedDict list keys list keys iteritems layers index set outputs _forward len iteritems _backward layers inputs index set len iteritems asarray extend copy next _batch itervalues forward len iteritems asarray backward extend copy next _batch itervalues zip_longest zip forward len ascontiguousarray concatenate itervalues zeros next range len data Pooling pool Convolution NetSpec Deconvolution conv Input NamedTemporaryFile str close write data Pooling pool1 conv2 pool2 ip1 relu1 SoftmaxWithLoss Convolution NetSpec DummyData ip2 ReLU InnerProduct label conv1 Pooling SoftmaxWithLoss Convolution DummyData ReLU InnerProduct data NetSpec DummyData Silence data2 error search add group clear compile compile compile SetOutputFormat SetCountingStyle SetFilters _Filters startswith IsErrorSuppressedByNolint _ShouldPrintError write IncrementErrorCount replace append Match group find startswith endswith range error FindNextMultiLineCommentEnd RemoveMultiLineCommentsFromRange FindNextMultiLineCommentStart rstrip find range len FindEndOfExpressionInLine range len FindStartOfExpressionInLine error min search I range len FileInfo RepositoryName sep sub ParseNolintSuppressions error startswith split GetHeaderGuardCPPVariable enumerate error enumerate error len error replace count error find error find error find error find error Search error match InnermostClass replace error escape Match Search error group Search Check error lines Count End group Begin NumLines Match raw_lines range Search error match group error Match group pop group append Search pop group append Search elided replace CheckSpacingForFunctionCall rfind error len group min CloseExpression NumLines sub find CheckComment Match range Search lines_without_raw_strings error group starting_linenum Match range Search error rfind len group ReverseCloseExpression Search Match CloseExpression find error Match CloseExpression find elided error strip group FindEndOfExpressionInLine find Match range CloseExpression len error Match finditer normalize isinstance GetLineWidth int InnermostClass CheckCheck error CheckAltTokens CheckBraces CheckSpacing CheckSectionSpacing CheckEmptyBlockBody CheckAccess GetHeaderGuardCPPVariable lines_without_raw_strings _DropCommonSuffixes RepositoryName match split CheckNextIncludeOrder CanonicalizeAlphabeticalOrder FileInfo error search group SetLastHeader match _ClassifyInclude Match pop end search set append values M rstrip replace CheckCStyleCast error _GetTextInside CheckIncludeLine search group lstrip startswith Match ResetSection Search split rfind error group ReverseCloseExpression lstrip findall Match range Search ReplaceAll error Match Search endswith replace setdefault group search CleanseComments open list FilesBelongToSameModule error search copy sub NumLines FullName keys range error search CheckPosixThreading ParseNolintSuppressions CheckVlogArguments CheckMakePairUsesDeduction CheckCaffeDataLayerSetUp CheckLanguage CheckInvalidIncrement CheckCaffeRandom CheckForNonConstReference check_fn Update CheckForNonStandardConstructs CheckStyle raw_lines CheckForMultilineCommentsAndStrings CheckCaffeAlternatives CheckForFunctionLengths CleansedLines _NestingState CheckForBadCharacters CheckForNewlineAtEOF _IncludeState RemoveMultiLineComments CheckForCopyright ResetNolintSuppressions CheckForHeaderGuard NumLines CheckCompletedBlocks CheckForIncludeWhatYouUse range ProcessLine _FunctionState Error rstrip endswith len write ProcessFileData _SetVerboseLevel range split write exit join write exit _VerboseLevel int getopt _SetOutputFormat set _SetVerboseLevel PrintCategories _SetFilters _OutputFormat PrintUsage _SetCountingStyle split getreader ParseArguments ResetErrorCounts stderr exit verbose_level PrintErrorCounts StreamReaderWriter ProcessFile getwriter int time write flush load join index int rfind datetime split getctime year strip extract_datetime_from_line get_start_time total_seconds strip write get_log_created_year close extract_datetime_from_line open float get_log_created_year compile fix_initial_nan_learning_rate search group OrderedDict append float join basename write_csv print excel parse_log save_csv_files output_dir logfile_path NetParameter decay_mult format name lr_mult append print zip len get join str format convolution_param list setdefault param kernel_size map set top bottom append type module layer enumerate print_table filename summarize_net read_net recv COCO_DS send sleep poll enumerate | # AttentionMask [AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects (ACCV 2018, accepted as oral)](https://www.inf.uni-hamburg.de/en/inst/ab/cv/people/wilms/attentionmask.html) We propose a novel approach for class-agnostic object proposal generation, which is efficient and especially well-suited to detect small objects. Efficiency is achieved by scale-specific objectness attention maps which focus the processing on promising parts of the image and reduce the amount of sampled windows strongly. This leads to a system, which is 33% faster than the state-of-the-art and clearly outperforming state-of-the-art in terms of average recall. Secondly, we add a module for detecting small objects, which are often missed by recent models. We show that this module improves the average recall for small objects by about 53%.  The system is based on [FastMask](https://arxiv.org/abs/1612.08843). If you find this software useful in your research, please cite our paper. ``` @inproceedings{WilmsFrintropACCV2018, title = {{AttentionMask}: Attentive, Efficient Object Proposal Generation Focusing on Small Objects}, author = {Christian Wilms and Simone Frintrop}, | 1,718 |
chwilms/superpixelRefinement | ['object proposal generation', 'instance segmentation', 'semantic segmentation'] | ['Superpixel-based Refinement for Object Proposal Generation'] | alchemy/utils/progress_bar.py caffe/python/caffe/classifier.py caffe/python/caffe/test/test_net.py python_layers/data/reshapeLayer.py caffe/examples/pycaffe/layers/pascal_multilabel_datalayers.py caffe/tools/extra/resize_and_crop_images.py python_layers/data/splitSegFlagsLayer.py caffe/examples/pycaffe/caffenet.py python_layers/data/boxSelectionLayerMP.py alchemy/datasets/coco.py alchemy/utils/image.py caffe/src/caffe/test/test_data/generate_sample_data.py alchemy/utils/timer.py generateFinalResults.py alchemy/test_all.py python_layers/data/segMasksTestLayer.py alchemy/utils/load_config.py caffe/python/caffe/coord_map.py python_layers/data/spxSamplingLayerTest.py trainSpxRefinedAttMask.py caffe/python/detect.py generateSpxJson.py caffe/tools/extra/summarize.py python_layers/data/spxSamplingLayer.py caffe/python/caffe/detector.py caffe/python/draw_net.py caffe/examples/finetune_flickr_style/assemble_data.py spiders/coco_ssm_spider.py caffe/tools/extra/extract_seconds.py utils.py alchemy/tests/utils/mask.py alchemy/items/base_item.py caffe/python/caffe/io.py caffe/python/caffe/test/test_layer_type_list.py python_layers/data/layers.py caffe/python/caffe/__init__.py caffe/examples/pycaffe/layers/pyloss.py caffe/examples/web_demo/app.py alchemy/datasets/base_dataset.py evalCOCONMS.py alchemy/spiders/dataset_spider.py caffe/python/classify.py alchemy/utils/__init__.py alchemy/items/coco.py caffe/python/caffe/draw.py caffe/examples/pycaffe/tools.py generateIntermediateResults.py splitJson.py python_layers/data/selectSegFlagsLayer.py alchemy/engines/caffe_python_layers.py alchemy/spiders/base_spider.py alchemy/tests/utils/type_assert.py caffe/python/caffe/test/test_python_layer_with_param_str.py alchemy/tests/utils/image.py caffe/scripts/download_model_binary.py caffe/tools/extra/parse_log.py spiders/base_coco_ssm_spider.py caffe/python/caffe/net_spec.py caffe/examples/web_demo/exifutil.py caffe/python/caffe/test/test_python_layer.py caffe/python/caffe/test/test_solver.py alchemy/utils/mask.py caffe/scripts/cpp_lint.py python_layers/data/createGTLayer.py alchemy/utils/type_assert.py config.py alchemy/tests/utils/load_config.py caffe/python/caffe/test/test_io.py caffe/scripts/copy_notebook.py caffe/python/caffe/pycaffe.py caffe/python/caffe/test/test_coord_map.py caffe/python/caffe/test/test_net_spec.py parse_args func colorDistFilter getNeighbours gaussian f parse_args parse_args f parse_args storeIntermediateResults upsample_filt expand_score transplant interp test_all BaseDataset Dummy COCO_DS AlchemyDataLayer BaseItem Field COCOItem BaseSpider DatasetSpider DatasetSpiderTest TestImage TestImage TestMask TestMasksAssert TestRLEAssert TestBoxesAssert resize_blob visualize_bbs sub_mean load_image image_to_data visualize_masks draw_attention load_config decode iou _masks_as_fortran_order bbs_in_bbs polygon_resize pts_in_bbs area crop _masks_as_c_order encode toBbox equal printProgress Timer is_RLE is_RLEs is_boxes is_masks is_box is_mask download_image make_net max_pool caffenet conv_relu fc_relu CaffeSolver SimpleTransformer print_info check_params PascalMultilabelDataLayerSync load_pascal_annotation BatchLoader EuclideanLossLayer start_tornado start_from_terminal embed_image_html classify_upload index allowed_file ImagenetClassifier classify_url open_oriented_im apply_orientation main main main parse_args Classifier coord_map UndefinedMapException conv_params coord_map_from_to AxisMismatchException inverse crop_params compose crop Detector get_edge_label draw_net get_layer_label get_pydot_graph choose_color_by_layertype get_pooling_types_dict draw_net_to_file Transformer blobproto_to_array datum_to_array array_to_blobproto array_to_datum resize_image arraylist_to_blobprotovector_str blobprotovector_str_to_arraylist load_image oversample Layers Function Parameters Top NetSpec assign_proto param_name_dict to_proto _Net_blobs _Net_forward_all _Net_set_input_arrays _Net_backward _Net_params _Net_forward _Net_outputs _Net_forward_backward_all _Net_blob_loss_weights _Net_batch _Net_get_id_name _Net_inputs TestCoordMap coord_net_spec TestBlobProtoToArray TestArrayToDatum TestLayerTypeList TestLevels TestStages simple_net_file TestNet TestAllInOne lenet TestNetSpec silent_net anon_lenet exception_net_file parameter_net_file SimpleLayer phase_net_file TestPythonLayer ParameterLayer PhaseLayer python_net_file ExceptionLayer SimpleParamLayer TestLayerWithParam python_param_net_file TestSolver ParseNolintSuppressions CheckVlogArguments CheckSectionSpacing FindNextMultiLineCommentEnd ReplaceAll CheckForFunctionLengths _SetOutputFormat _IsTestFilename _VerboseLevel CheckBraces RemoveMultiLineComments ResetNolintSuppressions CheckForNonStandardConstructs _SetVerboseLevel PrintUsage _NestingState CheckIncludeLine CheckAccess _CppLintState Search CheckInvalidIncrement RemoveMultiLineCommentsFromRange CleansedLines CheckForBadCharacters UpdateIncludeState FindPreviousMatchingAngleBracket CheckEmptyBlockBody FindNextMultiLineCommentStart Match _NamespaceInfo CheckMakePairUsesDeduction CheckCheck IsBlankLine _SetFilters ProcessLine _FunctionState CheckPosixThreading GetLineWidth GetHeaderGuardCPPVariable IsCppString _IncludeState CheckSpacing _ClassInfo CheckForCopyright IsErrorSuppressedByNolint ProcessFileData CheckForMultilineCommentsAndStrings CloseExpression _PreprocessorInfo _OutputFormat CheckForIncludeWhatYouUse CheckSpacingForFunctionCall FindEndOfExpressionInLine FindNextMatchingAngleBracket _SetCountingStyle ProcessFile _IncludeError CleanseRawStrings CheckAltTokens CheckForNewlineAtEOF ParseArguments CheckForNonConstReference PrintCategories _Filters main FilesBelongToSameModule CheckCStyleCast FileInfo _BlockInfo CheckForHeaderGuard CheckCaffeDataLayerSetUp ReverseCloseExpression CleanseComments _DropCommonSuffixes _ClassifyInclude CheckStyle CheckCaffeAlternatives FindStartOfExpressionInLine _ShouldPrintError CheckComment Error _GetTextInside CheckLanguage CheckCaffeRandom GetPreviousNonBlankLine reporthook parse_readme_frontmatter model_checks_out valid_dirname get_start_time extract_seconds extract_datetime_from_line get_log_created_year write_csv parse_log fix_initial_nan_learning_rate save_csv_files main parse_args parse_line_for_net_output ResizeCropImagesMapper PILResizeCrop OpenCVResizeCrop print_table printed_len summarize_net main read_net format_param NoLabelException dataSupply BoxSelectionLayer CreateGTLayer COCOSSMSpiderAttentionMask8_128 ReshapeLayer SegMasksTestLayer SelectSegFlagsLayer SplitSegFlags SpxSamplingLayer SpxSamplingLayer BaseCOCOSSMSpiderAttentionMaskSegs BaseCOCOSSMSpiderAttentionMaskSegGen NoLabelException BaseCOCOSSMSpiderAttentionMask BaseCOCOSSMSpiderAttSizeTest COCOSSMSpiderAttentionMask8_128 COCOSSMDemoSpiderSeg add_argument ArgumentParser sort astype reversed bool range len update list set gaussian zip data zeros_like where resize abs str getNeighbours append sum range colorDistFilter astype mean binary_closing unique zip float binary_opening enumerate load int min dstack split zeros array len argmax loadtxt tolist logical_and logical_or max exists annToMask print params range flat len data num print shape upsample_filt reshape forward savez_compressed str join exists endswith listdir Field zeros imread astype float transpose RGB_MEAN copy fromarray uint8 cmap astype delete append get_cmap resize transpose INTER_LINEAR show imshow figure zeros range draw_attention len show subplot imshow figure draw_attention items list transpose asfortranarray uint8 astype transpose ascontiguousarray asarray array _masks_as_fortran_order is_RLEs is_RLE _masks_as_c_order is_RLEs is_RLE is_RLE is_RLE is_RLEs is_masks encode is_mask asarray str int format write float round flush imread urlretrieve Convolution InnerProduct Data SoftmaxWithLoss LRN Accuracy max_pool InnerProduct conv_relu fc_relu Dropout join list getElementsByTagName get_data_from_tag csr_matrix dict zip zeros float range enumerate len print format get read info load_image classify_image StringIO join replace info secure_filename save filename open_oriented_im classify_image fromarray replace astype save resize StringIO items list listen HTTPServer format print start WSGIContainer update start_tornado add_option OptionParser debug port parse_args ImagenetClassifier forward run hasattr _getexif astype float32 tile apply_orientation open transpose model_def endswith ArgumentParser save mean_file channel_swap output_file dirname expanduser parse_args input_file predict Classifier set_mode_cpu load time isdir print add_argument set_mode_gpu pretrained_model gpu len DataFrame Detector format to_hdf detect_selective_search mean set_index to_csv detect_windows read_csv read NetParameter output_image_file rankdir Merge TRAIN draw_net_to_file TEST get params array get params array crop_params conv_params pop collect_bottoms add fn coord_map compose coord_map_from_to items list DESCRIPTOR batch_size str num_output get_pooling_types_dict add_edge get_edge_label list Dot exclude get_layer_label add_node values choose_color_by_layertype Edge Node bottom append type layer include top data array diff shape BlobProto extend flat extend BlobProtoVector ParseFromString BlobProtoVector extend tostring shape Datum flat data len float32 tile zoom tuple resize fill empty array concatenate shape tile empty array LayerParameter list NetParameter _to_proto extend Counter OrderedDict values iteritems hasattr isinstance extend add getattr setattr list OrderedDict _blobs _blob_names zip list _blob_loss_weights OrderedDict _blob_names zip OrderedDict list keys list keys iteritems layers index set outputs _forward len iteritems _backward layers inputs index set len iteritems asarray extend copy next _batch itervalues forward len iteritems asarray backward extend copy next _batch itervalues zip_longest zip forward len ascontiguousarray concatenate itervalues zeros next range len data Pooling pool Convolution NetSpec Deconvolution conv Input NamedTemporaryFile str close write data Pooling pool1 conv2 pool2 ip1 relu1 SoftmaxWithLoss Convolution NetSpec DummyData ip2 ReLU InnerProduct label conv1 Pooling SoftmaxWithLoss Convolution DummyData ReLU InnerProduct data NetSpec DummyData Silence data2 error search add group clear compile compile compile SetOutputFormat SetCountingStyle SetFilters _Filters startswith IsErrorSuppressedByNolint _ShouldPrintError write IncrementErrorCount replace append Match group find startswith endswith range error FindNextMultiLineCommentEnd RemoveMultiLineCommentsFromRange FindNextMultiLineCommentStart rstrip find range len FindEndOfExpressionInLine range len FindStartOfExpressionInLine error min search I range len FileInfo RepositoryName sep sub ParseNolintSuppressions error startswith split GetHeaderGuardCPPVariable enumerate error enumerate error len error replace count error find error find error find error find error Search error match InnermostClass replace error escape Match Search error group Search Check error lines Count End group Begin NumLines Match raw_lines range Search error match group error Match group pop group append Search pop group append Search elided replace CheckSpacingForFunctionCall rfind error len group min CloseExpression NumLines sub find CheckComment Match range Search lines_without_raw_strings error group starting_linenum Match range Search error rfind len group ReverseCloseExpression Search Match CloseExpression find error Match CloseExpression find elided error strip group FindEndOfExpressionInLine find Match range CloseExpression len error Match finditer normalize isinstance GetLineWidth int InnermostClass CheckCheck error CheckAltTokens CheckBraces CheckSpacing CheckSectionSpacing CheckEmptyBlockBody CheckAccess GetHeaderGuardCPPVariable lines_without_raw_strings _DropCommonSuffixes RepositoryName match split CheckNextIncludeOrder CanonicalizeAlphabeticalOrder FileInfo error search group SetLastHeader match _ClassifyInclude Match pop end search set append values M rstrip replace CheckCStyleCast error _GetTextInside CheckIncludeLine search group lstrip startswith Match ResetSection Search split rfind error group ReverseCloseExpression lstrip findall Match range Search ReplaceAll error Match Search endswith replace setdefault group search CleanseComments open list FilesBelongToSameModule error search copy sub NumLines FullName keys range error search CheckPosixThreading ParseNolintSuppressions CheckVlogArguments CheckMakePairUsesDeduction CheckCaffeDataLayerSetUp CheckLanguage CheckInvalidIncrement CheckCaffeRandom CheckForNonConstReference check_fn Update CheckForNonStandardConstructs CheckStyle raw_lines CheckForMultilineCommentsAndStrings CheckCaffeAlternatives CheckForFunctionLengths CleansedLines _NestingState CheckForBadCharacters CheckForNewlineAtEOF _IncludeState RemoveMultiLineComments CheckForCopyright ResetNolintSuppressions CheckForHeaderGuard NumLines CheckCompletedBlocks CheckForIncludeWhatYouUse range ProcessLine _FunctionState Error rstrip endswith len write ProcessFileData _SetVerboseLevel range split write exit join write exit _VerboseLevel int getopt _SetOutputFormat set _SetVerboseLevel PrintCategories _SetFilters _OutputFormat PrintUsage _SetCountingStyle split getreader ParseArguments ResetErrorCounts stderr exit verbose_level PrintErrorCounts StreamReaderWriter ProcessFile getwriter int time write flush load join index int rfind datetime split getctime year strip extract_datetime_from_line get_start_time total_seconds strip write get_log_created_year close extract_datetime_from_line open float get_log_created_year compile fix_initial_nan_learning_rate search group OrderedDict append float join basename write_csv print excel parse_log save_csv_files output_dir logfile_path NetParameter decay_mult format name lr_mult append print zip len get join str format convolution_param list setdefault param kernel_size map set top bottom append type module layer enumerate print_table filename summarize_net read_net recv COCO_DS send sleep poll enumerate | # Superpixel-based Refinement for Object Proposal Generation [Superpixel-based Refinement for Object Proposal Generation (ICPR 2020)](https://www.inf.uni-hamburg.de/en/inst/ab/cv/people-alt/wilms/spxrefinement.html) Precise segmentation of objects is an important problem in tasks like class-agnostic object proposal generation or instance segmentation. Deep learning-based systems usually generate segmentations of objects based on coarse feature maps, due to the inherent downsampling in CNNs. This leads to segmentation boundaries not adhering well to the object boundaries in the image. To tackle this problem, we introduce a new superpixel-based refinement approach on top of the state-of-the-art object proposal system AttentionMask. The refinement utilizes superpixel pooling for feature extraction and a novel superpixel classifier to determine if a high precision superpixel belongs to an object or not. Our experiments show an improvement of up to 26.0% in terms of average recall compared to original AttentionMask. Furthermore, qualitative and quantitative analyses of the segmentations reveal significant improvements in terms of boundary adherence for the proposed refinement compared to various deep learning-based state-of-the-art object proposal generation systems.  The system is based on [AttentionMask](https://www.inf.uni-hamburg.de/en/inst/ab/cv/people/wilms/attentionmask.html) and [FastMask](https://arxiv.org/abs/1612.08843). If you find this software useful in your research, please cite our paper. ``` @inproceedings{WilmsFrintropICPR2020, title = {{Superpixel-based Refinement for Object Proposal Generation}, author = {Christian Wilms and Simone Frintrop}, | 1,719 |
ciarapb/recovering_bandits | ['gaussian processes'] | ['Recovering Bandits'] | functs/drgpucb_functs.py setups/rgp_gp_l2_setup.py setups/rgp_logistic_l75_setup.py functs/rgpucb_functs.py setups/rgp_gamma_l25_setup.py functs/drgpts_opplan_functs.py setups/rgp_logistic_l25.py functs/rgpts_functs.py functs/gen_functs.py rgpts_manyl.py basic_alg.py rogueucbtuned.py setups/rgp_gp_l5_setup.py setups/rgp_gamma_l75_setup.py functs/rogueucbtuned_logistic_functs.py setups/rgp_logistic_l5_setup.py setups/rgp_gp_K30_l4_setup.py setups/rgp_gp_l05_setup.py rgpts_ds_opplan_l4.py rgpucb_manyl.py functs/drgpts_functs.py functs/rogueucbtuned_gamma_functs.py functs/ucbz_functs.py setups/rgp_gamma_l5_setup.py functs/gen_functs_opplan.py SampleFuncts GetTSSeqRep RGPTSds TSSeq GetTSSeq RGPTSop OptimisticPlanning SampleFuncts Bound CalcUCB RGPUCBds GetUCBSeq UCBSeq UCBSeqRep GetUCBSeqRep UpdateErews GetBestSeqRep UpdateHist ZSeq ErewSeq UpdateZs GetBestSeq ZPlayedSeq PlayArm UpdateErews Sequence GetBestSeqRep UpdateHist ZSeq ErewSeq UpdateZs GetBestSeq ZPlayedSeq PlayArm RGPTS GetTS CalcUCB RGPUCB S UpdateTheta DiffKL CalcUCB negrewfunct RogueUCBTuned NegLogLik consfunct Residuals KL S UpdateTheta DiffKL CalcUCB negrewfunct RogueUCBTuned NegLogLik consfunct Residuals KL CalcUCBz UCB rewfunct diffrew Arm rewfunct diffrew Arm rewfunct diffrew Arm Arm Arm Arm Arm rewfunct diffrew Arm rewfunct diffrew Arm rewfunct diffrew Arm list reshape dict posterior_samples_f zip append array range sum zeros ZPlayedSeq range len permutations TSSeq product TSSeq UpdatePosterior UpdateErews SampleFuncts GetTSSeqRep erew z ErewSeq UpdateZs GetBestSeq zeros GetBestSeqRep max range PlayArm GetTSSeq list max range remove value print depth Sequence max range indicies len UpdatePosterior UpdateErews SampleFuncts OptimisticPlanning z UpdateZs append zeros range PlayArm model sqrt array log predict sum model log sqrt zeros ZPlayedSeq array range predict len sum combinations model log set sqrt posterior_covariance_between_points zeros ZPlayedSeq array range predict len UCBSeq permutations UCBSeqRep product UpdatePosterior UpdateErews GetUCBSeq GetBestSeqRep erew z ErewSeq UpdateZs GetBestSeq zeros GetUCBSeqRep max range PlayArm sample min z rewfunct z vstack min z zeros range len ZSeq sum ZPlayedSeq len permutations ErewSeq ErewSeq product rvs model sqrt array predict UpdatePosterior UpdateErews erew z GetTS UpdateZs zeros max range PlayArm UpdatePosterior CalcUCB UpdateErews erew z UpdateZs zeros max range PlayArm rewfunct Residuals sum thetahat hess_inv x minimize rewfunct sum rewfunct sum diffrew DiffKL numplays thetahat zhist sum invobsinf S numplays min sqrt zhist thetahat KL log thetahat fun minimize UpdateTheta CalcUCB UpdateErews UpdateHist erew z UpdateZs zeros max range PlayArm sqrt float log int UpdateErews CalcUCBz erew z choice UpdateZs zeros setattr max range PlayArm exp exp log | # recovering_bandits Code corresponding to the paper: C.Pike-Burke & S.Grunewalder, Recovering Bandits, NeurIPS (2019). This repository contains all the code to run the experiments included in the above paper. It is in Python 2.7 and requires the GPy module (https://sheffieldml.github.io/GPy/). The code is written to be run in a directory with the following structure: ``` main ├── setups # files that contain the construction of arms and definition of priors ├── functs # files that contain functions to run the algorithms ├── saves # folder for saving output into | 1,720 |
ciaua/InstrumentPlayingDetection | ['action detection'] | ['Weakly-supervised Visual Instrument-playing Action Detection in Videos'] | jjtorch/layers.py jjtorch/vision.py jjtorch/measure.py scripts/YouTube8M/test.action.spatial.py jjtorch/load_data.py scripts/download_videos.py scripts/YouTube8M/compute_predictions.fragment.dense_optical_flow.no_resize.py scripts/AudioSet/extract_melspec.target_time.py jjtorch/__init__.py requirements.py jjtorch/data.py jjtorch/utils.py scripts/YouTube8M/test.action.temporal.py jjtorch/share_memory.py setup.py scripts/compute_predictions_for_sample_videos.fragment.dense_optical_flow.no_resize.py scripts/AudioSet/test.FCN.merged_tags.multilogmelspec.py jjtorch/optim.py scripts/YouTube8M/extract_image.fragment.no_padding.py MultiTensorDataset GaussianXPOU OldSpatialCrossMapLRN BumpXPOU get_max_mask GaussianX SpatialCrossMapLRNFunc BumpX GaussianWrong load_shared_tr_va load_by_file_fragment load2memory_tr_va load2memory load_shared all_exist f1_micro auc_y_classwise recall_micro confusion_mat precision f1 ap_y_classwise _score_to_rank precision_macro rmse_one hamming_loss map_x mean_precision coverage map LSD precision_score_one mean_average_precision precision_micro mean_auc map_y mean_auc_y f1_macro _prob2idx ranking_loss f1_score_one recall_macro _LSD recall precision_at_10_y_axis auc mean_auc_x accuracy_array2idx mcd recall_score_one accuracy precision_at_k_y_axis mcd_one mean_recall rmse average_precision one_error mean_f1 ConventionalSGD LSGD array_in_list delete_all get_array delete_array make_array_noreturn make_array save_params _load_one read_json append_line check_best_value _load_input_and_target_by_file_fragment_plus128 load_model save_structure_description save_record _load_one_plus128 read_csv make_iterator_minibatches_by_file_fragment _load_input_and_target_by_file_fragment load_params write_line make_iterator_minibatches_by_file_fragment_plus128 TrainingGANManager unpickle iterate_minibatches_by_file get_structure_description _load_input_and_target_by_file TrainingManager load_info write_csv load_structure_description decorator_for_save_best _load_one_plus128_npy write_lines get_current_time split_data save_info get_latest_epoch read_lines pickle save_best_params extract_one_dense_optical_flow extract_dense_optical_flows extract_images extract_dense_optical_flows_mp Net extract_one get_video_handler download main get_audio_from_video merge_anno Net Net extract_one get_video_handler do_one extract_one get_prediction upscale compute_euc_distances float max expand_as encode list format get_array format get_array format load join format print make_array_noreturn encode enumerate load join format print make_array_noreturn encode enumerate list sorted read_json append keys range len arange tolist argsort zeros array len argmax append array range roc_auc_score array roc_auc_score array ap mean auc symmetric_difference tolist set zip array len zip float argmax array len zip float _score_to_rank array len zip float sum array len _score_to_rank float sum array len _score_to_rank zip float sum array len sorted T list mean zip append float sum range len precision_at_k_y_axis append f1_score zip append recall_score zip append precision_score zip precision recall f1 mean zip append sum log mean zip append sum log append rmse_one zip mean confusion_matrix _prob2idx mean log10 mean log10 f1_score f1_score precision_score precision_score recall_score recall_score argmax create format create format attach delete list format name delete_array startswith list list range len comp_func save save load load_params load_state_dict replace write_lines get_structure_description read_lines pickle write_csv load get Thread arange print shuffle put object start split_data Queue task_done range len FloatTensor endswith tuple astype map split array open load randint FloatTensor len get Thread arange print shuffle put object start split_data Queue task_done range len FloatTensor endswith tuple reshape astype map split array open endswith tuple astype map split array open load randint FloatTensor len get Thread arange print shuffle put object start split_data Queue task_done range len join max stack astype iter_frames duration subclip round list transpose shape pad append range calcOpticalFlowFarneback COLOR_BGR2GRAY astype stack minimum int print reshape maximum cvtColor list calcOpticalFlowFarneback COLOR_BGR2GRAY duration transpose VideoFileClip astype subclip pad stack append range cvtColor minimum int list duration reshape print VideoFileClip close map maximum subclip stack shape append round Pool range subclip VideoFileClip resize round list transpose shape pad append range calcOpticalFlowFarneback COLOR_BGR2GRAY size astype stack float minimum int print reshape min maximum cvtColor print join format exists print call format exists load join T format print log get_audio_from_video save get_duration exists zeros max enumerate len margin iter_frames max join extract_one format replace print tuple VideoFileClip subclip save range exists makedirs FloatTensor Variable UpsamplingBilinear2d UpsamplingNearest2d numpy sqrt sum load join format | InstrumentPlayingDetection ========================== This repository contains the code and data used in the following paper: **_Weakly-supervised Visual Instrument-playing Action Detection in Videos_** authored by **Jen-Yu Liu, Yi-Hsuan Yang, and Shyh-Kang Jeng** It was submitted to a journal and is currently under review. The preprint version can be found here: [arXiv](https://arxiv.org/abs/1805.02031) ## Introduction In this work, we want to detect instrument-playing actions temporally and spatially from videos, that is, we want to know when and where the playing actions occur. The difficulty is in the lack of training data with detailed locations of actions. We deal with this problem by utilizing two auxiliary models: a sound model and an object model. The sound model predicts the temporal locations of instrument sounds and provides temporal supervision. The object model predicts the spatial locations of the instrument objects and provides spaital supervision. ### Proposed framework <p align="center"> | 1,721 |
cig-skoltech/deep_demosaick | ['demosaicking', 'denoising'] | ['Iterative Joint Image Demosaicking and Denoising using a Residual Denoising Network', 'Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks'] | modules/__init__.py data_loaders/dataset_loader.py data_loaders/transform.py main_xtrans.py data_loaders/rgb_transform.py data_loaders/mcm_dataset_loader.py modules/wmad_estimator.py l2proj.py data_loaders/kodak_dataset_loader.py MMNet_TBPTT.py residual_model_resdnet.py utils.py problems.py main.py data_loaders/__init__.py data_loaders/concat_dataset_loader.py mse_loss L2Proj worker_init_fn model_forward bilinear TBPTT MMNet downsampling Problem Demosaic conv3x3 BasicBlock ResNet_Den _f_gamma apply_colortransformation_gamma init_colortransformation_gamma _f_corr im2Tensor calculate_psnr_fast _f_color_t generate_mask tensor2Im load_resdnet_params calculate_psnr_fast_srgb loadmat ConcatDataset MSRDemosaicDataset KodakDataset MCMDataset _f_gamma apply_colortransformation_gamma init_colortransformation_gamma _f_corr _f_color_t load_theta_npy CenterCrop RandomShift RandomRotation ToTensor RandomVerticalFlip Onepixelshift RandomCrop Normalize RandomHorizontalFlip Identity Rescale Wmad_estimator test seed manual_seed exp fill_ criterion Variable clamp permute range cuda is_cuda detach conv2d cuda is_cuda FloatTensor arange transpose size repeat zeros cuda is_cuda expand_dims astype _f_corr _f_gamma _f_color_t where FloatTensor permute load_state_dict loadmat array range state_dict ceil zeros tile dstack size mean pow log10 append range apply_colortransformation_gamma init_colortransformation_gamma transpose astype float32 mean log10 append expand_dims range clip load astype Variable contiguous standard_normal shape estimate_sigma wmad assert_almost_equal Wmad_estimator imread numpy | cig-skoltech/deep_demosaick | 1,722 |
ciupakabra/conditional-neural-processes | ['gaussian processes'] | ['Conditional Neural Processes'] | mnist_completion.py models.py regression.py data.py MNISTDataGen GPDataGen main train_one_step train loss_fun Aggregator Decoder CNP Encoder main train_one_step train loss_fun log_prob trainable_variables apply_gradients gradient zip print train_one_step make_batch range constant decoder_arch batch_size train Adam lr max_num_context iter MNISTDataGen encoder_arch CNP GPDataGen ExponentiatedQuadratic | # conditional-neural-processes TF 2.0 implementation of https://arxiv.org/abs/1807.01613. Currently contains only the regression experiment. Some lines borrowed from https://github.com/deepmind/neural-processes/blob/master/conditional_neural_process.ipynb. | 1,723 |
cjbaq-origami/origami_inference | ['denoising'] | ['Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments'] | slalom/python/slalom/quant_layers.py cGAN-reconstruct-image/code_split_layer6/datasets.py slalom/python/slalom/sgxdnn.py slalom/python/slalom/scripts/eval_slalom_base.py slalom/python/slalom/mobilenet_sep.py slalom/python/slalom/keras_fix.py cGAN-reconstruct-image/code_split_layer6/model.py cGAN-reconstruct-image/split_layer6/split_vgg16.py slalom/python/preprocessing/cifarnet_preprocessing.py slalom/python/slalom/utils.py slalom/python/preprocessing/preprocessing_factory.py cGAN-reconstruct-image/code_split_layer6/main.py cGAN-reconstruct-image/code_split_layer6/miscc/utils.py slalom/python/slalom/resnet.py cGAN-reconstruct-image/code_split_layer6/miscc/datasets.py slalom/python/slalom/scripts/eval_slalom_old.py slalom/python/preprocessing/vgg_preprocessing.py slalom/python/preprocessing/lenet_preprocessing.py slalom/python/slalom/scripts/eval_slalom.py cGAN-reconstruct-image/code_split_layer6/miscc/config.py slalom/python/slalom/models.py slalom/python/preprocessing/inception_preprocessing.py slalom/python/imagenet.py slalom/python/slalom/scripts/benchmarks.py slalom/python/slalom/scripts/eval.py cGAN-reconstruct-image/code_split_layer6/trainer.py slalom/python/slalom/scripts/eval_gpu.py PairDataset parse_args CA_NET ResBlock D_NET conv3x3 upBlock G_NET D_GET_LOGITS GANTrainer cfg_from_file _merge_a_into_b TextDataset KL_loss save_model compute_generator_loss save_img_results mkdir_p weights_init save_img_results2 compute_discriminator_loss sparsify randomize resnet50_asitis thresholding fixed_padding main compute_accuracy get_split load_validation preprocess_image preprocess_for_train preprocess_for_eval distorted_bounding_box_crop preprocess_for_train preprocess_for_eval preprocess_image distort_color apply_with_random_selector preprocess_image get_preprocessing _aspect_preserving_resize preprocess_for_train _crop _central_crop _smallest_size_at_least _mean_image_subtraction preprocess_for_eval preprocess_image _random_crop _preprocess_conv2d_input_fixed preprocess_input _conv_block _depthwise_conv_block MobileNet_sep get_model preproc_tf preproc ActivationQ remainder Conv2DQ get_all_linear_layers Zeros64 fuse_bn DenseQ DepthwiseConv2DQ build_blinding_ops GlobalAveragePooling2DQ log2 transform prepare_blinding_factors ResNetBlock identity_block ResNet50 conv_block model_to_json mod_test SGXDNNUtils print_model_size get_topk_acc Results preprocess_vgg get_all_layers size_to_mb main main main main main main add_argument ArgumentParser Sequential conv3x3 Upsample ReLU BatchNorm2d items list ndarray isinstance type array _merge_a_into_b add_ mul_ data_parallel criterion size get_cond_logits BCELoss detach get_cond_logits BCELoss data_parallel criterion normal_ __name__ fill_ data VIS_COUNT save_image data VIS_COUNT save_image print save state_dict makedirs pad print shape multiply abs max float16 laplace preprocess_input img_to_array VGG16 load_img copy append expand_dims listdir compute_accuracy predict preprocess_input img_to_array VGG16 load_img function time print copy float16 shape append expand_dims listdir compute_accuracy get preprocess get_split DatasetDataProvider batch join TFExampleDecoder TFRecordReader to_float random_crop random_flip_left_right random_brightness pad image random_contrast expand_dims to_float resize_image_with_crop_or_pad expand_dims image random_uniform to_float resize_image_with_crop_or_pad div subtract greater_equal to_int32 logical_and Assert shape stack rank equal greater_equal logical_and extend Assert rank random_uniform append range equal len append _crop range split convert_to_tensor to_float to_int32 greater cond convert_to_tensor resize_bilinear squeeze shape set_shape _smallest_size_at_least expand_dims _aspect_preserving_resize set_shape random_uniform set_shape _aspect_preserving_resize transpose _obtain_input_shape set_image_data_format _depthwise_conv_block get_source_inputs _conv_block warn Model Input int int uint8 min astype shape resize set_shape get_weights int layers print_model_size print len placeholder summary get_preprocessing model_func Input set_floatx range set_weights constant log add_weight isinstance set_weights transpose get_input_at sqrt moving_variance moving_mean epsilon enumerate run layers float64 Sequential round set_weights fuse_bn add append get_weights get_config astype from_config type pop transform_layer isinstance print reshape get_operations summary get_all_layers print format get_all_linear_layers len get_all_linear_layers float64 activation round run slalom_set_z placeholder shape slalom_get_r range format relu astype enumerate print reshape float32 max_pool zeros len append get_all_layers isinstance enumerate str add str add _obtain_input_shape get_file get_source_inputs Model convert_all_kernels_in_model Input range layers isinstance extend layer_to_json append enumerate float64 fmod_pos run seed placeholder set_printoptions shape conv2d sum format astype upper fmod zip depthwise_conv2d_native isinstance print reshape float32 strides dot len list format print name from_iterable __class__ sum prod size_to_mb set_shape _aspect_preserving_resize cast list from_iterable use_sgx threads destroy benchmark SGXDNNUtils set_verbosity INFO set_random_seed | # origami_inference Private inference using hardware enclaves ## Folder Structure Folder|Description| ---|--- slalom| Contains code for baselines and Origami cGAN-reconstruct-image/split_layer6 | Contains code for collecting intermediate feature maps used for training c-GAN cGAN-reconstruct-image/code_split_layer6 | contains code for training c-GAN networks ## Prerequisites ``` | 1,724 |
cjc77/gaussian_processes_final_project | ['stochastic optimization'] | ['Adam: A Method for Stochastic Optimization'] | util/util.py acquisition/acquisition_functions.py setup.py hp_optimizers/hp_optimizer.py util/defs.py acquisition/acquisition_optimizers.py ExpectedImprovement ProbabilityOfImprovement AcquisitionFunction RandomAcquisitionOpt AcquisitionOptimizer ConstrainedAcquisitionOpt RandomSearchOptimizer GPROptimizer HPOptimizer ParamType scalar_or_1d_to_2d random_x_sample singleton_to_scalar uniform round random array | # gaussian_processes_final_project Implementation of Bayesian optimization for hyperparameter search. ## References [1] (2019) Hyperparameter optimization. (accessed: 03.12.2020). [Online]. Available: https://en.wikipedia.org/wiki/Hyperparameter_optimization [2] P. I. Frazier, “A tutorial on bayesian optimization,” Cornell University, Tech. Rep., 2018. [3] F. Hutter, H. Hoose, and K. Leyton-brown, “Sequential model-based optimiza- tion for general algorithm configuration (extended version),” University of British Columbia, Tech. Rep., 2010. | 1,725 |
cjshui/WAAL | ['active learning'] | ['Deep Active Learning: Unified and Principled Method for Query and Training'] | query_strategies/wasserstein_adversarial.py query_strategies/__init__.py dataset.py dataset_WA.py model.py Test.py DataHandler1 DataHandler3 DataHandler2 get_dataset get_handler get_CIFAR10 get_SVHN get_FashionMNIST Wa_datahandler3 get_dataset get_handler get_CIFAR10 get_SVHN get_FashionMNIST Wa_datahandler2 Wa_datahandler1 Net1_fea Net1_dis VGG_10_dis VGG_10_clf VGG_10_fea Net1_clf get_net set_requires_grad WAAL gradient_penalty FashionMNIST test_data train_labels train_data test_labels data labels from_numpy SVHN data targets from_numpy CIFAR10 array parameters norm critic mean requires_grad_ cuda | # WAAL Wasserstein Adversarial Active Learning A pytorch implementation of [Deep Active Learning: Unified and Principled Method for Query and Training](https://arxiv.org/abs/1911.09162) ## Prerequisites - Pytorch >=1.0, Torchvision >=0.2 - Scikit-learn >= 0.19.1 ## Models - 'Test.py': Active Learning model - 'query_startegies/wasserstien_adversarial.py': Model for WAAL ## How to cite | 1,726 |
cjxxx0/license | ['optical character recognition', 'scene text recognition'] | ['An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition'] | src/models/cnnmodel.py tools/create_xml_ssd_CCPD.py tools/inference_crnn_ctc.py src/models/resmodel.py tools/config.py tools/create_crnn_ctc_tfrecord_CCPD.py src/utils/stn.py src/models/pyramidcnnmodel.py src/models/model.py tools/eval_crnn_ctc.py tools/train_crnn_ctc_CCPD.py tools/create_image_list_CCPD.py src/utils/distribution.py build_network CRNNCTCNetwork SpatialAttention CFE build_network BilinearUpsampling BilinearDownsampling ChannelWiseAttention AtrousBlock Act resnet build_network Conv_unit residual_unit residual_unit_v1 channel_wise_attention affine_grid_generator get_pixel_value spatial_transformer_network bilinear_sampler generate_config _string_to_int convert_dataset _int64_feature write_tfrecord _bytes_feature main get_province_num write_to_files make_image_list find_key_by_value main progress _sparse_matrix_to_list _int_to_string _read_tfrecord main _eval_crnn_ctc main _sparse_matrix_to_list _int_to_string _inference_crnn_ctc _sparse_matrix_to_list _int_to_string _read_train_tfrecord main train_crnn_ctc _read_test_tfrecord as_list squeeze rgb info stn conv2d conv2d concat batch_norm AtrousBlock as_list as_list get_shape list fully_connected reshape multiply map reduce_mean tile list batch_norm map add sigmoid conv2d tile conv2d leaky_relu relu get reduce_mean Conv_unit get get bn_mom format print exit shape Conv_unit max_pool2d residual_unit range len resnet num_layers list Variable fully_connected reshape ones multiply map reduce_mean tile zeros reshape affine_grid_generator bilinear_sampler reshape shape stack tile range ones_like reshape matmul stack cast linspace tile meshgrid expand_dims cast clip_by_value floor add_n zeros expand_dims get_pixel_value items list load char_map_json_file append open join data_store_path makedirs load join int format data_store_path len shuffle train_ratio plate_map_json_file shuffle_list write_tfrecord trainval_targets range open convert_dataset seed join time int submit format basename float32 find_key_by_value lower writelines walk range open get join int print find_key_by_value walk join format dataset_root_path data_store_path get_province_num write_to_files print plate_map_json_file ThreadPoolExecutor mkdir info shutdown trainval_targets len make_image_list str SubElement Element write shape imread ElementTree load join list _int_to_string ones char_map_json_file dense_shape len indices append keys enumerate values open load list char_map_json_file keys open resize_images read TFRecordReader string_input_producer float32 set_shape cast int32 parse_single_example decode_jpeg load join _read_tfrecord ctc_beam_search_decoder batch_size latest_checkpoint data_dir char_map_json_file float32 placeholder sparse_placeholder model_dir Saver int32 tf_record_iterator ConfigProto batch open _eval_crnn_ctc plate_map_json_file plate_map_json_file load list ctc_beam_search_decoder model_stn_save_path latest_checkpoint assign_from_checkpoint_fn len placeholder plate_map_json_file model_save_path get_variables_to_restore int32 build_network global_variables_initializer bool stn keys open _inference_crnn_ctc read TFRecordReader string_input_producer float32 random_brightness set_shape resize_with_crop_or_pad cast int32 parse_single_example random_contrast decode_jpeg resize_images read TFRecordReader string_input_producer float32 set_shape cast int32 parse_single_example decode_jpeg ctc_beam_search_decoder batch_size localtime model_save_path Saver exponential_decay exists open decay_rate str list data_store_path model_stn_save_path ctc_loss decay_steps get_collection merge_all placeholder strftime cast build_network _read_train_tfrecord append stn format latest_checkpoint get_variables_to_restore create_global_step tf_record_iterator assign_from_checkpoint_fn ConfigProto _read_test_tfrecord keys batch enumerate load join time learning_rate makedirs float32 sparse_placeholder plate_map_json_file edit_distance reduce_mean UPDATE_OPS int32 save_root_path global_variables_initializer bool trainval_targets scalar len train_crnn_ctc | # crnn_ctc_.Tensorflow This software implements the Convolutional Recurrent Neural Network (CRNN), a combination of CNN, RNN and CTC loss for image-based sequence recognition tasks, such as scene text recognition and OCR. "An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition" : https://arxiv.org/abs/1507.05717 More details for CRNN and CTC loss (in chinese): https://zhuanlan.zhihu.com/p/43534801 ***The crnn+seq2seq+attention ocr code be found here [bai-shang/crnn_seq2seq_ocr.PyTorch](https://github.com/bai-shang/crnn_seq2seq_ocr.PyTorch)*** # Dependencies All dependencies should be installed are as follow: * Python2.7 or Python3 * tensorflow==1.8.0 * opencv-python | 1,727 |
ckbjimmy/clneg | ['text summarization'] | ['Clinical Text Summarization with Syntax-Based Negation and Semantic Concept Identification'] | src/create_neglist.py src/main.py src/concept_extraction.py src/negex.py src/tree_rules.py src/create_tokenization.py src/syntactic_parsing.py ctakes_concept_extraction extract_cuis get_cui_spans mimic_tokenize match_section_name print_out_result main sortRules negTagger synparse tregex_tsurgeon OpenNLP add set parse xpath get sort get_cui_spans items list str int system extend index zip DataFrame enumerate annotate lower set update join list annotate keys range len join print where set values join sort append IGNORECASE compile split writer getNegationFlag getScopes sortRules reader writerow readlines close negTagger append getNegTaggedSentence next open sorted print tolist append len replace communicate print system sub Popen | # Clinical Text Summarization Tool with Syntax-based Negation and Semantic Concept Identification * Edited by Wei-Hung Weng (MIT CSAIL) * Please contact the author with errors found. * ckbjimmy {AT} mit {DOT} edu This repository contains the codes for the method presented in the paper [Clinical Text Summarization with Syntax-Based Negation and Semantic Concept Identification](https://arxiv.org/abs/2003.00353) ## Introduction we utilized the power of computational linguistics with human experts-curated knowledge base for identifying clinical concepts with their corresponding negation information in the clinical narrative texts. We used the medical knowledge base UMLS along with Semantic Network, and take the advantage from the language hierarchical structure, the constituency tree, in order to identify the clinically relevant concepts and the negation information, which is extremely important for summarization. In this project, we used Stanford CoreNLP, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES), Unified Medical Language System (UMLS) and Semantic Network, to identify clinical concepts in the narrative texts. We also performed the negation detection in the clinical sentences through sentence pruning, syntactic analysis and parsing using Apache OpenNLP and Stanford Tregex/Tsurgeon. | 1,728 |
ckczzj/AAAI2020 | ['video summarization'] | ['Convolutional Hierarchical Attention Network for Query-Focused Video Summarization'] | runner/__init__.py runner/runner.py model/CHAN.py segment/cpd_nonlin.py dataset/dataset.py dataset/query_feature.py segment/cpd_auto.py model/__init__.py utils.py dataset/__init__.py main.py dataset/preprocess.py model/attention.py segment/__init__.py load_json load_pickle save_pickle UCTDataset Attention CHAN Runner estimate_vmax cpd_auto eval_score eval_cost centering calc_scatters cpd_nonlin arange argmin zeros float log cpd_nonlin centering trace list range len float log len list cumsum diag zeros range int inf calc_scatters print ones copy shape zeros max range | # Convolutional Hierarchical Attention Network for Query-Focused Video Summarization
code for ***Convolutional Hierarchical Attention Network for Query-Focused Video Summarization***，which is accepted by AAAI 2020 conference.
[arXiv](https://arxiv.org/abs/2002.03740) [paper](https://doi.org/10.1609/aaai.v34i07.6929)
## Prerequisites
- Python 3.5
| 1,729 |
ckirby19/style_transfer_snip | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | setup.py style_transfer_snip/__main__.py style_transfer_snip/SnippingTool.py style_transfer_snip/NeuralGUI.py main GUI SnippingWidget exec_ argv GUI exit QApplication pyqtSignal | # Style Transfer Snipping Tool This Python run lightweight GUI allows you to apply a number of style transfer models on a screenshot taken from your computer screen. The screenshot is taken just like the windows Snipping Tool, and can be used across multiple monitors. The Style Transfer Models can be cycled between and final image can be saved. ### Installation Please run `pip install -r requirements.txt` to get required modules ### How to run and use In command line, run `python style_transfer_snip` Once the GUI is open, press "New" in order to create your first screenshot. Your screen (or screens if you have multiple monitors) will turn slightly opaque, and by clicking and dragging your mouse, you can create a rectangular area which will become your input image. The style transfer model applied to the input can then be changed and the output is shown on the right. "Save" will then allow you to save the output. ### How it works This work is essentially the combination of the Style Transfer project from https://www.pyimagesearch.com/2018/08/27/neural-style-transfer-with-opencv/ and the screenshot project from https://github.com/harupy/snipping-tool #### Style Transfer | 1,730 |
clab/fast_align | ['word alignment', 'machine translation'] | ['A Simple, Fast, and Effective Reparameterization of IBM Model 2'] | src/force_align.py popen_io main Aligner start Popen readline format align strip write exit close Aligner flush | fast_align ========== `fast_align` is a simple, fast, unsupervised word aligner. If you use this software, please cite: * [Chris Dyer](http://www.cs.cmu.edu/~cdyer), [Victor Chahuneau](http://victor.chahuneau.fr), and [Noah A. Smith](http://www.cs.cmu.edu/~nasmith). (2013). [A Simple, Fast, and Effective Reparameterization of IBM Model 2](http://www.ark.cs.cmu.edu/cdyer/fast_valign.pdf). In *Proc. of NAACL*. The source code in this repository is provided under the terms of the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0.html). ## Input format Input to `fast_align` must be tokenized and aligned into parallel sentences. Each line is a source language sentence and its target language translation, separated by a triple pipe symbol with leading and trailing white space (` ||| `). An example 3-sentence German–English parallel corpus is: doch jetzt ist der Held gefallen . ||| but now the hero has fallen . neue Modelle werden erprobt . ||| new models are being tested . | 1,731 |
clarken92/Mixed-variate-RBM | ['outlier detection'] | ['Outlier Detection on Mixed-Type Data: An Energy-based Approach'] | utils/theano_utils.py utils/general_utils.py architecture/abstract/model.py architecture/unsupervised/rbm/mixed_rbm.py data_processing/toy/toy_gen.py architecture/abstract/regularizer.py utils/anomaly_utils.py utils/preprocess_utils.py architecture/unsupervised/rbm/sparse_rbm.py data_processing/toy/run.py architecture/abstract/lr_decay.py architecture/abstract/data_type.py architecture/unsupervised/rbm/rbm.py InputType BroadInputType LR_LinearDecay LR_StepDecay LR_Decay Model TrainMonitor l2_cost l1_cost l1_grad l2_grad MixedRBM VisibleLayer RBM SparseRBM rand_spm generate_toy_data one_hot roc_draw anomaly_poisson get_threshold anomaly_binary anomaly_detection compute_auc unfold_types_ranges draw_labeled_error_hist random_noise_on_data indices_for_each_class anomaly_categorical anomaly_gaussian separate_normal_anomaly iterate_minibatches init_weight draw_bias init_conv_bias scale_to_unit_interval shared_dataset iterate_minibatch_indices error_plot plot_3D_embedding init_conv_weight plot_2D_embedding draw_image draw_weight init_bias log_count load_rawdata_with_finetypes process_clean_mixed_data separate_train_test load_rawdata_with_broadtypes print_values_proportion standardize separate_clean_categorical normalize load_rawdata_without_type store_grads_in_update l1_cost grad l2_cost grad dot T randn permutation arange categorical argmax max gaussian uniform random_noise_on_data append binary range one_hot concatenate astype standardize rand_spm multivariate_normal print separate_train_test min int32 zeros len percentile max min print subplots max show axvline savefig legend append sum range format astype unique set_size_inches suptitle print min hist int32 len get unique append range len append range extend len uniform abs int anomaly_poisson arange std sort anomaly_binary shuffle unfold_types_ranges mean meshgrid array anomaly_categorical anomaly_gaussian len get int list asarray items concatenate shuffle logical_not where argsort in1d unique append range len get show set_size_inches subplots set_title plot set_xlabel set_ylabel savefig roc_draw format print min astype in1d linspace int32 append sum max auc print arange in1d len show set_size_inches plot print savefig read_csv show set_size_inches set_title add_subplot scatter savefig figure unique legend append range len show set_size_inches subplots set_title scatter savefig unique legend append range len copy uniform asarray zeros asarray sqrt uniform asarray prod zeros asarray shared asarray range shuffle len arange slice shuffle range len fromarray uint8 asarray print reshape transpose ones astype scale_to_unit_interval save range fromarray reshape scale_to_unit_interval save fromarray uint8 ones reshape astype scale_to_unit_interval save range get arange shuffle unique append zeros len format print unique sum range len min logical_and shape zeros max mean zeros std shape drop categorical concat get_dummies values list columns bool_ gaussian groups issubdtype append binary range poisson format astype separate_clean_categorical as_matrix items print extend int32 len get set_categories categories TypeError isinstance print len astype apply OrderedDict unique range read_csv drop get set_categories categories TypeError isinstance print len astype apply OrderedDict unique range read_csv drop get set_categories categories columns TypeError isinstance print len astype apply OrderedDict issubdtype unique object_ range read_csv drop dtype astype OrderedDict append shared zeros enumerate | # Mixed-variate RBM These codes are implementations of standard RBMs, Mixed-variate RBMs and sparse RBMs in Python/Theano which were used in the paper: "**_Outlier Detection on Mixed-Type Data: An Energy-based Approach_**", Kien Do, Truyen Tran, Dinh Phung, Svetha Venkatesh, ADMA 2016 Link Arxiv: https://arxiv.org/abs/1608.04830 | 1,732 |
classner/generating_people | ['semantic segmentation'] | ['A Generative Model of People in Clothing'] | generation/experiments/config/PM_class/options.py generation/data.py generation/experiments/config/PM/model.py generation/experiments/config/CSM/write_output.py generation/experiments/config/LSM/preprocessing.py generation/tools/06_render_bodies.py generation/experiments/config/PM/options.py generation/experiments/config/template/model.py generation/tools/07_create_additional_conditioning.py generation/test_runner.py generation/experiments/config/PM_class/write_output.py generation/experiments/config/CSM/model.py generation/experiments/config/LSM/config.py generation/experiments/config/CSM/summaries.py generation/experiments/config/PM/summaries.py generation/experiments/config/LSM/summaries.py generation/experiments/config/CSM/config.py generation/experiments/config/template/config.py generation/experiments/config/template/preprocessing.py generation/tools/02_create_clothdataset.py generation/experiments/config/template/options.py generation/experiments/config/template/summaries.py generation/experiments/config/PM_class/config.py generation/run.py generation/experiments/config/PM_class/summaries.py generation/experiments/config/PM_class/preprocessing.py gp_tools/tf.py generation/experiments/config/PM/config.py generation/experiments/config/LSM/options.py generation/tools/geometric_median.py generation/experiments/config/CSM/preprocessing.py generation/experiments/config/template/write_output.py gp_tools/write.py generation/experiments/config/LSM/write_output.py generation/experiments/config/PM/preprocessing.py generation/experiments/config/PM_class/model.py setup.py config.py generation/experiments/config/LSM/model.py generation/experiments/config/PM/write_output.py generation/experiments/config/CSM/options.py generation/tools/01_chic10k_to_fashion_joined.py cli cleanup prepare get_dataset cli get_unevaluated_checkpoint cli get_config adjust_config lrelu create_generator create_discriminator create_model deconv batchnorm conv transform preprocess prepare deprocess create_summaries postprocess_colormap save_images save_grid get_config adjust_config lrelu create_generator create_discriminator create_model deconv batchnorm conv transform preprocess prepare deprocess create_summaries postprocess_colormap save_images save_grid get_config adjust_config lrelu create_generator create_discriminator create_model deconv batchnorm conv transform preprocess prepare deprocess create_summaries postprocess_colormap save_images save_grid get_config adjust_config lrelu create_generator create_discriminator create_model deconv batchnorm conv transform preprocess prepare deprocess create_summaries postprocess_colormap save_images save_grid get_config adjust_config lrelu create_generator create_discriminator create_model deconv batchnorm conv transform preprocess prepare deprocess create_summaries postprocess_colormap save_images save_grid getface prepare cli convert cli lrswap_regions pad_width pad_height process_image cli process_image cli geometric_median one_hot get_val_or_initializer get_or_load_variable append_index print read TFRecordReader join int glob float mkdtemp extend ceil check_call info append scan_tfdb rmtree strip warn adjust_config Saver setLevel getfqdn Session list basename cleanup addHandler merge_all literal_eval load_source update setFormatter create_model latest_checkpoint get_config Graph close FileWriter info ConfigProto INFO FileHandler join items create_summaries Formatter global_variables_initializer makedirs debug sorted Reload info get_unevaluated_checkpoint check_call EventAccumulator sleep join dirname abspath info load_source constant ones len info append zeros enumerate concat range info constant conditioning concat inputs get_collection reduce_sum targets group float32 reduce_mean UPDATE_OPS cast info argmax equal resize_images random_flip_left_right BILINEAR NEAREST_NEIGHBOR cast floor random_uniform get one_hot float32 set_shape cast transform concat shuffle_batch_join batch list uint8 ones astype apply_colormap erode array range y gen_loss_recon z_mean gen_accuracy z z_log_sigma_sq histogram discrim_loss info gen_loss_latent gen_loss_GAN scalar join write dirname range exists open join str basename debug append_index OrderedDict splitext append range makedirs int debug min warn floor ceil argmax max range y clear_overlay fillPoly spredictor where x max top run list ones right add_overlay morphologyEx logical_and getface set_trace left imread sum range imresize MORPH_CLOSE copy float empty hit_enter_to_continue enumerate join uint8 items bottom image_window MORPH_BLACKHAT set_image seed sorted permutation zip glob TFRecordCreator unlink add_to_dset dirname mkdir open range empty_like vstack hstack imresize dstack lrswap_regions pad_width append float pad_height max convert_dset TFConverter exists int join basename format regions_to_classes dirname imread imsave six_region_groups sorted zeros_like geometric_median vstack unique append norm cdist min mean sum max len name debug convert_to_tensor name debug convert_to_tensor float32 as_list embedding_lookup constant reshape identity cast int32 join list basename items write dirname save exists open | # Generating People code repository Requirements: * OpenCV (on Ubuntu, e.g., install libopencv-dev and python-opencv). * SMPL (download at http://smpl.is.tue.mpg.de/downloads) and unzip to a place of your choice. * Edit the file `config.py` to set up the paths. * `tensorflow` or `tensorflow-gpu` in a version >=v1.1.0 (I did not want to add it to the requirements to force installation of the GPU or non-GPU version). * Only if you want to run pose estimation and 3D fitting to integrate new data into the dataset: set up the unite the people repository | 1,733 |
claudiashi57/dragonnet | ['causal inference'] | ['Adapting Neural Networks for the Estimation of Treatment Effects'] | src/experiment/data.py src/process_result/acic_ate.py src/process_result/ihdp_ate.py src/experiment/acic_main.py src/semi_parametric_estimation/helpers.py src/experiment/ihdp_main.py src/semi_parametric_estimation/ate.py src/experiment/models.py src/experiment/idhp_data.py src/semi_parametric_estimation/att.py _split_output run_acic turn_knob train_and_predict_ned train_and_predict_dragons main load_ufids load_params load_and_format_covariates main load_treatment_and_outcome main convert_file load_all_other_crap load_and_format_covariates_ihdp _split_output run_ihdp turn_knob train_and_predict_ned train_and_predict_dragons main treatment_accuracy dragonnet_loss_binarycross make_tarnet post_cut track_epsilon EpsilonLayer make_ned dead_loss make_dragonnet regression_loss binary_classification_loss make_tarreg_loss ned_loss load_truth make_table load_data main get_estimate load_truth make_table load_data main get_estimate psi_naive psi_aiptw _perturbed_model_bin_outcome ates_from_atts psi_tmle_cont_outcome psi_tmle_bin_outcome psi_very_naive main psi_iptw _perturbed_model psi_aiptw psi_plugin att_estimates psi_q_only psi_tmle psi_very_naive make_one_step_tmle truncate_by_g truncate_all_by_g mse calibrate_g cross_entropy format zeros_like print copy mean inverse_transform seed clear_session time make_tarnet arange concatenate print predict make_dragonnet set_random_seed make_tarreg_loss transform train_test_split range compile fit seed clear_session time post_cut arange concatenate make_ned predict set_random_seed transform train_test_split range compile fit join sorted format str print glob endswith train_and_predict_ned train_and_predict_dragons load_and_format_covariates savez_compressed enumerate load_treatment_and_outcome makedirs run_acic join add_argument output_base_dir turn_knob knob folder data_base_dir ArgumentParser parse_args read_csv join StandardScaler fit_transform read_csv values read_csv concatenate read_csv join load_and_format_covariates load_treatment_and_outcome astype values loadtxt loadtxt print summary join sorted format str print glob train_and_predict_ned train_and_predict_dragons enumerate savez_compressed load_all_other_crap load_and_format_covariates_ihdp makedirs run_ihdp binary_crossentropy reduce_sum square reduce_sum dl Model Input EpsilonLayer dl Model Input EpsilonLayer Model Input pop layers output Model input squeeze read_csv values load str format reshape psi_naive psi_tmle_cont_outcome deepcopy sorted format basename load_truth get_estimate glob Series print nanmean mean load_data append zeros abs range len print make_table load format logit ones_like zeros_like minimize truncate_all_by_g q1 ones_like std zeros_like truncate_all_by_g square mse mean sqrt sum q1 truncate_all_by_g mean list keys att_estimates ones_like expit logit zeros_like mean q1 ones_like zeros_like minimize truncate_all_by_g mean q2 mean truncate_all_by_g mean truncate_all_by_g mean psi_aiptw psi_plugin psi_q_only one_step_tmle psi_very_naive make_one_step_tmle reshape fit LogisticRegression logical_and truncate_by_g copy | # Introduction This repository contains software and data for "[Adapting Neural Networks for the Estimation of Treatment Effects](https://arxiv.org/pdf/1906.02120.pdf)". The paper describes approaches to estimating causal effects from observational data using neural networks. The high-level idea is to modify standard neural net design and training in order to induce a bias towards accurate estimates. # Requirements and setup You will need to install tensorflow 1.13, sklearn, numpy 1.15, keras 2.2.4 and, pandas 0.24.1 # Data 1. IHDP This dataset is based on a randomized experiment investigating the effect of home visits by specialists on future cognitive scores. It is generated via the npci package [`https://github.com/vdorie/npci`](https://github.com/vdorie/npci) (setting A) For convenience, we have also uploaded a portion of the simulated data in the dat folder. | 1,734 |
clevercool/TileSparsity | ['network pruning'] | ['Accelerating Sparse DNN Models without Hardware-Support via Tile-Wise Sparsity'] | Pruning/NMT/nmt/utils/evaluation_utils_test.py Pruning/NMT/nmt/utils/misc_utils.py Pruning/VGG/download_model.py Pruning/NMT/nmt/attention_model.py Pruning/BERT/utils/__init__.py Pruning/NMT/nmt/inference.py Pruning/VGG/imagenet.py Pruning/NMT/nmt/utils/common_test_utils.py Pruning/BERT/other_function/plot_model_weight.py Pruning/NMT/nmt/utils/iterator_utils.py Pruning/BERT/utils/dump_pruned_model.py Pruning/NMT/nmt/scripts/rouge.py Pruning/BERT/utils/data_processor.py Pruning/BERT/other_function/extract_features.py Pruning/BERT/other_function/calculate_model_sparsity.py Pruning/BERT/utils/modeling.py Pruning/BERT/utils/MyHook.py Pruning/NMT/nmt/model.py Pruning/NMT/nmt/model_test.py Pruning/NMT/nmt/utils/misc_utils_test.py Pruning/NMT/nmt/utils/vocab_utils_test.py Pruning/NMT/nmt/train.py Pruning/BERT/other_function/create_pretraining_data.py Pruning/VGG/pruning.py Pruning/BERT/run_squad.py Pruning/NMT/nmt/gnmt_model.py Pruning/VGG/imagenet_preprocessing.py Pruning/NMT/nmt/nmt.py Pruning/BERT/other_function/plot_weight_heatmap_bunch.py Pruning/BERT/utils/optimization.py Pruning/BERT/evaluate-squad-v1.1.py Pruning/NMT/nmt/myhook.py Pruning/NMT/nmt/scripts/bleu.py Pruning/NMT/nmt/utils/iterator_utils_test.py Pruning/VGG/prune_algo.py Pruning/NMT/nmt/model_helper.py Pruning/VGG/utils.py Pruning/NMT/nmt/utils/nmt_utils.py Pruning/VGG/myhook.py Pruning/NMT/nmt/nmt_test.py Pruning/NMT/nmt/utils/vocab_utils.py Pruning/NMT/nmt/prune_algo.py Pruning/BERT/utils/tokenization.py Pruning/NMT/nmt/utils/standard_hparams_utils.py Pruning/BERT/pruning_classifier.py Pruning/BERT/other_function/printTensor.py Pruning/NMT/nmt/utils/evaluation_utils.py Pruning/BERT/other_function/download_glue_data.py Pruning/NMT/nmt/inference_test.py normalize_answer metric_max_over_ground_truths evaluate f1_score exact_match_score create_model define_train_eval_input_fn input_fn_builder main model_fn_builder _check_is_max_context create_model _compute_softmax InputFeatures input_fn_builder get_final_text _improve_answer_span _get_best_indexes validate_flags_or_throw read_squad_examples convert_examples_to_features SquadExample FeatureWriter main write_predictions model_fn_builder TrainingInstance create_int_feature create_instances_from_document create_training_instances write_instance_to_example_files main create_float_feature truncate_seq_pair create_masked_lm_predictions download_and_extract format_mrpc get_tasks main download_diagnostic read_examples InputFeatures input_fn_builder InputExample _truncate_seq_pair convert_examples_to_features main model_fn_builder ColaProcessor MnliProcessor InputExample RteProcessor file_based_convert_examples_to_features QnliProcessor QqpProcessor StsbProcessor convert_examples_to_features PaddingInputExample DataProcessor AXProcessor InputFeatures _truncate_seq_pair WnliProcessor Sst2Processor convert_single_example MrpcProcessor file_based_input_fn_builder MisMnliProcessor dumpModel mask_dense get_shape_list embedding_postprocessor create_attention_mask_from_input_mask reshape_from_matrix reshape_to_matrix assert_rank layer_norm_and_dropout attention_layer layer_norm create_initializer get_assignment_map_from_checkpoint embedding_lookup dropout gelu BertConfig transformer_model get_activation BertModel exportPrunedModel SparseColumnPruningRank pause logPerformanceHook NetworkPruningHook TaylorRanker InitializeGlobalStepHook create_optimizer AdamWeightDecayOptimizer validate_case_matches_checkpoint convert_by_vocab FullTokenizer BasicTokenizer convert_ids_to_tokens WordpieceTokenizer printable_text convert_tokens_to_ids load_vocab whitespace_tokenize convert_to_unicode _is_whitespace _is_control _is_punctuation create_attention_mechanism AttentionModel _create_attention_images_summary GNMTModel gnmt_residual_fn GNMTAttentionMultiCell single_worker_inference start_sess_and_load_model _decode_inference_indices multi_worker_inference get_model_creator load_data inference InferenceTest TrainOutputTuple Model EvalOutputTuple BaseModel InferOutputTuple _create_or_load_embed gradient_clip create_train_model load_model create_emb_for_encoder_and_decoder get_device_str create_infer_model compute_perplexity _get_embed_device create_or_load_model TrainModel _cell_list get_initializer create_rnn_cell avg_checkpoints _create_pretrained_emb_from_txt InferModel EvalModel print_variables_in_ckpt create_eval_model ExtraArgs _single_cell ModelTest PruningHook SparseColumnPruningRank create_or_load_hparams run_main ensure_compatible_hparams _add_argument add_arguments extend_hparams main create_hparams NMTTest _update_flags img2col_forward block_wise img2col_back_ward vector_vise pruning_fun tw_mix_fn tiled_wise_2d tiled_wise_1d tiled_wise_1d_mix element_wise tiled_wise_2d_mix run_internal_and_external_eval get_model_creator _sample_decode init_stats run_internal_eval update_stats print_step_info run_full_eval before_train _format_results process_stats _internal_eval _external_eval add_info_summaries run_avg_external_eval run_external_eval get_session run_sample_decode get_best_results print_weights train _get_ngrams compute_bleu _len_lcs _get_ngrams rouge rouge_l_summary_level rouge_n _recon_lcs _split_into_words _lcs rouge_l_sentence_level _union_lcs _f_p_r_lcs _get_word_ngrams create_test_hparams create_test_iterator _clean evaluate _word_accuracy _accuracy _rouge _bleu _moses_bleu EvaluationUtilsTest get_iterator BatchedInput get_infer_iterator IteratorUtilsTest debug_tensor check_tensorflow_version format_spm_text format_text print_time print_hparams get_config_proto print_out load_hparams format_bpe_text add_summary safe_exp maybe_parse_standard_hparams save_hparams MiscUtilsTest get_translation decode_and_evaluate create_standard_hparams load_vocab load_embed_txt check_vocab tokens_to_bytes _string_to_bytes create_vocab_tables VocabUtilsTest download_all_tf_models download_file _single_thread_download download_arch download_tf_model _parse_example_proto input_fn get_filenames parse_record parse_record_t recover_from_alexnet process_record_dataset normalize_alexnet train test input_function input_fn_train normalize input_fn_eval _aspect_preserving_resize _decode_crop_and_flip alexnet_preprocess_image _central_crop _smallest_size_at_least _mean_image_subtraction _resize_image preprocess_image PruningHook SparseColumnPruningRank img2col_forward block_wise img2col_back_ward vector_vise pruning_fun tw_mix_fn tiled_wise_2d tiled_wise_1d tiled_wise_1d_mix element_wise tiled_wise_2d_mix main AlexNet VGG16 model_fn ensure_dir root_dir Counter split sum values len append metric_fn print list get_variable get_pooled_output value BertModel segment_ids label_id input_mask append input_ids get_train_examples init_checkpoint output_dir file_based_convert_examples_to_features validate_case_matches_checkpoint eval_batch_size max_seq_length data_dir get_labels do_lower_case use_tpu lower MakeDirs num_train_epochs info FullTokenizer int warmup_proportion join file_based_input_fn_builder get_dev_examples train_batch_size len TPUClusterResolver TPUEstimator set_verbosity from_json_file model_fn_builder list initialize tpu_name bert_config_file range ConfigProto INFO evaluate print_evaluating_result define_train_eval_input_fn sparsity PER_HOST_V2 train NetworkPruningHook RunConfig join is_whitespace whitespace_tokenize version_2_with_negative warning SquadExample append len _DocSpan printable_text _improve_answer_span orig_answer_text length convert_tokens_to_ids append range doc_tokens InputFeatures question_text start output_fn info tokenize enumerate join _check_is_max_context namedtuple len join tokenize range length start min enumerate reshape get_shape_list transpose get_sequence_output matmul unstack bias_add int64 FixedLenFeature strip end_logit _get_best_indexes sorted defaultdict get_final_text _NbestPrediction end_logits OrderedDict append start_logit replace _compute_softmax start_logits info enumerate join namedtuple text _PrelimPrediction version_2_with_negative split join _strip_spaces iteritems BasicTokenizer len info verbose_logging tokenize find sorted append range enumerate len append exp validate_case_matches_checkpoint init_checkpoint do_lower_case do_predict do_train validate_flags_or_throw output_dir do_train do_predict convert_examples_to_features predict num_features predict_batch_size input_fn_builder close shuffle MakeDirs num_train_epochs FeatureWriter info FullTokenizer int warmup_proportion Random read_squad_examples train_batch_size len segment_ids tokens list convert_tokens_to_ids masked_lm_labels SerializeToString OrderedDict Example append masked_lm_positions value create_int_feature TFRecordWriter close info keys enumerate join write create_float_feature len Feature Feature list extend shuffle create_instances_from_document keys range len TrainingInstance extend append randint truncate_seq_pair create_masked_lm_predictions range len int list sorted min len shuffle MaskedLmInstance set add index append label round max enumerate pop len max_seq_length Glob extend masked_lm_prob dupe_factor create_training_instances short_seq_prob write_instance_to_example_files random_seed max_predictions_per_seq split print remove urlretrieve print join urlretrieve mkdir print join urlretrieve mkdir append split download_and_extract path_to_mrpc data_dir add_argument format_mrpc get_tasks tasks mkdir ArgumentParser parse_args download_diagnostic input_type_ids unique_id text_b _truncate_seq_pair unique_id text_a pop len read_examples input_file join text_b isinstance InputFeatures convert_tokens_to_ids len _truncate_seq_pair tokenize guid info append text_a enumerate segment_ids create_int_feature TFRecordWriter write SerializeToString close OrderedDict Example input_mask info input_ids enumerate convert_single_example convert_single_example global_variables concatenate print len array range flush run pow tanh sqrt pi lower name group OrderedDict match list_variables info layer_norm dropout one_hot reshape get_shape_list matmul gather expand_dims get_variable one_hot reshape get_shape_list layer_norm_and_dropout matmul assert_less_equal get_variable ones reshape get_shape_list float32 cast dropout mask_dense multiply get_shape_list reshape transpose float32 matmul transpose_for_scores expand_dims sqrt cast softmax float reshape_to_matrix int get_shape_list append reshape_from_matrix range reshape_to_matrix as_list assert_rank name shape append enumerate reshape ndims get_shape_list name integer_types ndims isinstance Variable ones identity matmul activation get_variable read flush trainable_variables list constant get_or_create_global_step gradients clip_by_global_norm group float32 apply_gradients cast int32 zip polynomial_decay CrossShardOptimizer AdamWeightDecayOptimizer match group isinstance PY3 PY2 isinstance PY3 PY2 OrderedDict append strip split category category startswith category ord LuongAttention BahdanauAttention expand_dims stack image transpose assert_same_structure map_structure time print_out print_time GNMTModel Model AttentionModel Session single_worker_inference start_sess_and_load_model multi_worker_inference close get_model_creator create_infer_model inference_indices load_data int min load_data len src_vocab_file tgt_vocab_file Graph src_vocab_file tgt_vocab_file Graph Graph tgt_vocab_file src_vocab_file constant slice load_embed_txt load_vocab print_out array _create_pretrained_emb_from_txt fixed_size_partitioner BasicLSTMCell print_out DeviceWrapper DropoutWrapper NASCell LayerNormBasicLSTMCell GRUCell ResidualWrapper __name__ append print_out single_cell_fn range _cell_list clip_by_global_norm scalar global_norm append sorted NewCheckpointReader print_out keys get_variable_to_shape_map time restore tables_initializer print_out run join dtype get_checkpoint_state load_checkpoint get_tensor print_out MakeDirs list_variables zeros time load_model latest_checkpoint tables_initializer print_out eval global_variables_initializer run safe_exp eval time print_time register add_argument add_hparam setattr hasattr join embed_prefix vocab_prefix src share_vocab residual tgt Exists metrics _add_argument num_decoder_layers check_vocab print_out getattr MakeDirs out_dir num_encoder_layers list setattr hasattr num_layers print_out getattr add_hparam maybe_parse_standard_hparams keys values metrics ensure_compatible_hparams extend_hparams getattr load_hparams print_hparams maybe_parse_standard_hparams save_hparams ckpt subword_option seed inference_input_file repr print_out hparams_path dirname list_devices inference_list latest_checkpoint inference_output_file MakeDirs random_seed inference_ref_file create_or_load_hparams join jobid train_fn inference_fn evaluate metrics num_workers out_dir inference create_hparams run_main join get_temp_dir append reshape abs reshape shape append range len percentile tolist append abs array range len percentile concatenate len sqrt append range split percentile concatenate split append range len percentile concatenate transpose split append range len tiled_wise_1d tiled_wise_2d int transpose shape append zeros range len percentile norm shape append zeros range len batch_size_placeholder src_placeholder iterator _sample_decode _internal_eval iterator iterator load_data _external_eval infer_batch_size run_external_eval avg_ckpts num_keep_ckpts avg_checkpoints run_avg_external_eval run_external_eval metrics _format_results run_internal_eval test_prefix run_sample_decode batch_size print_out add_summary safe_exp print_out time initializer batch_size epoch_step init_stats print_out run _sess name trainable_variables warning global_variables num_train_steps avg_ckpts get_model_creator save Session create_train_model steps_per_stats pruning_type print_time GFile get_config_proto init_stats run_internal_eval update_stats create_infer_model print_out add_summary getattr print_step_info run_full_eval log_device_placement before_train close FileWriter process_stats steps_per_external_eval add_info_summaries run_avg_external_eval join time run_external_eval get_session metrics graph PruningHook sparsity run_sample_decode get_best_results load_data out_dir create_eval_model _HookedSession append metrics add_summary compute_perplexity initializer run decode initializer len print_out add_summary randint get_translation run join setattr initializer get_session metrics print_out getattr save out_dir add_summary decode_and_evaluate save_hparams run tuple range Counter len _get_ngrams exp Counter zip float sum range len add set _split_into_words _lcs dict max range tuple _lcs intersection _get_word_ngrams len _len_lcs _split_into_words len _recon_lcs set _split_into_words union len _split_into_words len mean list map zip create_standard_hparams constant index_table_from_tensor from_tensor_slices index_to_string_table_from_tensor _accuracy _rouge _word_accuracy _bleu lstrip strip sub _clean zip append compute_bleu split rouge check_output search group call float constant make_initializable_iterator EOS_CHAR_ID map get_next lookup cast int32 batching_func constant make_initializable_iterator EOS_CHAR_ID prefetch shard group_by_window shuffle skip map apply get_next lookup filter cast int32 zip batching_func exp print flush decode isinstance write encode flush PY2 sorted list print_out keys values join print_out Exists print_out join print_out name Summary ConfigProto encode append isinstance len time print_out evaluate format_spm_text format_text tolist format_bpe_text encode as_list uint8 decode_raw to_int32 concat reshape info fill join basename Exists load_vocab print_out len index_table_from_file dict mkdir str root_dir download_arch urlretrieve join format print rfind endswith extractall close mkdir download ZipFile open endswith sanity_check handle_frozen_graph download_file handle_checkpoint update concat transpose cast VarLenFeature parse_single_example expand_dims values _parse_example_proto reshape TFRecordDataset from_tensor_slices get_filenames flat_map shuffle _parse_example_proto preprocess_image cast TFRecordDataset from_tensor_slices get_filenames shard num_input_pipelines input_pipeline_id shuffle interleave info pre_transform_fn transform_fn shuffle map take repeat prefetch batch sample_distorted_bounding_box random_flip_left_right decode_and_crop_jpeg extract_jpeg_shape stack unstack shape expand_dims minimum int32 cast float32 shape _smallest_size_at_least _aspect_preserving_resize _decode_crop_and_flip _central_crop _resize_image set_shape decode_jpeg convert_to_tensor _aspect_preserving_resize _decode_crop_and_flip divide _central_crop _resize_image set_shape expand_dims decode_jpeg VGG16 get_or_create_global_step model MomentumOptimizer insert_masks sparse_softmax_cross_entropy minimize get_collection identity group accuracy mean AlexNet image add_n UPDATE_OPS in_top_k scalar mask_values batch_size init_checkpoint localtime mini_finetune_steps str pruning_type Estimator LoggingTensorHook strftime latest_checkpoint arch_name time finetune_steps deepcopy root_dir makedirs PruningHook WarmStartSettings pruning_steps dirname abspath dirname abspath makedirs | # Accelerating Sparse DNN Models without Hardware-Support via Tile-Wise Sparsity [[arXiv]](https://arxiv.org/abs/2008.13006) [](https://doi.org/10.5281/zenodo.3900188) ``` @inproceedings{guo2020accelerating, title={Accelerating sparse DNN models without hardware-support via tile-wise sparsity}, author={Guo, Cong and Hsueh, Bo Yang and Leng, Jingwen and Qiu, Yuxian and Guan, Yue and Wang, Zehuan and Jia, Xiaoying and Li, Xipeng and Guo, Minyi and Zhu, Yuhao}, booktitle={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis}, pages={1--15}, year={2020} | 1,735 |
clinicalml/cfrnet | ['generalization bounds', 'causal inference'] | ['Estimating individual treatment effect: generalization bounds and algorithms'] | cfr/plotting.py cfr/util.py cfr_param_search.py cfr/loader.py cfr/cfr_net.py cfr_net_train.py cfr/evaluation.py evaluate.py cfr/logger.py main train run is_used_cfg save_used_cfg read_used_cfgs sample_config run load_config cfg_string evaluate load_config sort_by_config cfr_net pdist2 evaluate_result evaluate policy_range cf_nn policy_val NaNException evaluate_bin_att evaluate_cont_ate pehe_nn load_results load_single_result load_data load_result_file load_config Logger table_str_bin cap evaluation_summary fill_bounds plot_option_correlation plot_evaluation_bin plot_cfr_evaluation_cont select_parameters plot_format fix_log_axes plot_evaluation_cont plot_with_fill plot_cfr_evaluation_bin safe_sqrt pdist2sq pdist2 save_config wasserstein pop_dist log lindisc mmd2_lin load_data simplex_project validation_split mmd2_rbf load_sparse batch_size pred_loss iterations h_rep_norm abs log run str list pop_dist append sum range projection concatenate square set mean sqrt nan sample varsel output t isnan simplex_project global_variables_initializer x len lrate_decay AdagradOptimizer weights_pred set_random_seed lrate output_csv RMSPropOptimizer experiments exponential_decay Session log open seed exit placeholder GradientDescentOptimizer savetxt append cfr_net range val_part save_config tot_loss close repetitions decay datadir swapaxes data_test validation_split varsel savez minimize Variable dataform dstack AdamOptimizer load_data train run mkdir strftime outdir list keys join sorted keys read_used_cfgs cfg_string set join is_used_cfg print save_used_cfg call sample_config load_config list keys argsort append array load dump plot_evaluation_bin print VERBOSE plot_evaluation_cont load_config open int list append randint float range len policy_range isnan argsort mean any append dot T sum square pdist2 mean sqrt cf_nn square policy_val mean abs log pehe_nn mean sqrt square pehe_nn list T cf_nn shape evaluate_bin_att array evaluate_cont_ate append keys range list evaluate_result load_results array load_data append keys len load dict float close open basename print VERBOSE load_result_file isfile max load_config print VERBOSE load_single_result isfile append len load mean plot set_xticklabels axes draw min dict xlim max gca grid set_axisbelow mean sqrt std mean fill_between fill_bounds plot upper join max range len list print argmin mean argsort sum keys range len rand xticks list sorted ylabel title savefig append range plot close set mean mkdir xlim int xlabel min index figure len join sorted plot_option_correlation evaluation_summary mean dict select_parameters load_data len evaluation_summary select_parameters plot_with_fill sorted plot_option_correlation ylabel ylim title plot_format savefig legend range plot astype close mean xlim join int xlabel text min float32 dict load_data len evaluation_summary_bin plot_with_fill list argmin ylabel title plot_format savefig legend append sum range plot xscale astype close mean fix_log_axes keys print xlabel float32 len plot_with_fill list argmin ylabel shape title plot_format savefig legend sum range plot xscale close copy mean fix_log_axes keys xlabel unravel_index len int list permutation range print join close write open loadtxt load_sparse open int todense loadtxt coo_matrix open safe_sqrt square reduce_sum sign reduce_mean gather gather reduce_mean square reduce_sum to_float exp square reduce_sum gather transpose reduce_sum square matmul to_float gather pdist2 to_float safe_sqrt exp pdist2sq dropout ones concat reduce_max transpose matmul reduce_sum shape reduce_mean stop_gradient gather range cumsum list maximum range | # cfrnet Counterfactual regression (CFR) by learning balanced representations, as developed by Johansson, Shalit & Sontag (2016) and Shalit, Johansson & Sontag (2016). cfrnet is implemented in Python using TensorFlow 0.12.0-rc1 and NumPy 1.11.3. The code has _not_ been tested with TensorFlow 1.0. # Code The core components of cfrnet, i.e. the TensorFlow graph, is contained in cfr/cfr_net.py. The training is performed by cfr_net_train.py. The file cfr_param_search.py takes a configuration file as input and allows the user to randomly sample from the supplied parameters (given that there are multiple values given in a list. See configs/example_ihdp.txt for an example. A typical experiment uses cfr_param_search.py and evaluate.py as sub-routines. cfr_param_search is best used to randomly search the parameter space. In the output directory, it creates a log of which configurations have been used so far, so that the same experiment is not repeated. evaluate.py goes through the predictions produced by the model and evaluates the error. ## cfr_param_search The script _cfr_param_search.py_ runs a random hyperparameter search given a configuration file. Usage: ``` python cfr_param_search.py <config_file> <num_runs> | 1,736 |
cloudygoose/negativetraining_acl2020 | ['response generation'] | ['Negative Training for Neural Dialogue Response Generation'] | seq2seq/lm_adv_attacks.py seq2seq/more_adv_attacks.py seq2seq/neg_mal_advtrain_seq2seq.py seq2seq/lm_baseline.py seq2seq/latent_baseline.py seq2seq/lib_pdf.py seq2seq/neg_freq_posttrain_seq2seq.py para_adv_lists/process_ini.py para_adv_lists/res_500/getvocab.py lib_pdf.py seq2seq/myutil.py seq2seq/text_eval.py seq2seq/models.py seq2seq/advinput_seq2seq.py para_adv_lists/aug_train.py myutil.py data/swda_dialogue/process_lm.py seq2seq/adv_attacks.py ex_sample_normal kl_normal_diag ex_compute_mmd ex_normal_diag_logp onehot_maskgen ex_logsoftmax_idxselect compute_mmd ex_kl_normal_diag gumbel_softmax_sample sample_normal kl_discrete gaussian_kernel logsoftmax_idxselect normal_diag_logp sample_gumbel ex_gumbel_softmax_sample ex_kl_discrete length_sort getVocab clean_sen add_log_fn setLogger idx2onehot init_lstm_hidden check_memory mask_gen DialogueBatches countSenAcc MyStatDic MyBatchSentences_v2 force_onehot get_adv_seq2seq_mb adv_model_forward adv_gibbs_enum_mb hashing adv_enumerate_all adv_gibbsenum build_lis_tgt_w mle_train softmax_idx_el_glue decoder_forward adversarial_latent_optimize sample_compare_bleu mask_gen adversarial_input_optimize ex_sample_normal kl_normal_diag ex_compute_mmd ex_normal_diag_logp onehot_maskgen ex_logsoftmax_idxselect compute_mmd ex_kl_normal_diag gumbel_softmax_sample sample_normal kl_discrete gaussian_kernel logsoftmax_idxselect normal_diag_logp sample_gumbel ex_gumbel_softmax_sample ex_kl_discrete lm_adv_check lm_model_forward get_opt train HighwayMLP LSTMLM_onehot LSTM_onehot_D CNN_KIM_TWOINPUT_D encoder_decoder_forward get_samples beam_search RNNAttDecoder RNNLatentDecoder adv_globalenum adv_greedyflip build_lis_tgt_w length_sort getVocab clean_sen add_log_fn setLogger idx2onehot init_lstm_hidden check_memory mask_gen DialogueBatches countSenAcc MyStatDic MyBatchSentences_v2 force_onehot get_decay_co mle_train adv_train sample_compare_bleu test_entropy inf_data_gen mask_gen text_entropy ex_text_entropy ByteTensor cuda fill_ FloatTensor size rand cuda neg_ sample_gumbel log softmax Variable gumbel_softmax_sample print FloatTensor data randn Variable size cuda Variable print sample_normal ones data log_softmax size onehot_maskgen masked_select LongTensor backward Variable ones print grad SGD logsoftmax_idxselect step log Variable kl_discrete print FloatTensor size sum log Variable kl_normal_diag print FloatTensor sum log fill_ backward print Variable FloatTensor Normal size sum exp size expand t mm cuda print compute_mmd cuda size cuda range range len sort list zip str info open split append len fill_ zero_ max range len setFormatter addHandler StreamHandler Formatter FileHandler Process exit getpid info float size cuda cuda format write close info open range len init_lstm_hidden logsoftmax_idxselect cuda view m_decode_w_rnn_dp permute append sum range m_encode_w_rnn_dp update size eval item calMeanLogp Variable min repeat train len update deepcopy sampleBatch adv_model_forward backward Variable sort size len tolist mean item append range detach update LongTensor size adv_gibbs_enum_mb append randint range len strip readlines len info append split str join int compare_output print readlines exit len min split save info append range build_lis_tgt_w load str format len gibbsenum_attack_mb split startswith info append save sum build_lis_tgt_w size squeeze repeat m_decode_w_rnn_dp clip_grad_norm_ zero_grad init_lstm_hidden logsoftmax_idxselect cuda values list view tolist decoder_forward permute sum m_encode_w_rnn_dp size mean eval item info backward Variable extend parameters train step len tuple init_lstm_hidden cuda open str list tolist exit permute generate m_encode_w_rnn append range generate_samplemin cat clean_sen size close info join write repeat sentence_bleu tanh sum view backward Variable step zero_grad SGD repeat logsoftmax_idxselect cuda info forward max range len softplus zero_grad init_lstm_hidden SGD logsoftmax_idxselect cuda max m_embed view decoder_forward permute m_encode_w_rnn append sum range softmax_idx_el_glue mean softmax info backward Variable sigmoid step len view model size initHidden eval train cuda idx2onehot len join MyBatchSentences_v2 min range info append numpy cuda lm_model_forward len parameters exit Adam SGD clip_grad_norm_ zero_grad cuda view expand_as masked_select sum initHidden size eval info rnn criterion backward parameters get_opt step idx2onehot len view Variable size squeeze init_lstm_hidden repeat logsoftmax_idxselect permute m_encode_w_rnn cuda m_decode_w_rnn len sorted view sort size init_lstm_hidden expand eval permute m_encode_w_rnn append forward cuda range len generate_samplemin generate size tolist beam_search init_lstm_hidden index eval repeat unsqueeze permute m_encode_w_rnn append cuda range cat load str format len split startswith info append save sum greedyflip_attack_mb build_lis_tgt_w load str globalenum_attack_mb format len split startswith info append save sum build_lis_tgt_w MyBatchSentences_v2 DialogueBatches info size max range get_decay_co clip_grad_norm_ zero_grad cuda values str list tolist append next sum range get_adv_seq2seq_mb size mean info item float backward print encoder_decoder_forward extend parameters train step len get_decay_co append next range get_adv_seq2seq_mb float print encoder_decoder_forward get_samples DialogueBatches text_entropy info join log float range len print text_entropy split | This is code for "Negative Training for Neural Dialogue Response Generation", which is to appear in ACL 2020. The code uses pytorch 0.4, python 2.7, cuda 9.0, and nltk. The Switchboard data is provided. The main files are in seq2seq/ dir. === Train Baseline Models === Call lm_baseline.py or latent_baseline.py to train baseline models Set EXP_ROOT at the beginning of the main files to set the place you want to save your models To train them, for example: python lm_baseline.py/latent_baseline.py "COMMAND='train';" After training, use the test command to get PPL results: | 1,737 |
clovaai/CLEval | ['optical character recognition'] | ['CLEval: Character-Level Evaluation for Text Detection and Recognition Tasks'] | file_utils.py script.py validation.py web.py box_types.py arg_parser.py rrc_evaluation_funcs.py config/config.py str2bool get_midpoints Box point_distance corner_continuous_check QUAD POLY point_angle decode_utf8 load_zip_file load_zip_file_keys parse_single_file validate_clockwise_points parse_values_from_single_line convert_LTRB2QUAD print_help main_validation main_evaluation granularity_score cleval_evaluation lcs GlobalResult SampleResult get_element_total_length harmonic_mean eval_single_result validate_point_inside_bounds validate_min_max_bounds validate_clockwise_points validate_lines_in_file validate_text_line_format validate_data get_sample_info get_samples image delete_all static_gt result_image static get_submission image_thumb exit subm_image static_custom gt_image edit_method gt_file sample favicon image_name_to_id get_sample_id_from_num evaluate get_all_submissions index delete_method get_sample_from_num method fabs group match namelist append ZipFile read replace dict namelist ZipFile decode BOM_UTF8 replace startswith encode write exit pop join list isdigit validate_point_inside_bounds int validate_min_max_bounds replace validate_clockwise_points group convert_LTRB2QUAD box_type match split append float len replace sort parse_values_from_single_line append split update items validate_data_fn writestr SUBMIT_PATH list OUTPUT_PATH dumps write close pprint evaluate_method_fn GT_PATH ZipFile makedirs validate_data_fn SUBMIT_PATH print exit GT_PATH max range len RS parse_single_file TRANSCRIPTION CONFIDENCES prepare_det SampleResult BOX_TYPE E2E CRLF evaluation prepare_gt submit GT_SAMPLE_NAME_2_ID DET_SAMPLE_NAME_2_ID GlobalResult ProcessPoolExecutor decode_utf8 E2E load_zip_file shutdown len GT_SAMPLE_NAME_2_ID DET_SAMPLE_NAME_2_ID TRANSCRIPTION validate_lines_in_file BOX_TYPE CRLF load_zip_file CONFIDENCES decode_utf8 replace split replace namelist ZipFile dirname abspath namelist ZipFile dirname abspath namelist dirname abspath append ZipFile get_samples get_all_submissions int close int get_submission get_samples redirect abspath isfile dirname ZipFile extract int str read get_submission list get_sample_id_from_num items get_all_submissions close get_samples loads dirname abspath open ZipFile append makedirs int read get_sample_id_from_num loads dirname abspath ZipFile int BytesIO thumbnail close getvalue dict save get_sample_from_num open dict int get_sample_from_num read dict dirname abspath ZipFile read dict dirname abspath ZipFile str read dict dirname abspath ZipFile str read dict dirname abspath ZipFile commit cursor rename abspath save str SUBMIT_PATH connect dirname filename get replace close splitext execute setattr main_evaluation remove isfile lastrowid join remove dirname abspath rmdir walk get join remove cursor commit isdir close connect dirname abspath execute rmdir walk get commit cursor close connect dirname abspath execute fetchall commit cursor close connect dirname abspath execute commit cursor close connect dirname abspath execute fetchone | # CLEval: Character-Level Evaluation for Text Detection and Recognition Tasks Official implementation of CLEval | [paper](https://arxiv.org/abs/2006.06244) ## Overview We propose a Character-Level Evaluation metric (CLEval). To perform fine-grained assessment of the results, *instance matching* process handles granularity difference and *scoring process* conducts character-level evaluation. Please refer to the paper for more details. This code is based on [ICDAR15 official evaluation code](http://rrc.cvc.uab.es/). ### Simplified Method Description  ## Notification * 15 Jun, 2020 | initial release - the reported evaluation results in our paper is measured by setting the ```CASE_SENSITIVE``` option as ```False```. ## Supported annotation types | 1,738 |
clovaai/CRAFT-pytorch | ['scene text detection'] | ['Character Region Awareness for Text Detection'] | file_utils.py refinenet.py imgproc.py test.py craft.py basenet/vgg16_bn.py craft_utils.py CRAFT double_conv warpCoord getDetBoxes_core getPoly_core adjustResultCoordinates getDetBoxes get_files list_files saveResult normalizeMeanVariance cvt2HeatmapImg resize_aspect_ratio loadImage denormalizeMeanVariance RefineNet copyStateDict str2bool test_net init_weights vgg16_bn matmul threshold roll max clip connectedComponentsWithStats argmin MORPH_RECT shape append minAreaRect range astype copy sqrt dilate int uint8 getStructuringElement reshape boxPoints min zeros array warpCoord line arange zeros inv float32 reversed shape array getPerspectiveTransform append median warpPerspective range enumerate len getPoly_core getDetBoxes_core len array range len list_files join lower splitext append walk basename imwrite mkdir splitext array COLOR_GRAY2RGB imread array cvtColor astype float32 uint8 astype copy shape max zeros resize applyColorMap uint8 astype COLORMAP_JET OrderedDict join items startswith unsqueeze numpy getDetBoxes cuda show_time normalizeMeanVariance cvt2HeatmapImg permute range format resize_aspect_ratio hstack copy canvas_size time Variable print adjustResultCoordinates len data isinstance fill_ Conv2d xavier_uniform_ normal_ zero_ BatchNorm2d Linear | ## CRAFT: Character-Region Awareness For Text detection Official Pytorch implementation of CRAFT text detector | [Paper](https://arxiv.org/abs/1904.01941) | [Pretrained Model](https://drive.google.com/open?id=1Jk4eGD7crsqCCg9C9VjCLkMN3ze8kutZ) | [Supplementary](https://youtu.be/HI8MzpY8KMI) **[Youngmin Baek](mailto:[email protected]), Bado Lee, Dongyoon Han, Sangdoo Yun, Hwalsuk Lee.** Clova AI Research, NAVER Corp. ### Sample Results ### Overview PyTorch implementation for CRAFT text detector that effectively detect text area by exploring each character region and affinity between characters. The bounding box of texts are obtained by simply finding minimum bounding rectangles on binary map after thresholding character region and affinity scores. <img width="1000" alt="teaser" src="./figures/craft_example.gif"> ## Updates | 1,739 |
clovaai/EXTD_Pytorch | ['face detection'] | ['EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse'] | bbox_setup.py logger.py mobileFacenet_64_PReLU.py data/factory.py data/egohand.py prepare_hand_dataset.py eval_tools/evaluation.py tools/eval_head.py EXTD_32.py tools/eval_hand.py EXTD_64.py train.py wider_test.py EXTD_48.py prepare_wider_data.py layers/functions/detection.py layers/bbox_utils.py layers/modules/l2norm.py data/config.py mobileFacenet_32_PReLU.py tools/pascal_test.py utils/augmentations.py layers/modules/multibox_loss.py data/vochead.py demo.py data/widerface.py tools/wider_test.py tools/afw_test.py tools/anchor_matching_test.py tools/fddb_test.py mobileFacenet_48_PReLU.py setup.py layers/functions/prior_box.py tools/detect.py detect EXTD upsample build_extd add_extras_dwc add_extras_mobileFace multibox add_extras mobileFacenet EXTD upsample build_extd add_extras_dwc add_extras_mobileFace multibox add_extras mobileFacenet EXTD upsample build_extd add_extras_dwc add_extras_mobileFace multibox add_extras mobileFacenet Logger conv_1x1_bn Max_AvgPool InvertedResidual InvertedResidual_dwc conv_bn Net DWC gated_conv1x1 conv_1x1_bn Max_AvgPool InvertedResidual InvertedResidual_dwc conv_bn Net DWC gated_conv1x1 conv_1x1_bn Max_AvgPool InvertedResidual InvertedResidual_dwc conv_bn Net DWC gated_conv1x1 generate_file parse_wider_file wider_data_file val compute_flops adjust_learning_rate train str2bool bbox_vote multi_scale_test flip_test get_data detect_face eval_wider HandDetection dataset_factory detection_collate VOCDetection VOCAnnotationTransform WIDERDetection detection_collate image_eval read_pred_file get_preds get_gt_boxes get_gt_boxes_from_txt voc_ap evaluation img_pr_info norm_score dataset_pr_info decode nms intersect match_ssd log_sum_exp jaccard center_size match point_form encode Detect PriorBox L2Norm detect_face anchor_match_count save_pkl anchor_match_ssd_count all_np plot_anchor_match dyna_anchor test_net evaluate_detections get_voc_results_file_template voc_ap write_voc_results_file Timer voc_eval do_python_eval get_output_dir parse_rec test_net evaluate_detections get_voc_results_file_template voc_ap write_voc_results_file Timer voc_eval do_python_eval get_output_dir detect_face detect_face bbox_vote multi_scale_test flip_test get_data detect_face eval_wider join basename imwrite Variable convert astype shape sqrt to_chw_bgr unsqueeze cuda resize save_dir array open Net enumerate enumerate print out_channels enumerate add_extras_mobileFace multibox mobileFacenet format len write close unique array open int strip split append range enumerate format len write close TRAIN_FILE VAL_FILE parse_wider_file range open isinstance out_channels groups in_channels Conv2d modules zero_grad report adjust_learning_rate cuda range val format lr item gamma net enumerate time criterion backward Variable print EPOCHES step scalar_summary eval param_groups lr data Variable astype to_chw_bgr unsqueeze resize numpy cuda net column_stack detect_face flip zeros shape detect_face row_stack minimum maximum delete row_stack tile zeros sum max join WIDER_DIR loadmat format load list print NUM_CLASSES build_extd get_data eval load_state_dict keys WIDERDetection VAL_FILE TRAIN_FILE VOCDetection DIR append FloatTensor join loadmat load join list dump print readlines close len astype append exists open astype join read_pred_file tqdm dict set_description listdir items list min max ones copy zeros bbox_overlaps range len astype range len zeros range concatenate size maximum sum range set_description img_pr_info image_eval str list append range format get_preds astype norm_score dataset_pr_info print get_gt_boxes tqdm voc_ap savemat zeros len clamp size min expand max intersect expand_as squeeze_ lt sort size jaccard clone gt index_fill_ eq point_form encode sum max range squeeze_ size jaccard index_fill_ point_form encode max range log cat max mul sort new clamp index_select resize_as_ long int size copy shape Tensor range max decode OVERLAP_THRESH all_np INPUT_SIZE view shape expand_as range LongTensor concatenate astype sqrt enumerate int print clone match Tensor len decode int LongTensor view print match_ssd concatenate clone astype sqrt shape all_np expand_as Tensor INPUT_SIZE range enumerate len size array unique anchor_match_count dump close anchor_match_ssd_count open show subplot list plot xlabel set_yticks grid ylabel save_pkl figure legend zeros array range len arange max cumsum argmax max range eps format astype mkdir float enumerate minimum join print sort maximum voc_ap argsort zeros array len join makedirs join makedirs print format get_voc_results_file_template enumerate join format print get_voc_results_file_template mean boxes fnames mkdir voc_eval enumerate data imwrite to_chw_bgr unsqueeze resize cuda view shape tic range format evaluate_detections size astype sqrt pull_image toc join Variable print float32 t rectangle numpy get_output_dir len write_voc_results_file do_python_eval int parse findall text append find parse_rec sum bool flip_test row_stack cuda open encode range format multi_scale_test build_s3fd sqrt detect_face enumerate join time bbox_vote convert write tqdm array makedirs | ## EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse ##
A PyTorch Implementation of Extremely Tiny Face Detector via Iterative Filter Reuse
YoungJoon Yoo, Dongyoon Han, Sangdoo Yun
https://arxiv.org/abs/1906.06579
| 1,740 |
clovaai/SATRN | ['scene text recognition'] | ['On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention'] | src/networks/Network.py src/inference.py src/dataset.py src/networks/CRNN.py src/networks/GRCNN.py src/networks/SAR.py src/flags.py src/networks/SATRN.py src/networks/RARE.py src/train.py src/eval.py src/networks/AON.py src/networks/layers.py src/utils.py src/constant.py src/create_tfrecord.py src/networks/FAN.py get_image make_example _int64_feature gen_data parse_gt _bytes_feature main process_fn gen_shard DatasetLodaer get_stoi_table Flags dict_to_namedtuple inference set_seed log_formatted _average_gradients _clip_gradients create_model_dir main get_logger validate get_network get_error get_optimizer adjust_string get_labels get_string count_available_gpus add_special_symbol_to_charset get_session_config text_length single_tower load_charset get_init_trained get_session get_itos_table cyclic_learning_rate _get_init_pretrained get_scaffold AON CRNN FAN GRCNN residual_block pool_layer norm_layer dense_layer rnn_layer attention_decoder rnn_layers conv_layer _attention_decoder Network RARE SAR depthwise_conv_layer SATRN DepthwiseConv2D makedirs parse_gt sum max len join str format print isfile range gen_shard get_image format make_example TFRecordWriter print write SerializeToString close zip join glob strip close lower dirname open append split size read BytesIO open join Example len print Fire time format items sorted list namedtuple FLAGSTuple eval keys lowercase alphanumeric use_rgb charset get_network set_shape Session run placeholder IMREAD_COLOR get_string expand_dims imread sparse_to_dense get get_logits restore_model IMREAD_GRAYSCALE model_path load_charset preprocess_image get_init_trained uint8 print reshape get_prediction adjust_string len append stack reduce_mean zip clip_by_global_norm list zip join makedirs RotatingFileHandler join format getLogger addHandler StreamHandler setLevel INFO makedirs seed format print set_random_seed info batch_size SUMMARIES cpu_count charset get_network extra_update_ops model_dir Saver create_model_dir get_optimizer seed set_seed get_collection available _average_gradients get_default_graph append count_available_gpus get_logger range get get_or_create_global_step tune_from get_session_config single_tower prediction FileWriter info load_charset optimizer merge get_init_trained join log_formatted DatasetLodaer text reduce_mean total_parameters profile loss get_scaffold Saver SUMMARIES group get_collection tables_initializer Scaffold global_variables_initializer local_variables_initializer merge _sess decode int replace get_string zip append adjust_string run list_local_devices len ConfigProto Saver GLOBAL_VARIABLES get_collection join count_nonzero to_int64 text reduce_join edit_distance lookup cast int32 sparse_tensor_to_dense get_itos_table len format import_module getattr network net_cls load readline add_special_symbol_to_charset splitext exists open list learning_rate Variable GradientDescentOptimizer AdamOptimizer use_clr RMSPropOptimizer AdadeltaOptimizer cyclic_learning_rate scalar join lower decode relu name norm_layer conv2d conv_layer batch_norm name norm_layer activation conv2d max_pooling2d batch_normalization concat bidirectional_dynamic_rnn stack _Linear output_size batch_norm name norm_layer activation xavier_initializer | ## On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention <p align="center"> <br> <img width="650" alt="teaser" src="./figures/teaser-satrn.png"> <br> <b>Figure. Self-attention text recognition network(SATRN)</b> </p> <br> <p align="center"> <img alt="att" src="./figures/att.png"> | 1,741 |
clovaai/TedEval | ['scene text detection'] | ['TedEval: A Fair Evaluation Metric for Scene Text Detectors'] | config/config.py web.py rrc_evaluation_funcs.py script.py validate_point_inside_bounds load_zip_file_keys validate_clockwise_points validate_lines_in_file decode_utf8 print_help main_validation get_tl_line_values load_zip_file get_tl_line_values_from_file_contents validate_tl_line main_evaluation evaluation_imports evaluate_method default_evaluation_params validate_data get_sample_info subm_xml get_samples image delete_all static_gt result_image static get_submission image_thumb exit subm_image result_xml static_custom gt_image edit_method gt_file sample favicon image_name_to_id get_sample_id_from_num evaluate get_all_submissions index delete_method get_sample_from_num gt_video method write exit group match namelist append ZipFile append namelist ZipFile replace decode BOM_UTF8 replace startswith encode decode_utf8 replace split get_tl_line_values validate_point_inside_bounds int replace validate_clockwise_points group match float replace argsort append get_tl_line_values split update default_evaluation_params_fn validate_data_fn writestr list items write dumps close dict print_help evaluate_method_fn ZipFile makedirs update default_evaluation_params_fn validate_data_fn print exit dict load_zip_file validate_lines_in_file compute_ap char_fill str list one_to_one_match many_to_one_match decode_utf8 append sum polygon_from_points range polygon_to_points aspectRatio set import_module load_zip_file empty get_tl_line_values_from_file_contents float items gtBoxtoChars namedtuple int8 one_to_many_match rectangle_to_polygon Rectangle get_intersection zeros center_distance len replace namelist ZipFile dirname abspath namelist ZipFile dirname abspath namelist dirname abspath append ZipFile get_samples get_all_submissions int close int get_submission get_samples redirect abspath isfile dirname ZipFile extract int str read get_submission list get_sample_id_from_num items get_all_submissions close get_samples loads dirname abspath open ZipFile append makedirs int read get_sample_id_from_num loads dirname abspath ZipFile int BytesIO thumbnail close getvalue dict save get_sample_from_num open dict int get_sample_from_num read dict dirname abspath ZipFile read dict dirname abspath ZipFile read dict dirname abspath ZipFile str read dict dirname abspath ZipFile str read dict dirname abspath ZipFile str read dict dirname abspath ZipFile str read dict dirname abspath ZipFile commit cursor rename save abspath validate_data str list evaluate_method default_evaluation_params connect dirname filename get replace close import_module splitext execute main_evaluation items remove isfile lastrowid join remove dirname abspath rmdir walk get join remove cursor commit isdir close connect dirname abspath execute rmdir walk get commit cursor close connect dirname abspath execute fetchall commit cursor close connect dirname abspath execute commit cursor close connect dirname abspath execute fetchone | clovaai/TedEval | 1,742 |
clovaai/WCT2 | ['image stylization', 'style transfer'] | ['Deep Photo Style Transfer', 'A Closed-form Solution to Photorealistic Image Stylization', 'Photorealistic Style Transfer via Wavelet Transforms'] | utils/io.py utils/core.py transfer.py model.py WavePool WaveUnpool WaveDecoder get_wav WaveEncoder is_image_file WCT2 get_all_transfer run_bulk svd feature_wct get_rank get_squeeze_feat wct_core wct_core_segment Timer change_seg open_image mkdir compute_label_info load_segment ones transpose Conv2d expand sqrt unsqueeze ConvTranspose2d net append set content join get_all_transfer print style transfer_at_encoder output add set tqdm load_segment device splitext transfer_at_decoder to listdir transfer_at_skip image_size size clone mean t div expand_as mm size squeeze range svd min mean get_rank pow t clamp_ expand_as get_squeeze_feat mm max diag squeeze transpose clone index_select resize get_index index_copy_ wct_core wct_core view_as wct_core_segment CenterCrop size ToTensor Compose append open sum asarray zeros abs range change_seg CenterCrop size Resize transform open size where unique zeros max makedirs | <img src='./figures/day2sunset.gif' align="right" width=400>
# WCT2 (ICCV 2019 accepted)
Photorealistic Style Transfer via Wavelet Transforms | [paper](https://arxiv.org/abs/1903.09760) | [supplementary materials](https://github.com/clovaai/WCT2/blob/master/%5Bsupplementary%20materials%5D%20Photorealistic_Style_Transfer_via_Wavelet_Transforms.pdf) | [video stylization results](https://youtu.be/o-AgHt1VA30)
| 1,743 |
clulab/timenorm | ['semantic parsing', 'semantic composition'] | ['From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations'] | src/main/python/subToSuperInterval/analyze_duplicates.py src/main/python/model_training.py src/main/python/genranddates.py src/main/python/ruleLinking.py src/main/python/preprocess.py src/main/python/anafora_funct.py src/main/python/output.py src/main/python/read_files.py src/main/python/subToSuperInterval/sub_to_super_interval.py src/main/python/process_functions.py src/main/python/preprocess_functions.py get_types get_schema trainging load_hdf5 span2xmlfiles main sentence_length generate_output_multiclass get_train features_extraction get_sample_weights_multiclass document_level_2_sentence_level output_encoding get_list_name xml_tag_in_sentence create_class_weight main split_by_sentence get_idx_from_sent extract_xmltag_anafora extract_xmltag_anafora_pred word_pos_2_character_pos text_normalize get_unicode get_pos_sentence get_explict_label split_sentence_based_on_rules add_start_end rule_based_tokenizer tokenize_span addannotation_to_dict get_implict_label extract_xmltag_timeml get_words spans found_location_with_constraint get_gold_dict metrics evaluate make_prediction_function_multiclass prob2classes_multiclasses loc2span prob2classes_multiclasses_multioutput get_counts hot_vectors2class_index calculate_score pro2classes_binaryclass readfrom_txt textfile2list create_folder save_hdf5 movefiles counterList2Dict load_hdf5 readfrom_json readfrom_pickle savein_json savein_pickle process_doc get_relation main sub_to_super get parse dict findall bool split dict split close open list Bidirectional Explicit_operator Implicit_operator Gru_out_1 save Gru_out_2 Input set_weights ModelCheckpoint load_model Interval_output Model get_weights Gru_out_6 Gru_out_3 Dense Gru_out_4 compile print fit Gru_out_5 CSVLogger history summary LSTM makedirs str AnaforaEntity append AnaforaData indent append range found_location_with_constraint join list textfile2list create_folder counterList2Dict make_prediction_function_multiclass len loc2span span2xmlfiles dict readfrom_json to_file save append range savein_pickle enumerate int list load_model sentence_length divide range float round generate_output_multiclass len replace savein_json textfile2list readfrom_json savein_json list print len rule_based_tokenizer sent_tokenize append regexp_span_tokenize spans sorted list keys append append range count_nonzero items list asarray counterList2Dict dict zeros float sum range values enumerate append list create_class_weight readfrom_txt join list defaultdict text_normalize extract_xmltag_anafora sort xml_tag_in_sentence range split_by_sentence savein_json len join list asarray save_hdf5 print makedirs readfrom_json append range get_idx_from_sent len get_explict_label get_sample_weights_multiclass save textfile2list list defaultdict counterList2Dict get_implict_label append sum range asarray save_hdf5 set readfrom_json enumerate join int print repeat zeros len features_extraction output_encoding list word_tokenize append find append type items sorted text from_file annotations dict OrderedDict _tag_to_property_xml addannotation_to_dict items sorted from_file annotations dict OrderedDict addannotation_to_dict items sorted print from_file annotations dict OrderedDict append append list find append list print search split split_sentence_based_on_rules popleft add_start_end deque append spans list word_tokenize StanfordPOSTagger print tag append tokenize_span list print append list print match append bool range len append list category append list argmax makedirs prob2classes_multiclasses predict prob2classes_multiclasses_multioutput append list index append list range len append list len print items list items list get_counts print get_gold_dict join extract_xmltag_anafora_pred readfrom_txt metrics readfrom_json calculate_score range exists len join makedirs print close create_folder close create_folder read readfrom_txt list splitlines append create_folder File create_dataset range len create_folder replace copy dict rstrip endswith search open str list sorted map strftime title append parse get_relation Element close listdir pop join read remove text2num text makedirs write extend dict sub split findall find join sorted items print from_file annotations Counter walk join from_file annotations id to_file walk makedirs | # timenorm The timenorm library provides models for finding natural language expressions of dates and times and converting them to a normalized form. ## Text to time expressions with the neural parser The primary entry point for the library is the `TemporalNeuralParser` class, which implements a character-based recurrent neural network for finding and normalizing time expressions, as described in: > Egoitz Laparra, Dongfang Xu, and Steven Bethard. 2018. > [From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations](https://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00025). > In: Transactions of the Association for Computational Linguistics 2018, Vol. 6, pp. 343–356 | 1,744 |
cmry/amica | ['domain generalization'] | ['Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity'] | corpora/xu/to_csv.py neural.py corpora/vanhee/nl/askfm_parse.py corpora/vanhee/nl/tag_ask.py evaluation.py experiments.py corpora/bretschneider/to_csv.py corpora/vanhee/to_csv.py corpora/xu/retrieve.py models.py corpora/vanhee/en/askfm_parse.py corpora/kontostathis/myspace/get_labels.py reader.py utils.py corpora/kontostathis/myspace/to_csv.py corpora/vanhee/to_json.py Evaluation DutchCompare EnglishCompare select_model BertFeatures WordEmbeddings MajorityBaseline BayesFeatures VocabularyProcessor AttLayer ReproductionNeuralNetwork merge_datasets Dataset Reader block_printing test_report call_experiment sk_clf_report debug_tests clean_xml entry_to_data convert dict_to_csv annotations err_check files_to_dict get_batch batcher attach_api pop deepcopy list author tuple subset test id append train items format asarray unique_labels warn dict startswith zip precision_recall_fscore_support max _check_targets len run print deepcopy call_experiment __dict__ print test_report findall str format replace join sorted format glob print close add print append items list err_check entry_to_data convert annotations writer join dump list items writerow open OAuthHandler set_access_token str list format print index id zip statuses_lookup | # Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity Repository for the work described in [Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity](https://arxiv.org/abs/1910.11922). Code is released under the GPL-v3 license. > **Access to data**: If you'd like access to the Ask.fm or Simulated corpus please contact [us](https://github.com/cmry). Also see [here](https://github.com/cmry/amica/blob/master/README.md#data). If you use anything related to the repository or paper, please cite the following work: ``` @article{emmery2019current, title={Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity}, author={Emmery, Chris and Verhoeven, Ben and De Pauw, Guy and Jacobs, Gilles and Van Hee, Cynthia and Lefever, Els and Desmet, Bart and Hoste, | 1,745 |
cmry/style-obfuscation | ['style transfer'] | ['Style Obfuscation by Invariance'] | src/train_sae.py src/train_seq2seq.py src/data.py src/utils.py src/adversary.py src/obfuscate.py BibleAdversary load_sents load_pairs add_noise chunks conditional_report report make_report_hook make_report_hook report make_check_hook append tolist conditional translate_beam zip enumerate conditional translate_beam tolist zip | # Style Obfuscation by Invariance Repository for the work described in [Style Obfuscation by Invariance](http://aclweb.org/anthology/C18-1084), presented at [COLING 2018](http://www.coling2018.org/). Code is released under the MIT license, the data under [CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/). If you use anything in our repository, please cite the following work: ``` @inproceedings{emmery2018style, title={Style Obfuscation by Invariance}, author={Emmery, Chris and Manjavacas, Enrique and Chrupa{\l}a, Grzegorz}, journal={COLING 2018}, pages={984--996}, year={2018} | 1,746 |
cnap/grammaticality-metrics | ['grammatical error correction'] | ["There's No Comparison: Reference-less Evaluation Metrics in Grammatical Error Correction"] | codalab/scoring_program/m2scorer/scripts/levenshtein.py heilman-et-al/linkparser.py codalab/scoring_program/m2scorer/scripts/token_offsets.py heilman-et-al/feature_extractor.py codalab/scoring_program/imeasure/candgen.py heilman-et-al/grammatical_model.py codalab/scoring_program/m2scorer/scripts/nuclesgmlparser.py codalab/scoring_program/imeasure/align.py codalab/scoring_program/m2scorer/scripts/Tokenizer.py codalab/scoring_program/gleu.py codalab/scoring_program/imeasure/ieval.py codalab/scoring_program/imeasure/m2_to_ixml.py codalab/scoring_program/m2scorer/m2scorer.py codalab/scoring_program/m2scorer/scripts/combiner.py codalab/scoring_program/m2scorer/scripts/m2scorer.py codalab/scoring_program/m2scorer/scripts/util.py codalab/scoring_program/evaluate.py codalab/scoring_program/sentence_scores.py compute_m2 compute_im call_lt compute_gleu GLEU Alignment Candidate CandidateGenerator SentCorrectionsIndex IMeasure get_type edit_has_overlap cluster_has_overlap group_by_alternatives print_usage load_annotation print_usage load_annotation best_edit_seq_bf transitive_arcs matchEdit comp_r shrinkEdit precision matchSeq f1 set_weights get_edits batch_pre_rec_f1 get_distance next_identical_edge batch_recall batch_multi_pre_rec_f1 batch_f1 levenshtein_distance pre_rec_f1 merge_edits recall edit_graph merge_graph prev_identical_edge comp_f1 f1_suffstats levenshtein_matrix comp_p equals_ignore_whitespace_casing get_prev_edges batch_precision get_next_edges print_usage load_annotation nuclesgmlparser PTBTokenizer DummyTokenizer fix_cp1252codes pairs max_dict intersect paragraphs frange clean_utf8 isASCII smart_open min_dict softmax randint sort_dict uniq TooLongError check_path FeatureLoader FluencyModel LinkParser load_sources print run_iterations write GLEU load_references load_m2_annotation write IMeasure write str int communicate write split array Popen len append sort groupby set decode read int join items paragraphs close smart_open splitlines append split print int list best_edit_seq_bf transitive_arcs print set_weights len reversed levenshtein_matrix matchSeq encode edit_graph split merge_graph int best_edit_seq_bf items transitive_arcs zip comp_f1 print set_weights comp_r len levenshtein_matrix comp_p matchSeq encode float edit_graph split list best_edit_seq_bf transitive_arcs zip comp_f1 print set_weights comp_r reversed levenshtein_matrix comp_p matchSeq encode edit_graph split deepcopy len split deepcopy print matchEdit reversed append range len best_edit_seq_bf transitive_arcs matchSeq levenshtein_matrix split edit_graph set_weights best_edit_seq_bf transitive_arcs print len matchSeq levenshtein_matrix split encode float edit_graph set_weights append float range len append append deepcopy sorted list print reversed append keys range len remove get_distance print merge_edits append float range len append deepcopy sorted list print min append keys levenshtein_matrix append range len endswith append idfun list items sorted sort append is_separator decode str isinstance search sub type __iter__ next append len max | # Metrics for evaluating grammatical error corrections These metrics were used in [Courtney Napoles, Keisuke Sakaguchi, and Joel Tetreault. _There's No Comparison: Reference-less Evaluation Metrics in Grammatical Error Correction_. EMNLP 2016](https://www.aclweb.org/anthology/D/D16/D16-1228.pdf) If you use this code or the accompanying CodaLab evaluation, please cite: ``` @InProceedings{napoles-sakaguchi-tetreault:2016:EMNLP2016, author = {Napoles, Courtney and Sakaguchi, Keisuke and Tetreault, Joel}, title = {There's No Comparison: Reference-less Evaluation Metrics in Grammatical Error Correction}, booktitle = {Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing}, month = {November}, | 1,747 |
cnrpman/procedural-extraction | ['relation extraction'] | ['Eliciting Knowledge from Experts:Automatic Transcript Parsing for Cognitive Task Analysis', 'Eliciting Knowledge from Experts: Automatic Transcript Parsing for Cognitive Task Analysis'] | fuzzy_matching/__init__.py pipeline/target_processor.py utils/spacytokenizer.py action_phrase_extraction.py fuzzy_matching/dist.py models/bert_modeling_posattention.py pattern_extraction/corenlp.py script/reduce.py paper/figure_sample.py fuzzy_matching/dist_exact.py paper/curve_context_acc.py pipeline/source_processor.py pipeline/target_matching.py fuzzy_matching/dist_exbert.py fuzzy_matching/manual_rules.py models/__init__.py extract_samples.py paper/figure_pipeline.py pipeline/__init__.py utils/input_context_sample.py utils/__init__.py pipeline/dsbuilder_relation.py models/bert_modeling_mask.py paper/tablebuilder_sampling.py paper/figure_predict.py pipeline/dsbuilder.py utils/path.py paper/curve_context.py fuzzy_matching/dist_embavg.py fuzzy_matching/measurer_glove.py paper/figure_context.py models/bert_extractor.py paper/figure_mask2.py train_bert_context_classifier.py fuzzy_matching/measurer_embed.py fuzzy_matching/dist_manual.py paper/tablebuilder_context.py pattern_extraction/__init__.py pipeline/dsbuilder_seqlabel.py pipeline/relation_preprocessor.py utils/utilities.py create_dataset.py models/bert_modeling_inputoffsetemb.py main main InputFeatures _truncate_seq_pair warmup_linear convert_examples_to_features main get_nearest_method get_method_names register_dist_adaptor embavg_adaptor exact_adaptor extracted_bert_adaptor manual_adaptor manual_rules L2norm EmbeddingMeasurer mle dot GloveMeasurer read_examples InputFeatures BertExtractor InputExample _truncate_seq_pair convert_examples_to_features BertOffsetembModel BertOffsetEmbeddings BertOffsetForSequenceClassification BertMaskForSequenceClassification BertPosattnForSequenceClassification ln_func ln_func ln_func2 ln_func ln_func ln_func retrieve_head retrieve_result retrieve_head retrieve_result contain_ban_word get_token get_name get_dep filter_verb split_s filter_sen save_aps filter_if get_token_str get_reg main load_aps get_next_punct get_ann register_dsbuilder get_builder_names get_builder builder_relation_dataset builder_seqlabel_dataset RelationProcessor inflate_examples DataProcessor SourceProcessor test match retrieve split TargetProcessor InputContextSampleSentence InputContextSample Tokenizer convert_int posstr test_prune prune basicConfig list dir_extracted get_builder add_argument extracted map parse_known_args builder ArgumentParser info type split datasetid src_ref str tgt retrieve src_retok eval enumerate dir_data SourceProcessor src print output match inflate_examples join InputFeatures convert_tokens_to_ids _truncate_seq_pair info append enumerate len pop len gradient_accumulation_steps from_pretrained do_eval get_train_examples BertAdam DataParallel device output_dir do_train open seed data_dir get_labels comment device_count dir_check parse_args to manual_seed_all get_dataloader SummaryWriter format dump close do_eval_on_train lower num_train_epochs manual_seed trange do_manual load int join log_dir bert_model train_epoch named_parameters train_batch_size len GloveMeasurer add_argument_group add_argument Tokenizer parse_known_args dir_glove Tokenizer parse_known_args BertExtractor add_argument add_argument_group add_argument parse_known_args text_b tokenize text_a append strip InputExample text text append enumerate load join zip append open lower range enumerate len enumerate range range len contain_ban_word range append get_token format get_dep get_token_str append get_next_punct get_token annotate append filter_sen split_s replace strip get_reg get_ann print format isfile seed parse_args add_argument parse_args add_argument list right left convert_block append enumerate print SourceProcessor split_ifthen_cite prune update add_labels list print retrive_positions copy TargetProcessor set shrink_next update_next enumerate split get_toked_ngrams_line open list parse_known_args append range get_toked_ngrams nearest set eval zip info enumerate load addbin print src_sens extend dict no_ref get_nearest_method split method len int range len len strip split print prune | cnrpman/procedural-extraction | 1,748 |
coby1729/neural_style | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | python/model.py python/neural_transfer.py python/util.py Normalization StyleLoss gram_matrix ContentLoss get_style_model_and_losses get_input_optimizer run_style_transfer image_loader show_images convert_to_image save t size mm view children format isinstance Sequential StyleLoss MaxPool2d add_module Conv2d len eval ReLU ContentLoss BatchNorm2d to range append detach LBFGS format clamp_ save get_style_model_and_losses step get_input_optimizer cpu squeeze unloader ToPILImage show subplot convert_to_image subplots_adjust imshow title figure range len convert_to_image unsqueeze Compose convert | Este repo es una implementación en pytorch de este paper: https://arxiv.org/abs/1508.06576 | 1,749 |
code-Assasin/A_Little_fog_for_a_Large_Turn | ['autonomous navigation', 'adversarial attack'] | ['A Little Fog for a Large Turn'] | DistanceGAN/cyclegan_arch/mnist_to_svhn/solver.py DistanceGAN/cyclegan_arch/data/aligned_data_loader.py cycle_gan_code/data/unaligned_dataset.py DistanceGAN/cyclegan_arch/networks.py DistanceGAN/cyclegan_arch/cyclegan_arch_options/base_options.py DistanceGAN/cyclegan_arch/data/base_data_loader.py DistanceGAN/cyclegan_arch/data/image_folder.py cycle_gan_code/models/colorization_model.py cycle_gan_code/options/base_options.py DistanceGAN/cyclegan_arch/distance_gan_model.py DistanceGAN/train.py steering_models/AutoPilot/autopilot_128.py cycle_gan_code/scripts/eval_cityscapes/util.py DistanceGAN/datasets/combine_A_and_B.py DistanceGAN/cyclegan_arch/mnist_to_svhn/main.py cycle_gan_code/data/__init__.py DistanceGAN/cyclegan_arch/mnist_to_svhn/model.py DistanceGAN/cyclegan_arch/cyclegan_arch_options/train_options.py steering_models/Comma_ai/comma_ai.py cycle_gan_code/data/aligned_dataset.py cycle_gan_code/scripts/eval_cityscapes/evaluate.py cycle_gan_code/models/base_model.py steering_models/AutoPilot/test_autopilot.py cycle_gan_code/models/steering_model_loader.py DistanceGAN/cyclegan_arch/cycle_gan_model.py steering_models/script_degree_file_matcher.py DistanceGAN/cyclegan_arch/data/unaligned_data_loader.py cycle_gan_code/models/cycle_gan_model.py DistanceGAN/cyclegan_arch/util/png.py steering_models/Comma_ai/test_comma_ai.py DistanceGAN/cyclegan_arch/steering_model_loader.py DistanceGAN/cyclegan_arch/util/html.py cycle_gan_code/util/get_data.py cycle_gan_code/test.py DistanceGAN/cyclegan_arch/base_model.py cycle_gan_code/data/template_dataset.py cycle_gan_code/util/util.py cycle_gan_code/datasets/combine_A_and_B.py cycle_gan_code/models/template_model.py cycle_gan_code/scripts/eval_cityscapes/cityscapes.py scripts/iqa.py cycle_gan_code/data/single_dataset.py cycle_gan_code/models/cycle_gan_fool_model.py cycle_gan_code/options/train_options.py cycle_gan_code/models/test_model.py DistanceGAN/cyclegan_arch/util/image_pool.py cycle_gan_code/data/colorization_dataset.py DistanceGAN/test.py DistanceGAN/cyclegan_arch/util/visualizer.py cycle_gan_code/scripts/test_before_push.py cycle_gan_code/data/image_folder.py cycle_gan_code/train.py cycle_gan_code/models/__init__.py DistanceGAN/cyclegan_arch/models.py cycle_gan_code/data/base_dataset.py DistanceGAN/cyclegan_arch/cyclegan_arch_options/test_options.py cycle_gan_code/models/networks.py cycle_gan_code/scripts/edges/batch_hed.py cycle_gan_code/datasets/make_dataset_aligned.py cycle_gan_code/util/image_pool.py DistanceGAN/cyclegan_arch/tester.py DistanceGAN/cyclegan_arch/mnist_to_svhn/data_loader.py cycle_gan_code/util/html.py cycle_gan_code/util/visualizer.py DistanceGAN/cyclegan_arch/util/util.py DistanceGAN/datasets/download.py cycle_gan_code/util/__init__.py cycle_gan_code/models/pix2pix_model.py DistanceGAN/cyclegan_arch/data/data_loader.py cycle_gan_code/options/test_options.py DistanceGAN/cyclegan_arch/gan_model.py cycle_gan_code/options/__init__.py AlignedDataset BaseDataset __flip get_transform __crop __make_power_2 __print_size_warning __scale_width get_params ColorizationDataset is_image_file ImageFolder default_loader make_dataset SingleDataset TemplateDataset UnalignedDataset CustomDatasetDataLoader get_option_setter find_dataset_using_name create_dataset get_file_paths align_images BaseModel ColorizationModel CycleGANFoolModel CycleGANModel get_norm_layer PixelDiscriminator Identity GANLoss ResnetGenerator ResnetBlock define_D UnetGenerator UnetSkipConnectionBlock init_weights get_scheduler init_net NLayerDiscriminator cal_gradient_penalty define_G Pix2PixModel AutoPilot comma_ai_model_loader Comma_ai autopilot_model_loader TemplateModel TestModel get_option_setter create_model find_model_using_name BaseOptions TestOptions TrainOptions run parse_args cityscapes main segrun fast_hist get_out_scoremap feed_net get_scores GetData HTML ImagePool print_numpy diagnose_network mkdirs mkdir save_image tensor2im save_images Visualizer CycleGANModel DistanceGANModel GANModel create_model get_norm_layer GANLoss ResnetGenerator ResnetBlock define_D UnetGenerator UnetSkipConnectionBlock weights_init print_network NLayerDiscriminator define_G AutoPilot comma_ai_model_loader Comma_ai autopilot_model_loader LoadTrainBatch weight_init unison_shuffled_copies LoadValBatch BaseOptions TestOptions TrainOptions PairedData AlignedDataLoader BaseDataLoader CreateDataLoader is_image_file ImageFolder default_loader make_dataset UnalignedDataLoader PairedData get_loader main str2bool deconv G21 D1 conv D2 G12 Solver HTML ImagePool encode print_numpy varname diagnose_network mkdirs mkdir info save_image tensor2im Visualizer download_facescrub unzip preprocess_facescrub download_pix2pix download_cyclegan download_celeb_a download AutoPilot weight_init unison_shuffled_copies LoadTrainBatch LoadValBatch AutoPilot LoadTrainBatch autopilot_model_loader weight_init unison_shuffled_copies LoadTrainBatch Comma_ai LoadValBatch LoadTrainBatch Comma_ai comma_ai_model_loader crop_size randint maximum load_size Grayscale Lambda Resize crop_size RandomCrop RandomHorizontalFlip append int size round __print_size_warning int size size print is_image_file join sorted append walk items list import_module replace find_dataset_using_name CustomDatasetDataLoader load_data join sorted abspath append walk join format new makedirs len paste save range open BatchNorm2d partial InstanceNorm2d LambdaLR CosineAnnealingLR ReduceLROnPlateau StepLR print apply init_weights to DataParallel ResnetGenerator UnetGenerator get_norm_layer NLayerDiscriminator PixelDiscriminator get_norm_layer view size rand grad mean requires_grad_ netD AutoPilot parameters load load_state_dict load parameters Comma_ai load_state_dict items list replace print exit import_module find_model_using_name print __name__ find_model_using_name model print system exit add_argument ArgumentParser list_label_frames gpu_id output_dir load_label palette cityscapes cityscapes_dir open caffemodel_dir str set_device len TEST imsave imresize Net preprocess classes get_scores enumerate segrun result_dir print set_mode_gpu split zeros array makedirs reshape forward feed_net bincount astype sum diag data isinstance transpose tile Tensor numpy print parameters fromarray save print float64 flatten astype mkdir makedirs join list basename items get_image_dir imresize add_images shape add_header append save_image tensor2im initialize DistanceGANModel CycleGANModel GANModel name normal_ __name__ fill_ print print apply DataParallel cuda print apply DataParallel cuda print parameters list shuffle zip zeros range rollaxis imresize zeros range rollaxis imresize data kaiming_normal_ initialize print name UnalignedDataLoader AlignedDataLoader MNIST DataLoader Compose SVHN sample get_loader model_path train sample_path Solver append BatchNorm2d ConvTranspose2d append BatchNorm2d Conv2d print join search join int read str print write close urlopen flush open print remove dirname join remove print rename download exists system system join print cpu_count close map mkdir append range Pool exists len join remove imwrite exists print system mkdir imread range hexdigest len | # A Little Fog for a Large_Turn Code for the WACV 2020 paper, "A Little Fog for a Large Turn" <br> Arxiv paper link : [https://arxiv.org/abs/2001.05873](https://arxiv.org/abs/2001.05873)<br> Website link : [https://code-assasin.github.io/little_fog/](https://code-assasin.github.io/little_fog/) ## Abstract Small, carefully crafted perturbations called adversarial perturbations can easily fool neural networks. However, these perturbations are largely additive and not naturally found. We turn our attention to the field of Autonomous navigation wherein adverse weather conditions such as fog have a drastic effect on the predictions of these systems. These weather conditions are capable of acting like natural adversaries that can help in testing models. To this end, we introduce a general notion of adversarial perturbations, which can be created using generative models and provide a methodology inspired by Cycle-Consistent Generative Adversarial Networks to generate adversarial weather conditions for a given image. Our formulation and results show that these images provide a suitable testbed for steering models used in Autonomous navigation models. Our work also presents a more natural and general definition of Adversarial perturbations based on Perceptual Similarity. ## Examples | 1,750 |
code-terminator/DilatedRNN | ['sequential image classification'] | ['Dilated Recurrent Neural Networks'] | models/classification_models.py models/drnn.py drnn_classification _contruct_cells _rnn_reformat multi_dRNN_with_dilations dRNN BasicLSTMCell GRUCell BasicRNNCell append reshape transpose split _contruct_cells _rnn_reformat Variable concat multi_dRNN_with_dilations matmul add range random_normal enumerate zeros_like print static_rnn append range len dRNN copy zip | # Dilated Recurrent Neural Networks Tensorflow implementation of [Dilated Recurrent Neural Networks](https://arxiv.org/abs/1710.02224) (DilatedRNN). <p align="center"> <img src="./assets/combined_drnn.png"> </p> For more about DilatedRNN, Please see our NIPS [paper](https://arxiv.org/abs/1710.02224). If you find this work useful and use it on your own research, please cite our paper. ``` @article{chang2017dilated, title={Dilated Recurrent Neural Networks}, author={Chang, Shiyu and Zhang, Yang and Han, Wei and Yu, Mo and Guo, Xiaoxiao and Tan, Wei and Cui, Xiaodong and Witbrock, Michael and Hasegawa-Johnson, Mark and Huang, Thomas}, | 1,751 |
code-terminator/classwise_rationale | ['sentiment analysis'] | ['A Game Theoretic Approach to Class-wise Selective Rationalization'] | core/train_utils.py core/misc.py core/eval_utils.py core/visualize.py core/dataset.py core/beer.py core/metric.py core/model.py core/language.py run_beer.py get_beer_dataset BeerProcessor get_beer_annotation get_dataset convert_examples_to_np_arrays DataProcessor convert_single_text validate flush get_pretained_glove LanguageIndex get_sparsity_loss compute_micro_stats compute_detail_micro_stats compute_accuracy get_continuity_loss str2bool Embedding RNN TargetRNN train gen_nl_loss show_binary_rationale show_binary_rationale_with_annotation seed int get_train_examples print min sample get_dev_examples BeerProcessor append array append len strip split append array convert_single_text convert_examples_to_np_arrays word2idx from_tensor_slices LanguageIndex argmax model ones concat write show_binary_rationale float32 close shape cast int32 zeros expand_dims numpy range enumerate open compute_micro_stats model concat argmax open ones shape cast expand_dims range close show_binary_rationale_with_annotation zeros flush enumerate print write float32 int32 randint numpy items list randn print zeros load_glove_embedding len reduce_sum int argmax greater_equal cast float32 reduce_sum greater_equal cast float32 reduce_sum cast softmax float32 reduce_sum concat gradient list ones shape apply_gradients compute_accuracy discriminator_trainable_variables generator_pos_trainable_variables generator_neg_trainable_variables show_binary_rationale zip zeros flush enumerate idx2word print int32 randint numpy print flush enumerate print flush enumerate | # A Game Theoretic Approach to Class-wise Selective Rationalization This repo contains the Tensorflow implementation of [A Game Theoretic Approach to Class-wise Selective Rationalization](https://arxiv.org/abs/1910.12853) (CAR). To make this repo neat and light-weight, we release the core code with a single multi-aspect dataset (i.e. the [beer review](http://snap.stanford.edu/data/web-BeerAdvocate.html)) for the demo purpose. If you are interested in reproducing the exact results for other datasets, please contact us, and we are very happy to provide the code and help. A short video explains the main concepts of our work. For more detail about CAR, Please see our [NeurIPS 2019 paper](https://arxiv.org/abs/1910.12853). If you find this work useful and use it in your research, please consider citing our paper. [](https://youtu.be/DFtJL7PcGFA) ``` @article{chang2019rationale, title={A Game Theoretic Approach to Class-wise Selective Rationalization}, author={Chang, Shiyu and Zhang, Yang and Yu, Mo and Jaakkola, Tommi}, journal={arXiv preprint arXiv:1910.12853}, year={2019} | 1,752 |
coderwangson/Learn-Convolutional-Neural-Network-for-Face-Anti-Spoofing_pytorch | ['face anti spoofing'] | ['Learn Convolutional Neural Network for Face Anti-Spoofing'] | train.py Data.py generate_frames_and_bbox.py statistics.py Nets.py crop_images.py process_db_casia crop_face Data read generate_frames_and_bbox Net HTER EER val train int copyMakeBorder min shape resize ceil float max join list imwrite crop_face print makedirs map writelines split imread open join VideoCapture read imwrite COLOR_BGR2RGB print glob close detect_faces writelines open release cvtColor makedirs print split open sorted list abs float print min argmin auc mean zip append matrix maxsize array range len sorted abs float print min auc append matrix maxsize range len val backward print step zero_grad extend range numpy net cross_entropy enumerate print zero_grad extend EER eval HTER numpy net enumerate | # Learn Convolutional Neural Network for Face Anti-Spoofing using pytorch ## requirements * pytorch * cv2 * tensorflow * [mtcnn][1] ## Step 1 run `generate_frames_and_bbox.py`,video is sampled as a frame,also generate a file_list containing the list of files_name and the bbox of the face **like this:** file_name x y w h label | 1,753 |
codetendolkar/tacotron-2-explained | ['speech synthesis'] | ['Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions'] | train.py model/input_fn.py model/wrappers.py model/modules.py model/helpers.py model/external/zoneout_wrapper.py model/model_fn.py model/external/attention.py model/loss.py model/utils.py CustomTrainingHelper CustomTestHelper train_input_fn parse_csv_line composite_loss create_model decoder_prenet n_layer_1d_convolution postnet Vocabulary ConcatOutputAndAttentionWrapper DecoderPrenetWrapper OutputProjectionWrapper LocationSensitiveAttention _location_sensitive_score ZoneoutWrapper concat indices abs string_split values SparseTensor tensordot list decode_csv text2idx squeeze transpose value size dense_shape map_fn tile sparse_tensor_to_dense zip decode_audio stft linear_to_mel_weight_matrix dict read_file padded_batch shuffle map skip make_one_shot_iterator repeat TextLineDataset mean_squared_error scalar conv1d range dropout dense dropout conv2d expand_dims range dropout get_variable | # Tacotron 2 Explained This repository is meant to teach the intricacies of writing advanced Recurrent Neural Networks in Tensorflow. The code is used as a guide, in weekly Deep Learning meetings at Ohio State University, for teaching - 1. How to read a paper 2. How to implement it in Tensorflow I choose Tacotron 2 because - 1. Encoder-Decoder architectures contain more complexities then standard DNNs. Implementing one helps you master concepts you would otherwise overlook 2. Tachotron 2 was released less than a year ago (as of 2018) and is a relatively simple model (compared to something like GNTM). The associated paper explains the architecture well 3. Other public implementations offer a benchmark to compare results 4. Public datasets are available to achieve state of the art results 4. Training requires ~10 days given access to a GPU (comparable to GTX 1080) | 1,754 |
codogogo/xling-eval | ['word embeddings', 'bilingual lexicon induction', 'cross lingual word embeddings', 'cross lingual transfer'] | ['How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions'] | code/cca.py code/projection.py code/emb_serializer.py code/simple_stats.py code/sims.py code/map.py code/emb_deserializer.py code/util.py code/eval.py CCA project_pinv build_matrices get_seeds project_proc_bootstrap project_proc_bootstrap_reproduce project_proc project_cca covariance_matrix kullback_leibler cosine sign_mismatches most_similar similarity most_similar_index check_in_vocabulary write_text deser_simple deserialize_embs big_matrix_multiplication write_lines big_matrix_csls load_embs write_embs serialize_embs prefix_lang mat_normalize load_and_serialize_embs load_lines sort append get_seeds dot pinv build_matrices CCA transform build_matrices min correlate svd transpose build_matrices matmul str int print sort transpose min mat_normalize big_matrix_multiplication project_proc len str print sort transpose extend mat_normalize big_matrix_multiplication project_proc len len range sign range len multiply transpose matmul add zeros float array range print transpose dot print transpose dot print str close write open close write open print array str join print len write close range open dump print save mat_normalize open load open load mat_normalize open print shape load_embs serialize_embs len items list str print function_on_result extend dot range str print reshape extend dot zeros argmax range | # XLing-Eval Code and resources for inducing and evaluating cross-lingual embedding spaces This repository accompanies the following ACL 2019 publication: Goran Glavaš, Robert Litschko, Sebastian Ruder and Ivan Vulić. How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), pages 710-721, Florence, 2019. If you are using the BLI datasets and/or the code in your work, please cite the above paper. Here's a Bibtex entry: ``` @inproceedings{glavas-etal-2019-properly, title = "How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions", author = "Glava{\v{s}}, Goran and Litschko, Robert and | 1,755 |
cogaplex-bts/bts | ['depth estimation', 'monocular depth estimation'] | ['From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation'] | pytorch/bts_main.py tensorflow/bts_main.py tensorflow/run_bts_eval_schedule.py pytorch/bts_test.py pytorch/distributed_sampler_no_evenly_divisible.py tensorflow/average_gradients.py tensorflow/custom_layer/_local_planar_guidance_grad.py pytorch/run_bts_eval_schedule.py pytorch/bts_dataloader.py tensorflow/bts_eval.py tensorflow/bts_live_3d.py tensorflow/bts_sequence.py pytorch/bts_live_3d.py utils/eval_with_pngs.py tensorflow/bts.py tensorflow/resnet_v1.py utils/download_from_gdrive.py utils/extract_official_train_test_set_from_mat.py tensorflow/bts_dataloader.py pytorch/bts.py pytorch/bts_eval.py tensorflow/bts_test.py bts weights_init_xavier BtsModel reduction_1x1 silog_loss upconv local_planar_guidance encoder bn_init_as_tf atrous_conv _is_numpy_image preprocessing_transforms ToTensor BtsDataLoader _is_pil_image DataLoadPreprocess convert_arg_line_to_args compute_errors test eval get_num_lines toc load_model np_to_qimage Window GLWidget tic edges qimage_to_np main_worker enable_print online_eval convert_arg_line_to_args normalize_result colorize compute_errors block_print set_misc main get_num_lines convert_arg_line_to_args test get_num_lines DistributedSamplerNoEvenlyDivisible run_eval average_gradients BtsModel BtsDataloader convert_arg_line_to_args compute_errors test eval main get_num_lines toc load_model np_to_qimage Window GLWidget tic edges qimage_to_np get_tensors_in_checkpoint_file convert_arg_line_to_args build_tensors_in_checkpoint_file main train get_num_lines test_sequence main main convert_arg_line_to_args test get_num_lines resnet_v1_152 NoOpScope resnet_v1_101 bottleneck resnet_v1_200 resnet_v1_50 resnet_v1 resnet_v1_block run_eval _local_planar_guidance_grad_cc download_file_from_google_drive convert_arg_line_to_args compute_errors test eval main convert_image BatchNorm2d eval isinstance isinstance Conv2d xavier_uniform_ zeros_ bias weight split maximum mean sqrt log10 abs log readlines close open rstrip checkpoint_path BtsDataLoader filenames_file DataParallel cuda exists str sorted add load_state_dict append imread range SummaryWriter format astype BtsModel set mean eval flush load int remove join time isdir add_scalar print gt_path float32 output_directory model_name get_num_lines filenames_file garg_crop max_depth_eval len logical_and shape append do_kb_crop range format mean eigen_crop int min_depth_eval print compute_errors float32 zeros get_num_lines load checkpoint_path BtsModel DataParallel eval load_state_dict cuda time print time format copy convertToFormat Format_ARGB32 sobel Signal devnull open __stdout__ cmapper get_cmap log10 bn_no_track_stats named_children fix_first_conv_block print apply named_parameters any fix_first_conv_blocks data new_group multiprocessing_distributed garg_crop cuda max_depth_eval logical_and shape do_kb_crop range format item enumerate int min_depth_eval print compute_errors tqdm all_reduce cpu zeros eigen_crop data bool set_misc checkpoint_path batch_size multiprocessing_distributed model BtsDataLoader zero_grad where DataParallel DistributedDataParallel save forward cuda exists set_device do_online_eval log_directory apply silog_loss log_freq rank load_state_dict sleep to sum range SummaryWriter format enable_print normalize_result init_process_group param_groups close BtsModel eval_summary_directory distributed eval retrain item num_epochs flush add_image load int join time learning_rate online_eval enumerate backward print AdamW Variable add_scalar system set_epoch isnan block_print model_name isfile cpu zeros train step gpu len eval_freq world_size basename checkpoint_path format spawn print multiprocessing_distributed do_online_eval log_directory system device_count dirname model_name main_worker empty_cache gpu uint16 imwrite save_lpg list log10 sum imsave replace mkdir tqdm data_path amax print system now concat reduce_mean zip append expand_dims initializer getmtime get_next Saver Session run make_initializable_iterator start_queue_runners global_variables_initializer FileWriter ConfigProto local_variables_initializer Coordinator BtsDataloader bts_parameters test start_queue_runners bts_parameters Coordinator Saver global_variables_initializer local_variables_initializer run sorted NewCheckpointReader get_tensor append get_variable_to_shape_map list add set append get_tensor_by_name enumerate train Saver Session run placeholder image_path append start_queue_runners glob BtsModel ConfigProto local_variables_initializer join constant print sort float32 Coordinator global_variables_initializer len test_sequence get get_confirm_token save_response_content Session pred_path filter walk len int imwrite uint16 astype zeros makedirs | # BTS From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation [arXiv](https://arxiv.org/abs/1907.10326) [Supplementary material](https://arxiv.org/src/1907.10326v4/anc/bts_sm.pdf) ## Video Demo 1 [](https://www.youtube.com/watch?v=2fPdZYzx9Cg) ## Video Demo 2 [](https://www.youtube.com/watch?v=1J-GSb0fROw) ## Note This repository contains TensorFlow and PyTorch implementations of BTS. | 1,756 |
cogsys-tuebingen/FourierNet | ['instance segmentation', 'semantic segmentation'] | ['FourierNet: Compact mask representation for instance segmentation using differentiable shape decoders'] | mmdet/models/roi_extractors/__init__.py configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x.py tests/test_heads.py mmdet/core/utils/misc.py mmdet/models/detectors/__init__.py mmdet/models/mask_heads/fcn_mask_head.py demo/inference_demo.py configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x.py mmdet/models/detectors/fcos.py mmdet/core/utils/dist_utils.py configs/scratch/scratch_mask_rcnn_r50_fpn_gn_6x.py configs/fcos/fcos_mstrain_640_800_r101_caffe_fpn_gn_2x_4gpu.py configs/htc/htc_without_semantic_r50_fpn_1x.py mmdet/models/utils/conv_ws.py mmdet/models/losses/accuracy.py configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws_2x.py mmdet/models/mask_heads/grid_head.py configs/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py mmdet/models/detectors/single_stage.py configs/gn+ws/mask_rcnn_r50_fpn_gn_ws_2x.py configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x.py mmdet/datasets/__init__.py mmdet/datasets/pipelines/loading.py configs/fast_rcnn_r101_fpn_1x.py mmdet/core/anchor/point_target.py configs/faster_rcnn_r50_caffe_c4_1x.py tools/test.py mmdet/datasets/cityscapes.py configs/wider_face/ssd300_wider_face.py mmdet/core/post_processing/merge_augs.py configs/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py mmdet/models/detectors/grid_rcnn.py configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py configs/foveabox/fovea_align_gn_ms_r50_fpn_4gpu_2x.py mmdet/models/anchor_heads/ssd_head.py mmdet/models/utils/weight_init.py mmdet/models/utils/__init__.py mmdet/models/detectors/fouriernet.py tests/test_async.py mmdet/core/bbox/assign_sampling.py configs/fast_mask_rcnn_r101_fpn_1x.py mmdet/models/backbones/hrnet.py configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e.py mmdet/models/plugins/generalized_attention.py configs/cascade_mask_rcnn_x101_32x4d_fpn_1x.py mmdet/core/bbox/assigners/atss_assigner.py mmdet/ops/affine_grid/affine_grid.py configs/gcnet/mask_rcnn_r50_fpn_sbn_1x.py mmdet/models/losses/utils.py configs/htc/htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e.py mmdet/models/mask_heads/htc_mask_head.py configs/reppoints/reppoints_moment_r50_fpn_1x.py configs/reppoints/reppoints_moment_r101_dcn_fpn_2x_mt.py tests/test_soft_nms.py mmdet/models/losses/iou_loss.py configs/cascade_mask_rcnn_r101_fpn_1x.py configs/reppoints/reppoints_moment_r101_fpn_2x.py configs/faster_rcnn_r101_fpn_1x.py mmdet/ops/dcn/deform_conv.py tests/async_benchmark.py configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x.py configs/fouriernet/fourier_768_1x_r50_36_60.py mmdet/datasets/registry.py mmdet/core/bbox/assigners/approx_max_iou_assigner.py configs/rpn_x101_32x4d_fpn_1x.py mmdet/models/necks/__init__.py configs/retinanet_x101_64x4d_fpn_1x.py mmdet/utils/profiling.py mmdet/models/losses/__init__.py mmdet/models/anchor_heads/anchor_head.py mmdet/models/anchor_heads/rpn_head.py mmdet/models/__init__.py mmdet/datasets/pipelines/compose.py mmdet/ops/dcn/deform_pool.py configs/fp16/faster_rcnn_r50_fpn_fp16_1x.py configs/fast_mask_rcnn_r50_fpn_1x.py tools/upgrade_model_version.py configs/foveabox/fovea_align_gn_ms_r101_fpn_4gpu_2x.py mmdet/core/fp16/decorators.py configs/mask_rcnn_r50_caffe_c4_1x.py mmdet/models/necks/nas_fpn.py configs/dcn/faster_rcnn_dpool_r50_fpn_1x.py configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x.py mmdet/core/optimizer/__init__.py mmdet/models/detectors/fovea.py mmdet/models/utils/scale.py configs/reppoints/reppoints_moment_r50_fpn_2x.py tools/analyze_logs.py mmdet/models/detectors/reppoints_detector.py mmdet/models/anchor_heads/atss_head.py tests/test_config.py configs/fast_mask_rcnn_r50_caffe_c4_1x.py mmdet/core/utils/__init__.py configs/htc/htc_r50_fpn_20e.py configs/htc/htc_r50_fpn_1x.py configs/free_anchor/retinanet_free_anchor_x101-32x4d_fpn_1x.py mmdet/datasets/pipelines/instaboost.py configs/gn/mask_rcnn_r101_fpn_gn_2x.py mmdet/core/mask/utils.py mmdet/ops/grid_sampler/__init__.py mmdet/models/anchor_heads/__init__.py mmdet/models/utils/conv_module.py configs/reppoints/reppoints_moment_r50_no_gn_fpn_1x.py mmdet/models/losses/mse_loss.py configs/cascade_rcnn_r101_fpn_1x.py demo/webcam_demo.py mmdet/models/necks/hrfpn.py mmdet/utils/collect_env.py configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x.py configs/scratch/scratch_faster_rcnn_r50_fpn_gn_6x.py mmdet/datasets/loader/__init__.py mmdet/ops/nms/__init__.py configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x.py configs/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py configs/faster_rcnn_x101_64x4d_fpn_1x.py configs/hrnet/faster_rcnn_hrnetv2p_w18_1x.py configs/reppoints/reppoints_partial_minmax_r50_fpn_1x.py mmdet/ops/roi_align/roi_align.py configs/pascal_voc/ssd300_voc.py mmdet/models/bbox_heads/convfc_bbox_head.py mmdet/models/detectors/mask_rcnn.py tools/convert_datasets/pascal_voc.py mmdet/core/evaluation/class_names.py mmdet/core/anchor/anchor_target.py mmdet/ops/carafe/__init__.py docs/conf.py mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py configs/cascade_rcnn_x101_32x4d_fpn_1x.py configs/fast_rcnn_r50_caffe_c4_1x.py mmdet/ops/affine_grid/__init__.py mmdet/core/fp16/__init__.py mmdet/models/anchor_heads/ga_rpn_head.py mmdet/models/detectors/cascade_rcnn.py configs/mask_rcnn_r50_fpn_1x.py mmdet/utils/logger.py configs/htc/htc_r101_fpn_20e.py mmdet/datasets/pipelines/contour.py mmdet/models/detectors/rpn.py configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x.py mmdet/utils/contextmanagers.py configs/reppoints/reppoints_moment_x101_dcn_fpn_2x.py mmdet/models/necks/bfp.py configs/hrnet/mask_rcnn_hrnetv2p_w18_1x.py configs/ssd300_coco.py tools/test_robustness.py configs/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py mmdet/datasets/pipelines/__init__.py configs/hrnet/fcos_hrnetv2p_w32_gn_1x_4gpu.py configs/ghm/retinanet_ghm_r50_fpn_1x.py mmdet/models/backbones/__init__.py mmdet/ops/carafe/grad_check.py configs/albu_example/mask_rcnn_r50_fpn_1x.py configs/retinanet_r101_fpn_1x.py mmdet/ops/nms/nms_wrapper.py configs/cascade_mask_rcnn_r50_fpn_1x.py configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py mmdet/core/optimizer/builder.py mmdet/apis/__init__.py configs/fp16/mask_rcnn_r50_fpn_fp16_1x.py mmdet/core/evaluation/mean_ap.py mmdet/datasets/dataset_wrappers.py configs/retinanet_r50_fpn_1x.py configs/gn+ws/mask_rcnn_r50_fpn_gn_ws_20_23_24e.py mmdet/datasets/pipelines/formating.py mmdet/models/losses/ghm_loss.py configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x.py mmdet/models/anchor_heads/retina_sepbn_head.py configs/mask_rcnn_r101_fpn_1x.py mmdet/models/detectors/mask_scoring_rcnn.py mmdet/core/bbox/samplers/__init__.py configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x.py mmdet/core/bbox/samplers/combined_sampler.py mmdet/ops/roi_pool/gradcheck.py configs/double_heads/dh_faster_rcnn_r50_fpn_1x.py mmdet/utils/flops_counter.py mmdet/models/plugins/non_local.py mmdet/core/fp16/utils.py mmdet/datasets/loader/sampler.py mmdet/datasets/pipelines/test_aug.py mmdet/ops/carafe/setup.py mmdet/models/mask_heads/maskiou_head.py configs/reppoints/reppoints_moment_r50_fpn_2x_mt.py configs/reppoints/reppoints_moment_r101_fpn_2x_mt.py tools/detectron2pytorch.py mmdet/models/detectors/test_mixins.py mmdet/core/anchor/point_generator.py mmdet/core/evaluation/__init__.py mmdet/datasets/pipelines/transforms.py mmdet/ops/masked_conv/__init__.py configs/faster_rcnn_x101_32x4d_fpn_1x.py mmdet/core/bbox/samplers/pseudo_sampler.py configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py mmdet/core/post_processing/bbox_nms.py mmdet/models/detectors/double_head_rcnn.py configs/gn/mask_rcnn_r50_fpn_gn_contrib_2x.py mmdet/models/anchor_heads/free_anchor_retina_head.py configs/fcos/fcos_mstrain_640_800_x101_64x4d_fpn_gn_2x.py configs/gn+ws/faster_rcnn_r50_fpn_gn_ws_1x.py configs/fcos/fcos_r50_caffe_fpn_gn_1x_4gpu.py mmdet/datasets/builder.py configs/hrnet/faster_rcnn_hrnetv2p_w40_1x.py configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x.py mmdet/core/bbox/geometry.py tools/convert_datasets/cityscapes.py mmdet/models/losses/polar_iou_loss.py setup.py mmdet/models/bbox_heads/double_bbox_head.py configs/faster_rcnn_r50_fpn_1x.py mmdet/utils/__init__.py mmdet/models/losses/balanced_l1_loss.py configs/cascade_rcnn_r50_fpn_1x.py mmdet/models/anchor_heads/fovea_head.py tools/get_flops.py mmdet/models/losses/focal_loss.py mmdet/core/bbox/demodata.py configs/mask_rcnn_x101_64x4d_fpn_1x.py configs/nas_fpn/retinanet_crop640_r50_fpn_50e.py mmdet/models/utils/upsample.py mmdet/core/evaluation/recall.py mmdet/core/optimizer/copy_of_sgd.py configs/dcn/faster_rcnn_mdpool_r50_fpn_1x.py mmdet/core/__init__.py configs/reppoints/reppoints_moment_x101_dcn_fpn_2x_mt.py configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x.py mmdet/models/backbones/resnext.py mmdet/models/mask_heads/fused_semantic_head.py mmdet/ops/__init__.py configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x.py mmdet/models/anchor_heads/fcos_head.py configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x.py mmdet/core/bbox/samplers/base_sampler.py configs/pascal_voc/ssd512_voc.py configs/fast_rcnn_r50_fpn_1x.py configs/foveabox/fovea_align_gn_r50_fpn_4gpu_2x.py configs/guided_anchoring/ga_rpn_r101_caffe_rpn_1x.py mmdet/ops/roi_align/__init__.py mmdet/core/bbox/transforms.py configs/dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py mmdet/ops/roi_align/gradcheck.py mmdet/core/optimizer/registry.py mmdet/core/bbox/assigners/max_iou_assigner.py mmdet/models/anchor_heads/reppoints_head.py mmdet/core/bbox/assigners/point_assigner.py mmdet/models/anchor_heads/retina_head.py mmdet/models/detectors/faster_rcnn.py tests/test_assigner.py configs/rpn_r50_caffe_c4_1x.py configs/libra_rcnn/libra_retinanet_r50_fpn_1x.py mmdet/models/roi_extractors/single_level.py mmdet/ops/sigmoid_focal_loss/sigmoid_focal_loss.py mmdet/core/anchor/__init__.py mmdet/ops/sigmoid_focal_loss/__init__.py mmdet/core/anchor/anchor_generator.py mmdet/core/mask/mask_target.py configs/foveabox/fovea_align_gn_r101_fpn_4gpu_2x.py mmdet/ops/masked_conv/masked_conv.py configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x.py configs/rpn_r101_fpn_1x.py mmdet/core/post_processing/__init__.py configs/instaboost/ssd300_coco_instaboost_4x.py mmdet/models/detectors/base.py mmdet/core/bbox/samplers/ohem_sampler.py mmdet/datasets/custom.py mmdet/datasets/wider_face.py tools/coco_error_analysis.py mmdet/core/evaluation/bbox_overlaps.py configs/hrnet/htc_hrnetv2p_w32_20e.py configs/free_anchor/retinanet_free_anchor_r50_fpn_1x.py mmdet/ops/roi_pool/roi_pool.py mmdet/utils/util_mixins.py mmdet/models/backbones/resnet.py mmdet/core/bbox/samplers/random_sampler.py mmdet/models/mask_heads/__init__.py configs/fouriernet/fourier_768_1x_x101_36_90.py mmdet/ops/dcn/__init__.py configs/nas_fpn/retinanet_crop640_r50_nasfpn_50e.py mmdet/ops/grid_sampler/grid_sampler.py configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x.py configs/hrnet/mask_rcnn_hrnetv2p_w32_1x.py configs/dcn/mask_rcnn_mdconv_c3-c5_r50_fpn_1x.py configs/gcnet/mask_rcnn_r4_gcb_c3-c5_r50_fpn_syncbn_1x.py mmdet/__init__.py configs/gcnet/mask_rcnn_r16_gcb_c3-c5_r50_fpn_1x.py configs/grid_rcnn/grid_rcnn_gn_head_x101_32x4d_fpn_2x.py configs/free_anchor/retinanet_free_anchor_r101_fpn_1x.py configs/carafe/mask_rcnn_r50_fpn_carafe_1x.py mmdet/core/bbox/bbox_target.py mmdet/models/backbones/ssd_vgg.py mmdet/ops/context_block.py mmdet/core/bbox/assigners/__init__.py configs/reppoints/reppoints_moment_r101_dcn_fpn_2x.py configs/gcnet/mask_rcnn_r16_gcb_c3-c5_r50_fpn_syncbn_1x.py mmdet/core/bbox/assigners/base_assigner.py mmdet/core/evaluation/eval_hooks.py mmdet/models/necks/fpn_carafe.py mmdet/models/shared_heads/res_layer.py mmdet/core/bbox/assigners/assign_result.py configs/cascade_rcnn_r50_caffe_c4_1x.py configs/gn/mask_rcnn_r50_fpn_gn_2x.py mmdet/models/anchor_heads/fouriernet_head.py mmdet/models/builder.py mmdet/datasets/loader/build_loader.py mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py mmdet/models/registry.py mmdet/core/bbox/samplers/sampling_result.py mmdet/models/utils/norm.py configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e.py configs/rpn_x101_64x4d_fpn_1x.py configs/dcn/faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x.py tools/robustness_eval.py configs/mask_rcnn_x101_32x4d_fpn_1x.py mmdet/models/bbox_heads/__init__.py configs/htc/htc_x101_64x4d_fpn_20e_16gpu.py mmdet/models/detectors/htc.py mmdet/models/plugins/__init__.py configs/cascade_rcnn_x101_64x4d_fpn_1x.py mmdet/models/anchor_heads/guided_anchor_head.py configs/rpn_r50_fpn_1x.py mmdet/apis/inference.py configs/atss/atss_r50_fpn_1x.py configs/gcnet/mask_rcnn_r4_gcb_c3-c5_r50_fpn_1x.py tests/test_forward.py configs/cascade_mask_rcnn_x101_64x4d_fpn_1x.py mmdet/core/fp16/hooks.py mmdet/models/anchor_heads/ga_retina_head.py tests/test_sampler.py configs/htc/htc_x101_32x4d_fpn_20e_16gpu.py mmdet/models/detectors/atss.py configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x.py mmdet/models/detectors/fast_rcnn.py tools/publish_model.py configs/cascade_mask_rcnn_r50_caffe_c4_1x.py mmdet/models/bbox_heads/bbox_head.py mmdet/ops/roi_pool/__init__.py configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x.py configs/reppoints/bbox_r50_grid_fpn_1x.py tools/train.py mmdet/models/detectors/retinanet.py mmdet/models/losses/cross_entropy_loss.py mmdet/models/losses/smooth_l1_loss.py mmdet/datasets/voc.py configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py mmdet/ops/carafe/carafe.py tests/test_nms.py configs/faster_rcnn_ohem_r50_fpn_1x.py configs/foveabox/fovea_r50_fpn_4gpu_1x.py mmdet/models/necks/fpn.py mmdet/datasets/xml_style.py mmdet/datasets/coco.py configs/ssd512_coco.py configs/reppoints/reppoints_minmax_r50_fpn_1x.py mmdet/models/detectors/two_stage.py configs/grid_rcnn/grid_rcnn_gn_head_r50_fpn_2x.py mmdet/ops/utils/__init__.py mmdet/core/mask/__init__.py configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x.py configs/retinanet_x101_32x4d_fpn_1x.py tests/test_utils.py mmdet/utils/registry.py mmdet/core/anchor/guided_anchor_target.py mmdet/core/bbox/__init__.py configs/carafe/faster_rcnn_r50_fpn_carafe_1x.py configs/hrnet/faster_rcnn_hrnetv2p_w32_1x.py configs/reppoints/bbox_r50_grid_center_fpn_1x.py configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x.py mmdet/apis/train.py configs/fp16/retinanet_r50_fpn_fp16_1x.py mmdet/models/shared_heads/__init__.py make_cuda_ext write_version_py readme get_version parse_requirements get_git_hash get_hash main parse_args save_result_pyplot inference_detector show_result_pyplot LoadImage init_detector show_result _dist_train set_random_seed batch_processor _non_dist_train train_detector parse_losses AnchorGenerator anchor_target unmap anchor_inside_flags images_to_levels anchor_target_single ga_loc_target ga_shape_target_single calc_region images_to_levels ga_shape_target PointGenerator images_to_levels point_target unmap point_target_single assign_and_sample build_assigner build_sampler bbox_target_single expand_target bbox_target ensure_rng random_boxes bbox_overlaps delta2bbox roi2bbox bbox_flip bbox_mask2result distance2bbox bbox2delta bbox_mapping bbox2result bbox_mapping_back bbox2roi ApproxMaxIoUAssigner AssignResult ATSSAssigner BaseAssigner MaxIoUAssigner PointAssigner BaseSampler CombinedSampler InstanceBalancedPosSampler IoUBalancedNegSampler OHEMSampler PseudoSampler RandomSampler SamplingResult bbox_overlaps get_classes imagenet_vid_classes voc_classes imagenet_det_classes coco_classes cityscapes_classes wider_face_classes DistEvalHook eval_map tpfp_imagenet print_map_summary average_precision get_cls_results tpfp_default plot_iou_recall set_recall_param print_recall_summary _recalls eval_recalls plot_num_recall force_fp32 auto_fp16 Fp16OptimizerHook wrap_fp16_model patch_forward_method patch_norm_fp32 cast_tensor_type mask_target mask_target_single split_combined_polys build_optimizer CopyOfSGD register_torch_optimizers multiclass_nms multiclass_nms_with_mask merge_aug_scores merge_aug_masks merge_aug_bboxes merge_aug_proposals DistOptimizerHook allreduce_grads _allreduce_coalesced unmap tensor2imgs multi_apply build_dataset _concat_dataset CityscapesDataset CocoDataset CustomDataset RepeatDataset ConcatDataset VOCDataset WIDERFaceDataset XMLDataset build_dataloader worker_init_fn GroupSampler DistributedSampler DistributedGroupSampler Compose get_centerpoint polar_centerness_target get_polar_coordinates ConvertToContour DefaultFormatBundle Transpose ToTensor Collect to_tensor ImageToTensor ToDataContainer InstaBoost LoadImageFromFile LoadProposals LoadAnnotations MultiScaleFlipAug RandomFlip Pad Corrupt PhotoMetricDistortion MinIoURandomCrop SegRescale Resize RandomCrop Albu Normalize Expand build_shared_head build_detector build_loss build build_backbone build_roi_extractor build_head build_neck AnchorHead reduce_mean ATSSHead FCOSHead get_polar_coordinates polar_centerness_target get_mask_sample_region FourierNetHead get_points_single FeatureAlign FoveaHead FreeAnchorRetinaHead GARetinaHead GARPNHead FeatureAdaption GuidedAnchorHead RepPointsHead RetinaHead RetinaSepBNHead RPNHead SSDHead HRModule HRNet ResNet BasicBlock make_res_layer Bottleneck ResNeXt make_res_layer Bottleneck SSDVGG L2Norm BBoxHead SharedFCBBoxHead ConvFCBBoxHead DoubleConvFCBBoxHead BasicResBlock ATSS BaseDetector CascadeRCNN DoubleHeadRCNN FasterRCNN FastRCNN FCOS FourierNet FOVEA GridRCNN HybridTaskCascade MaskRCNN MaskScoringRCNN RepPointsDetector RetinaNet RPN SingleStageDetector MaskTestMixin BBoxTestMixin RPNTestMixin TwoStageDetector Accuracy accuracy BalancedL1Loss balanced_l1_loss binary_cross_entropy mask_cross_entropy _expand_binary_labels CrossEntropyLoss cross_entropy sigmoid_focal_loss py_sigmoid_focal_loss FocalLoss _expand_binary_labels GHMR GHMC bounded_iou_loss iou_loss IoULoss BoundedIoULoss GIoULoss giou_loss MSELoss PolarIOULoss smooth_l1_loss SmoothL1Loss weight_reduce_loss weighted_loss reduce_loss FCNMaskHead FusedSemanticHead GridHead HTCMaskHead MaskIoUHead BFP FPN FPN_CARAFE HRFPN NASFPN SumCell GPCell MergingCell GeneralizedAttention NonLocal2D SingleRoIExtractor ResLayer ConvModule build_conv_layer conv_ws_2d ConvWS2d build_norm_layer Scale build_upsample_layer PixelShufflePack xavier_init bias_init_with_prob uniform_init normal_init kaiming_init last_zero_init ContextBlock affine_grid _AffineGridGenerator CARAFEPack CARAFENaive CARAFENaiveFunction CARAFE CARAFEFunction DeformConvFunction ModulatedDeformConv DeformConvPack ModulatedDeformConvPack DeformConv ModulatedDeformConvFunction DeformRoIPoolingPack DeformRoIPoolingFunction ModulatedDeformRoIPoolingPack DeformRoIPooling _GridSampler grid_sample MaskedConv2dFunction MaskedConv2d nms soft_nms RoIAlign RoIAlignFunction RoIPool RoIPoolFunction SigmoidFocalLoss SigmoidFocalLossFunction collect_env add_flops_counting_methods add_flops_counter_hook_function bn_flops_counter_hook reset_flops_count gn_flops_counter_hook relu_flops_counter_hook deconv_flops_counter_hook get_model_parameters_number add_flops_mask flops_to_string params_to_string remove_flops_mask remove_batch_counter_hook_function start_flops_count add_batch_counter_variables_or_reset pool_flops_counter_hook empty_flops_counter_hook add_flops_mask_variable_or_reset add_batch_counter_hook_function get_model_complexity_info conv_flops_counter_hook remove_flops_counter_hook_function batch_counter_hook add_flops_counter_variable_or_reset is_supported_instance stop_flops_count upsample_flops_counter_hook linear_flops_counter_hook compute_average_flops_cost print_model_with_flops print_log get_root_logger profile_time build_from_cfg Registry NiceRepr test_approx_iou_assigner_with_empty_boxes test_point_assigner test_max_iou_assigner test_approx_iou_assigner_with_empty_gt test_point_assigner_with_empty_gt test_random_assign_result test_approx_iou_assigner test_point_assigner_with_empty_boxes_and_gt test_max_iou_assigner_with_empty_boxes_and_gt test_max_iou_assigner_with_empty_boxes test_max_iou_assigner_with_empty_gt test_max_iou_assigner_with_ignore test_approx_iou_assigner_with_empty_boxes_and_gt test_max_iou_assigner_with_empty_boxes_and_ignore MaskRCNNDetector AsyncTestCase AsyncInferenceTestCase test_config_build_detector _get_config_directory _get_config_directory _get_config_module test_cascade_forward test_faster_rcnn_forward _get_detector_cfg test_faster_rcnn_ohem_forward _demo_mm_inputs test_rpn_forward test_retina_ghm_forward test_ssd300_forward _demodata_refine_boxes test_anchor_head_loss test_bbox_head_loss test_refine_boxes test_nms_device_and_dtypes_cpu test_nms_device_and_dtypes_gpu test_random_sampler_empty_pred test_ohem_sampler _context_for_ohem test_ohem_sampler_empty_gt test_random_sampler_empty_gt test_random_sampler test_random_sample_result test_ohem_sampler_empty_pred test_soft_nms_device_and_dtypes_cpu test_params_to_string cal_train_time plot_curve load_json_logs main parse_args add_plot_parser add_time_parser main analyze_results analyze_individual_category makeplot main convert convert_bn convert_conv_fc main parse_args main parse_args process_checkpoint get_distortions_from_results print_coco_results get_distortions_from_file get_coco_style_results get_voc_style_results get_results main single_gpu_test collect_results_cpu collect_results_gpu main multi_gpu_test MultipleKVAction parse_args voc_eval_with_return single_gpu_test collect_results coco_eval_with_return main multi_gpu_test parse_args main parse_args main convert collect_annotations cvt_annotations load_img_info main collect_files parse_args main cvt_annotations parse_xml parse_args decode _minimal_ext_cmd exists get_hash list gen_packages_items add_argument ArgumentParser config VideoCapture camera_id read CLASSES print waitKey inference_detector init_detector parse_args show_result checkpoint get_classes isinstance model load_checkpoint warn eval build_detector fromfile to Compose cfg dict test_pipeline device seed int bool concat_list isinstance concatenate imshow_det_bboxes astype copy vstack imread bgr2rgb imshow show_result figure join bgr2rgb figure show_result imsave seed manual_seed_all manual_seed items list isinstance clone get_world_size OrderedDict mean all_reduce item div_ Tensor sum dict parse_losses model log_level _non_dist_train get_root_logger _dist_train workflow MMDistributedDataParallel DistSamplerSeedHook cuda run total_epochs build_optimizer checkpoint_config work_dir get val load_from DistEvalHook resume_from register_training_hooks resume optimizer DistOptimizerHook lr_config load_checkpoint register_hook Runner log_config Fp16OptimizerHook workflow cuda run total_epochs build_optimizer checkpoint_config work_dir optimizer_config get load_from resume_from register_training_hooks resume optimizer lr_config load_checkpoint Runner log_config Fp16OptimizerHook multi_apply images_to_levels any sum range cat len append stack squeeze assign_and_sample zeros_like PseudoSampler pos_gt_bboxes size pos_weight anchor_inside_flags unmap sample new_zeros build_assigner assign pos_inds bbox2delta allowed_border neg_inds pos_bboxes assigner uint8 type new_full clamp long new_full zeros_like calc_region size sqrt log2 floor full_like item append zeros float sum long range len multi_apply images_to_levels any append sum range cat len ga_assigner build_sampler ga_sampler PseudoSampler zeros_like reshape pos_gt_bboxes size unmap build_assigner assign pos_inds sample neg_inds pos_bboxes multi_apply images_to_levels any sum range cat len assign_and_sample zeros_like PseudoSampler pos_gt_bboxes size pos_weight unmap new_zeros build_assigner assign pos_inds sample neg_inds assigner BaseAssigner isinstance BaseSampler isinstance build_sampler sampler build_assigner assign sample assigner multi_apply cat bbox2delta size new_zeros squeeze new_zeros _rand RandomState isinstance minimum astype float32 maximum from_numpy ensure_rng clamp size min max stack unsqueeze div_ float log exp clamp size repeat expand_as view_as abs log addcmul Tensor ndarray isinstance clone bbox_flip new_full new_zeros append cat enumerate cpu append unique numpy clamp reshape tolist permute append frPyObjects numpy range minimum T astype maximum float32 zeros range items list eval is_str arange ones hstack maximum zeros sum range minimum zeros_like concatenate argsort vstack zeros bbox_overlaps range enumerate len max zeros_like concatenate argsort vstack zeros argmax bbox_overlaps enumerate len append empty starmap cumsum tuple vstack Pool get_cls_results list print_map_summary append range eps mean item zip enumerate maximum argsort any average_precision zeros len get_classes ndarray format isinstance table len is_str print_log AsciiTable append zeros range enumerate sum sort hstack copy zeros float argmax fliplr range enumerate array isinstance min set_recall_param print_recall_summary _recalls array append zeros bbox_overlaps range len arange table insert size tolist print_log AsciiTable append array enumerate show ndarray plot isinstance xlabel tolist axis ylabel figure show ndarray plot isinstance xlabel tolist axis ylabel figure hasattr patch_norm_fp32 modules half children isinstance half patch_forward_method float forward ndarray isinstance Iterable Tensor Mapping list map cat mask_size imresize size astype maximum range new_zeros shape int32 device append to numpy clip _pair tolist append slice_list range len pop get named_modules hasattr replace endswith search copy named_parameters dict parameters append module dir getattr startswith register_module append optim pop new_full sort copy nms_op new_zeros getattr append range cat pop new_full sort copy nms_op new_zeros getattr append range cat nms nms_thr sort min clone max_num zip append bbox_mapping_back cat append mean bbox_mapping_back zip Tensor isinstance average mean array list _take_tensors _flatten_dense_tensors zip _unflatten_dense_tensors OrderedDict all_reduce copy_ div_ append type values all_reduce _allreduce_coalesced get_world_size div_ uint8 transpose size astype ascontiguousarray append array range list map get deepcopy isinstance append build_dataset range len get isinstance ConcatDataset _concat_dataset build_from_cfg RepeatDataset DistributedSampler get_dist_info DistributedGroupSampler DataLoader seed arctan2 astype pi argsort sqrt zeros max range sqrt min max range len Tensor ndarray isinstance isinstance all_reduce clone get_world_size div_ where new_zeros shape stack enumerate int sort atan2 tensor meshgrid stack arange block Sequential build_conv_layer append range expansion topk isinstance size t eq mul_ expand_as append sum max log abs e where float weight_reduce_loss new_full size squeeze expand size weight_reduce_loss binary_cross_entropy_with_logits _expand_binary_labels float squeeze arange type_as sigmoid pow weight_reduce_loss binary_cross_entropy_with_logits _sigmoid_focal_loss weight_reduce_loss view clamp view zeros_like size min where abs max clamp min max abs where get_enum sum reduce_loss dict conv_layer pop copy size view pop str setdefault norm_layer copy parameters _specify_ddp_gpu_num pop upsample copy hasattr bias xavier_uniform_ xavier_normal_ weight constant_ hasattr bias normal_ weight constant_ hasattr bias uniform_ weight constant_ kaiming_uniform_ hasattr bias weight kaiming_normal_ constant_ float Sequential isinstance constant_init dim range ndarray isinstance new_zeros Tensor to numpy is_cuda ndarray isinstance from_numpy cpu Tensor show join str format defaultdict replace list items check_output strip get_compiler_version device_count __version__ get_compiling_cuda_version platform is_available range append flops_model get_model_parameters_number input_constructor stop_flops_count add_flops_counting_methods start_flops_count compute_average_flops_cost new_empty print_model_with_flops print compute_average_flops_cost apply sum __get__ reset_flops_count apply __batch_counter__ is_supported_instance modules add_batch_counter_hook_function apply remove_batch_counter_hook_function apply add_batch_counter_variables_or_reset apply apply apply type issubclass numel shape affine prod shape affine prod groups kernel_size out_channels in_channels list kernel_size out_channels groups in_channels expand sum prod print len register_forward_hook hasattr remove hasattr is_supported_instance items issubclass hasattr register_forward_hook type is_supported_instance remove is_supported_instance hasattr is_supported_instance setFormatter basicConfig get_dist_info getLogger addHandler Formatter setLevel hasHandlers FileHandler isinstance print get_root_logger Logger log record_event monotonic Event pop get list items setdefault copy is_str isclass FloatTensor assign LongTensor MaxIoUAssigner LongTensor FloatTensor assign MaxIoUAssigner Tensor FloatTensor assign LongTensor MaxIoUAssigner LongTensor FloatTensor assign MaxIoUAssigner empty LongTensor FloatTensor assign MaxIoUAssigner Tensor empty assign empty MaxIoUAssigner LongTensor assign PointAssigner FloatTensor LongTensor assign PointAssigner FloatTensor assign PointAssigner FloatTensor FloatTensor assign LongTensor ApproxMaxIoUAssigner FloatTensor assign LongTensor ApproxMaxIoUAssigner FloatTensor assign empty ApproxMaxIoUAssigner assign empty ApproxMaxIoUAssigner random int getenv join dirname join format _get_config_directory model print build_detector import_module_from_path train_cfg test_cfg len join _get_config_directory import_module_from_path Config deepcopy model _get_config_module train_cfg test_cfg pop _get_detector_cfg _demo_mm_inputs build_detector forward pop _get_detector_cfg _demo_mm_inputs build_detector forward pop _get_detector_cfg _demo_mm_inputs build_detector is_available forward cuda pop _get_detector_cfg _demo_mm_inputs item build_detector float forward pop _get_detector_cfg _demo_mm_inputs item build_detector float forward pop _get_detector_cfg _demo_mm_inputs item build_detector float forward T RandomState LongTensor FloatTensor rand append randint range clip Config sum AnchorHead forward loss Config _dummy_bbox_sampling forward rand get_target BBoxHead sum loss format print BBoxHead refine_bboxes _demodata_refine_boxes int random_boxes group_items astype from_numpy ensure_rng numpy randint empty long cat nms FloatTensor float64 astype float32 DoubleTensor array nms format print astype float32 skip device_count to array range LongTensor FloatTensor RandomSampler assign MaxIoUAssigner sample Tensor FloatTensor RandomSampler assign MaxIoUAssigner sample empty long LongTensor FloatTensor RandomSampler assign MaxIoUAssigner sample empty build_detector _get_detector_cfg LongTensor FloatTensor OHEMSampler _context_for_ohem assign MaxIoUAssigner sample Tensor LongTensor FloatTensor OHEMSampler _context_for_ohem assign MaxIoUAssigner sample Tensor empty LongTensor FloatTensor OHEMSampler _context_for_ohem assign MaxIoUAssigner sample Tensor empty random range FloatTensor float64 astype float32 DoubleTensor soft_nms array params_to_string assert_equal list format std print argmin mean array append argmax keys include_outliers enumerate arange backend max out show list title savefig legend gca append format plot concatenate cla keys enumerate json_logs switch_backend print style xlabel set_xticks set_style array len add_argument add_parser add_argument add_parser add_plot_parser add_subparsers add_time_parser zip json_logs load_json_logs vstack subplot ylabel shape ylim title savefig legend range format plot insert close xlim xlabel figure zeros fill_between len deepcopy format evaluate COCOeval print createIndex accumulate getImgIds append getCatIds enumerate deepcopy format evaluate COCOeval print makeplot COCO accumulate getImgIds recThrs loadRes dirname vstack getCatIds enumerate makedirs analyze_results ann add_argument result types ArgumentParser ones size add from_numpy zeros from_numpy add load format convert_conv_fc print len set OrderedDict dict save range convert_bn enumerate src convert dst depth forward_dummy format hasattr tuple get_model_complexity_info shape eval fromfile cuda load decode format rstrip save Popen in_file process_checkpoint out_file zeros _print load list format basename print_coco_results isinstance print mean zeros keys enumerate len load list format basename isinstance print mean zeros keys enumerate len print get_coco_style_results get_voc_style_results load append replace enumerate task get_results filename update show_result size ProgressBar eval append dataset range enumerate len update get_dist_info size ProgressBar eval collect_results_cpu collect_results_gpu append dataset range enumerate len rstrip tensor broadcast list get_dist_info mkdtemp encode append range dump format bytearray zip load join barrier extend rmtree mkdir_or_exist full list get_dist_info bytearray dumps extend tobytes shape loads all_gather zip append tensor max zeros str local_rank model tmpdir launcher MMDistributedDataParallel format_only show get_dist_info format_results build_detector build_dataset gpu_collect get dump init_dist single_gpu_test build_dataloader wrap_fp16_model test evaluate load_checkpoint multi_gpu_test out MMDataParallel list evaluate COCOeval summarize is_str COCO accumulate getImgIds loadRes stats load eval_map CLASSES img_norm_cfg collect_results rstrip tensor broadcast list get_dist_info mkdtemp encode append range dump format bytearray zip load join barrier extend rmtree mkdir_or_exist full set_random_seed coco_eval_with_return final_prints seed corruptions voc_eval_with_return final_prints_aggregate obj_from_dict workers_per_gpu insert iou_thr results2json enumerate deepcopy join coco severities dict localtime autoscale_lr abspath train_detector strftime get_root_logger work_dir append val resume_from info deterministic gpus text collect_env mkdir_or_exist pipeline pop list items replace search join format print glob append len print track_progress track_parallel_progress int decode asarray basename area id dict dirname unique append imread toBbox pop dump labels dict append gt_dir list items img_dir cityscapes_path int parse findall text getroot append zeros array find join list format print track_progress extend zip list_from_file isdir devkit_path cvt_annotations | # FourierNet: Compact mask representation for instance segmentation using differentiable shape decoders This git repo contains the official code for **[FourierNet](https://arxiv.org/abs/2002.02709)**. | | | |:-------------------------:|:-------------------------:| | 2 fourier coefficients |  5 fourier coefficients| | 10 fourier coefficients|  20 fourier coefficients| FourierNet is a single shot, anchor-free, fully convolutional instance segmentation method, which predicts a shape vector that is converted into contour points using a numerical transformation. Compared to previous methods, we introduce a new training technique, where we utilize a differentiable shape decoder, which achieves automatic weight balancing of the shape vector’s coefficients. | 1,757 |
coinse/sadl | ['out of distribution detection', 'autonomous driving'] | ['Guiding Deep Learning System Testing using Surprise Adequacy', 'A Review and Refinement of Surprise Adequacy'] | sa.py train_model.py utils.py run.py fetch_lsa _get_lsa fetch_dsa _get_train_target_ats _get_kdes find_closest_at _get_saved_path _aggr_output get_sc get_ats train compute_roc_auc infog warn info compute_roc fail Colors join zip print predict_classes map Model array save info append Pool predict norm load d format save_path print infog _get_saved_path exists get_ats print _get_train_target_ats tqdm find_closest_at info append enumerate var_threshold is_classification format num_classes list print gaussian_kde transpose infog delete warn tqdm append range len delete _get_lsa is_classification print _get_train_target_ats _get_kdes tqdm info append enumerate digitize linspace format d print reshape Sequential to_categorical astype add load_data save summary Activation compile fit auc roc_curve concatenate fit array compute_roc concatenate | > [Update April, 2021] Checkout a recent [paper](https://arxiv.org/abs/2103.05939) with fast, efficient implementation of SA: https://github.com/testingautomated-usi/surprise-adequacy. Big thanks to the authors! :smiley:
# Guiding Deep Learning System Testing using Surprise Adequacy
[](https://zenodo.org/badge/latestdoi/159278402)
Code release of a paper ["Guiding Deep Learning System Testing using Surprise Adequacy"](https://arxiv.org/abs/1808.08444)
If you find this paper helpful, consider cite the paper:
```
@inproceedings{Kim2019aa,
| 1,758 |
coldmanck/zero-shot-indoor-localization-release | ['indoor localization', 'visual place recognition'] | ['Zero-Shot Multi-View Indoor Localization via Graph Location Networks'] | lib/datasets.py eval-zs_gln.py compute-loc_vec.py eval-gln.py lib/models.py train evaluate evaluate evaluate IndoorDataset MapDataset LocationNet LocationGraphNet MapGraphNet criterion model backward zero_grad to step print eval norm float to | # Zero-Shot Indoor Localization [[Paper]](https://arxiv.org/abs/2008.02492) [[Poster]](https://coldmanck.github.io/files/zero-shot-indoor-localization-poster.pdf) [[Video]](https://dl.acm.org/doi/10.1145/3394171.3413856#sec-supp) The official evaluation code of the paper **Zero-Shot Multi-View Indoor Localization via Graph Location Networks** which has been accepted at [ACM MM 2020](https://2020.acmmm.org/). This repo also includes two datasets (ICUBE & WCP) used in the paper and useful code snippets for reading datasets. <div align="center"> <img src="figs/intro.jpg" width="400"> <img src="figs/zero-shot-indoor-localization.jpg" width="800"> </div> Please cite our paper if you use our code/datasets or feel inspired by our work :) ``` @inproceedings{chiou2020zero, | 1,759 |
colemiller94/gatedgan | ['style transfer'] | ['Gated-GAN: Adversarial Gated Networks for Multi-Collection Style Transfer'] | utils.py models.py data.py ImageDataset ReplayBuffer Transformer ResidualBlock Generator Decoder Discriminator Encoder Identity TVLoss tensor2image LambdaLR label2tensor weights_init_normal Logger size range fill_ numpy tile data normal_ constant __name__ | # Gated-Gan for Multi-Style Transfer PyTorch Implementation of https://github.com/xinyuanc91/Gated-GAN <br> The goal of this project is to translate (from lua to python) and train the model described in this paper: https://arxiv.org/abs/1904.02296<br> I use the general architecture of this repository as a baseline: https://github.com/aitorzip/PyTorch-CycleGAN<br> and modify it to the paper's specifications. ## Notebooks <ul> <li> Builder Book: Train the model <li> Demo: Interact with most recent model using jpeg/jpg in working directory </ul> | 1,760 |
comp-syn/comp-syn | ['word embeddings', 'image retrieval'] | ['comp-syn: Perceptually Grounded Word Embeddings with Color'] | compsyn/trial.py tests/test_analysis.py compsyn/utils.py tests/test_imagedata.py compsyn/visualisation.py compsyn/vectors.py compsyn/vector.py tests/test_vectors.py tests/test_utils.py compsyn/__init__.py tests/test_s3.py compsyn/wordnet_functions.py compsyn/s3.py compsyn/color.py compsyn/helperfunctions.py data/dataset.py tests/test_visualisation.py tests/test_texture.py compsyn/wordtocolor_vector.py compsyn/jzazbz.py compsyn/config.py compsyn/analysis.py tests/test_config.py compsyn/logger.py setup.py compsyn/texture.py compsyn/datahelper.py install_requires merge_vectors_to_image_analysis ImageAnalysis UnknownColorSpaceError avg_rgb get_color MissingArgumentError js_divergence bin_img bin_hsv color_distribution RGB2HEX ColorSpaceConversionError rgb_array_to_jzazbz_array avg_hsv kl_divergence CompsynConfig ImageLoadingError ImageData write_to_json get_google_application_args run_google_vision write_img_classifications_to_file get_jzazbz_array get_jzazbz_args get_logger s3_object_exists NoS3DataError list_object_paths_in_s3 upload_file_to_s3 download_file_from_s3 get_s3_args NoObjectInS3Error get_s3_client S3Error get_coefficents perform_scattering_transform get_wavelet_embedding Trial get_trial_args get_trial_from_env get_logger_args EnvDefault human_bytes env_default compress_image set_env_var MissingRevisionNameError Vector BadPickleError load_vector_pickle VectorNotGeneratedError Visualisation getImage expandTree get_tree_structure get_branching_factor get_wordnet_tree_data WordToColorVector get_parser directory_with_raw_images test_merge_vectors_to_image_analysis test_CompsynConfig test_load_image_dict_from_folder test_load_rgb_and_jzazbz_arrays test_s3_object_exists test_download_file_from_s3 test_get_s3_client test_upload_file_to_s3 test_s3_list_object_paths_in_s3 arrays_within_n_percent test_get_wavelet_embedding test_set_env_var test_compress_image validate_w2cv test_w2cv_fresh_run test_w2cv_s3_pull test_w2cv_s3_push test_w2cv_produce_known_analysis_results test_w2cv_fresh_run_with_related test_Visualisation read_text image_data ImageAnalysis compute_color_distributions get_composite_image info append label get_logger entropy entropy reshape transpose get_jzazbz_array ravel _check_required_args bin_img bin_hsv rgb_array_to_jzazbz_array rgb_to_hsv sum ravel mean ravel rgb_to_hsv reshape KMeans cluster_centers_ Counter array crop fit_predict add_argument_group add_argument ArgumentParser list ImageAnnotatorClient Image score keys label_annotations getenv info get_logger label_detection update write_text dumps strftime read_text joinpath loads mkdir info get_logger is_file add_argument_group add_argument ArgumentParser parse_known_args debug stdout setFormatter getLogger addHandler debug StreamHandler Formatter Path DEBUG setLevel FileHandler add_argument_group add_argument ArgumentParser parse_known_args get_s3_client get_logger get_object parse_known_args get_s3_client list_objects_v2 get_logger filterwarnings debug parse_known_args put_object get_s3_client get_logger write_bytes read filterwarnings debug parse_known_args mkdir get_object get_s3_client get_logger is_file append range int scattering Scattering2D astype float32 extend get_coefficents mean unique append sum array range len add_argument_group add_argument ArgumentParser parse_known_args add_argument_group add_argument ArgumentParser str debug upper getenv get_logger filterwarnings name joinpath Path mkdir save open len set hyponyms extend synsets hypernyms list values chdir synset set mkdir append to_json DataFrame keys get_branching_factor chdir expandTree get_logger get_tree_structure add_argument add_subparsers ArgumentParser add_parser list CompsynConfig Trial merge_vectors_to_image_analysis load_data WordToColorVector append items list CompsynConfig print get_trial_from_env load_rgb_image rgb_array_to_jzazbz_array ImageData zip load_image_dict_from_folder ImageData parse_known_args get_s3_client upload_file_to_s3 parent list_object_paths_in_s3 unlink with_suffix download_file_from_s3 abs zip CompsynConfig Trial print run_analysis load_data WordToColorVector list plot xlabel close ylabel unlink joinpath compress_image ylim savefig clf resolve iterdir append getenv set_env_var enumerate validate_w2cv CompsynConfig Trial run_analysis WordToColorVector time run_image_capture print rgb_dist run_analysis WordToColorVector save rgb_ratio jzazbz_dist time run_image_capture print run_analysis WordToColorVector load validate_w2cv push WordToColorVector validate_w2cv pull delete_local_images run_analysis WordToColorVector entropy_computations list CompsynConfig plot_labels_in_space Trial merge_vectors_to_image_analysis close compress_color_data clf load_data WordToColorVector append Visualisation | comp-syn/comp-syn | 1,761 |
compstorylab/contagiograms | ['time series'] | ['Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter'] | contagiograms/cli.py contagiograms/consts.py setup.py contagiograms/utils.py contagiograms/__init__.py contagiograms/contagiograms.py valid_windowsize NegateAction valid_date parse_args valid_timescale get_parser SortedMenu main plot flipbook plot_contagiograms match int add_argument get_parser rglob str sorted write close PdfFileReader Path info append PdfFileMerger items get_ngram get_lang list Storywrangler plot_contagiograms nparser mkdir info append enumerate len time plot flipbook info parse_args arange set_yticklabels grid add_subplot set_visible set_minor_locator vstack set_major_formatter axhline tick_params fillna set_major_locator get_display idxmin set_yscale tolist pcolormesh colorbar savefig legend add_gridspec DateFormatter update get inset_axes set_minor_formatter replace plot set_xticklabels set_xlim LogLocator mean set_label_position annotate get_cmap zeros enumerate ListedColormap to_datetime text min reshape set_yticks index invert_yaxis subplots_adjust AutoDateLocator YearLocator figure fill_between dropna set_ylim split |  # Contagiograms As part of our [Storywrangler](https://gitlab.com/compstorylab/storywrangler) project, we present a Python package for visualizing contagiograms. ## Description With these expanded time series visualizations, we convey the degree to which an n-gram τ is retweeted both overall and relative to the background level of retweeting for a given language ℓ. We show both rates as retweet rates change strongly over time and variably so across languages.  | 1,762 |
compstorylab/covid19ngrams | ['time series'] | ['Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter'] | src/vis.py src/consts.py src/query.py src/cli.py src/utils.py src/filter.py src/update.py parse_args SortedMenu main Query main query_lang_array update_timeseries filter_ngrams plot_cases cases add_argument add_subparsers ArgumentParser add_parser time parent glob print filter_ngrams mkdir rglob plot jhu update_timeseries contagiograms Path info parse_args cases set_index print glob to_csv index drop enumerate any mkdir Path info append read_csv compile split Path exists Query list day get reset_index combine_first month today pivot_table timedelta nan info keys year datetime to_csv read_csv info endswith glob index query_lang_array mkdir Path Language read_csv Query items T list plot_cases query_timeseries info to_datetime len index split append read_csv diff arange set_yticklabels grid add_subplot set_minor_locator set_major_formatter tick_params fillna set_major_locator set_title set_yscale twinx savefig set_color add_gridspec append legend DateFormatter update set_minor_formatter plot set_xlim LogLocator today mean timedelta annotate datetime to_datetime invert_yaxis set_yticks text index subplots_adjust AutoDateLocator YearLocator set_ylabel figure set_ylim | # COVID19 related n-gram time series for 24 languages on Twitter - [Website](http://compstorylab.org/covid19ngrams/) - [arXiv](https://arxiv.org/abs/2003.12614) In confronting the global spread of the coronavirus disease COVID-19 pandemic we must have coordinated medical, operational, and political responses. In all efforts, data is crucial. Fundamentally, and in the possible absence of a vaccine for 12 to 18 months, we need universal, well-documented testing for both the presence of the disease as well as confirmed recovery through serological tests for antibodies, and we need to track major socioeconomic indices. | 1,763 |
compstorylab/storywrangling | ['time series'] | ['Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter'] | storywrangling/storywrangler.py tests/test_realtime.py storywrangling/regexr.py setup.py storywrangling/__init__.py storywrangling/realtime_query.py storywrangling/realtime.py tests/test_storywrangler.py storywrangling/query.py Query Realtime RealtimeQuery html2unicode hex2unicode remove_whitespaces nparser ngram_parser get_ngram_int Storywrangler RealtimeTesting NgramsTesting join strip sub sub list ngram_parser zip int | compstorylab/storywrangling | 1,764 |
computationalpathologygroup/hooknet | ['type prediction', 'whole slide images', 'semantic segmentation'] | ['HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images'] | apply.py train.py source/model.py source/image/imagereader.py source/generator/batchgenerator.py argconfigparser/argconfigparser.py source/trainer.py source/image/deamons.py source/image/imagewriter.py source/inference.py is_valid_file train is_valid_file RecursiveLoader _str2value ArgumentConfigParser normalize Inference HookNet UNet HookNetTrainer Trainer RandomBatchGenerator ImageProcessor fit_data PngMaskReader MaskReader TifMaskReader WSIReaderDeamon WSIWriterDeamon ImageReader ImageWriter error print HookNet ArgumentConfigParser RandomBatchGenerator parse_args HookNetTrainer type | # HookNet ## Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images. ### This repository is outdated. You can find the new and updated repository here: # https://github.com/DIAGNijmegen/pathology-hooknet | 1,765 |
concordiaca/3D-ResNets-PyTorch | ['action recognition'] | ['Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?'] | utils/eval_ucf101.py utils/video_jpg.py opts.py models/resnext.py train.py datasets/hmdb51.py dataset.py models/wide_resnet.py models/densenet.py utils/ucf101_json.py utils.py utils/eval_kinetics.py datasets/activitynet.py models/pre_act_resnet.py temporal_transforms.py test.py utils/kinetics_json.py datasets/ucf101.py utils/eval_hmdb51.py utils/hmdb51_json.py mean.py utils/n_frames_ucf101_hmdb51.py datasets/kinetics.py main.py target_transforms.py model.py utils/n_frames_kinetics.py utils/video_jpg_ucf101_hmdb51.py utils/fps.py validation.py spatial_transforms.py models/resnet.py utils/video_jpg_kinetics.py get_training_set get_test_set get_validation_set get_std get_mean generate_model parse_opts MultiScaleCornerCrop CenterCrop MultiScaleRandomCrop ToTensor Compose Scale Normalize RandomHorizontalFlip CornerCrop ClassLabel VideoID Compose TemporalBeginCrop LoopPadding TemporalCenterCrop TemporalRandomCrop calculate_video_results test calculate_accuracy AverageMeter Logger load_value_file modify_frame_indices get_class_labels load_annotation_data video_loader get_end_t make_dataset ActivityNet accimage_loader get_default_image_loader get_default_video_loader make_untrimmed_dataset pil_loader get_video_names_and_annotations get_class_labels load_annotation_data video_loader make_dataset accimage_loader HMDB51 get_default_image_loader get_default_video_loader pil_loader get_video_names_and_annotations get_class_labels load_annotation_data video_loader make_dataset accimage_loader Kinetics get_default_image_loader get_default_video_loader pil_loader get_video_names_and_annotations UCF101 get_class_labels load_annotation_data video_loader make_dataset accimage_loader get_default_image_loader get_default_video_loader pil_loader get_video_names_and_annotations get_fine_tuning_parameters DenseNet densenet201 densenet169 densenet264 _DenseLayer _DenseBlock _Transition densenet121 conv3x3x3 get_fine_tuning_parameters resnet50 downsample_basic_block resnet152 PreActivationBasicBlock resnet34 resnet200 PreActivationBottleneck resnet18 PreActivationResNet resnet101 conv3x3x3 get_fine_tuning_parameters ResNet downsample_basic_block resnet50 Bottleneck resnet152 resnet34 resnet200 resnet18 resnet10 BasicBlock resnet101 ResNeXtBottleneck conv3x3x3 get_fine_tuning_parameters resnet50 downsample_basic_block ResNeXt resnet152 resnet101 conv3x3x3 get_fine_tuning_parameters WideBottleneck resnet50 downsample_basic_block WideResNet HMDBclassification compute_video_hit_at_k get_blocked_videos KINETICSclassification compute_video_hit_at_k UCFclassification compute_video_hit_at_k convert_hmdb51_csv_to_activitynet_json get_labels convert_csv_to_dict load_labels convert_kinetics_csv_to_activitynet_json convert_csv_to_dict class_process class_process load_labels convert_ucf101_csv_to_activitynet_json convert_csv_to_dict class_process class_process video_path UCF101 ActivityNet Kinetics annotation_path HMDB51 video_path UCF101 n_val_samples ActivityNet Kinetics annotation_path HMDB51 video_path UCF101 ActivityNet Kinetics annotation_path HMDB51 get_fine_tuning_parameters in_features densenet264 DataParallel ft_begin_index resnet34 resnet152 cuda load_state_dict resnet200 resnet101 resnet18 format resnet50 resnet10 n_finetune_classes Linear load densenet169 densenet201 print pretrain_path densenet121 parse_args set_defaults add_argument ArgumentParser topk size mean stack append range update time format model print Variable cpu AverageMeter size eval softmax calculate_video_results append range enumerate len topk view size t eq join format image_loader append exists get_default_image_loader append enumerate append items list format append join format items list format join get_class_labels deepcopy load_annotation_data print modify_frame_indices len load_value_file ceil max range append get_video_names_and_annotations sort listdir items list format join get_class_labels deepcopy load_annotation_data print modify_frame_indices len load_value_file get_end_t ceil max range append get_video_names_and_annotations int min DenseNet DenseNet DenseNet DenseNet append format range named_parameters data isinstance FloatTensor Variable zero_ avg_pool3d cuda cat PreActivationResNet PreActivationResNet PreActivationResNet PreActivationResNet PreActivationResNet PreActivationResNet ResNet ResNet ResNet ResNet ResNet ResNet ResNet ResNeXt ResNeXt ResNeXt WideResNet reset_index size tolist mean unique zeros values enumerate Request urlopen format ceil join read_csv append listdir range len append join listdir update get_labels convert_csv_to_dict read_csv update load_labels convert_csv_to_dict join int print sort append listdir split append range update load_labels convert_csv_to_dict format call mkdir splitext exists | # 3D ResNets for Action Recognition ## Update (2018/2/21) Our paper "Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?" is accepted to CVPR2018! We update the paper information. ## Update (2018/01/16) We uploaded some of fine-tuned models on UCF-101 and HMDB-51. * ResNeXt-101 fine-tuned on UCF-101 (split1) * ResNeXt-101 (64 frame inputs) fine-tuned on UCF-101 (split1) * ResNeXt-101 fine-tuned on HMDB-51 (split1) * ResNeXt-101 (64 frame inputs) fine-tuned on HMDB-51 (split1) | 1,766 |
congasix/DeepNC | ['link prediction'] | ['DeepNC: Deep Generative Network Completion'] | baselines/mmsb.py baselines/graphvae/model.py build/lib/snap.py ged4py/algorithm/abstract_graph_edit_dist.py fastPFP.py baselines/graphvae/train.py src/node2vec.py data.py args.py ForestFire.py main_test.py plot.py baselines/baseline_simple.py main_DeepGMG.py evaluate.py utils.py baselines/graphvae/data.py main_train.py ged4py/algorithm/edge_edit_dist.py create_graphs.py node2vec.py createtruegraph.py model.py snap.py ged4py/algorithm/graph_edit_dist.py src/main.py dataprocess.py setup.py deg_distribution.py create_dataset.py Args create_dataset_artificial create encode_adj_flexible decode_adj_flexible Graph_sequence_sampler_bfs_permute_truncate_multigraph GraphDataset_adj_batch parse_index_file test_graph_load_DD encode_adj_full encode_adj GraphDataset_adj_batch_1 Graph_sequence_sampler_pytorch_nll decode_adj_full bfs_seq Graph_sequence_sampler_pytorch Graph_load_batch decode_adj Graph_sequence_sampler_pytorch_canonical preprocess Graph_synthetic Graph_sequence_sampler_truncate test_encode_decode_adj_full Graph_load GraphDataset_adj GraphDataset Graph_sequence_sampler_flexible test_encode_decode_adj Graph_sequence_sampler_fast Graph_sequence_sampler_pytorch_nobfs load_graph_list eval_performance clean_graphs process_kron eval_list_fname Args_evaluate find_nearest_idx compute_basic_stats load_ground_truth eval_list eval_single_list evaluation_epoch evaluation extract_result_id_and_epoch fastPFP fastPFP_faster greedy_assignment loss ForestFire train_DGMG_epoch Args_DGMG train_DGMG_forward_epoch train_DGMG train_DGMG_nll test_DGMG_epoch DGM_graphs sample_tensor message_passing GraphConv MLP_plain binary_cross_entropy_weight imputation CNN_decoder GRU_plain sample_sigmoid_supervised_simple_origin CNN_decoder_share GCN_decoder sample_sigmoid GCN_encoder LSTM_plain CNN_decoder_attention calc_graph_embedding sample_sigmoid_greedy MLP_token_plain Graph_generator_LSTM sample_sigmoid_supervised MLP_VAE_plain sample_sigmoid_supervised_greedy preprocess Graphsage_Encoder GCN_generator sample_sigmoid_supervised_greedy2 Graph_generator_LSTM_output_generator Graph_RNN_structure gumbel_sigmoid GCN_encoder_graph MLP_VAE_conditional_plain Graph_generator_LSTM_output_discriminator calc_init_embedding sample_sigmoid_supervised_simple sample_sigmoid_supervised_greedy_lp gumbel_softmax Graph alias_draw alias_setup getdynpath TFlt_GetPrcStr iterhashset LoadConnList TBPGraph_New TMOut itervec TNEANetAIntI PlotShortPathDistr_PNEANet TStrFltFltTrV_SwapI GetNodeTriads_PNGraph GenCircle_PUNGraph LoadEdgeListNet TStrIntVHI TSStr TIntIntHHI GetTriangleCnt_PDirNet DrawGViz DelSelfEdges_PUNGraph IsWeaklyConn_PUndirNet GetTreeSig_PUNGraph TFltIntIntTrV_SwapI TUInt64V_SwapI TFltIntIntIntQuV ToGraph_PUNGraph TInt_GetMn TStrIntKdV_SwapI GetCmnNbrs TFltStrPrV GetDegCnt GetDegreeCentr GetSccSzCnt_PUndirNet TCh_IsHex GetKCoreEdges_PUNGraph GetMxScc_PDirNet TIntUInt64KdV TStrIntPrVH TFltUInt64PrV DrawGViz_PUNGraph TNEGraph_New TBool_GetStr TFltUInt64PrV_SwapI TInt_GetInRng GetMxBiCon_PUNGraph GenRndGnm_PUndirNet TExcept_ThrowFull GenCircle_PDirNet TStrPrBoolH DelZeroDegNodes_PUndirNet TStr_GetNrNumFExt TChV_SwapI TIntPrStrVHI GetBfsEffDiam_PUNGraph IsConnected_PUndirNet TCliqueOverlap_CalculateOverlapMtx ConvertGraph_PNEANet_PUNGraph PlotInDegDistr GetOutDegCnt_PUndirNet TStrStrIntPrVHI GetTreeRootNId_PUndirNet PrintGraphStatTable_PUNGraph TUInt64Pr GenFull_PDirNet LoadEdgeListStr_PUndirNet MakeUnDir_PUNGraph ConvertGraph_PDirNet_PDirNet TUInt_JavaUIntToCppUInt TNEANetMPEdgeI PercentMxWcc TForestFire_GenGraph GetPageRank_v1_PDirNet GetPageRankMP_PDirNet TNotify_DfOnNotify TFltPrV GetTriads_PUndirNet DelSelfEdges TIntBoolPr TStrAscFltKd TFltIntKd TStrFltKdV_GetV Edges TUInt64IntKdV GetHits TIntIntVIntTrV_SwapI GetKCoreEdges GetClustCf_PNEANet TNEANetAFltI TStrIntPrH PlotKCoreEdges_PUNGraph GetKCoreEdges_PUndirNet PFltV TStrIntVH SaveGViz_PUndirNet GetEdgesInOut_PNEANet TStrUtil_SplitWords PTable TFltStrKdV_GetV TChA_LoadTxt GetMxWccSz_PNGraph TNotify_OnNotify CntUniqUndirEdges_PDirNet CntUniqDirEdges GetPageRankMP_PNEANet TStrStrVPrV_SwapI TUIntV_GetV ConvertESubGraph_PNGraph_PNEANet PlotKCoreNodes_PNEANet ReebSimplify TUInt64IntKdV_GetV PlotOutDegDistr_PNGraph PAscFltV_New GetMxInDegNId GenBaraHierar_PNEANet TBoolFltPr MxDegree_PNEANet TestAnf_PDirNet TStrPrV_SwapI GetEigenVectorCentr TSFlt TRowIteratorWithRemove GetWccs_PDirNet GenRMatEpinions GetNodeClustCf_PDirNet PNEANet TAscFltV_GetV GetKCoreEdges_PNGraph TUNGraphEdgeI GetClustCf_PUndirNet TChAV_SwapI TRStr_CmpI TStrFltVHI MxDegree_PUNGraph TDirNet_New TFltTrV_GetV TStrVIntVHI TAGMUtil_TotalMemberships PlotOutDegDistr PercentDegree GetWccs_PNEANet GetSubTreeSz_PNGraph TIntV_GetV TFltRect_Intersection ConvertSubGraph LoadDyNetGraphV PlotWccDistr_PNGraph GenGrid_PNGraph GetAnfEffDiam_PDirNet GetBfsTree_PNGraph GetWeightedFarnessCentr LoadConnListStr_PNEANet GetNodeWcc_PDirNet TIntStrStrTrV_GetV print_array ToGraphMP TAscFltVP_New ToNetwork GetMxScc_PUNGraph GetMxSccSz_PDirNet EventImportance TUIntIntPr TAscFltPr TTable_LoadSS GenTree_PUNGraph TStrFltHI LoadConnList_PDirNet GetGroupClosenessCentr TIntIntFltTr TStrUtil_SplitLines DelNodes_PNEANet TStrFltFltTrV GetMxWcc_PUNGraph TFltFltStrTrV_GetV TGUtil GetNodesAtHop_PNEANet LoadEdgeList_PUndirNet TFlt PlotOutDegDistr_PDirNet MaxCPGreedyBetter1 GetTreeRootNId_PDirNet TIntIntIntVTrV_GetV LoadConnListStr GetRndSubGraph_PNEANet TStrVStrH TInt_IsOdd TStrHashF_Md5 GetMxInDegNId_PNGraph SavePajek_PNEANet ConvertSubGraph_PNGraph_PNEANet GenRndGnm_PDirNet ConvertSubGraph_PNGraph_PUNGraph SavePajek_PDirNet PlotSngVec CntUniqDirEdges_PUndirNet GetHits_PUNGraph TStrUtil_GetWIdV TStrHashF_DJB GetMxSccSz_PUNGraph TFlt_GetInRng GetNodeTriads PlotSccDistr TStrPool64_Load GenStar_PUndirNet GetSubTreeSz_PUNGraph TCRef MakeUnDir_PNEANet LoadModeNetToNet TFlt_GetMegaStr GetInvParticipRat PIntVecPool TExcept_GetOnExceptF GetTriadEdges_PNEANet TAscFltIntKdV TFltIntIntIntQuV_GetV TStrPrIntH PlotKCoreEdges TFltFltStrTr TIntIntVIntTrV LoadMode TStrKdV_SwapI SaveMatlabSparseMtx_PNGraph GetDegSeqV_PDirNet TIntQuV TIntIntPrVHI IsTree TFltIntIntTrV_GetV getitem_hashset TStrVIntVH TUndirFFire TAGMUtil_GetNodeMembership PlotInDegDistr_PNGraph TStrVStrVHI GetMxSccSz_PNGraph ToGraphMP3_PNGraphMP PercentMxScc_PNEANet TFlt_GetMx GetTriadEdges GetBfsFullDiam TInt_SaveFrugalInt TAGMUtil_SaveGephi PercentDegree_PUNGraph TUNGraph_New CntUniqDirEdges_PDirNet TIntFltPrKd GetKCoreNodes TStrPrIntHI TStrUtil_GetTmFromStr TAscFltVQ GetInvParticipRatEig GetPageRank_PDirNet CmtyEvolutionJson PlotEigValDistr GetNodeEcc TStrVV TStrVIntPrV IterHash TStrHashF_Md5_GetSecHashCd TFltIntPrKdV_GetV CalcEffDiamPdf TUndirNet_Load_V1 TInt_GetKiloStr TAGMUtil_RewireCmtyNID Intersect TMem GetDegSeqV_PNGraph PlotSngValDistr TStrHashF_OldGLib TPredicate GetDegCnt_PUNGraph TFltPr TFfGGen _swig_setattr_nondynamic PUndirNet_New IterHashSet TStrIntPrIntHI PyToTIntV GetRndESubGraph_PNGraph GetNodesAtHops_PNEANet PNEANet_New SaveEdgeList_PUndirNet GetModularity_PUndirNet TNGraphMP_Load TVoid TAscFlt PrintGraphStatTable_PDirNet TAscFltStrPrV TIntTrIntH TNEANetMP_Load GetHitsMP_PUndirNet ReebRefine CntNonZNodes_PUNGraph PercentMxWcc_PDirNet GetRndSubGraph_PUNGraph TStr_GetStr GetTriadParticip_PUNGraph GetWeightedBetweennessCentr TIntPrFltH TStr_AddToFMid PlotSccDistr_PNEANet CntDegNodes_PNGraph CntDegNodes_PDirNet TBPGraph_GetSmallGraph WarnNotify TStrIntStrVTrTree GetId TGUtil_MakeExpBins TFltKd GetKCoreNodes_PUNGraph TIntStrH PlotHops_PDirNet GetNodeTriads_PUndirNet TStrStrIntTrV_SwapI TInt_Abs SavePajek_PUndirNet LoadPajek_PUNGraph GetPageRank GetRndESubGraph_PNEANet TNotify ConvertESubGraph_PUndirNet_PNEANet PercentDegree_PUndirNet CntDegNodes_PUNGraph GetAnfEffDiam TCrossNet ConvertSubGraph_PDirNet_PDirNet TChAIn_New TStrQuV TIntPrIntH GetNodeClustCf_PNGraph AddSelfEdges_PNGraph GetNodesAtHop_PUNGraph GetModularity TSIn TIntFltFltTr GetLen2Paths TIntSFltKd IsConnected GenStar_PNGraph TTable_GetFltNodePropertyTable MMNodes PlotInvParticipRat GetKCoreNodes_PNEANet TFltVP_Load TCliqueOverlap_Intersection TCliqueOverlap_GetIntersection TUNGraph IsTree_PDirNet TFltStrPrV_GetV TTableRow PStrV_New TStrIntKd TTable_GetMapPageRank PlotKCoreEdges_PDirNet TIntIntPrPr ToNetworkMP_PNEANetMP CntInDegNodes_PUNGraph TAGMUtil_GetNbhCom TStrStrIntIntQu GetTriadParticip_PDirNet TIntKdV TFltUInt64KdV_SwapI TAscFltV GetWccSzCnt_PUndirNet TPairHashImpl1_GetHashCd TIntFltHI TUChUInt64PrV GetRndESubGraph_PUNGraph TIntVecPool GenStar_PNEANet MaxCPGreedyBetter2 TNEANetMP TAscFltV_SwapI LoadEdgeListStr_PUNGraph TChV Get1CnCom GetFarnessCentr_PNEANet TFltPrV_GetV ConvertSubGraph_PDirNet_PUNGraph TUInt64H Schema_GetV TMMNet_Load PlotClustCf_PUndirNet GetMxInDegNId_PNEANet ConvertESubGraph_PNEANet_PNEANet CntInDegNodes_PNEANet GetShortPath_PNGraph CalcAvgDiamPdf TIntUInt64KdV_GetV TStrHashF_DJB_GetPrimHashCd TFltIntPrV_SwapI _swig_setattr_nondynamic_method TNotify_OnLn GetNodeInDegV_PDirNet TIntStrIntIntQuV_SwapI IsConnected_PNEANet TIntStrPrPrV_SwapI GetBfsTree PyTFltV IsWeaklyConn TFltFltStrTrV_SwapI TStrTrV TIntFltPrV TNEANet_Load_V1 GetSccSzCnt_PNEANet TIntKdV_SwapI TAGMUtil_SaveBipartiteGephi TChTr GetKCore_PDirNet TIntIntPrPrV_SwapI GetCmnNbrs_PDirNet ConvertGraph_PNGraph_PUNGraph TSInOut ConvertGraph_PNGraph_PNGraph CntUniqUndirEdges_PNEANet GetMxOutDegNId_PUNGraph TRnd_GetNrmDevStep TUInt64FltKdV_GetV GenBaraHierar PlotClustCf_PDirNet TFltIntIntIntQuV_SwapI GetEigVals GetTriadEdges_PUndirNet GetPageRank_v1_PUNGraph TFIn_New len_hash GroupStmt TFile_Del GetPageRankMP_PNGraph TTable_GetMP PrintInfo_PUNGraph GetWccSzCnt PercentMxScc_PDirNet GetEdgesInOut_PUndirNet TUChIntPrV_GetV GetTriangleCnt_PUNGraph TIntVecPool_Load TFltIntKdV TCrossNetAStrI TAGMUtil_GenCmtyVVFromPL TExcept GenGrid_PDirNet SaveGViz_PNGraph TBPGraph_Load TTable_GetMapHitsIterator TUInt64IntKd TFltStrPrPr CntNonZNodes_PNEANet MakeUnDir_PDirNet CntEdgesToSet PercentMxWcc_PUndirNet TIntUInt64H TStrIntPrVHI GetBfsTree_PNEANet ConvertSubGraph_PNEANet_PUNGraph GenFull_PNEANet TStr_GetNrAbsFPath PrintGraphStatTable_PNGraphMP TFltRect TStrStrPrHI CntSelfEdges_PDirNet GetMxScc_PUndirNet TIntPrQ NodesGTEDegree TUInt64 PlotShortPathDistr_PUNGraph MxSccSz TIntH TFltTree GetSubGraph_PDirNet TStrPrStrH TIntStrVH TIntUInt64HI ExeStop GetHits_PNEANet TIntKdV_GetV GetMxOutDegNId_PNGraph TStdIn_New GetSccSzCnt TIntStrPrPrV_GetV TStrPool_New CntSelfEdges TStrUtil_GetAddWIdV TIntKd TStr_PutFBaseIfEmpty TIntTrV_SwapI GenTree GetInDegCnt_PDirNet TDirNetNodeI TUChUInt64PrV_SwapI LoadCrossNetToNet TStrKd GetNodesAtHops_PUndirNet PercentDegree_PNEANet GetMxSccSz_PNEANet TIntIntStrTrV_GetV TIntStrVHI GetMxOutDegNId TUInt64StrKdV_SwapI TModeNetNodeI TStrFltKdV GetTriads_PUNGraph TFltIntKdV_GetV GetBfsEffDiam_PNGraph TStr_Fmt PercentMxScc_PNGraph CntOutDegNodes_PUndirNet TUInt64StrPrV_SwapI TStrUtil_IsLatinStr PIntVecPool_New GetNodeTriads_PDirNet TFltBoolKdV_SwapI GenCopyModel GetNodeOutDegV_PNGraph GetNodeEcc_PUNGraph TIntPrFltKdV TIntTr TNEANet TMemIn GetBfsTree_PDirNet TStrBoolHI LoadEdgeList_PNEANet WrNotify PrintGraphStatTable_PNEANet GetMxDegNId_PNGraph GetWeightedClosenessCentr GetTriangleCnt_PNGraph GetDegSeqV TFIn LoadEdgeListStr GenForestFire TUInt64IntKdV_SwapI TPredicate_EvalStrAtom DrawGViz_PNGraph TMem_LoadMem TIntTrV TFlt_GetKiloStr CntEdgesToSet_PUNGraph CntOutDegNodes_PNGraph TPairHashImpl2_GetHashCd TFltIntPrKdV TUndirNetNodeI TUInt64StrPrV GetMxDegNId_PUNGraph TStrQuV_SwapI TIntPrIntVHI PercentMxScc_PUNGraph TAGMUtil GetMxDegNId_PDirNet GetSubGraph_PUNGraph TFlt_Round TStopwatch_GetInstance CntUniqBiDirEdges_PUNGraph GenConfModel len_vec TestAnf_PUNGraph TFltKdV_GetV PercentMxWcc_PUNGraph CntUniqBiDirEdges TIntIntPrPrV TNotify_OnTxt GetTreeSig_PUndirNet TSFltVV TFltUInt64Kd Schema GetInDegCnt TStr_GetNrFPath TAGMFit GetTriadEdges_PUNGraph TUInt64FltPrV GetUnDir TGUtil_GetPdf TUInt_GetStr LoadEdgeListStr_PNEANet ConvertSubGraph_PUNGraph_PUNGraph GenGrid_PUndirNet ToNetworkMP2_PNEANetMP TTable TIntIntH IsTree_PUNGraph GetOutDegCnt_PUNGraph TNGraphMP_GetSmallGraph TPairHashImpl1 TFltTrV_SwapI GetTriadParticip_PNEANet CntUniqDirEdges_PUNGraph TCh_IsAlNum TIntUInt64KdV_SwapI TStrAscFltKdV_SwapI TStrPool TAGM_GenAGM TFlt_GetMn TExcept_IsOnExceptF PlotKCoreNodes_PUndirNet TIntStrPrQ GetRndESubGraph_PUndirNet GetMxScc_PNGraph GetHitsMP_PDirNet TAGMFast TStrStrVPrV_GetV CntUniqBiDirEdges_PDirNet GetNodeClustCf TFfGGen_GenFFGraphs TAGMUtil_DumpCmtyVV TIntStrStrTrV IsWeaklyConn_PUNGraph GetInDegCnt_PNGraph PlotClustCf_PUNGraph PNEANetV_GetV GetNodeOutDegV_PUNGraph CntUniqUndirEdges_PNGraph TUInt_IsIpv6Str SaveMatlabSparseMtx_PNEANet TIntVIntHI TNEANet_New TStrStrIntPrVH LoadDyNet GetMxWccSz_PUNGraph SaveEdgeListNet TStrIntKdV_GetV PlotWccDistr_PNEANet TInt_GetRnd TCnComV GetDegCnt_PDirNet TUIntV_SwapI TIntPr TIntIntIntVTr TIntStrVPrV_SwapI GetClosenessCentr_PUNGraph TStrUtil_CountWords ConvertSubGraph_PUndirNet_PNEANet GetFarnessCentr_PUNGraph TFltPrV_SwapI TStrIn_New GetMxDegNId PlotSccDistr_PUndirNet TBool_GetRnd TStrQ TFltIntIntTr GetNodesAtHops_PUNGraph GetBfsEffDiam_PDirNet GetDegCnt_PUndirNet TStrPr MakeUnDir TMMNet GetCmnNbrs_PNEANet GetWccSzCnt_PNGraph GetShortPath GetDegSeqV_PNEANet TStrIntFltPrHI TStrUInt64VHI ConvertGraph_PDirNet_PUNGraph TUndirNet TNGraphMPNodeI TNGraphMP_New CntOutDegNodes_PUNGraph TFltVV TUNGraphMtx TUInt64FltKdV_SwapI TStr_MkClone TMIn TStrStrVH TStrIntKdV ConvertSubGraph_PNEANet_PNEANet TCallbackNotify_New TUInt64StrKdV TAscFltStrPrV_GetV CmtyTest TStrUtil_GetXmlTagNmVal TNEANetNodeI GetMxBiCon GetSubGraph_PUndirNet TIntFltPrHI PlotClustCf GenRndDegK MaxCPGreedyBetter GetMxDegNId_PUndirNet TModeNet GetUnDir_PUNGraph CntUniqDirEdges_PNEANet TStrUtil_GetWebsiteNm PrintGraphStatTable_PNGraph PlotKCoreNodes_PNGraph LoadConnList_PNEANet ConvertSubGraph_PDirNet_PNEANet GetNodeTriads_PUNGraph SavePajek_PUNGraph TIntUInt64Kd GetBfsFullDiam_PUNGraph TCh_GetNum GetWccs_PUndirNet InfomapOnline TStrStrVPrV StatNotify TLnRet TStr_GetSpaceStr PlotShortPathDistr PlotOutDegDistr_PUNGraph TIntStrIntIntQuV_GetV TUInt_GetRnd TUndirNetEdgeI DelDegKNodes_PDirNet TStr_GetDChStr GenRewire GetTriadParticip_PUndirNet DelSelfEdges_PNEANet GetSccs_PNEANet LoadConnList_PUNGraph GetNodesAtHop_PUndirNet TNEGraph LoadEdgeList getitem_hash TFltUInt64KdV_GetV TIntHI LoadCrossNet Schema_SwapI TCh_IsUc TUChV_GetV TUInt64StrKdV_GetV TAGM TCh_IsNum GetBetweennessCentr GetSngVals TMemOut_New TSInt GetMxOutDegNId_PDirNet TIntFltIntTr GetBfsTree_PUNGraph GetAnfEffDiam_PNGraph TFltStrPrV_SwapI GetWccSzCnt_PNEANet GetEdgesInOut_PNGraph GetMxOutDegNId_PUndirNet TFltVP node2vec TChIntIntTr TMemIn_New GenGrid_PNEANet TStrStrIntTr TCnCom_SaveTxt TNullNotify TUInt64StrVH GetTreeRootNId GetWccSzCnt_PUNGraph GetModularity_PDirNet GetInDegCnt_PNEANet TUNGraphNodeI TStrPrFltH TFlt_Abs TStrVHI TIntStrIntIntQuV PlotEigValRank PNEANetV_SwapI GetESubGraph_PNEANet PlotShortPathDistr_PDirNet GetBiConSzCnt PlotOutDegDistr_PUndirNet LoadConnListStr_PDirNet TIntPrV_SwapI Save TStrTrV_SwapI GetMxInDegNId_PDirNet TBoolVV TNativeCallbackNotify_New TIntPrStrHI TUIntIntKd ConvertGraph_PUndirNet_PNGraph CntDegNodes GetOutEdges GetNodeOutDegV_PNEANet TCrossNetAFltI Intersect1 TIntFltPrH LoadEdgeListStr_PDirNet TUChV PAscFltV TIntFltKd TNEANet_Load TIntVVV TStrUtil_GetNormalizedUrl CntNonZNodes_PNGraph GetModularity_PNEANet GetNodeEcc_PNEANet TNGraphEdgeI TIntStrIntTrV_SwapI TAscFltIntPrV ToNetworkMP TUInt64FltPr TStrHashF_DJB_GetSecHashCd TTable_TableFromHashMap DelZeroDegNodes_PNEANet NodesGTEDegree_PUNGraph ErrNotify TIntS LoadPajek_PNEANet TIntIntPrPrV_GetV TUInt64FltPrV_SwapI GetAnf_PNGraph TDbStr TStrFltKdV_SwapI ToNetwork_PNEANet LoadConnList_PNGraph GetPageRank_v1_PNEANet PrintInfo_PNGraph SaveGViz TDirNet_Load_V1 TDirNet_GetSmallGraph TBoolChS TNGraphMPEdgeI GetRndSubGraph_PUndirNet GetTriangleCnt_PNEANet TChRet TIntPrFltKd TIntIntHH TStrBoolH CalcEffDiam TIntIntStrTr TAGMUtil_GetConductance GetKCore_PUndirNet TStrPool64_New TStrKdV_GetV TStrTrIntHI TModeNetEdgeI TStrKdV TIntVToPy GetNodeInDegV_PUNGraph GetHits_PUndirNet GetNodeOutDegV TMMNetModeNetI GenTree_PDirNet TInt_IsEven TFile_DelWc TStrVIntPrV_GetV TNGraphMtx TUInt64FltPrV_GetV GenRndGnm_PUNGraph TIntFltH GetTreeSig_PDirNet TMOut_New TIntStrIntIntQu TStrUtil_SplitSentences TStrFltFltTr GetBfsTree_PUndirNet TNEANetMP_New TSFltV_SwapI TGUtil_GetCCdf TIntFltTrHI TInt_GetMx TLFlt_GetStr TUNGraph_GetSmallGraph GetBfsFullDiam_PNEANet GetMxInDegNId_PUNGraph TIntIntFltTrV TIntIntVH GetCmnNbrs_PNGraph TFltBoolKdV TIntPrFltKdV_GetV TFltStrPrPrV_GetV CntDegNodes_PUndirNet TIntUInt64PrV TChAIn TStrPrStrVHI GetModularity_PNGraph GetShortPath_PUNGraph TNullNotify_New TStrTr TStr_GetFNmStr TIntStrPrPrV TUInt64_GetMegaStr GetPageRank_v1_PNGraph CntInDegNodes_PDirNet TFltFltStrTrV MakeUnDir_PUndirNet GetTriangleCnt GetBfsFullDiam_PDirNet TInt_TestFrugalInt GenTree_PNGraph TStr_PutFExtIfEmpty GetArtPoints TCliqueOverlap_GetMaxCliques PlotWccDistr_PUndirNet TUChV_SwapI TFltIntPrV TFltQu TNGraph Nodes GetRndESubGraph_PDirNet LoadPajek_PUndirNet TCliqueOverlap_GetOverlapCliques TFltStrKdV_SwapI GetUnDir_PDirNet TIntFltKdV TLogNotify_New TIntIntPrHI TStrPrFltHI TStdNotify_New TIntIntStrTrV TUIntHI TIntStrPrV_SwapI TMem_New TUInt64HI GetMxSccSz TIntPrIntVH ConvertGraph_PUNGraph_PNEANet TStrVIntPr GenFull_PNGraph GetMxInDegNId_PUndirNet ConvertESubGraph_PDirNet_PNEANet NodesGTEDegree_PNEANet AddSelfEdges_PUndirNet CntUniqBiDirEdges_PUndirNet PNEANetV GetTriads_PDirNet ConvertSubGraph_PUNGraph_PNGraph TNEANetAStrI TIntTree TUInt_GetMegaStr ConvertGraph_PNEANet_PNGraph TStrUtil_SplitOnCh TConv_Pt64Ints32 TLFlt TStr_GetNrFNm DelZeroDegNodes DelDegKNodes_PUNGraph TFltTr PercentDegree_PNGraph TFltV_SwapI TUndirNet_GetSmallGraph TAscFltIntKdV_SwapI GetBfsEffDiam_PUndirNet CntUniqUndirEdges_PUndirNet TDirNetEdgeI CntEdgesToSet_PNGraph TStrStrHI TInt_Sign TStrStrIntKdVH TIntIntVPr TFltVP_New GetPageRank_PUndirNet TArtPointVisitor TIntStrKdV GenPrefAttach TIntUInt64PrV_GetV GetUnDir_PNEANet PlotHops_PNGraph TFltFltHI CntEdgesToSet_PUndirNet MxWccSz TIntFltPrV_GetV TStrVP CntOutDegNodes_PNEANet PNEANetMP TUNGraph_Load PNGraphMP TUnionFind PDirNet_New TIntFltTrH SaveEdgeList_PNGraph PlotWccDistr_PDirNet TFile DelDegKNodes_PNEANet GenCircle_PUndirNet GenRndPowerLaw TStrStrIntTrV AddSelfEdges_PUNGraph GetNodeOutDegV_PDirNet GetBetweennessCentr_PNEANet delitem_hash TTable_Load ConvertGraph_PNEANet_PNEANet TStrAscFltKdV TStrStrKdVHI delitem_hashset TStrIntSH GenBaraHierar_PDirNet TDirNet TIntVV GetSccs_PUndirNet TAGMUtil_ConnectCmtyVV TUCh TCrossNetAIntI GetAnfEffDiam_PUNGraph TCh_GetHexCh TDirNet_Load GetMxWcc_PNEANet GetInDegCnt_PUNGraph GetNodeWcc_PNEANet TIntIntVIntTr TStrIntPrTree GetSubGraph_PNEANet PlotShortPathDistr_PUndirNet ToGraphMP_PNGraphMP TFltStrPrPrV_SwapI TIntQ GetWccs TStr_IsAbsFPath TestAnf_PUndirNet TIntStrHI TUIntUIntPr GetNodeWcc_PNGraph TBigStrPool IsTree_PNGraph TRnd_LoadTxt GetPageRankMP_PUNGraph TBool_GetValFromStr GetNodeEcc_PDirNet TIntStrIntTrV TAtomicPredicate TStdIn GetRndWalkRestart_PNEANet TFlt_GetStr GetRndWalkRestart_PNGraph TStrUtil_GetStdNameV TStrUInt64VH TCnCom_Dump TStdOut CntEdgesToSet_PDirNet TStrIn ConvertGraph_PUNGraph_PNGraph CntInDegNodes GenGrid_PUNGraph GetNodeWcc TAGMUtil_GetIntersection GenStar_PUNGraph GetPageRank_PNEANet TStr_GetNrFMid GetGroupDegreeCentr InfoNotify TIntPrFltHI GetNodesAtHops_PNGraph _swig_repr TCh_IsAlpha GVizDoLayout TCh_GetHex TIntIntIntVTrV TIntIntVV_GetV TStrStrPrH GenFull_PUNGraph TStrTAttrPr TUInt64StrKd GetSccSzCnt_PDirNet TIntQu TUIntV TBool_Get01Str TForestFire TestAnf_PNGraph GetHitsMP_PUNGraph PlotWccDistr GetMxSccSz_PUndirNet GetWeightedPageRank GetWccSzCnt_PDirNet TInt swig_import_helper TTable_New Infomap TBoolV_GetV GenSmallWorld GetClosenessCentr_PNEANet GetUnDir_PUndirNet TFlt_Eq6 GenCircle GetClosenessCentr TMMNet_New GetTriangleCnt_PUndirNet DelDegKNodes_PUndirNet GetBetweennessCentr_PUNGraph GenStar TMMNetCrossNetI GetTriads TStrTrV_GetV CntUniqBiDirEdges_PNGraph SavePajek_PNGraph TChAV_GetV GenStar_PDirNet TCnCom GetSccSzCnt_PUNGraph TUndirNet_Load GetKCore TIntBoolH GetNodeClustCf_PUndirNet GetKCore_PUNGraph AddSelfEdges PercentMxWcc_PNEANet GetSubGraph_PNGraph TCliqueOverlap_GetCPMCommunities GetWccs_PNGraph TTable_GetEdgeTablePN TIntIntFltTrV_SwapI GenCircle_PNGraph GetTriadParticip_PNGraph TStrHashF_OldGLib_GetSecHashCd GenRndGnm_PNEANet TFltFltIntTr TUndirNet_New TIntVIntH GetESubGraph ConvertGraph TStrVStrVH GetEdgesInOut_PUNGraph GetOutDegCnt TIntV TStrIntH CommunityCNM TSOut NodesGTEDegree_PNGraph PrintInfo TChAV TIntIntStrTrV_SwapI TTable_NormalizeColName GetKCoreEdges_PDirNet TFltUInt64Pr TIntPrIntPrVHI TIntFltKdV_GetV PMMNet_New TFltStrPr TRnd_GetUniDevStep TAscFltIntPr CmtyEvolutionFileBatch PlotOutDegDistr_PNEANet PlotHops_PUndirNet PlotWccDistr_PUNGraph TAscFltVP_Load GetSccs_PUNGraph TStr_GetNumFNm TUIntH TMIn_New PlotClustCf_PNGraph GetTreeRootNId_PUNGraph TFltFltH GetSccs_PNGraph TFile_Rename TIntFltPrKdV_GetV PlotInDegDistr_PDirNet GetMxWcc_PUndirNet PlotInDegDistr_PUndirNet GetNodeEcc_PUndirNet TInt_GetMegaStr GenGeoPrefAttach TUIntKd TSFltV_GetV ToGraph GetRndSubGraph_PNGraph LoadEdgeList_PNGraph GenFull PlotKCoreNodes_PUNGraph TUInt64_GetStr ConvertGraph_PUndirNet_PUndirNet GetMxOutDegNId_PNEANet TCs NodesGTEDegree_PUndirNet TFile_Exists TChA DelSelfEdges_PUndirNet PlotSccDistr_PDirNet TStrUtil_GetDomNm GetMxWccSz_PUndirNet TStrStrIntKdVHI TBigStrPool_Load DrawGViz_PNEANet GenRndGnm_PNGraph GetAnf_PUndirNet GetSubTreeSz GetTriadParticip TCliqueOverlap TFltTrV GetTreeSig TAscFltIntPrV_SwapI ConvertSubGraph_PNGraph_PNGraph TFlt_Sign DelZeroDegNodes_PNGraph TInt_LoadFrugalIntV GetMxWccSz_PNEANet GetNodesAtHop_PDirNet GetHitsMP_PNEANet TStrIntPrHI ConvertGraph_PUNGraph_PUNGraph TStrQu LoadConnListStr_PNGraph TIntIntFltTrV_GetV TStrUtil_GetShorStr TFltStrKdV DelSelfEdges_PNGraph GetRndWalkRestart_PUNGraph GenDegSeq TIntFltVHI SaveMatlabSparseMtx PlotInDegDistr_PUNGraph TStrStrKdVH TStrIntIntTr GetWeightedPageRankMP TFltIntPr TStrV_SwapI TStrStrIntTrV_GetV TFltIntIntIntQu LoadPajek_PNGraph TAscFltIntKdV_GetV TUInt64StrVHI GetMxBiCon_PNEANet GetSccSzCnt_PNGraph TAscFltStrPrV_SwapI CntDegNodes_PNEANet TExcept_New GetModularity_PUNGraph _swig_getattr GenRndGnm AddSelfEdges_PNEANet TNGraph_Load TTable_SetMP TSBase GenBaraHierar_PUNGraph TChVV TStrUtil GetWeightedShortestPath SaveMatlabSparseMtx_PUNGraph TIntSet Empty PercentMxScc TStrUtil_RemoveHtmlTags CntSelfEdges_PNGraph PNGraphMP_New CntNonZNodes_PUndirNet IsWeaklyConn_PDirNet TTable_GetEdgeTable TIntHSI GetKCoreNodes_PUndirNet PlotKCoreNodes TFltStrPrPrV CntNonZNodes DelNodes_PNGraph TStr_LoadTxt PercentMxScc_PUndirNet PUndirNet TFltV_GetV TFltIntPrKdV_SwapI GetEgonet GetNodesAtHop_PNGraph GetSubTreeSz_PDirNet LoadPajek_PDirNet TStr_GetNrFExt TCh_IsHashCh TStrPrBoolHI GetNodesAtHop TStrV GetBfsFullDiam_PNGraph TFltBoolKdV_GetV TStrUtil_GetXmlTagNmVal2 TStrPrStrHI GetSccs_PDirNet TIntFltPrKdV_SwapI CntOutDegNodes GetMxScc_PNEANet TCh_IsWs PStrV TStdNotify TUInt64FltKd CntUniqDirEdges_PNGraph TIntIntHI TIntIntPrVH GetPageRank_PNGraph TIntIntVV_SwapI TUChUInt64PrV_GetV TRowIterator GetClustCf_PUNGraph GetFlagStr MxDegree_PUndirNet ConvertGraph_PUndirNet_PNEANet TIntTrIntHI IsTree_PNEANet TBool_IsValStr CntUniqUndirEdges GetOutDegCnt_PNGraph TFltQ PlotSngValRank GetHits_PNGraph TIntFltIntTrV_GetV GetOutDegCnt_PNEANet GVizGetLayoutStr GenBaraHierar_PNGraph TNativeCallbackNotify TIntIntVV PlotSccDistr_PNGraph TBoolV_SwapI GetTriadEdges_PDirNet TAscFltIntKd LoadConnList_PUndirNet GetMxBiCon_PNGraph TUInt64IntPrV_GetV TIntIntVIntTrV_GetV GetDegCnt_PNGraph TStrUInt64H PNEANetMP_New TUInt_IsIpStr GetDegSeqV_PUndirNet DelSelfEdges_PDirNet CntSelfEdges_PNEANet TTableContext GetNodeEcc_PNGraph Get1CnComSzCnt PUNGraph_New TCnComV_GetV ConvertSubGraph_PUNGraph_PNEANet GetAnfEffDiam_PUndirNet GetHitsMP_PNGraph GetDegSeqV_PUNGraph TIntIntFltFltQu TUInt64_GetKiloStr TAGMUtil_RewireCmtyVV TIntFltKdV_SwapI TIntStrPrVH GetSubTreeSz_PNEANet TIntTrFltH TNotify_OnStatus GetPageRankMP_PUndirNet TCliqueOverlap_GetRelativeComplement TUInt GetNodesAtHops_PDirNet PlotKCoreEdges_PNEANet MxDegree_PDirNet PlotInDegDistr_PNEANet TUInt64StrPrV_GetV DrawGViz_PDirNet PlotKCoreNodes_PDirNet PMMNet getitem_vec TIntIntIntVTrV_SwapI GetAnf_PDirNet TAGM_RndConnectInsideCommunity TBoolV GetSccs CntUniqBiDirEdges_PNEANet TNGraphMP setitem_vec GetClustCf_PNGraph TNEANetMPNodeI TFltIntPrV_GetV TBPGraph _swig_setattr PNGraph_New TStrIntStrVTr TStrFltVH delitem_vec TStopwatch GenRMat ConvertESubGraph_PUNGraph_PNEANet TNGraph_New TUInt64V TStrIntPrV_GetV GetPageRank_v1_PUndirNet TStrVIntPrV_SwapI GetInDegCnt_PUndirNet TCh TStrVStrHI NodesGTEDegree_PDirNet GenGrid TBool_GetYesNoStr SaveMatlabSparseMtx_PUndirNet ConvertGraph_PNGraph_PNEANet TIntStrStrTrV_SwapI TIntStrKdV_SwapI CntEdgesToSet_PNEANet TFltKdV_SwapI TUChIntPrV TUInt64FltKdV GetBetweennessCentr_PNGraph GetCommon PercentDegree_PDirNet TStrPool64 TStr_PutFExt TStrQuV_GetV PlotShortPathDistr_PNGraph TAGMUtil_Intersection SaveEdgeList TUInt64IntPr SaveEdgeList_PNEANet GetRndSubGraph TIntQuV_GetV TIntFltVH TIntSet_GetSet contains_hashset TStrStrVPr TStrTrIntH ConvertSubGraph_PNEANet_PNGraph TStrIntPrV TFltIntKdV_SwapI DrawGViz_PUndirNet GetKCoreNodes_PDirNet TMemOut GetUnDir_PNGraph PlotKCoreEdges_PNGraph TIntStrPrIntHI DelNodes TIntPrIntPr TStr DelDegKNodes_PNGraph TStrPrV TStrV_GetV PFltV_New GetAnf CmtyGirvanNewmanStep PlotKCoreEdges_PUndirNet GenRndBipart TStrH TIntIntVHI TUInt_GetStrFromIpUInt IsWeaklyConn_PNGraph TBiConVisitor GetTreeSig_PNEANet TestAnf GetCmnNbrs_PUNGraph TChV_GetV GetClustCf GetHitsMP TStrIntFltPrH TStr_GetNullStr LoadEdgeListStr_PNGraph AddSelfEdges_PDirNet GetTreeRootNId_PNEANet IsConnected_PNGraph TStrFltKd SaveMatlabSparseMtx_PDirNet TStrPrV_GetV PlotHops LoadEdgeList_PUNGraph IterVec TIntBoolHI TIntUInt64PrV_SwapI TPrGraph TIntQuV_SwapI TAscFltVP GetKCore_PNGraph GetWccs_PUNGraph TAGMUtil_LoadCmtyVV TBool TStrFltPrV_GetV TIntVecPool_New TIntFltPrKdV TStrFltFltTrV_GetV GenTree_PNEANet IsConnected_PUNGraph GenFull_PUndirNet LoadEdgeList_PDirNet TRStr_GetNullRStr TUInt64StrPr TCrossNetEdgeI PrintInfo_PUndirNet GetKCoreEdges_PNEANet PercentMxWcc_PNGraph setitem_hash TIntIntPrH TCnComV_SwapI GetPageRankMP TIntPrStrH GetNodeInDegV_PNGraph TIntStrKd TStrVP_New CntInDegNodes_PNGraph TUIntIntKdV_GetV SaveEdgeList_PDirNet GetMxWcc_PDirNet TStrVH PUNGraph SaveGViz_PNEANet PrintGraphStatTable_PNEANetMP PrintInfo_PNEANet TCh_GetUc TUIntIntKdV_SwapI GetInEdges PrintGraphStatTable_PUndirNet DelZeroDegNodes_PUNGraph GetMxWccSz_PDirNet GetEigVec IsWeaklyConn_PNEANet TStrTree TTable_NormalizeColNameV TFltIntPrKd ToGraphMP3 TStdOut_New TStrUtil_GetDomNm2 TStrAscFltKdV_GetV GetBfsEffDiam TStrPool_Load TStrUtil_GetStdName TIntPrIntPrVH TInt_LoadFrugalInt TFile_GetUniqueFNm TNotify_GetTypeStr GetTreeSig_PNGraph TIntStrPrVHI GetTriadEdges_PNGraph GetTriads_PNGraph TFltV PNGraph TIntPrFltKdV_SwapI TUInt64IntPrV_SwapI IsConnected_PDirNet GetMxDegNId_PNEANet DelNodes_PUndirNet TRnd_GetExpDevStep TAttrPair TIntStrPrIntH TStrStrPrVHI GetFarnessCentr SaveToErrLog GenCircle_PNEANet TStrFltH TFltStrKd TExcept_PutOnExceptF GetClustCf_PDirNet TIntFltIntTrV_SwapI TPrimitive GetHits_PDirNet PDirNet GetNodeInDegV PrintInfo_PDirNet GetEdgesInOut_PDirNet TIntStrPrV TGUtil_Normalize GetEdgesInOut TStrIntHI LoadNodeList TInt_GetHexStr TStrStrH TFltVQ TFltUInt64PrV_GetV CmtyEvolutionFileBatchV LoadConnListStr_PUNGraph DelNodes_PUNGraph TIntV_SwapI TLogRegPredict TCh_GetUsFromYuAscii GetNodesAtHops TCh_GetStr TIntStrPrPr GetBfsFullDiam_PUndirNet TStrUInt64HI CntUniqUndirEdges_PUNGraph TAGMFastUtil TNGraph_GetSmallGraph GetRndESubGraph GetNodeOutDegV_PUndirNet MxDegree count TUIntIntKdV TIntTrFltHI PTable_New TTableIterator GetAnf_PNEANet GetShortPath_PUndirNet TRnd DelZeroDegNodes_PDirNet TUInt64IntPrV TUInt_GetKiloStr TCallbackNotify TUChIntPrV_SwapI CntSelfEdges_PUNGraph PlotSccDistr_PUNGraph TFltIntIntTrV GetCmnNbrs_PUndirNet TGUtil_GetCdf GetMxWccSz TFltUIntKd GetMxBiCon_PUndirNet TIntStrVPrV_GetV GetDegCnt_PNEANet TIntStrVPr GetSubTreeSz_PUndirNet TFltBoolKd TStrStrVHI TStdErrNotify_New TAttr GetClosenessCentr_PNGraph TStrHI TestAnf_PNEANet TStrFltPrV ConvertGraph_PDirNet_PNEANet ConvertESubGraph GenBaraHierar_PUndirNet TStrIntPrV_SwapI TFOut GetNodeInDegV_PNEANet TBool_GetYNStr TAGMUtil_GenPLSeq GetNodeTriads_PNEANet CntSelfEdges_PUndirNet GetNodeWcc_PUndirNet TAGMUtil_FindComsByAGM PlotHops_PUNGraph GetNodeInDegV_PUndirNet ToNetworkMP2 TCs_GetCsFromBf TUInt64_GetHexStr TRStr TFltUInt64KdV TIntStrIntTrV_GetV TIntPrStrVH iterhash GetKCore_PNEANet ConvertSubGraph_PUndirNet_PUndirNet TStrVP_Load CntNonZNodes_PDirNet Clr GetNodeClustCf_PUNGraph TPairHashImpl2 TFltIntBoolPrKd TIntPrVV GetShortPath_PDirNet ConvertSubGraph_PUndirNet_PNGraph TStrUtil_GetCleanWrdStr TIntFltPr TNGraphNodeI TIntStrPr TIntStrKdV_GetV TSFltV GetBfsEffDiam_PNEANet TUInt64V_GetV TStr_GetChStr TIntTrV_GetV TStrBoolKd TChATr GetSubGraph TInt_SaveFrugalIntV GetKCoreNodes_PNGraph TBigStrPool_New GetMxScc CntOutDegNodes_PDirNet TStdErrNotify TAscFltStrPr SaveGViz_PDirNet SaveGViz_PUNGraph TStrUtil_GetXmlTagVal GetFarnessCentr_PNGraph GetNodeWcc_PUNGraph SaveEdgeList_PUNGraph DelDegKNodes TStrUtil_StripEnd TStrIntPr TIntPrV_GetV TStrHashF_Md5_GetPrimHashCd TExcept_Throw CntInDegNodes_PUndirNet IsTree_PUndirNet TStrFltPr TIntStrPrV_GetV TIntUInt64Pr TNEGraph_Load GetPageRank_PUNGraph TSOutMnp GetNodeClustCf_PNEANet TUInt_GetUIntFromIpStr GetSngVec GetAnfEffDiam_PNEANet GetEdgeBridges GetMxWcc_PNGraph GenTree_PUndirNet MaxCPGreedyBetter3 GetRndSubGraph_PDirNet TFlt_GetGigaStr TIntFltIntTrV TFOut_New TLogRegPredict_Load GetAnf_PUNGraph PlotClustCf_PNEANet GetOutDegCnt_PDirNet ToGraph_PUndirNet SavePajek ToGraph_PDirNet MxDegree_PNGraph TIntStrIntTr GetShortPath_PNEANet TStrPrStrVH TTable_GetNodeTable DelNodes_PDirNet TStrUtil_GetCleanStr LoadPajek PrintGraphStatTable TUInt64Tr TIntStrVPrV TStrHashF_OldGLib_GetPrimHashCd TUChIntIntTr TStrFltPrV_SwapI TFltVVV TLogRegFit TFltKdV TInt_Swap TIntPrV LoadConnListStr_PUndirNet GetMxWcc TNEANetEdgeI TStrStrPrVH CommunityGirvanNewman GetTriads_PNEANet TStrIntPrIntH GetMxBiCon_PDirNet TLogNotify MakeUnDir_PNGraph TPredicateNode TFlt_GetRnd PlotHops_PNEANet GetBiCon GetPageRank_v1 TIntPrIntHI TStr_PutFBase GetTreeRootNId_PNGraph ToGraph_PNGraph TAscFltIntPrV_GetV TIntFltPrV_SwapI caveman_special perturb export_graphs_to_txt deledges perturb_new save_prediction_histogram draw_graph_list pick_connected_component get_graph ForestFire imsave draw_graph pick_connected_component_new decode_graph load_graph_list snap_txt_output_to_nx citeseer_ego n_community test_perturbed save_graph_list Graph_generator_baseline Graph_generator_baseline_train_rulebased Loss Graph_generator_baseline_train_optimizationbased optimizer_brute Graph_generator_baseline_test emd_distance disjoint_cliques_test_graph graph_gen_from_blockmodel mmsb arg_parse GraphAdjSampler GraphVAE arg_parse main train build_model TFlt_GetPrcStr iterhashset LoadConnList TBPGraph_New TMOut itervec TNEANetAIntI PlotShortPathDistr_PNEANet TStrFltFltTrV_SwapI GetNodeTriads_PNGraph GenCircle_PUNGraph LoadEdgeListNet TStrIntVHI TSStr TIntIntHHI GetTriangleCnt_PDirNet DrawGViz DelSelfEdges_PUNGraph IsWeaklyConn_PUndirNet GetTreeSig_PUNGraph TFltIntIntTrV_SwapI TUInt64V_SwapI TFltIntIntIntQuV ToGraph_PUNGraph TInt_GetMn TStrIntKdV_SwapI GetCmnNbrs TFltStrPrV GetDegCnt GetDegreeCentr GetSccSzCnt_PUndirNet TCh_IsHex GetKCoreEdges_PUNGraph GetMxScc_PDirNet TIntUInt64KdV TStrIntPrVH TFltUInt64PrV DrawGViz_PUNGraph TNEGraph_New TBool_GetStr TFltUInt64PrV_SwapI TInt_GetInRng GetMxBiCon_PUNGraph GenRndGnm_PUndirNet TExcept_ThrowFull GenCircle_PDirNet TStrPrBoolH DelZeroDegNodes_PUndirNet TStr_GetNrNumFExt TChV_SwapI TIntPrStrVHI GetBfsEffDiam_PUNGraph IsConnected_PUndirNet TCliqueOverlap_CalculateOverlapMtx ConvertGraph_PNEANet_PUNGraph PlotInDegDistr GetOutDegCnt_PUndirNet TStrStrIntPrVHI GetTreeRootNId_PUndirNet PrintGraphStatTable_PUNGraph TUInt64Pr GenFull_PDirNet LoadEdgeListStr_PUndirNet MakeUnDir_PUNGraph ConvertGraph_PDirNet_PDirNet TUInt_JavaUIntToCppUInt TNEANetMPEdgeI PercentMxWcc TForestFire_GenGraph GetPageRank_v1_PDirNet GetPageRankMP_PDirNet TNotify_DfOnNotify TFltPrV GetTriads_PUndirNet DelSelfEdges TIntBoolPr TStrAscFltKd TFltIntKd TStrFltKdV_GetV Edges TUInt64IntKdV GetHits TIntIntVIntTrV_SwapI GetKCoreEdges GetClustCf_PNEANet TNEANetAFltI TStrIntPrH PlotKCoreEdges_PUNGraph GetKCoreEdges_PUndirNet PFltV TStrIntVH SaveGViz_PUndirNet GetEdgesInOut_PNEANet TStrUtil_SplitWords PTable TFltStrKdV_GetV TChA_LoadTxt GetMxWccSz_PNGraph TNotify_OnNotify CntUniqUndirEdges_PDirNet CntUniqDirEdges GetPageRankMP_PNEANet TStrStrVPrV_SwapI TUIntV_GetV ConvertESubGraph_PNGraph_PNEANet PlotKCoreNodes_PNEANet ReebSimplify TUInt64IntKdV_GetV PlotOutDegDistr_PNGraph PAscFltV_New GetMxInDegNId GenBaraHierar_PNEANet TBoolFltPr MxDegree_PNEANet TestAnf_PDirNet TStrPrV_SwapI GetEigenVectorCentr TSFlt TRowIteratorWithRemove GetWccs_PDirNet GenRMatEpinions GetNodeClustCf_PDirNet PNEANet TAscFltV_GetV GetKCoreEdges_PNGraph TUNGraphEdgeI GetClustCf_PUndirNet TChAV_SwapI TRStr_CmpI TStrFltVHI MxDegree_PUNGraph TDirNet_New TFltTrV_GetV TStrVIntVHI TAGMUtil_TotalMemberships PlotOutDegDistr PercentDegree GetWccs_PNEANet GetSubTreeSz_PNGraph TIntV_GetV TFltRect_Intersection ConvertSubGraph LoadDyNetGraphV PlotWccDistr_PNGraph GenGrid_PNGraph GetAnfEffDiam_PDirNet GetBfsTree_PNGraph GetWeightedFarnessCentr LoadConnListStr_PNEANet GetNodeWcc_PDirNet TIntStrStrTrV_GetV print_array ToGraphMP TAscFltVP_New ToNetwork GetMxScc_PUNGraph GetMxSccSz_PDirNet EventImportance TUIntIntPr TAscFltPr TTable_LoadSS GenTree_PUNGraph TStrFltHI LoadConnList_PDirNet GetGroupClosenessCentr TIntIntFltTr TStrUtil_SplitLines DelNodes_PNEANet TStrFltFltTrV GetMxWcc_PUNGraph TFltFltStrTrV_GetV TGUtil GetNodesAtHop_PNEANet LoadEdgeList_PUndirNet TFlt PlotOutDegDistr_PDirNet MaxCPGreedyBetter1 GetTreeRootNId_PDirNet TIntIntIntVTrV_GetV LoadConnListStr GetRndSubGraph_PNEANet TStrVStrH TInt_IsOdd TStrHashF_Md5 GetMxInDegNId_PNGraph SavePajek_PNEANet ConvertSubGraph_PNGraph_PNEANet GenRndGnm_PDirNet ConvertSubGraph_PNGraph_PUNGraph SavePajek_PDirNet PlotSngVec CntUniqDirEdges_PUndirNet GetHits_PUNGraph TStrUtil_GetWIdV TStrHashF_DJB GetMxSccSz_PUNGraph TFlt_GetInRng GetNodeTriads PlotSccDistr TStrPool64_Load GenStar_PUndirNet GetSubTreeSz_PUNGraph TCRef MakeUnDir_PNEANet LoadModeNetToNet TFlt_GetMegaStr GetInvParticipRat PIntVecPool TExcept_GetOnExceptF GetTriadEdges_PNEANet TAscFltIntKdV TFltIntIntIntQuV_GetV TStrPrIntH PlotKCoreEdges TFltFltStrTr TIntIntVIntTrV LoadMode TStrKdV_SwapI SaveMatlabSparseMtx_PNGraph GetDegSeqV_PDirNet TIntQuV TIntIntPrVHI IsTree TFltIntIntTrV_GetV getitem_hashset TStrVIntVH TUndirFFire TAGMUtil_GetNodeMembership PlotInDegDistr_PNGraph TStrVStrVHI GetMxSccSz_PNGraph ToGraphMP3_PNGraphMP PercentMxScc_PNEANet TFlt_GetMx GetTriadEdges GetBfsFullDiam TInt_SaveFrugalInt TAGMUtil_SaveGephi PercentDegree_PUNGraph TUNGraph_New CntUniqDirEdges_PDirNet TIntFltPrKd GetKCoreNodes TStrPrIntHI TStrUtil_GetTmFromStr TAscFltVQ GetInvParticipRatEig GetPageRank_PDirNet CmtyEvolutionJson PlotEigValDistr GetNodeEcc TStrVV TStrVIntPrV IterHash TStrHashF_Md5_GetSecHashCd TFltIntPrKdV_GetV CalcEffDiamPdf TUndirNet_Load_V1 TInt_GetKiloStr TAGMUtil_RewireCmtyNID Intersect TMem GetDegSeqV_PNGraph PlotSngValDistr TStrHashF_OldGLib TPredicate GetDegCnt_PUNGraph TFltPr TFfGGen _swig_setattr_nondynamic PUndirNet_New IterHashSet TStrIntPrIntHI PyToTIntV GetRndESubGraph_PNGraph GetNodesAtHops_PNEANet PNEANet_New SaveEdgeList_PUndirNet GetModularity_PUndirNet TNGraphMP_Load TVoid TAscFlt PrintGraphStatTable_PDirNet TAscFltStrPrV TIntTrIntH TNEANetMP_Load GetHitsMP_PUndirNet ReebRefine CntNonZNodes_PUNGraph PercentMxWcc_PDirNet GetRndSubGraph_PUNGraph TStr_GetStr GetTriadParticip_PUNGraph GetWeightedBetweennessCentr TIntPrFltH TStr_AddToFMid PlotSccDistr_PNEANet CntDegNodes_PNGraph CntDegNodes_PDirNet TBPGraph_GetSmallGraph WarnNotify TStrIntStrVTrTree GetId TGUtil_MakeExpBins TFltKd GetKCoreNodes_PUNGraph TIntStrH PlotHops_PDirNet GetNodeTriads_PUndirNet TStrStrIntTrV_SwapI TInt_Abs SavePajek_PUndirNet LoadPajek_PUNGraph GetPageRank GetRndESubGraph_PNEANet TNotify ConvertESubGraph_PUndirNet_PNEANet PercentDegree_PUndirNet CntDegNodes_PUNGraph GetAnfEffDiam TCrossNet ConvertSubGraph_PDirNet_PDirNet TChAIn_New TStrQuV TIntPrIntH GetNodeClustCf_PNGraph AddSelfEdges_PNGraph GetNodesAtHop_PUNGraph GetModularity TSIn TIntFltFltTr GetLen2Paths TIntSFltKd IsConnected GenStar_PNGraph TTable_GetFltNodePropertyTable MMNodes PlotInvParticipRat GetKCoreNodes_PNEANet TFltVP_Load TCliqueOverlap_Intersection TCliqueOverlap_GetIntersection TUNGraph IsTree_PDirNet TFltStrPrV_GetV TTableRow PStrV_New TStrIntKd TTable_GetMapPageRank PlotKCoreEdges_PDirNet TIntIntPrPr ToNetworkMP_PNEANetMP CntInDegNodes_PUNGraph TAGMUtil_GetNbhCom TStrStrIntIntQu GetTriadParticip_PDirNet TIntKdV TFltUInt64KdV_SwapI TAscFltV GetWccSzCnt_PUndirNet TPairHashImpl1_GetHashCd TIntFltHI TUChUInt64PrV GetRndESubGraph_PUNGraph TIntVecPool GenStar_PNEANet MaxCPGreedyBetter2 TNEANetMP TAscFltV_SwapI LoadEdgeListStr_PUNGraph TChV Get1CnCom GetFarnessCentr_PNEANet TFltPrV_GetV ConvertSubGraph_PDirNet_PUNGraph TUInt64H Schema_GetV TMMNet_Load PlotClustCf_PUndirNet GetMxInDegNId_PNEANet ConvertESubGraph_PNEANet_PNEANet CntInDegNodes_PNEANet GetShortPath_PNGraph CalcAvgDiamPdf TIntUInt64KdV_GetV TStrHashF_DJB_GetPrimHashCd TFltIntPrV_SwapI _swig_setattr_nondynamic_method TNotify_OnLn GetNodeInDegV_PDirNet TIntStrIntIntQuV_SwapI IsConnected_PNEANet TIntStrPrPrV_SwapI GetBfsTree PyTFltV IsWeaklyConn TFltFltStrTrV_SwapI TStrTrV TIntFltPrV TNEANet_Load_V1 GetSccSzCnt_PNEANet TIntKdV_SwapI TAGMUtil_SaveBipartiteGephi TChTr GetKCore_PDirNet TIntIntPrPrV_SwapI GetCmnNbrs_PDirNet ConvertGraph_PNGraph_PUNGraph TSInOut ConvertGraph_PNGraph_PNGraph CntUniqUndirEdges_PNEANet GetMxOutDegNId_PUNGraph TRnd_GetNrmDevStep TUInt64FltKdV_GetV GenBaraHierar PlotClustCf_PDirNet TFltIntIntIntQuV_SwapI GetEigVals GetTriadEdges_PUndirNet GetPageRank_v1_PUNGraph TFIn_New len_hash GroupStmt TFile_Del GetPageRankMP_PNGraph TTable_GetMP PrintInfo_PUNGraph GetWccSzCnt PercentMxScc_PDirNet GetEdgesInOut_PUndirNet TUChIntPrV_GetV GetTriangleCnt_PUNGraph TIntVecPool_Load TFltIntKdV TCrossNetAStrI TAGMUtil_GenCmtyVVFromPL TExcept GenGrid_PDirNet SaveGViz_PNGraph TBPGraph_Load TTable_GetMapHitsIterator TUInt64IntKd TFltStrPrPr CntNonZNodes_PNEANet MakeUnDir_PDirNet CntEdgesToSet PercentMxWcc_PUndirNet TIntUInt64H TStrIntPrVHI GetBfsTree_PNEANet ConvertSubGraph_PNEANet_PUNGraph GenFull_PNEANet TStr_GetNrAbsFPath PrintGraphStatTable_PNGraphMP TFltRect TStrStrPrHI CntSelfEdges_PDirNet GetMxScc_PUndirNet TIntPrQ NodesGTEDegree TUInt64 PlotShortPathDistr_PUNGraph MxSccSz TIntH TFltTree GetSubGraph_PDirNet TStrPrStrH TIntStrVH TIntUInt64HI ExeStop GetHits_PNEANet TIntKdV_GetV GetMxOutDegNId_PNGraph TStdIn_New GetSccSzCnt TIntStrPrPrV_GetV TStrPool_New CntSelfEdges TStrUtil_GetAddWIdV TIntKd TStr_PutFBaseIfEmpty TIntTrV_SwapI GenTree GetInDegCnt_PDirNet TDirNetNodeI TUChUInt64PrV_SwapI LoadCrossNetToNet TStrKd GetNodesAtHops_PUndirNet PercentDegree_PNEANet GetMxSccSz_PNEANet TIntIntStrTrV_GetV TIntStrVHI GetMxOutDegNId TUInt64StrKdV_SwapI TModeNetNodeI TStrFltKdV GetTriads_PUNGraph TFltIntKdV_GetV GetBfsEffDiam_PNGraph TStr_Fmt PercentMxScc_PNGraph CntOutDegNodes_PUndirNet TUInt64StrPrV_SwapI TStrUtil_IsLatinStr PIntVecPool_New GetNodeTriads_PDirNet TFltBoolKdV_SwapI GenCopyModel GetNodeOutDegV_PNGraph GetNodeEcc_PUNGraph TIntPrFltKdV TIntTr TNEANet TMemIn GetBfsTree_PDirNet TStrBoolHI LoadEdgeList_PNEANet WrNotify PrintGraphStatTable_PNEANet GetMxDegNId_PNGraph GetWeightedClosenessCentr GetTriangleCnt_PNGraph GetDegSeqV TFIn LoadEdgeListStr GenForestFire TUInt64IntKdV_SwapI TPredicate_EvalStrAtom DrawGViz_PNGraph TMem_LoadMem TIntTrV TFlt_GetKiloStr CntEdgesToSet_PUNGraph CntOutDegNodes_PNGraph TPairHashImpl2_GetHashCd TFltIntPrKdV TUndirNetNodeI TUInt64StrPrV GetMxDegNId_PUNGraph TStrQuV_SwapI TIntPrIntVHI PercentMxScc_PUNGraph TAGMUtil GetMxDegNId_PDirNet GetSubGraph_PUNGraph TFlt_Round TStopwatch_GetInstance CntUniqBiDirEdges_PUNGraph GenConfModel len_vec TestAnf_PUNGraph TFltKdV_GetV PercentMxWcc_PUNGraph CntUniqBiDirEdges TIntIntPrPrV TNotify_OnTxt GetTreeSig_PUndirNet TSFltVV TFltUInt64Kd Schema GetInDegCnt TStr_GetNrFPath TAGMFit GetTriadEdges_PUNGraph TUInt64FltPrV GetUnDir TGUtil_GetPdf TUInt_GetStr LoadEdgeListStr_PNEANet ConvertSubGraph_PUNGraph_PUNGraph GenGrid_PUndirNet ToNetworkMP2_PNEANetMP TTable TIntIntH IsTree_PUNGraph GetOutDegCnt_PUNGraph TNGraphMP_GetSmallGraph TPairHashImpl1 TFltTrV_SwapI GetTriadParticip_PNEANet CntUniqDirEdges_PUNGraph TCh_IsAlNum TIntUInt64KdV_SwapI TStrAscFltKdV_SwapI TStrPool TAGM_GenAGM TFlt_GetMn TExcept_IsOnExceptF PlotKCoreNodes_PUndirNet TIntStrPrQ GetRndESubGraph_PUndirNet GetMxScc_PNGraph GetHitsMP_PDirNet TAGMFast TStrStrVPrV_GetV CntUniqBiDirEdges_PDirNet GetNodeClustCf TFfGGen_GenFFGraphs TAGMUtil_DumpCmtyVV TIntStrStrTrV IsWeaklyConn_PUNGraph GetInDegCnt_PNGraph PlotClustCf_PUNGraph PNEANetV_GetV GetNodeOutDegV_PUNGraph CntUniqUndirEdges_PNGraph TUInt_IsIpv6Str SaveMatlabSparseMtx_PNEANet TIntVIntHI TNEANet_New TStrStrIntPrVH LoadDyNet GetMxWccSz_PUNGraph SaveEdgeListNet TStrIntKdV_GetV PlotWccDistr_PNEANet TInt_GetRnd TCnComV GetDegCnt_PDirNet TUIntV_SwapI TIntPr TIntIntIntVTr TIntStrVPrV_SwapI GetClosenessCentr_PUNGraph TStrUtil_CountWords ConvertSubGraph_PUndirNet_PNEANet GetFarnessCentr_PUNGraph TFltPrV_SwapI TStrIn_New GetMxDegNId PlotSccDistr_PUndirNet TBool_GetRnd TStrQ TFltIntIntTr GetNodesAtHops_PUNGraph GetBfsEffDiam_PDirNet GetDegCnt_PUndirNet TStrPr MakeUnDir TMMNet GetCmnNbrs_PNEANet GetWccSzCnt_PNGraph GetShortPath GetDegSeqV_PNEANet TStrIntFltPrHI TStrUInt64VHI ConvertGraph_PDirNet_PUNGraph TUndirNet TNGraphMPNodeI TNGraphMP_New CntOutDegNodes_PUNGraph TFltVV TUNGraphMtx TUInt64FltKdV_SwapI TStr_MkClone TMIn TStrStrVH TStrIntKdV ConvertSubGraph_PNEANet_PNEANet TCallbackNotify_New TUInt64StrKdV TAscFltStrPrV_GetV CmtyTest TStrUtil_GetXmlTagNmVal TNEANetNodeI GetMxBiCon GetSubGraph_PUndirNet TIntFltPrHI PlotClustCf GenRndDegK MaxCPGreedyBetter GetMxDegNId_PUndirNet TModeNet GetUnDir_PUNGraph CntUniqDirEdges_PNEANet TStrUtil_GetWebsiteNm PrintGraphStatTable_PNGraph PlotKCoreNodes_PNGraph LoadConnList_PNEANet ConvertSubGraph_PDirNet_PNEANet GetNodeTriads_PUNGraph SavePajek_PUNGraph TIntUInt64Kd GetBfsFullDiam_PUNGraph TCh_GetNum GetWccs_PUndirNet InfomapOnline TStrStrVPrV StatNotify TLnRet TStr_GetSpaceStr PlotShortPathDistr PlotOutDegDistr_PUNGraph TIntStrIntIntQuV_GetV TUInt_GetRnd TUndirNetEdgeI DelDegKNodes_PDirNet TStr_GetDChStr GenRewire GetTriadParticip_PUndirNet DelSelfEdges_PNEANet GetSccs_PNEANet LoadConnList_PUNGraph GetNodesAtHop_PUndirNet TNEGraph LoadEdgeList getitem_hash TFltUInt64KdV_GetV TIntHI LoadCrossNet Schema_SwapI TCh_IsUc TUChV_GetV TUInt64StrKdV_GetV TAGM TCh_IsNum GetBetweennessCentr GetSngVals TMemOut_New TSInt GetMxOutDegNId_PDirNet TIntFltIntTr GetBfsTree_PUNGraph GetAnfEffDiam_PNGraph TFltStrPrV_SwapI GetWccSzCnt_PNEANet GetEdgesInOut_PNGraph GetMxOutDegNId_PUndirNet TFltVP node2vec TChIntIntTr TMemIn_New GenGrid_PNEANet TStrStrIntTr TCnCom_SaveTxt TNullNotify TUInt64StrVH GetTreeRootNId GetWccSzCnt_PUNGraph GetModularity_PDirNet GetInDegCnt_PNEANet TUNGraphNodeI TStrPrFltH TFlt_Abs TStrVHI TIntStrIntIntQuV PlotEigValRank PNEANetV_SwapI GetESubGraph_PNEANet PlotShortPathDistr_PDirNet GetBiConSzCnt PlotOutDegDistr_PUndirNet LoadConnListStr_PDirNet TIntPrV_SwapI Save TStrTrV_SwapI GetMxInDegNId_PDirNet TBoolVV TNativeCallbackNotify_New TIntPrStrHI TUIntIntKd ConvertGraph_PUndirNet_PNGraph CntDegNodes GetOutEdges GetNodeOutDegV_PNEANet TCrossNetAFltI Intersect1 TIntFltPrH LoadEdgeListStr_PDirNet TUChV PAscFltV TIntFltKd TNEANet_Load TIntVVV TStrUtil_GetNormalizedUrl CntNonZNodes_PNGraph GetModularity_PNEANet GetNodeEcc_PNEANet TNGraphEdgeI TIntStrIntTrV_SwapI TAscFltIntPrV ToNetworkMP TUInt64FltPr TStrHashF_DJB_GetSecHashCd TTable_TableFromHashMap DelZeroDegNodes_PNEANet NodesGTEDegree_PUNGraph ErrNotify TIntS LoadPajek_PNEANet TIntIntPrPrV_GetV TUInt64FltPrV_SwapI GetAnf_PNGraph TDbStr TStrFltKdV_SwapI ToNetwork_PNEANet LoadConnList_PNGraph GetPageRank_v1_PNEANet PrintInfo_PNGraph SaveGViz TDirNet_Load_V1 TDirNet_GetSmallGraph TBoolChS TNGraphMPEdgeI GetRndSubGraph_PUndirNet GetTriangleCnt_PNEANet TChRet TIntPrFltKd TIntIntHH TStrBoolH CalcEffDiam TIntIntStrTr TAGMUtil_GetConductance GetKCore_PUndirNet TStrPool64_New TStrKdV_GetV TStrTrIntHI TModeNetEdgeI TStrKdV TIntVToPy GetNodeInDegV_PUNGraph GetHits_PUndirNet GetNodeOutDegV TMMNetModeNetI GenTree_PDirNet TInt_IsEven TFile_DelWc TStrVIntPrV_GetV TNGraphMtx TUInt64FltPrV_GetV GenRndGnm_PUNGraph TIntFltH GetTreeSig_PDirNet TMOut_New TIntStrIntIntQu TStrUtil_SplitSentences TStrFltFltTr GetBfsTree_PUndirNet TNEANetMP_New TSFltV_SwapI TGUtil_GetCCdf TIntFltTrHI TInt_GetMx TLFlt_GetStr TUNGraph_GetSmallGraph GetBfsFullDiam_PNEANet GetMxInDegNId_PUNGraph TIntIntFltTrV TIntIntVH GetCmnNbrs_PNGraph TFltBoolKdV TIntPrFltKdV_GetV TFltStrPrPrV_GetV CntDegNodes_PUndirNet TIntUInt64PrV TChAIn TStrPrStrVHI GetModularity_PNGraph GetShortPath_PUNGraph TNullNotify_New TStrTr TStr_GetFNmStr TIntStrPrPrV TUInt64_GetMegaStr GetPageRank_v1_PNGraph CntInDegNodes_PDirNet TFltFltStrTrV MakeUnDir_PUndirNet GetTriangleCnt GetBfsFullDiam_PDirNet TInt_TestFrugalInt GenTree_PNGraph TStr_PutFExtIfEmpty GetArtPoints TCliqueOverlap_GetMaxCliques PlotWccDistr_PUndirNet TUChV_SwapI TFltIntPrV TFltQu TNGraph Nodes GetRndESubGraph_PDirNet LoadPajek_PUndirNet TCliqueOverlap_GetOverlapCliques TFltStrKdV_SwapI GetUnDir_PDirNet TIntFltKdV TLogNotify_New TIntIntPrHI TStrPrFltHI TStdNotify_New TIntIntStrTrV TUIntHI TIntStrPrV_SwapI TMem_New TUInt64HI GetMxSccSz TIntPrIntVH ConvertGraph_PUNGraph_PNEANet TStrVIntPr GenFull_PNGraph GetMxInDegNId_PUndirNet ConvertESubGraph_PDirNet_PNEANet NodesGTEDegree_PNEANet AddSelfEdges_PUndirNet CntUniqBiDirEdges_PUndirNet PNEANetV GetTriads_PDirNet ConvertSubGraph_PUNGraph_PNGraph TNEANetAStrI TIntTree TUInt_GetMegaStr ConvertGraph_PNEANet_PNGraph TStrUtil_SplitOnCh TConv_Pt64Ints32 TLFlt TStr_GetNrFNm DelZeroDegNodes DelDegKNodes_PUNGraph TFltTr PercentDegree_PNGraph TFltV_SwapI TUndirNet_GetSmallGraph TAscFltIntKdV_SwapI GetBfsEffDiam_PUndirNet CntUniqUndirEdges_PUndirNet TDirNetEdgeI CntEdgesToSet_PNGraph TStrStrHI TInt_Sign TStrStrIntKdVH TIntIntVPr TFltVP_New GetPageRank_PUndirNet TArtPointVisitor TIntStrKdV GenPrefAttach TIntUInt64PrV_GetV GetUnDir_PNEANet PlotHops_PNGraph TFltFltHI CntEdgesToSet_PUndirNet MxWccSz TIntFltPrV_GetV TStrVP CntOutDegNodes_PNEANet PNEANetMP TUNGraph_Load PNGraphMP TUnionFind PDirNet_New TIntFltTrH SaveEdgeList_PNGraph PlotWccDistr_PDirNet TFile DelDegKNodes_PNEANet GenCircle_PUndirNet GenRndPowerLaw TStrStrIntTrV AddSelfEdges_PUNGraph GetNodeOutDegV_PDirNet GetBetweennessCentr_PNEANet delitem_hash TTable_Load ConvertGraph_PNEANet_PNEANet TStrAscFltKdV TStrStrKdVHI delitem_hashset TStrIntSH GenBaraHierar_PDirNet TDirNet TIntVV GetSccs_PUndirNet TAGMUtil_ConnectCmtyVV TUCh TCrossNetAIntI GetAnfEffDiam_PUNGraph TCh_GetHexCh TDirNet_Load GetMxWcc_PNEANet GetInDegCnt_PUNGraph GetNodeWcc_PNEANet TIntIntVIntTr TStrIntPrTree GetSubGraph_PNEANet PlotShortPathDistr_PUndirNet ToGraphMP_PNGraphMP TFltStrPrPrV_SwapI TIntQ GetWccs TStr_IsAbsFPath TestAnf_PUndirNet TIntStrHI TUIntUIntPr GetNodeWcc_PNGraph TBigStrPool IsTree_PNGraph TRnd_LoadTxt GetPageRankMP_PUNGraph TBool_GetValFromStr GetNodeEcc_PDirNet TIntStrIntTrV TAtomicPredicate TStdIn GetRndWalkRestart_PNEANet TFlt_GetStr GetRndWalkRestart_PNGraph TStrUtil_GetStdNameV TStrUInt64VH TCnCom_Dump TStdOut CntEdgesToSet_PDirNet TStrIn ConvertGraph_PUNGraph_PNGraph CntInDegNodes GenGrid_PUNGraph GetNodeWcc TAGMUtil_GetIntersection GenStar_PUNGraph GetPageRank_PNEANet TStr_GetNrFMid GetGroupDegreeCentr InfoNotify TIntPrFltHI GetNodesAtHops_PNGraph _swig_repr TCh_IsAlpha GVizDoLayout TCh_GetHex TIntIntIntVTrV TIntIntVV_GetV TStrStrPrH GenFull_PUNGraph TStrTAttrPr TUInt64StrKd GetSccSzCnt_PDirNet TIntQu TUIntV TBool_Get01Str TForestFire TestAnf_PNGraph GetHitsMP_PUNGraph PlotWccDistr GetMxSccSz_PUndirNet GetWeightedPageRank GetWccSzCnt_PDirNet TInt swig_import_helper TTable_New Infomap TBoolV_GetV GenSmallWorld GetClosenessCentr_PNEANet GetUnDir_PUndirNet TFlt_Eq6 GenCircle GetClosenessCentr TMMNet_New GetTriangleCnt_PUndirNet DelDegKNodes_PUndirNet GetBetweennessCentr_PUNGraph GenStar TMMNetCrossNetI GetTriads TStrTrV_GetV CntUniqBiDirEdges_PNGraph SavePajek_PNGraph TChAV_GetV GenStar_PDirNet TCnCom GetSccSzCnt_PUNGraph TUndirNet_Load GetKCore TIntBoolH GetNodeClustCf_PUndirNet GetKCore_PUNGraph AddSelfEdges PercentMxWcc_PNEANet GetSubGraph_PNGraph TCliqueOverlap_GetCPMCommunities GetWccs_PNGraph TTable_GetEdgeTablePN TIntIntFltTrV_SwapI GenCircle_PNGraph GetTriadParticip_PNGraph TStrHashF_OldGLib_GetSecHashCd GenRndGnm_PNEANet TFltFltIntTr TUndirNet_New TIntVIntH GetESubGraph ConvertGraph TStrVStrVH GetEdgesInOut_PUNGraph GetOutDegCnt TIntV TStrIntH CommunityCNM TSOut NodesGTEDegree_PNGraph PrintInfo TChAV TIntIntStrTrV_SwapI TTable_NormalizeColName GetKCoreEdges_PDirNet TFltUInt64Pr TIntPrIntPrVHI TIntFltKdV_GetV PMMNet_New TFltStrPr TRnd_GetUniDevStep TAscFltIntPr CmtyEvolutionFileBatch PlotOutDegDistr_PNEANet PlotHops_PUndirNet PlotWccDistr_PUNGraph TAscFltVP_Load GetSccs_PUNGraph TStr_GetNumFNm TUIntH TMIn_New PlotClustCf_PNGraph GetTreeRootNId_PUNGraph TFltFltH GetSccs_PNGraph TFile_Rename TIntFltPrKdV_GetV PlotInDegDistr_PDirNet GetMxWcc_PUndirNet PlotInDegDistr_PUndirNet GetNodeEcc_PUndirNet TInt_GetMegaStr GenGeoPrefAttach TUIntKd TSFltV_GetV ToGraph GetRndSubGraph_PNGraph LoadEdgeList_PNGraph GenFull PlotKCoreNodes_PUNGraph TUInt64_GetStr ConvertGraph_PUndirNet_PUndirNet GetMxOutDegNId_PNEANet TCs NodesGTEDegree_PUndirNet TFile_Exists TChA DelSelfEdges_PUndirNet PlotSccDistr_PDirNet TStrUtil_GetDomNm GetMxWccSz_PUndirNet TStrStrIntKdVHI TBigStrPool_Load DrawGViz_PNEANet GenRndGnm_PNGraph GetAnf_PUndirNet GetSubTreeSz GetTriadParticip TCliqueOverlap TFltTrV GetTreeSig TAscFltIntPrV_SwapI ConvertSubGraph_PNGraph_PNGraph TFlt_Sign DelZeroDegNodes_PNGraph TInt_LoadFrugalIntV GetMxWccSz_PNEANet GetNodesAtHop_PDirNet GetHitsMP_PNEANet TStrIntPrHI ConvertGraph_PUNGraph_PUNGraph TStrQu LoadConnListStr_PNGraph TIntIntFltTrV_GetV TStrUtil_GetShorStr TFltStrKdV DelSelfEdges_PNGraph GetRndWalkRestart_PUNGraph GenDegSeq TIntFltVHI SaveMatlabSparseMtx PlotInDegDistr_PUNGraph TStrStrKdVH TStrIntIntTr GetWeightedPageRankMP TFltIntPr TStrV_SwapI TStrStrIntTrV_GetV TFltIntIntIntQu LoadPajek_PNGraph TAscFltIntKdV_GetV TUInt64StrVHI GetMxBiCon_PNEANet GetSccSzCnt_PNGraph TAscFltStrPrV_SwapI CntDegNodes_PNEANet TExcept_New GetModularity_PUNGraph _swig_getattr GenRndGnm AddSelfEdges_PNEANet TNGraph_Load TTable_SetMP TSBase GenBaraHierar_PUNGraph TChVV TStrUtil GetWeightedShortestPath SaveMatlabSparseMtx_PUNGraph TIntSet Empty PercentMxScc TStrUtil_RemoveHtmlTags CntSelfEdges_PNGraph PNGraphMP_New CntNonZNodes_PUndirNet IsWeaklyConn_PDirNet TTable_GetEdgeTable TIntHSI GetKCoreNodes_PUndirNet PlotKCoreNodes TFltStrPrPrV CntNonZNodes DelNodes_PNGraph TStr_LoadTxt PercentMxScc_PUndirNet PUndirNet TFltV_GetV TFltIntPrKdV_SwapI GetEgonet GetNodesAtHop_PNGraph GetSubTreeSz_PDirNet LoadPajek_PDirNet TStr_GetNrFExt TCh_IsHashCh TStrPrBoolHI GetNodesAtHop TStrV GetBfsFullDiam_PNGraph TFltBoolKdV_GetV TStrUtil_GetXmlTagNmVal2 TStrPrStrHI GetSccs_PDirNet TIntFltPrKdV_SwapI CntOutDegNodes GetMxScc_PNEANet TCh_IsWs PStrV TStdNotify TUInt64FltKd CntUniqDirEdges_PNGraph TIntIntHI TIntIntPrVH GetPageRank_PNGraph TIntIntVV_SwapI TUChUInt64PrV_GetV TRowIterator GetClustCf_PUNGraph GetFlagStr MxDegree_PUndirNet ConvertGraph_PUndirNet_PNEANet TIntTrIntHI IsTree_PNEANet TBool_IsValStr CntUniqUndirEdges GetOutDegCnt_PNGraph TFltQ PlotSngValRank GetHits_PNGraph TIntFltIntTrV_GetV GetOutDegCnt_PNEANet GVizGetLayoutStr GenBaraHierar_PNGraph TNativeCallbackNotify TIntIntVV PlotSccDistr_PNGraph TBoolV_SwapI GetTriadEdges_PDirNet TAscFltIntKd LoadConnList_PUndirNet GetMxBiCon_PNGraph TUInt64IntPrV_GetV TIntIntVIntTrV_GetV GetDegCnt_PNGraph TStrUInt64H PNEANetMP_New TUInt_IsIpStr GetDegSeqV_PUndirNet DelSelfEdges_PDirNet CntSelfEdges_PNEANet TTableContext GetNodeEcc_PNGraph Get1CnComSzCnt PUNGraph_New TCnComV_GetV ConvertSubGraph_PUNGraph_PNEANet GetAnfEffDiam_PUndirNet GetHitsMP_PNGraph GetDegSeqV_PUNGraph TIntIntFltFltQu TUInt64_GetKiloStr TAGMUtil_RewireCmtyVV TIntFltKdV_SwapI TIntStrPrVH GetSubTreeSz_PNEANet TIntTrFltH TNotify_OnStatus GetPageRankMP_PUndirNet TCliqueOverlap_GetRelativeComplement TUInt GetNodesAtHops_PDirNet PlotKCoreEdges_PNEANet MxDegree_PDirNet PlotInDegDistr_PNEANet TUInt64StrPrV_GetV DrawGViz_PDirNet PlotKCoreNodes_PDirNet PMMNet getitem_vec TIntIntIntVTrV_SwapI GetAnf_PDirNet TAGM_RndConnectInsideCommunity TBoolV GetSccs CntUniqBiDirEdges_PNEANet TNGraphMP setitem_vec GetClustCf_PNGraph TNEANetMPNodeI TFltIntPrV_GetV TBPGraph _swig_setattr PNGraph_New TStrIntStrVTr TStrFltVH delitem_vec TStopwatch GenRMat ConvertESubGraph_PUNGraph_PNEANet TNGraph_New TUInt64V TStrIntPrV_GetV GetPageRank_v1_PUndirNet TStrVIntPrV_SwapI GetInDegCnt_PUndirNet TCh TStrVStrHI NodesGTEDegree_PDirNet GenGrid TBool_GetYesNoStr SaveMatlabSparseMtx_PUndirNet ConvertGraph_PNGraph_PNEANet TIntStrStrTrV_SwapI TIntStrKdV_SwapI CntEdgesToSet_PNEANet TFltKdV_SwapI TUChIntPrV TUInt64FltKdV GetBetweennessCentr_PNGraph GetCommon PercentDegree_PDirNet TStrPool64 TStr_PutFExt TStrQuV_GetV PlotShortPathDistr_PNGraph TAGMUtil_Intersection SaveEdgeList TUInt64IntPr SaveEdgeList_PNEANet GetRndSubGraph TIntQuV_GetV TIntFltVH TIntSet_GetSet contains_hashset TStrStrVPr TStrTrIntH ConvertSubGraph_PNEANet_PNGraph TStrIntPrV TFltIntKdV_SwapI DrawGViz_PUndirNet GetKCoreNodes_PDirNet TMemOut GetUnDir_PNGraph PlotKCoreEdges_PNGraph TIntStrPrIntHI DelNodes TIntPrIntPr TStr DelDegKNodes_PNGraph TStrPrV TStrV_GetV PFltV_New GetAnf CmtyGirvanNewmanStep PlotKCoreEdges_PUndirNet GenRndBipart TStrH TIntIntVHI TUInt_GetStrFromIpUInt IsWeaklyConn_PNGraph TBiConVisitor GetTreeSig_PNEANet TestAnf GetCmnNbrs_PUNGraph TChV_GetV GetClustCf GetHitsMP TStrIntFltPrH TStr_GetNullStr LoadEdgeListStr_PNGraph AddSelfEdges_PDirNet GetTreeRootNId_PNEANet IsConnected_PNGraph TStrFltKd SaveMatlabSparseMtx_PDirNet TStrPrV_GetV PlotHops LoadEdgeList_PUNGraph IterVec TIntBoolHI TIntUInt64PrV_SwapI TPrGraph TIntQuV_SwapI TAscFltVP GetKCore_PNGraph GetWccs_PUNGraph TAGMUtil_LoadCmtyVV TBool TStrFltPrV_GetV TIntVecPool_New TIntFltPrKdV TStrFltFltTrV_GetV GenTree_PNEANet IsConnected_PUNGraph GenFull_PUndirNet LoadEdgeList_PDirNet TRStr_GetNullRStr TUInt64StrPr TCrossNetEdgeI PrintInfo_PUndirNet GetKCoreEdges_PNEANet PercentMxWcc_PNGraph setitem_hash TIntIntPrH TCnComV_SwapI GetPageRankMP TIntPrStrH GetNodeInDegV_PNGraph TIntStrKd TStrVP_New CntInDegNodes_PNGraph TUIntIntKdV_GetV SaveEdgeList_PDirNet GetMxWcc_PDirNet TStrVH PUNGraph SaveGViz_PNEANet PrintGraphStatTable_PNEANetMP PrintInfo_PNEANet TCh_GetUc TUIntIntKdV_SwapI GetInEdges PrintGraphStatTable_PUndirNet DelZeroDegNodes_PUNGraph GetMxWccSz_PDirNet GetEigVec IsWeaklyConn_PNEANet TStrTree TTable_NormalizeColNameV TFltIntPrKd ToGraphMP3 TStdOut_New TStrUtil_GetDomNm2 TStrAscFltKdV_GetV GetBfsEffDiam TStrPool_Load TStrUtil_GetStdName TIntPrIntPrVH TInt_LoadFrugalInt TFile_GetUniqueFNm TNotify_GetTypeStr GetTreeSig_PNGraph TIntStrPrVHI GetTriadEdges_PNGraph GetTriads_PNGraph TFltV PNGraph TIntPrFltKdV_SwapI TUInt64IntPrV_SwapI IsConnected_PDirNet GetMxDegNId_PNEANet DelNodes_PUndirNet TRnd_GetExpDevStep TAttrPair TIntStrPrIntH TStrStrPrVHI GetFarnessCentr SaveToErrLog GenCircle_PNEANet TStrFltH TFltStrKd TExcept_PutOnExceptF GetClustCf_PDirNet TIntFltIntTrV_SwapI TPrimitive GetHits_PDirNet PDirNet GetNodeInDegV PrintInfo_PDirNet GetEdgesInOut_PDirNet TIntStrPrV TGUtil_Normalize GetEdgesInOut TStrIntHI LoadNodeList TInt_GetHexStr TStrStrH TFltVQ TFltUInt64PrV_GetV CmtyEvolutionFileBatchV LoadConnListStr_PUNGraph DelNodes_PUNGraph TIntV_SwapI TLogRegPredict TCh_GetUsFromYuAscii GetNodesAtHops TCh_GetStr TIntStrPrPr GetBfsFullDiam_PUndirNet TStrUInt64HI CntUniqUndirEdges_PUNGraph TAGMFastUtil TNGraph_GetSmallGraph GetRndESubGraph GetNodeOutDegV_PUndirNet MxDegree count TUIntIntKdV TIntTrFltHI PTable_New TTableIterator GetAnf_PNEANet GetShortPath_PUndirNet TRnd DelZeroDegNodes_PDirNet TUInt64IntPrV TUInt_GetKiloStr TCallbackNotify TUChIntPrV_SwapI CntSelfEdges_PUNGraph PlotSccDistr_PUNGraph TFltIntIntTrV GetCmnNbrs_PUndirNet TGUtil_GetCdf GetMxWccSz TFltUIntKd GetMxBiCon_PUndirNet TIntStrVPrV_GetV GetDegCnt_PNEANet TIntStrVPr GetSubTreeSz_PUndirNet TFltBoolKd TStrStrVHI TStdErrNotify_New TAttr GetClosenessCentr_PNGraph TStrHI TestAnf_PNEANet TStrFltPrV ConvertGraph_PDirNet_PNEANet ConvertESubGraph GenBaraHierar_PUndirNet TStrIntPrV_SwapI TFOut GetNodeInDegV_PNEANet TBool_GetYNStr TAGMUtil_GenPLSeq GetNodeTriads_PNEANet CntSelfEdges_PUndirNet GetNodeWcc_PUndirNet TAGMUtil_FindComsByAGM PlotHops_PUNGraph GetNodeInDegV_PUndirNet ToNetworkMP2 TCs_GetCsFromBf TUInt64_GetHexStr TRStr TFltUInt64KdV TIntStrIntTrV_GetV TIntPrStrVH iterhash GetKCore_PNEANet ConvertSubGraph_PUndirNet_PUndirNet TStrVP_Load CntNonZNodes_PDirNet Clr GetNodeClustCf_PUNGraph TPairHashImpl2 TFltIntBoolPrKd TIntPrVV GetShortPath_PDirNet ConvertSubGraph_PUndirNet_PNGraph TStrUtil_GetCleanWrdStr TIntFltPr TNGraphNodeI TIntStrPr TIntStrKdV_GetV TSFltV GetBfsEffDiam_PNEANet TUInt64V_GetV TStr_GetChStr TIntTrV_GetV TStrBoolKd TChATr GetSubGraph TInt_SaveFrugalIntV GetKCoreNodes_PNGraph TBigStrPool_New GetMxScc CntOutDegNodes_PDirNet TStdErrNotify TAscFltStrPr SaveGViz_PDirNet SaveGViz_PUNGraph TStrUtil_GetXmlTagVal GetFarnessCentr_PNGraph GetNodeWcc_PUNGraph SaveEdgeList_PUNGraph DelDegKNodes TStrUtil_StripEnd TStrIntPr TIntPrV_GetV TStrHashF_Md5_GetPrimHashCd TExcept_Throw CntInDegNodes_PUndirNet IsTree_PUndirNet TStrFltPr TIntStrPrV_GetV TIntUInt64Pr TNEGraph_Load GetPageRank_PUNGraph TSOutMnp GetNodeClustCf_PNEANet TUInt_GetUIntFromIpStr GetSngVec GetAnfEffDiam_PNEANet GetEdgeBridges GetMxWcc_PNGraph GenTree_PUndirNet MaxCPGreedyBetter3 GetRndSubGraph_PDirNet TFlt_GetGigaStr TIntFltIntTrV TFOut_New TLogRegPredict_Load GetAnf_PUNGraph PlotClustCf_PNEANet GetOutDegCnt_PDirNet ToGraph_PUndirNet SavePajek ToGraph_PDirNet MxDegree_PNGraph TIntStrIntTr GetShortPath_PNEANet TStrPrStrVH TTable_GetNodeTable DelNodes_PDirNet TStrUtil_GetCleanStr LoadPajek PrintGraphStatTable TUInt64Tr TIntStrVPrV TStrHashF_OldGLib_GetPrimHashCd TUChIntIntTr TStrFltPrV_SwapI TFltVVV TLogRegFit TFltKdV TInt_Swap TIntPrV LoadConnListStr_PUndirNet GetMxWcc TNEANetEdgeI TStrStrPrVH CommunityGirvanNewman GetTriads_PNEANet TStrIntPrIntH GetMxBiCon_PDirNet TLogNotify MakeUnDir_PNGraph TPredicateNode TFlt_GetRnd PlotHops_PNEANet GetBiCon GetPageRank_v1 TIntPrIntHI TStr_PutFBase GetTreeRootNId_PNGraph ToGraph_PNGraph TAscFltIntPrV_GetV TIntFltPrV_SwapI AbstractGraphEditDistance EdgeEditDistance compare GraphEditDistance main parse_args learn_embeddings read_graph Graph alias_draw alias_setup median exp print rand distance_matrix max number_of_nodes caveman_special subgraph ladder_graph barabasi_albert_graph perturb_new LFR_benchmark_graph max connected_component_subgraphs str list grid_2d_graph node2vec_walk nodes edges append range balanced_tree Graph Graph_load_batch shuffle choice load_graph_list ego_graph startswith read_edgelist Graph_load clear int print convert_node_labels_to_integers n_community preprocess_transition_probs max_prev_node len str list add_edges_from max arange isolates number_of_nodes print loadtxt Graph subgraph astype map append remove_nodes_from range add_node draw_graph_list switch_backend print Graph_load_batch close shuffle hist savefig append int strip open load list format lil_matrix from_dict_of_lists tolil tuple sort len min adjacency_matrix parse_index_file append max range open dict get pop bfs_successors zeros max range tril T zeros max range tril print tril append amin range len T len zeros range tril asarray grid_2d_graph number_of_nodes encode_adj_flexible connected_caveman_graph ladder_graph print decode_adj_flexible len range decode_adj from_numpy_matrix randint bfs_seq karate_club_graph array to_numpy_matrix encode_adj zeros range amin tril zeros T range amax asarray print decode_adj_full encode_adj_full from_numpy_matrix randint bfs_seq karate_club_graph array to_numpy_matrix flatten dot eye sum diag len randn rand abs values seed sorted list ones sum degree_histogram range add_edge Graph remove_nodes_from isolates print repeat eye zeros argmin int len find sorted list perturb print shuffle clustering_stats load_graph_list extract_result_id_and_epoch keys len clustering_stats degree_stats find_nearest_idx array shuffle append str load_graph_list num_layers print degree_stats load_ground_truth clustering_stats orbit_stats_all len print evaluation_epoch info perturb delete find_nearest_idx str list compute_basic_stats append range format close shuffle load_graph_list orbit_stats_all items print min write array len list graph_save_path eval_list_fname eval_list fname_test range join endswith snap_txt_output_to_nx append listdir fsdecode T ones print dot eye zeros abs max loss T ones print dot zeros sum max clip min copy shape any unravel_index zeros argmax seed Add GetDeg GetNbrNId GetId randint GetNI range number_of_nodes message_passing binary_cross_entropy zero_grad cuda relabel_nodes f_an list nodes expand epochs adjacency_list permute f_ae append calc_graph_embedding node_embedding_size range cat format size shuffle graph_type softmax zip backward print dict f_s calc_init_embedding zeros train step len number_of_nodes message_passing binary_cross_entropy zero_grad cuda relabel_nodes f_an list nodes expand adjacency_list permute f_ae append calc_graph_embedding range cat size shuffle softmax zip dict f_s calc_init_embedding zeros train len sample_tensor message_passing from_dict_of_lists f_an topk list expand permute f_ae append calc_graph_embedding range cat size eval softmax zip test_graph_num int dict f_s calc_init_embedding gumbel_softmax len train_DGMG_epoch timing_save_path MultiStepLR model_save_path fname_pred save str list graph_save_path Adam load_state_dict state_dict format load_epoch lr load time print fname parameters zeros epochs test_DGMG_epoch save_graph_list load str note load_epoch model_save_path fname graph_type load_state_dict nll_save_path arange binary_cross_entropy ones size repeat float cuda size rand softmax neg_ cuda size rand sigmoid cuda log size sigmoid any float cuda range size sigmoid any float cuda range data all size sigmoid any float cuda range size sigmoid any float cuda range FloatTensor size sigmoid any zeros float cuda range len FloatTensor size sigmoid any zeros float cuda range len FloatTensor size sigmoid any zeros float cuda range has_edge index has_edge FloatTensor size freeze_support sigmoid any zeros float numpy cuda range f_n_1 size expand m_uv_1 append sum cuda range cat len mul f_gate sum f_m mul f_m_init f_init sum f_gate_init size range matmul pow cuda pop len append zeros enumerate int rand floor len search find compile getfile find_module load_module get get __repr__ delete_TCRef _swig_property delete_TSStr _swig_property delete_TConv_Pt64Ints32 _swig_property delete_TPairHashImpl1 staticmethod _swig_property delete_TPairHashImpl2 staticmethod _swig_property delete_TRnd staticmethod _swig_property staticmethod _swig_property delete_TMem staticmethod _swig_property delete_TMemIn staticmethod _swig_property delete_TMemOut delete_TChA staticmethod _swig_property delete_TChAIn staticmethod _swig_property TRStr_Refs_get _swig_property delete_TRStr TRStr_Refs_set TRStr_Bf_set staticmethod TRStr_Bf_get staticmethod _swig_property delete_TStr staticmethod _swig_property delete_TStrIn TDbStr_Str1_set TDbStr_Str1_get delete_TDbStr _swig_property TDbStr_Str2_get TDbStr_Str2_set staticmethod _swig_property delete_TStrPool staticmethod _swig_property delete_TStrPool64 delete_TVoid _swig_property delete_TBool _swig_property TBool_Rnd_set TBool_Val_set staticmethod TBool_Rnd_get TBool_Val_get TCh_Val_get TCh_Val_set delete_TCh _swig_property staticmethod TUCh_Val_set TUCh_Val_get _swig_property delete_TUCh delete_TSInt _swig_property TSInt_Val_set TSInt_Val_get TInt_Rnd_get _swig_property delete_TInt TInt_Val_get staticmethod TInt_Rnd_set TInt_Val_set TUInt_Rnd_set TUInt_Val_set TUInt_Rnd_get _swig_property staticmethod TUInt_Val_get delete_TUInt delete_TUInt64 _swig_property TUInt64_Val_get TUInt64_Val_set staticmethod TFlt_Rnd_get _swig_property TFlt_Val_set TFlt_Rnd_set delete_TFlt staticmethod TFlt_Val_get delete_TAscFlt _swig_property TSFlt_Val_get delete_TSFlt _swig_property TSFlt_Val_set delete_TLFlt _swig_property TLFlt_Val_set staticmethod TLFlt_Val_get TFltRect_MnX_set TFltRect_MnY_get TFltRect_MxX_set _swig_property TFltRect_MnY_set staticmethod TFltRect_MxY_get TFltRect_MnX_get delete_TFltRect TFltRect_MxX_get TFltRect_MxY_set delete_TCs staticmethod _swig_property _swig_property delete_TSOutMnp _swig_property delete_TSBase delete_TSIn _swig_property _swig_property delete_TSOut _swig_property delete_TSInOut delete_TStdIn staticmethod _swig_property delete_TStdOut staticmethod _swig_property staticmethod _swig_property delete_TFIn staticmethod _swig_property delete_TFOut staticmethod _swig_property delete_TMIn staticmethod _swig_property delete_TMOut delete_TChRet _swig_property delete_TLnRet _swig_property delete_TFile staticmethod _swig_property delete_TNotify staticmethod _swig_property staticmethod _swig_property delete_TNullNotify staticmethod _swig_property delete_TCallbackNotify delete_TNativeCallbackNotify staticmethod _swig_property delete_TStdNotify staticmethod _swig_property staticmethod _swig_property delete_TStdErrNotify delete_TLogNotify staticmethod _swig_property TExcept_OnExceptF_get delete_TExcept TExcept_OnExceptF_set _swig_property staticmethod delete_TUnionFind _swig_property delete_TGUtil staticmethod _swig_property staticmethod _swig_property delete_TStrUtil TStopwatch_Postprocess TStopwatch_CopyNodes TStopwatch_PopulateGraph TStopwatch_Sort2 TStopwatch_ConstructGraph TStopwatch_AddEdges TStopwatch_NEXPS TStopwatch_ComputeOffset TStopwatch_ExtractEdges TStopwatch_CopyColumns TStopwatch_LoadTables TStopwatch_EstimateSizes _swig_property TStopwatch_AllocateColumnCopies TStopwatch_MergeNeighborhoods TStopwatch_BuildSubgraph TStopwatch_ExtractNbrETypes delete_TStopwatch TStopwatch_Compute TStopwatch_AddNeighborhoods TStopwatch_StoreOutputs TStopwatch_Preprocess TStopwatch_Group TStopwatch_ComputeETypes staticmethod TStopwatch_InitGraph TStopwatch_Sort staticmethod _swig_property delete_TBigStrPool staticmethod _swig_property delete_TStrHashF_OldGLib staticmethod _swig_property delete_TStrHashF_Md5 delete_TStrHashF_DJB staticmethod _swig_property staticmethod _swig_property delete_TUNGraph staticmethod _swig_property delete_TNGraph delete_TNEGraph staticmethod _swig_property _swig_property TBPGraph_bgsBoth staticmethod delete_TBPGraph TBPGraph_bgsLeft TBPGraph_bgsUndef TBPGraph_bgsRight delete_TNGraphMP staticmethod _swig_property delete_TNEANet staticmethod _swig_property TNEANet_CRef_get staticmethod _swig_property delete_TUndirNet staticmethod _swig_property delete_TDirNet _swig_property delete_TModeNet _swig_property delete_TCrossNet TMMNet_CRef_get _swig_property staticmethod delete_TMMNet staticmethod _swig_property delete_TNEANetMP delete_TAtomicPredicate _swig_property TPredicateNode_Result_set delete_TPredicateNode _swig_property TPredicateNode_Parent_get TPredicateNode_Left_get TPredicateNode_Result_get TPredicateNode_Op_set TPredicateNode_Atom_set TPredicateNode_Op_get TPredicateNode_Right_get TPredicateNode_Atom_get TPredicateNode_Right_set TPredicateNode_Left_set TPredicateNode_Parent_set staticmethod _swig_property delete_TPredicate _swig_property delete_TTableContext delete_TPrimitive _swig_property delete_TTableRow _swig_property delete_GroupStmt _swig_property delete_TRowIterator _swig_property _swig_property delete_TRowIteratorWithRemove _swig_property delete_TTableIterator staticmethod _swig_property delete_TTable delete_TAttr _swig_property _swig_property delete_TAttrPair TCnCom_NIdV_set TCnCom_NIdV_get _swig_property delete_TCnCom staticmethod delete_TArtPointVisitor TArtPointVisitor_ParentH_set TArtPointVisitor_Time_get TArtPointVisitor_VnLowH_get TArtPointVisitor_ArtSet_set _swig_property TArtPointVisitor_VnLowH_set TArtPointVisitor_ArtSet_get TArtPointVisitor_ParentH_get TArtPointVisitor_Time_set TBiConVisitor_Time_get TBiConVisitor_VnLowH_get TBiConVisitor_CnComV_get TBiConVisitor_CnComV_set _swig_property TBiConVisitor_NSet_get TBiConVisitor_ParentH_get TBiConVisitor_ParentH_set TBiConVisitor_Time_set TBiConVisitor_Stack_set TBiConVisitor_VnLowH_set TBiConVisitor_Stack_get delete_TBiConVisitor TBiConVisitor_NSet_set delete_TForestFire staticmethod _swig_property TFfGGen_srUndef TFfGGen_TimeLimitSec_set _swig_property staticmethod TFfGGen_srFlood delete_TFfGGen TFfGGen_srOk TFfGGen_TimeLimitSec_get TFfGGen_srTimeLimit delete_TUndirFFire _swig_property delete_TNGraphMtx _swig_property _swig_property delete_TUNGraphMtx delete_TCliqueOverlap staticmethod _swig_property delete_TAGM staticmethod _swig_property staticmethod _swig_property delete_TAGMUtil delete_TLogRegFit _swig_property delete_TLogRegPredict staticmethod _swig_property TAGMFast_MaxVal_set TAGMFast_NegWgt_set TAGMFast_MaxVal_get TAGMFast_PNoCom_set _swig_property delete_TAGMFast TAGMFast_NegWgt_get TAGMFast_HOVIDSV_get TAGMFast_MinVal_set TAGMFast_MinVal_get TAGMFast_HOVIDSV_set TAGMFast_DoParallel_set TAGMFast_PNoCom_get TAGMFast_DoParallel_get delete_TAGMFastUtil _swig_property _swig_property delete_TAGMFit TIntPr_Val1_set delete_TIntPr _swig_property TIntPr_Val1_get TIntPr_Val2_get TIntPr_Val2_set TFltPr_Val1_set _swig_property TFltPr_Val1_get TFltPr_Val2_get delete_TFltPr TFltPr_Val2_set TStrIntPr_Val2_get TStrIntPr_Val1_get TStrIntPr_Val2_set delete_TStrIntPr _swig_property TStrIntPr_Val1_set TIntTr_Val3_get delete_TIntTr TIntTr_Val2_get TIntTr_Val2_set TIntTr_Val1_set _swig_property TIntTr_Val3_set TIntTr_Val1_get TIntFltKd_Key_set TIntFltKd_Dat_set _swig_property TIntFltKd_Dat_get delete_TIntFltKd TIntFltKd_Key_get delete_TIntV staticmethod _swig_property staticmethod _swig_property delete_TFltV staticmethod _swig_property delete_TStrV staticmethod _swig_property delete_TIntPrV staticmethod _swig_property delete_TFltPrV delete_TStrIntPrV _swig_property staticmethod staticmethod _swig_property delete_TIntTrV staticmethod _swig_property delete_TIntFltKdV TIntStrPr_Val1_set TIntStrPr_Val2_get _swig_property TIntStrPr_Val2_set delete_TIntStrPr TIntStrPr_Val1_get staticmethod _swig_property delete_TIntIntVV staticmethod _swig_property delete_PNEANetV TBoolFltPr_Val2_get TBoolFltPr_Val2_set _swig_property TBoolFltPr_Val1_set delete_TBoolFltPr TBoolFltPr_Val1_get TIntBoolPr_Val1_get TIntBoolPr_Val2_set _swig_property TIntBoolPr_Val2_get delete_TIntBoolPr TIntBoolPr_Val1_set delete_TIntUInt64Pr _swig_property TIntUInt64Pr_Val2_get TIntUInt64Pr_Val1_set TIntUInt64Pr_Val2_set TIntUInt64Pr_Val1_get delete_TIntIntPrPr _swig_property TIntIntPrPr_Val2_set TIntIntPrPr_Val1_set TIntIntPrPr_Val1_get TIntIntPrPr_Val2_get TIntIntVPr_Val2_set TIntIntVPr_Val1_get delete_TIntIntVPr _swig_property TIntIntVPr_Val1_set TIntIntVPr_Val2_get TIntFltPr_Val1_set delete_TIntFltPr _swig_property TIntFltPr_Val2_set TIntFltPr_Val2_get TIntFltPr_Val1_get delete_TIntStrVPr _swig_property TIntStrVPr_Val1_set TIntStrVPr_Val2_set TIntStrVPr_Val1_get TIntStrVPr_Val2_get TIntPrIntPr_Val2_get delete_TIntPrIntPr _swig_property TIntPrIntPr_Val2_set TIntPrIntPr_Val1_get TIntPrIntPr_Val1_set _swig_property TUIntUIntPr_Val2_set TUIntUIntPr_Val1_get delete_TUIntUIntPr TUIntUIntPr_Val1_set TUIntUIntPr_Val2_get TUIntIntPr_Val1_set TUIntIntPr_Val1_get _swig_property delete_TUIntIntPr TUIntIntPr_Val2_set TUIntIntPr_Val2_get delete_TUInt64IntPr TUInt64IntPr_Val2_set _swig_property TUInt64IntPr_Val1_set TUInt64IntPr_Val1_get TUInt64IntPr_Val2_get TUInt64Pr_Val2_get delete_TUInt64Pr TUInt64Pr_Val1_get _swig_property TUInt64Pr_Val1_set TUInt64Pr_Val2_set delete_TUInt64FltPr _swig_property TUInt64FltPr_Val1_get TUInt64FltPr_Val2_set TUInt64FltPr_Val2_get TUInt64FltPr_Val1_set TUInt64StrPr_Val1_set _swig_property TUInt64StrPr_Val2_get TUInt64StrPr_Val2_set delete_TUInt64StrPr TUInt64StrPr_Val1_get TFltIntPr_Val2_get TFltIntPr_Val2_set _swig_property TFltIntPr_Val1_get delete_TFltIntPr TFltIntPr_Val1_set TFltUInt64Pr_Val2_set TFltUInt64Pr_Val1_set TFltUInt64Pr_Val1_get _swig_property delete_TFltUInt64Pr TFltUInt64Pr_Val2_get delete_TFltStrPr _swig_property TFltStrPr_Val2_get TFltStrPr_Val1_set TFltStrPr_Val1_get TFltStrPr_Val2_set TAscFltIntPr_Val1_set TAscFltIntPr_Val1_get _swig_property TAscFltIntPr_Val2_set TAscFltIntPr_Val2_get delete_TAscFltIntPr _swig_property delete_TAscFltPr TAscFltPr_Val2_set TAscFltPr_Val1_set TAscFltPr_Val2_get TAscFltPr_Val1_get TAscFltStrPr_Val2_get TAscFltStrPr_Val2_set _swig_property TAscFltStrPr_Val1_set TAscFltStrPr_Val1_get delete_TAscFltStrPr TStrFltPr_Val1_set TStrFltPr_Val2_get TStrFltPr_Val2_set TStrFltPr_Val1_get _swig_property delete_TStrFltPr _swig_property TStrPr_Val2_set TStrPr_Val1_set TStrPr_Val2_get TStrPr_Val1_get delete_TStrPr delete_TStrStrVPr TStrStrVPr_Val1_get _swig_property TStrStrVPr_Val2_set TStrStrVPr_Val1_set TStrStrVPr_Val2_get TStrVIntPr_Val2_set _swig_property delete_TStrVIntPr TStrVIntPr_Val1_set TStrVIntPr_Val1_get TStrVIntPr_Val2_get TIntStrPrPr_Val2_get TIntStrPrPr_Val1_get _swig_property TIntStrPrPr_Val2_set TIntStrPrPr_Val1_set delete_TIntStrPrPr TFltStrPrPr_Val1_get TFltStrPrPr_Val1_set _swig_property TFltStrPrPr_Val2_get TFltStrPrPr_Val2_set delete_TFltStrPrPr TChTr_Val2_set _swig_property TChTr_Val1_set delete_TChTr TChTr_Val3_set TChTr_Val1_get TChTr_Val3_get TChTr_Val2_get TChIntIntTr_Val1_set TChIntIntTr_Val2_set _swig_property delete_TChIntIntTr TChIntIntTr_Val3_set TChIntIntTr_Val1_get TChIntIntTr_Val2_get TChIntIntTr_Val3_get TUChIntIntTr_Val1_get TUChIntIntTr_Val1_set TUChIntIntTr_Val3_get delete_TUChIntIntTr _swig_property TUChIntIntTr_Val2_set TUChIntIntTr_Val3_set TUChIntIntTr_Val2_get TUInt64Tr_Val1_set TUInt64Tr_Val2_get _swig_property TUInt64Tr_Val3_get TUInt64Tr_Val3_set TUInt64Tr_Val1_get TUInt64Tr_Val2_set delete_TUInt64Tr TIntStrIntTr_Val3_get _swig_property TIntStrIntTr_Val2_get delete_TIntStrIntTr TIntStrIntTr_Val1_get TIntStrIntTr_Val1_set TIntStrIntTr_Val3_set TIntStrIntTr_Val2_set TIntIntStrTr_Val2_get _swig_property delete_TIntIntStrTr TIntIntStrTr_Val3_get TIntIntStrTr_Val3_set TIntIntStrTr_Val1_get TIntIntStrTr_Val1_set TIntIntStrTr_Val2_set TIntIntFltTr_Val3_set TIntIntFltTr_Val2_set delete_TIntIntFltTr _swig_property TIntIntFltTr_Val2_get TIntIntFltTr_Val1_get TIntIntFltTr_Val1_set TIntIntFltTr_Val3_get TIntFltIntTr_Val3_get _swig_property TIntFltIntTr_Val3_set TIntFltIntTr_Val1_get TIntFltIntTr_Val2_set TIntFltIntTr_Val1_set delete_TIntFltIntTr TIntFltIntTr_Val2_get TIntFltFltTr_Val1_get TIntFltFltTr_Val1_set TIntFltFltTr_Val3_set TIntFltFltTr_Val3_get _swig_property delete_TIntFltFltTr TIntFltFltTr_Val2_get TIntFltFltTr_Val2_set delete_TIntIntVIntTr TIntIntVIntTr_Val2_get TIntIntVIntTr_Val2_set TIntIntVIntTr_Val1_set _swig_property TIntIntVIntTr_Val3_set TIntIntVIntTr_Val1_get TIntIntVIntTr_Val3_get TIntIntIntVTr_Val3_set TIntIntIntVTr_Val1_set TIntIntIntVTr_Val1_get _swig_property delete_TIntIntIntVTr TIntIntIntVTr_Val2_set TIntIntIntVTr_Val3_get TIntIntIntVTr_Val2_get TFltTr_Val2_get _swig_property TFltTr_Val1_set delete_TFltTr TFltTr_Val3_get TFltTr_Val3_set TFltTr_Val1_get TFltTr_Val2_set TFltIntIntTr_Val1_get delete_TFltIntIntTr TFltIntIntTr_Val1_set _swig_property TFltIntIntTr_Val3_get TFltIntIntTr_Val2_set TFltIntIntTr_Val3_set TFltIntIntTr_Val2_get TFltFltIntTr_Val1_set _swig_property TFltFltIntTr_Val3_get delete_TFltFltIntTr TFltFltIntTr_Val3_set TFltFltIntTr_Val2_set TFltFltIntTr_Val1_get TFltFltIntTr_Val2_get TFltFltStrTr_Val2_set TFltFltStrTr_Val3_get _swig_property TFltFltStrTr_Val1_get TFltFltStrTr_Val1_set TFltFltStrTr_Val2_get TFltFltStrTr_Val3_set delete_TFltFltStrTr TChATr_Val1_set TChATr_Val3_set TChATr_Val1_get TChATr_Val2_set _swig_property delete_TChATr TChATr_Val2_get TChATr_Val3_get TStrTr_Val1_get TStrTr_Val3_set _swig_property TStrTr_Val2_get TStrTr_Val1_set TStrTr_Val3_get delete_TStrTr TStrTr_Val2_set delete_TStrIntIntTr TStrIntIntTr_Val1_set TStrIntIntTr_Val1_get TStrIntIntTr_Val3_get _swig_property TStrIntIntTr_Val3_set TStrIntIntTr_Val2_get TStrIntIntTr_Val2_set TStrFltFltTr_Val3_set TStrFltFltTr_Val2_set TStrFltFltTr_Val2_get TStrFltFltTr_Val3_get _swig_property TStrFltFltTr_Val1_set TStrFltFltTr_Val1_get delete_TStrFltFltTr TStrStrIntTr_Val1_set TStrStrIntTr_Val2_get delete_TStrStrIntTr TStrStrIntTr_Val3_get _swig_property TStrStrIntTr_Val1_get TStrStrIntTr_Val3_set TStrStrIntTr_Val2_set TStrIntStrVTr_Val3_get TStrIntStrVTr_Val3_set _swig_property TStrIntStrVTr_Val1_set TStrIntStrVTr_Val2_get delete_TStrIntStrVTr TStrIntStrVTr_Val1_get TStrIntStrVTr_Val2_set TStrStrIntIntQu_Val3_get TStrStrIntIntQu_Val3_set delete_TStrStrIntIntQu TStrStrIntIntQu_Val2_get _swig_property TStrStrIntIntQu_Val1_set TStrStrIntIntQu_Val2_set TStrStrIntIntQu_Val1_get TStrStrIntIntQu_Val4_set TStrStrIntIntQu_Val4_get TStrQu_Val2_set TStrQu_Val3_set TStrQu_Val1_set TStrQu_Val4_get TStrQu_Val4_set _swig_property TStrQu_Val2_get TStrQu_Val1_get delete_TStrQu TStrQu_Val3_get TIntQu_Val3_set TIntQu_Val4_set TIntQu_Val2_set TIntQu_Val2_get _swig_property TIntQu_Val1_set TIntQu_Val1_get TIntQu_Val4_get delete_TIntQu TIntQu_Val3_get TFltQu_Val3_set TFltQu_Val1_get TFltQu_Val2_set TFltQu_Val4_set delete_TFltQu _swig_property TFltQu_Val2_get TFltQu_Val4_get TFltQu_Val3_get TFltQu_Val1_set delete_TFltIntIntIntQu TFltIntIntIntQu_Val4_get TFltIntIntIntQu_Val2_get TFltIntIntIntQu_Val3_get TFltIntIntIntQu_Val1_set _swig_property TFltIntIntIntQu_Val1_get TFltIntIntIntQu_Val4_set TFltIntIntIntQu_Val2_set TFltIntIntIntQu_Val3_set TIntStrIntIntQu_Val3_get TIntStrIntIntQu_Val2_get TIntStrIntIntQu_Val3_set TIntStrIntIntQu_Val4_get _swig_property TIntStrIntIntQu_Val1_get TIntStrIntIntQu_Val4_set TIntStrIntIntQu_Val1_set delete_TIntStrIntIntQu TIntStrIntIntQu_Val2_set TIntIntFltFltQu_Val2_set TIntIntFltFltQu_Val1_get delete_TIntIntFltFltQu TIntIntFltFltQu_Val4_get _swig_property TIntIntFltFltQu_Val1_set TIntIntFltFltQu_Val3_set TIntIntFltFltQu_Val4_set TIntIntFltFltQu_Val2_get TIntIntFltFltQu_Val3_get TIntKd_Key_set _swig_property TIntKd_Dat_set delete_TIntKd TIntKd_Dat_get TIntKd_Key_get _swig_property delete_TIntUInt64Kd TIntUInt64Kd_Key_set TIntUInt64Kd_Dat_get TIntUInt64Kd_Key_get TIntUInt64Kd_Dat_set delete_TIntPrFltKd TIntPrFltKd_Key_set _swig_property TIntPrFltKd_Dat_get TIntPrFltKd_Key_get TIntPrFltKd_Dat_set TIntFltPrKd_Key_set _swig_property TIntFltPrKd_Dat_set TIntFltPrKd_Dat_get TIntFltPrKd_Key_get delete_TIntFltPrKd delete_TIntSFltKd TIntSFltKd_Dat_get TIntSFltKd_Key_set _swig_property TIntSFltKd_Dat_set TIntSFltKd_Key_get TIntStrKd_Dat_get _swig_property TIntStrKd_Key_set TIntStrKd_Key_get TIntStrKd_Dat_set delete_TIntStrKd TUIntIntKd_Key_set TUIntIntKd_Dat_set TUIntIntKd_Key_get _swig_property TUIntIntKd_Dat_get delete_TUIntIntKd delete_TUIntKd TUIntKd_Dat_get TUIntKd_Dat_set _swig_property TUIntKd_Key_get TUIntKd_Key_set TUInt64IntKd_Key_set _swig_property TUInt64IntKd_Key_get TUInt64IntKd_Dat_set delete_TUInt64IntKd TUInt64IntKd_Dat_get TUInt64FltKd_Dat_get TUInt64FltKd_Dat_set _swig_property delete_TUInt64FltKd TUInt64FltKd_Key_get TUInt64FltKd_Key_set TUInt64StrKd_Key_get delete_TUInt64StrKd _swig_property TUInt64StrKd_Dat_get TUInt64StrKd_Dat_set TUInt64StrKd_Key_set delete_TFltBoolKd TFltBoolKd_Dat_get _swig_property TFltBoolKd_Key_set TFltBoolKd_Key_get TFltBoolKd_Dat_set TFltIntKd_Key_get TFltIntKd_Key_set TFltIntKd_Dat_set TFltIntKd_Dat_get _swig_property delete_TFltIntKd TFltUInt64Kd_Dat_set _swig_property TFltUInt64Kd_Key_set delete_TFltUInt64Kd TFltUInt64Kd_Dat_get TFltUInt64Kd_Key_get TFltIntPrKd_Dat_get _swig_property delete_TFltIntPrKd TFltIntPrKd_Key_get TFltIntPrKd_Dat_set TFltIntPrKd_Key_set TFltUIntKd_Key_get TFltUIntKd_Dat_set delete_TFltUIntKd _swig_property TFltUIntKd_Key_set TFltUIntKd_Dat_get TFltKd_Key_get _swig_property TFltKd_Dat_set delete_TFltKd TFltKd_Key_set TFltKd_Dat_get delete_TFltStrKd TFltStrKd_Key_get _swig_property TFltStrKd_Dat_set TFltStrKd_Dat_get TFltStrKd_Key_set TFltIntBoolPrKd_Dat_set TFltIntBoolPrKd_Dat_get _swig_property TFltIntBoolPrKd_Key_set TFltIntBoolPrKd_Key_get delete_TFltIntBoolPrKd TAscFltIntKd_Key_get delete_TAscFltIntKd _swig_property TAscFltIntKd_Key_set TAscFltIntKd_Dat_get TAscFltIntKd_Dat_set TStrBoolKd_Key_get TStrBoolKd_Dat_set delete_TStrBoolKd _swig_property TStrBoolKd_Dat_get TStrBoolKd_Key_set TStrIntKd_Key_get delete_TStrIntKd _swig_property TStrIntKd_Key_set TStrIntKd_Dat_get TStrIntKd_Dat_set TStrFltKd_Dat_get delete_TStrFltKd TStrFltKd_Key_get _swig_property TStrFltKd_Dat_set TStrFltKd_Key_set TStrAscFltKd_Dat_set TStrAscFltKd_Dat_get delete_TStrAscFltKd _swig_property TStrAscFltKd_Key_get TStrAscFltKd_Key_set delete_TStrKd _swig_property TStrKd_Dat_set TStrKd_Dat_get TStrKd_Key_set TStrKd_Key_get staticmethod _swig_property delete_TBoolV staticmethod _swig_property delete_TChV staticmethod _swig_property delete_TUChV staticmethod _swig_property delete_TUIntV staticmethod _swig_property delete_TUInt64V delete_TSFltV staticmethod _swig_property staticmethod _swig_property delete_TAscFltV delete_TChAV staticmethod _swig_property staticmethod _swig_property delete_TIntQuV delete_TFltTrV staticmethod _swig_property delete_TIntKdV staticmethod _swig_property delete_TUChIntPrV staticmethod _swig_property delete_TUChUInt64PrV staticmethod _swig_property staticmethod _swig_property delete_TIntUInt64PrV delete_TIntUInt64KdV staticmethod _swig_property staticmethod _swig_property delete_TIntFltPrV staticmethod _swig_property delete_TIntFltPrKdV staticmethod _swig_property delete_TFltIntPrV delete_TFltUInt64PrV staticmethod _swig_property delete_TFltStrPrV staticmethod _swig_property delete_TAscFltStrPrV staticmethod _swig_property delete_TIntStrPrV staticmethod _swig_property staticmethod _swig_property delete_TIntIntStrTrV staticmethod _swig_property delete_TIntIntFltTrV delete_TIntFltIntTrV staticmethod _swig_property staticmethod _swig_property delete_TIntStrIntTrV delete_TIntStrStrTrV staticmethod _swig_property delete_TUIntIntKdV staticmethod _swig_property staticmethod _swig_property delete_TIntPrFltKdV staticmethod _swig_property delete_TIntStrKdV delete_TIntStrPrPrV staticmethod _swig_property delete_TIntStrVPrV staticmethod _swig_property staticmethod _swig_property delete_TIntIntVIntTrV staticmethod _swig_property delete_TIntIntIntVTrV delete_TUInt64IntPrV _swig_property staticmethod staticmethod _swig_property delete_TUInt64FltPrV staticmethod _swig_property delete_TUInt64StrPrV delete_TUInt64IntKdV staticmethod _swig_property delete_TUInt64FltKdV staticmethod _swig_property delete_TUInt64StrKdV staticmethod _swig_property staticmethod _swig_property delete_TFltBoolKdV delete_TFltIntKdV _swig_property staticmethod delete_TFltUInt64KdV staticmethod _swig_property staticmethod _swig_property delete_TFltIntPrKdV delete_TFltKdV staticmethod _swig_property staticmethod _swig_property delete_TFltStrKdV staticmethod _swig_property delete_TFltStrPrPrV staticmethod _swig_property delete_TFltIntIntTrV delete_TFltFltStrTrV staticmethod _swig_property staticmethod _swig_property delete_TAscFltIntPrV staticmethod _swig_property delete_TAscFltIntKdV delete_TStrPrV staticmethod _swig_property staticmethod _swig_property delete_TStrFltPrV staticmethod _swig_property delete_TStrIntKdV delete_TStrFltKdV staticmethod _swig_property delete_TStrAscFltKdV _swig_property staticmethod delete_TStrTrV _swig_property staticmethod staticmethod _swig_property delete_TStrQuV staticmethod _swig_property delete_TStrFltFltTrV staticmethod _swig_property delete_TStrStrIntTrV delete_TStrKdV staticmethod _swig_property delete_TStrStrVPrV staticmethod _swig_property staticmethod _swig_property delete_TStrVIntPrV staticmethod _swig_property delete_TFltIntIntIntQuV delete_TIntStrIntIntQuV staticmethod _swig_property delete_TIntIntPrPrV staticmethod _swig_property staticmethod _swig_property delete_TIntVecPool delete_PIntVecPool staticmethod _swig_property _swig_property TFltVP_V_get delete_TFltVP staticmethod TFltVP_V_set PFltV_V_get _swig_property PFltV_V_set staticmethod delete_PFltV TAscFltVP_V_set _swig_property delete_TAscFltVP TAscFltVP_V_get staticmethod PAscFltV_V_get _swig_property delete_PAscFltV staticmethod PAscFltV_V_set TStrVP_V_set TStrVP_V_get _swig_property staticmethod delete_TStrVP delete_PStrV _swig_property PStrV_V_set staticmethod PStrV_V_get delete_TBoolVV _swig_property _swig_property delete_TChVV delete_TIntVV _swig_property _swig_property delete_TSFltVV delete_TFltVV _swig_property delete_TStrVV _swig_property _swig_property delete_TIntPrVV _swig_property delete_TIntVVV _swig_property delete_TFltVVV _swig_property delete_TIntTree _swig_property delete_TFltTree delete_TStrTree _swig_property _swig_property delete_TStrIntPrTree _swig_property delete_TStrIntStrVTrTree _swig_property delete_TIntS delete_TBoolChS _swig_property _swig_property delete_TIntQ delete_TFltQ _swig_property delete_TStrQ _swig_property _swig_property delete_TIntPrQ _swig_property delete_TIntStrPrQ _swig_property delete_TFltVQ delete_TAscFltVQ _swig_property delete_TIntH TIntH_HashPrimes _swig_property TIntIntH_HashPrimes delete_TIntIntH _swig_property TIntFltH_HashPrimes delete_TIntFltH _swig_property delete_TIntStrH _swig_property TIntStrH_HashPrimes TIntPrFltH_HashPrimes delete_TIntPrFltH _swig_property delete_TStrIntH _swig_property TStrIntH_HashPrimes _swig_property delete_TStrIntSH _swig_property delete_TIntHI _swig_property delete_TIntIntHI delete_TIntFltHI _swig_property _swig_property delete_TIntStrHI delete_TIntPrFltHI _swig_property delete_TStrIntHI _swig_property _swig_property TUInt64H_HashPrimes delete_TUInt64H TIntBoolH_HashPrimes delete_TIntBoolH _swig_property TIntUInt64H_HashPrimes _swig_property delete_TIntUInt64H delete_TIntIntVH TIntIntVH_HashPrimes _swig_property TIntIntHH_HashPrimes delete_TIntIntHH _swig_property TIntFltPrH_HashPrimes delete_TIntFltPrH _swig_property TIntFltTrH_HashPrimes _swig_property delete_TIntFltTrH TIntFltVH_HashPrimes delete_TIntFltVH _swig_property delete_TIntStrVH _swig_property TIntStrVH_HashPrimes delete_TIntIntPrH _swig_property TIntIntPrH_HashPrimes TIntIntPrVH_HashPrimes _swig_property delete_TIntIntPrVH delete_TIntStrPrVH _swig_property TIntStrPrVH_HashPrimes delete_TUInt64StrVH _swig_property TUInt64StrVH_HashPrimes delete_TIntPrIntH TIntPrIntH_HashPrimes _swig_property TIntPrIntVH_HashPrimes _swig_property delete_TIntPrIntVH TIntPrIntPrVH_HashPrimes _swig_property delete_TIntPrIntPrVH _swig_property delete_TIntTrIntH TIntTrIntH_HashPrimes TIntVIntH_HashPrimes delete_TIntVIntH _swig_property _swig_property TUIntH_HashPrimes delete_TUIntH TIntTrFltH_HashPrimes _swig_property delete_TIntTrFltH delete_TIntPrStrH TIntPrStrH_HashPrimes _swig_property TIntPrStrVH_HashPrimes _swig_property delete_TIntPrStrVH TIntStrPrIntH_HashPrimes delete_TIntStrPrIntH _swig_property delete_TFltFltH _swig_property TFltFltH_HashPrimes TStrH_HashPrimes _swig_property delete_TStrH TStrBoolH_HashPrimes _swig_property delete_TStrBoolH TStrIntPrH_HashPrimes _swig_property delete_TStrIntPrH TStrIntVH_HashPrimes _swig_property delete_TStrIntVH TStrUInt64H_HashPrimes _swig_property delete_TStrUInt64H delete_TStrUInt64VH _swig_property TStrUInt64VH_HashPrimes delete_TStrIntPrVH TStrIntPrVH_HashPrimes _swig_property TStrFltH_HashPrimes _swig_property delete_TStrFltH delete_TStrFltVH TStrFltVH_HashPrimes _swig_property _swig_property delete_TStrStrH TStrStrH_HashPrimes _swig_property delete_TStrStrPrH TStrStrPrH_HashPrimes TStrStrVH_HashPrimes delete_TStrStrVH _swig_property delete_TStrStrPrVH _swig_property TStrStrPrVH_HashPrimes TStrStrKdVH_HashPrimes delete_TStrStrKdVH _swig_property TStrIntFltPrH_HashPrimes delete_TStrIntFltPrH _swig_property _swig_property delete_TStrStrIntPrVH TStrStrIntPrVH_HashPrimes TStrStrIntKdVH_HashPrimes _swig_property delete_TStrStrIntKdVH TStrPrBoolH_HashPrimes delete_TStrPrBoolH _swig_property TStrPrIntH_HashPrimes delete_TStrPrIntH _swig_property TStrPrFltH_HashPrimes _swig_property delete_TStrPrFltH delete_TStrPrStrH TStrPrStrH_HashPrimes _swig_property TStrPrStrVH_HashPrimes _swig_property delete_TStrPrStrVH TStrTrIntH_HashPrimes delete_TStrTrIntH _swig_property TStrIntPrIntH_HashPrimes delete_TStrIntPrIntH _swig_property TStrVH_HashPrimes _swig_property delete_TStrVH delete_TStrVIntVH _swig_property TStrVIntVH_HashPrimes delete_TStrVStrH TStrVStrH_HashPrimes _swig_property TStrVStrVH_HashPrimes _swig_property delete_TStrVStrVH delete_TUInt64HI _swig_property _swig_property delete_TIntBoolHI _swig_property delete_TIntUInt64HI delete_TIntIntVHI _swig_property delete_TIntIntHHI _swig_property _swig_property delete_TIntFltPrHI _swig_property delete_TIntFltTrHI delete_TIntFltVHI _swig_property delete_TIntStrVHI _swig_property _swig_property delete_TIntIntPrHI delete_TIntIntPrVHI _swig_property delete_TIntStrPrVHI _swig_property delete_TUInt64StrVHI _swig_property _swig_property delete_TIntPrIntHI _swig_property delete_TIntPrIntVHI _swig_property delete_TIntPrIntPrVHI _swig_property delete_TIntTrIntHI delete_TIntVIntHI _swig_property delete_TUIntHI _swig_property delete_TIntTrFltHI _swig_property delete_TIntPrStrHI _swig_property _swig_property delete_TIntPrStrVHI _swig_property delete_TIntStrPrIntHI delete_TFltFltHI _swig_property _swig_property delete_TStrHI _swig_property delete_TStrBoolHI delete_TStrIntPrHI _swig_property delete_TStrIntVHI _swig_property delete_TStrUInt64HI _swig_property delete_TStrUInt64VHI _swig_property _swig_property delete_TStrIntPrVHI delete_TStrFltHI _swig_property _swig_property delete_TStrFltVHI _swig_property delete_TStrStrHI delete_TStrStrPrHI _swig_property _swig_property delete_TStrStrVHI delete_TStrStrPrVHI _swig_property _swig_property delete_TStrStrKdVHI delete_TStrIntFltPrHI _swig_property _swig_property delete_TStrStrIntPrVHI _swig_property delete_TStrStrIntKdVHI delete_TStrPrBoolHI _swig_property _swig_property delete_TStrPrIntHI delete_TStrPrFltHI _swig_property delete_TStrPrStrHI _swig_property _swig_property delete_TStrPrStrVHI delete_TStrTrIntHI _swig_property delete_TStrIntPrIntHI _swig_property _swig_property delete_TStrVHI delete_TStrVIntVHI _swig_property delete_TStrVStrHI _swig_property delete_TStrVStrVHI _swig_property delete_TCnComV staticmethod _swig_property TStrTAttrPr_Val1_get TStrTAttrPr_Val1_set TStrTAttrPr_Val2_get _swig_property delete_TStrTAttrPr TStrTAttrPr_Val2_set staticmethod _swig_property delete_Schema staticmethod _swig_property delete_TIntSet TIntHSI_Mega_get TIntHSI_Val_get TIntHSI_Mx_get delete_TIntHSI TIntHSI_Giga_get _swig_property TIntHSI_Mn_get TIntHSI_Rnd_get TIntHSI_Kilo_get delete_TNGraphNodeI _swig_property delete_TDirNetNodeI _swig_property _swig_property delete_TNGraphMPNodeI delete_TNGraphEdgeI _swig_property delete_TDirNetEdgeI _swig_property _swig_property delete_TNGraphMPEdgeI delete_TUNGraphNodeI _swig_property delete_TUndirNetNodeI _swig_property delete_TUNGraphEdgeI _swig_property _swig_property delete_TUndirNetEdgeI _swig_property delete_TNEANetNodeI delete_TNEANetEdgeI _swig_property delete_TNEANetAIntI _swig_property delete_TNEANetAStrI _swig_property _swig_property delete_TNEANetAFltI _swig_property delete_TNEANetMPNodeI delete_TNEANetMPEdgeI _swig_property delete_TModeNetNodeI _swig_property delete_TModeNetEdgeI _swig_property delete_TCrossNetEdgeI _swig_property delete_TCrossNetAIntI _swig_property _swig_property delete_TCrossNetAStrI _swig_property delete_TCrossNetAFltI _swig_property delete_TMMNetModeNetI _swig_property delete_TMMNetCrossNetI SetVal Del AddDat DelKey DelKey delete_PNEANet staticmethod _swig_property PNEANet_CRef_get PMMNet_CRef_get delete_PMMNet staticmethod _swig_property staticmethod _swig_property delete_PNGraph delete_PUNGraph staticmethod _swig_property delete_PDirNet staticmethod _swig_property staticmethod _swig_property delete_PUndirNet delete_PNGraphMP staticmethod _swig_property staticmethod _swig_property delete_PNEANetMP Next BegNI Next BegEI GetOutDeg range GetInDeg range Next BegMMNI staticmethod _swig_property delete_PTable connected_component_subgraphs number_of_nodes convert_node_labels_to_integers append range max Graph_load ego_graph connected_component_subgraphs int list add_edge caveman_graph edges ceil randint range max remove_edge connected_component_subgraphs add_edge list len nodes range disjoint_union_all add_edge list number_of_nodes nodes copy range number_of_edges edges binomial sum remove_edge append len list print copy edges remove_edge add_edge list number_of_nodes copy edges append randint range remove_edge Figure figimage savefig FigureCanvas imsave histogram linspace zeros range spring_layout degree_histogram switch_backend axis close loglog draw_networkx savefig append best_partition array range len spring_layout subplot draw_networkx_nodes spectral_layout switch_backend axis tight_layout subplots_adjust close savefig draw_networkx_edges enumerate diameter sorted format number_of_nodes list append print asmatrix is_connected cycle_basis draw_graph number_of_edges average_shortest_path_length from_numpy_matrix sum degree_histogram values len from_numpy_matrix asmatrix node_connected_component connected_component_subgraphs list subgraph min adjacency_list max range enumerate str write index edges open Graph perturb print barabasi_albert_graph append range sqrt float range len int barabasi_albert_graph sqrt fast_gnp_random_graph append float range len emd hstack astype float max len int list barabasi_albert_graph rint histogram emd_distance fast_gnp_random_graph sum array degree_histogram values append arange Loss print optimizer_brute range len int list print rint min barabasi_albert_graph append fast_gnp_random_graph keys range len disjoint_union_all update initialize Bernoulli Dirichlet n_iter print Beta MAP PointMass print_progress finalize Multinomial range run set_defaults add_argument ArgumentParser transpose dot binomial array len GraphVAE list model print float backward zero_grad Adam MultiStepLR parameters step cuda range enumerate add_mutually_exclusive_group arg_parse str max_num_nodes format grid_2d_graph print Graph_load_batch GraphAdjSampler DataLoader cuda append train max range len GraphEditDistance normalized_distance print_matrix set_defaults add_argument ArgumentParser to_undirected weighted edges input read_edgelist save_word2vec_format output Word2Vec read_graph p Graph num_walks simulate_walks walk_length preprocess_transition_probs directed q | # DeepNC: Deep Generative Network Completion This repository is the implemention of DeepNC, a method to recover the missing part of a network. Reference: https://arxiv.org/abs/1907.07381. # Test run ``` python main_test.py ``` # Descriptions The code is being clean. Further descriptions shall be available soon. # Copyright This repository is built based on an open source implementation of GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Model, ICML 2018. Source: https://github.com/JiaxuanYou/graph-generation. | 1,767 |
conormdurkan/autoregressive-energy-machines | ['density estimation'] | ['Autoregressive Energy Machines'] | tensorflow/train_AEM_UCI_data.py pytorch/data_/power.py pytorch/data_/bsds300.py pytorch/utils/torchutils.py pytorch/data_/miniboone.py pytorch/models/energy.py tensorflow/utils/data_generators_2D.py pytorch/models/made.py tensorflow/utils/energy_nets.py tensorflow/train_AEM_2D_data.py pytorch/uci.py pytorch/data_/__init__.py pytorch/face.py pytorch/data_/plane.py pytorch/data_/hepmass.py pytorch/utils/io.py pytorch/models/aem.py pytorch/data_/base.py tensorflow/utils/aem.py pytorch/utils/uciutils.py pytorch/utils/__init__.py pytorch/plane.py pytorch/models/__init__.py tensorflow/eval_AEM_UCI_data.py tensorflow/utils/made_utils.py pytorch/utils/plane.py pytorch/data_/gas.py tensorflow/utils/data_utils.py pytorch/probability/distributions_.py get_uci_dataset_range load_plane_dataset InfiniteLoader load_uci_dataset BSDS300Dataset test main GasDataset test HEPMASSDataset test main MiniBooNEDataset test TestGridDataset CheckerboardDataset PlaneDataset test TwoSpiralsDataset FaceDataset GaussianGridDataset main PowerDataset AEM main ResidualEnergyNet ResidualBlock EnergyNet MADE MaskedLinear get_mask check_conditional MaskedResidualBlock main check_masks ResidualMADE check_connectivity main MixtureSameFamily Normal_ get_data_root get_pytorch_root get_project_root get_log_root get_checkpoint_root get_image_root get_timestamp main get_output_root create_two_spirals_data create_einstein_data create_checkerboard_data create_gaussian_grid_data tensor2numpy parse_activation test get_n_parameters tile preprocess_and_save_miniboone load_hepmass download_and_extract preprocess_and_save_hepmass load_miniboone preprocess_and_save_gas download_data load_power preprocess_and_save_power load_gas load_bsds300 parse_args eval_model train_model parse_args train_model parse_args AEM get_activation create_gaussian_grid_data create_two_spirals_data gen_2D_data create_einstein_data create_checkerboard_data download_and_extract preprocess_gas preprocess_miniboone load_UCI_np download_preprocess_data UCI preprocess_UCI_data preprocess_power preprocess_bsds300 download_data preprocess_hepmass Datasets2D contextual_res_net _get_mask ResMADE masked_residual_block masked_dense minimum maximum load_uci_dataset data show T subplots print reshape len BSDS300Dataset tight_layout shape DataLoader hist type enumerate GasDataset where test HEPMASSDataset MiniBooNEDataset int set_yticks set_xticks hist2d GaussianGridDataset data show print reshape shape hist PowerDataset type AEM ResidualEnergyNet ResidualMADE sample_from_proposal arange min tile float max uint8 list format backward model sort print ResidualMADE astype append float range isinstance print t modules ResidualMADE print ResidualMADE made randn check_masks check_connectivity check_conditional real strftime get_log_root list randn astype float32 choice eye array range rand cos astype pi float32 sqrt vstack sin rand astype float32 floor randint join list reshape rgb2gray astype float32 choice get_image_root resize imread sum array range len reshape transpose repeat parameters arange tile join remove print rmtree eval input get_data_root download_and_extract print exit eval input get_data_root join format load_power save get_data_root join get_data_root join format save load_gas get_data_root join get_data_root load_hepmass join format save get_data_root join get_data_root join format load_miniboone save get_data_root join File add_argument ArgumentParser seed AEM format batch_size print UCI close set_random_seed n_importance_samples sqrt Saver model_name split dataset placeholder_with_default open set_random_seed log_prob_est_data Saver dataset seed list merge_all cosine_decay cast learning_rate_start format FileWriter max_steps Datasets2D AEM items T constant minimize Variable proposal_log_prob_data float32 AdamOptimizer reduce_mean model_name array scalar makedirs batch_size tuple UCI use_subset_val train_AEM placeholder_with_default tanh leaky_relu relu identity elu permutation constant from_tensor_slices load_UCI_np float32 shuffle make_one_shot_iterator get_next repeat batch permutation constant from_tensor_slices float32 gen_2D_data shuffle make_one_shot_iterator get_next repeat batch load join download_preprocess_data join load join join join join File join list format items preprocess_fn save makedirs rename makedirs download_data print preprocess_UCI_data dense dropout concat activation range activation range masked_residual_block masked_dense min max arange repeat glorot_normal_initializer T value masked_dense dropout activation | # Autoregressive Energy Machines This repo contains Tensorflow and Pytorch implementations of the Autoregressive Energy Machine, outlined in the paper: > C. Nash, C. Durkan, _Autoregressive Energy Machines_. 2019. [[arXiv]](https://arxiv.org/abs/1904.05626) <p align="center"> <img width="210" height="210" src="./img/einstein-data.png"> <img width="210" height="210" src="./img/einstein-aem.png"> <img width="210" height="210" src="./img/einstein-aem-samples.png"> </p> | 1,768 |
conradry/max-deeplab | ['panoptic segmentation'] | ['MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers'] | max_deeplab/blocks.py util/misc.py pretrain_imagenet.py util/visualize.py max_deeplab/backbone.py max_deeplab/model.py datasets/coco_panoptic.py datasets/panoptic_eval.py max_deeplab/losses.py validate AverageMeter accuracy save_checkpoint ProgressMeter adjust_learning_rate main_worker main train make_coco_transforms CocoPanoptic build PanopticEvaluator MaXDeepLabSBackbone MaXDeepLabSImageNet conv_bn_relu InceptionStem MaskHead AxialMultiHeadAttention linear_bn_relu DecoderBottleneck AxialBottleneck DualPathXF cdice_similarity dice_score InstanceDiscLoss MaskIDLoss SemanticSegmentationLoss PQLoss HungarianMatcher MaXDeepLabLoss MaXDeepLabS MaXDeepLabSEncoder MaXDeepLabSDecoder is_dist_avail_and_initialized setup_for_distributed get_sha SmoothedValue MetricLogger init_distributed_mode _max_by_axis nested_tensor_from_tensor_list reduce_dict get_world_size accuracy save_on_master collate_fn interpolate all_gather get_rank NestedTensor is_main_process roll_image random_colors display_instances apply_mask seed int world_size spawn multiprocessing_distributed warn device_count manual_seed main_worker parse_args gpu workers data validate batch_size multiprocessing_distributed SGD DataParallel DistributedDataParallel ImageFolder DataLoader adjust_learning_rate save_checkpoint features cuda max MaXDeepLabSImageNet set_device DistributedSampler rank load_state_dict to range format init_process_group Compose start_epoch distributed lr resume Normalize load int join evaluate print set_epoch parameters isfile train epochs gpu model zero_grad cuda display update size item is_available enumerate time criterion backward AverageMeter accuracy ProgressMeter step gpu len len eval AverageMeter ProgressMeter copyfile save param_groups lr Compose Sequential join CocoPanoptic sum unsqueeze tuple sum range ndimension from_buffer dumps get_world_size loads zip append tensor to empty max cat get_world_size check_output _run dirname abspath keys nested_tensor_from_tensor_list zip len max enumerate dtype _max_by_axis copy_ device zeros enumerate print is_main_process save int setup_for_distributed format init_process_group print set_device dist_url barrier device_count rank gpu topk size t eq mul_ expand_as append sum max _output_size list rollaxis list shuffle range where show uint8 subplots text axis astype where copy apply_mask imshow median enumerate len | # MaX-DeepLab Unofficial implementation of MaX-DeepLab for Instance Segmentation: https://arxiv.org/abs/2012.00759v1. <figure> <img height=300 src="./architecture.png"></img> </figure> ## Status Only the MaX-DeepLab-S architecture is putatively implemented. Primarily, this code is intended as a reference; I can't make any guarantees that it will reproduce the results of the paper. - [x] Axial Attention block - [x] Dual Path Transformer block - [x] MaX-DeepLab-S architecture | 1,769 |
constantinpape/cluster_tools | ['instance segmentation', 'graph partitioning', 'semantic segmentation'] | ['Leveraging Domain Knowledge to Improve Microscopy Image Segmentation with Lifted Multicuts'] | publications/leveraging_domain_knowledge/2_false_merge_detection/resolve_separately.py cluster_tools/lifted_multicut/solve_lifted_global.py cluster_tools/affinities/to_boundaries.py cluster_tools/lifted_features/sparse_lifted_neighborhood.py cluster_tools/postprocess/size_filter_blocks.py cluster_tools/write/__init__.py cluster_tools/connected_components/merge_assignments.py cluster_tools/masking/minfilter.py cluster_tools/mutex_watershed/two_pass_assignments.py example/skeletons.py publications/leveraging_domain_knowledge/5_lifted_solver/initial_multicut.py cluster_tools/ilastik/__init__.py cluster_tools/lifted_features/costs_from_node_labels.py cluster_tools/masking/__init__.py cluster_tools/postprocess/filter_blocks.py example/multicut.py cluster_tools/node_labels/__init__.py test/downscaling/test_downscaling.py publications/leveraging_domain_knowledge/4_nucleus_segmentation/4_evaluate.py test/utils/test_volume_utils.py publications/leveraging_domain_knowledge/3_instance_segmentation_of_small_organelles/1_prediction.py cluster_tools/inference/multiscale_inference_vis.py cluster_tools/masking/blocks_from_mask_workflow.py cluster_tools/copy_volume/__init__.py cluster_tools/costs/__init__.py cluster_tools/evaluation/__init__.py cluster_tools/meshes/__init__.py cluster_tools/postprocess/postprocess_workflow.py publications/leveraging_domain_knowledge/4_nucleus_segmentation/3_lmc.py cluster_tools/debugging/check_components.py cluster_tools/lifted_features/clear_lifted_edges_from_labels.py cluster_tools/masking/blocks_from_mask.py cluster_tools/features/image_filter.py cluster_tools/multicut/sub_solutions.py cluster_tools/connected_components/__init__.py cluster_tools/costs/costs_workflow.py cluster_tools/node_labels/node_label_workflow.py publications/leveraging_domain_knowledge/2_false_merge_detection/resolving/edges_to_problem.py test/watershed/test_watershed_without_mask.py cluster_tools/relabel/find_labeling.py cluster_tools/features/merge_edge_features.py cluster_tools/paintera/label_block_mapping.py cluster_tools/transformations/transformix_coordinate.py cluster_tools/write/write.py cluster_tools/lifted_multicut/__init__.py cluster_tools/__init__.py publications/leveraging_domain_knowledge/5_lifted_solver/set_up_problem.py publications/leveraging_domain_knowledge/3_instance_segmentation_of_small_organelles/2_extract_boundaries.py cluster_tools/skeletons/__init__.py cluster_tools/evaluation/evaluation_workflow.py cluster_tools/watershed/two_pass_watershed.py cluster_tools/copy_sources/__init__.py publications/leveraging_domain_knowledge/3_instance_segmentation_of_small_organelles/3_segment.py test/mutex_watershed/test_mws_with_mask.py cluster_tools/affinities/__init__.py cluster_tools/features/block_edge_features.py publications/leveraging_domain_knowledge/2_false_merge_detection/resolving/io.py cluster_tools/statistics/__init__.py publications/leveraging_domain_knowledge/5_lifted_solver/eval_solvers.py cluster_tools/features/__init__.py cluster_tools/relabel/merge_uniques.py cluster_tools/stitching/stitching_multicut.py cluster_tools/inference/frameworks.py cluster_tools/relabel/find_uniques.py cluster_tools/multicut/reduce_problem.py cluster_tools/postprocess/background_size_filter.py setup.py cluster_tools/ilastik/prediction.py cluster_tools/relabel/relabel_workflow.py cluster_tools/features/merge_region_features.py cluster_tools/evaluation/object_iou.py cluster_tools/statistics/block_statistics.py cluster_tools/inference/inference.py cluster_tools/transformations/__init__.py cluster_tools/downscaling/scale_to_boundaries.py cluster_tools/threshold/threshold.py cluster_tools/inference/inference_embl.py cluster_tools/costs/probs_to_costs.py cluster_tools/multicut/multicut_workflow.py test/relabel/test_relabel.py example/downscale.py test/label_multisets/test_label_multisets.py test/retry/test_retry.py cluster_tools/multicut/__init__.py cluster_tools/graph/map_edge_ids.py cluster_tools/learning/__init__.py cluster_tools/watershed/watershed_workflow.py publications/leveraging_domain_knowledge/2_false_merge_detection/3_skeletonize.py cluster_tools/debugging/__init__.py cluster_tools/postprocess/filling_size_filter.py test/mutex_watershed/test_mws.py test/watershed/test_watershed_with_mask.py cluster_tools/distances/__init__.py cluster_tools/downscaling/upscaling.py cluster_tools/ilastik/stack_predictions.py cluster_tools/stitching/stitch_faces.py cluster_tools/graph/merge_sub_graphs.py test/utils/test_function_utils.py cluster_tools/debugging/check_ws_workflow.py cluster_tools/copy_sources/copy_sources.py cluster_tools/inference/prep_model.py cluster_tools/transformations/transformix.py cluster_tools/utils/function_utils.py cluster_tools/relabel/__init__.py publications/leveraging_domain_knowledge/5_lifted_solver/run_solvers.py test/write/test_write.py publications/leveraging_domain_knowledge/3_instance_segmentation_of_small_organelles/4_validate.py cluster_tools/evaluation/object_vi.py cluster_tools/graph/initial_sub_graphs.py cluster_tools/morphology/__init__.py cluster_tools/label_multisets/label_multiset_workflow.py cluster_tools/node_labels/merge_node_labels.py cluster_tools/mutex_watershed/mws_blocks.py cluster_tools/mutex_watershed/mws_workflow.py publications/leveraging_domain_knowledge/2_false_merge_detection/resolving/resolving_workflow.py publications/leveraging_domain_knowledge/2_false_merge_detection/evaluate/__init__.py cluster_tools/learning/edge_labels.py cluster_tools/agglomerative_clustering/agglomerative_clustering.py cluster_tools/postprocess/id_filter.py cluster_tools/transformations/transformation_workflows.py test/skeletons/test_skeletons.py cluster_tools/stitching/__init__.py cluster_tools/evaluation/measures.py cluster_tools/watershed/watershed_from_seeds.py test/transformations/test_linear.py cluster_tools/connected_components/merge_faces.py test/statistics/test_statistics.py cluster_tools/skeletons/skeleton_workflow.py publications/leveraging_domain_knowledge/1_axon_dendrite_attribution/2_lifted_multicut_simple.py test/graph/test_graph.py cluster_tools/label_multisets/create_multiset.py cluster_tools/watershed/agglomerate.py cluster_tools/costs/predict.py cluster_tools/postprocess/graph_watershed_assignments.py cluster_tools/graph/__init__.py publications/leveraging_domain_knowledge/2_false_merge_detection/2_multicut.py cluster_tools/debugging/check_sub_graphs.py cluster_tools/skeletons/skeleton_evaluation.py cluster_tools/debugging/check_sub_graphs_workflow.py cluster_tools/copy_volume/copy_volume.py cluster_tools/distances/distance_workflow.py cluster_tools/graph/graph_workflow.py cluster_tools/learning/learning_workflow.py cluster_tools/postprocess/__init__.py test/morphology/test_morphology.py example/postprocessing.py cluster_tools/mutex_watershed/two_pass_mws.py cluster_tools/affinities/embedding_distances.py publications/leveraging_domain_knowledge/2_false_merge_detection/5_roc.py cluster_tools/ilastik/ilastik_workflow.py cluster_tools/workflows.py cluster_tools/downscaling/downscaling_workflow.py cluster_tools/multicut/solve_global.py publications/leveraging_domain_knowledge/2_false_merge_detection/resolving/problem_workflow.py test/connected_components/connected_components.py publications/leveraging_domain_knowledge/2_false_merge_detection/make_roc_fig.py cluster_tools/morphology/region_centers.py cluster_tools/watershed/watershed.py test/postprocess/test_postprocess.py test/retry/failing_task.py cluster_tools/transformations/affine.py cluster_tools/downscaling/__init__.py cluster_tools/stitching/simple_stitch_edges.py cluster_tools/distances/object_distances.py cluster_tools/morphology/merge_morphology.py test/evaluation/test_evaluation.py cluster_tools/lifted_multicut/solve_lifted_subproblems.py cluster_tools/connected_components/connected_components_workflow.py cluster_tools/paintera/conversion_workflow.py cluster_tools/affinities/insert_affinities_workflow.py cluster_tools/downscaling/downscaling.py test/lifted_features/lifted_features.py cluster_tools/lifted_features/__init__.py cluster_tools/lifted_features/merge_lifted_problems.py cluster_tools/utils/volume_utils.py cluster_tools/utils/parse_utils.py publications/leveraging_domain_knowledge/2_false_merge_detection/resolving/__init__.py test/workflows/lifted_multicut_workflow.py cluster_tools/skeletons/upsample_skeletons.py cluster_tools/features/features_workflow.py cluster_tools/utils/task_utils.py publications/leveraging_domain_knowledge/2_false_merge_detection/resolving/resolve_individual_objects.py test/features/test_edge_features.py cluster_tools/watershed/slice_agglomeration.py publications/leveraging_domain_knowledge/1_axon_dendrite_attribution/4_view_results.py test/node_labels/test_node_labels.py publications/leveraging_domain_knowledge/4_nucleus_segmentation/1_watershed.py cluster_tools/stitching/stitching_workflows.py cluster_tools/ilastik/carving.py publications/leveraging_domain_knowledge/4_nucleus_segmentation/2_mc.py publications/leveraging_domain_knowledge/1_axon_dendrite_attribution/1_multicut_simple.py cluster_tools/inference/__init__.py cluster_tools/stitching/simple_stitch_assignments.py test/features/test_region_features.py cluster_tools/lifted_features/lifted_feature_workflow.py cluster_tools/ilastik/merge_predictions.py publications/leveraging_domain_knowledge/2_false_merge_detection/resolve_jointly.py publications/leveraging_domain_knowledge/2_false_merge_detection/resolving/edges_from_skeletons.py test/base.py test/lifted_features/sparse_lifted_neighborhood.py publications/leveraging_domain_knowledge/2_false_merge_detection/evaluate/evaluate_fib.py cluster_tools/statistics/statistics_workflow.py publications/leveraging_domain_knowledge/2_false_merge_detection/4_oracle.py cluster_tools/skeletons/skeletonize.py cluster_tools/node_labels/block_node_labels.py cluster_tools/meshes/compute_meshes.py cluster_tools/lifted_multicut/reduce_lifted_problem.py cluster_tools/transformations/linear.py publications/leveraging_domain_knowledge/2_false_merge_detection/1_prediction.py cluster_tools/lifted_multicut/lifted_multicut_workflow.py cluster_tools/paintera/unique_block_labels.py example/ilastik/carving.py cluster_tools/affinities/gradients.py publications/leveraging_domain_knowledge/1_axon_dendrite_attribution/0_data.py cluster_tools/version.py cluster_tools/multicut/solve_subproblems.py cluster_tools/postprocess/orphan_assignments.py cluster_tools/label_multisets/__init__.py cluster_tools/paintera/assignments.py publications/leveraging_domain_knowledge/5_lifted_solver/extract_test_data.py cluster_tools/watershed/__init__.py cluster_tools/learning/learn_rf.py cluster_tools/paintera/__init__.py cluster_tools/statistics/merge_statistics.py cluster_tools/affinities/insert_affinities.py cluster_tools/connected_components/connected_component_blocks.py cluster_tools/features/region_features.py cluster_tools/morphology/block_morphology.py test/workflows/multicut_workflow.py cluster_tools/bigcat/__init__.py cluster_tools/threshold/__init__.py cluster_tools/label_multisets/downscale_multiset.py cluster_tools/cluster_tasks.py publications/leveraging_domain_knowledge/2_false_merge_detection/evaluate/copy_and_crop.py cluster_tools/mutex_watershed/__init__.py cluster_tools/meshes/mesh_workflow.py cluster_tools/postprocess/graph_connected_components.py cluster_tools/inference/multiscale_inference.py cluster_tools/morphology/morphology_workflow.py cluster_tools/bigcat/bigcat_workflow.py SlurmTask LocalTask LSFTask BaseClusterTask FailedJobsError WorkflowBase AgglomerativeClusteringWorkflow MulticutSegmentationWorkflow SimpleStitchingWorkflow LiftedMulticutSegmentationWorkflow SegmentationWorkflowBase MulticutStitchingWorkflow ProblemWorkflow SubLiftedSolutionsWorkflow ReducedLiftedSolutionWorkflow LiftedMulticutWorkflowBase LiftedMulticutWorkflow ReduceLiftedProblemLSF ReduceLiftedProblemLocal _get_new_edges ReduceLiftedProblemSlurm _serialize_new_problem _load_cut_edges _merge_nodes ReduceLiftedProblemBase reduce_lifted_problem SolveLiftedGlobalLocal SolveLiftedGlobalSlurm solve_lifted_global SolveLiftedGlobalLSF SolveLiftedGlobalBase _solve_block_problem solve_lifted_subproblems SolveLiftedSubproblemsBase SolveLiftedSubproblemsSlurm SolveLiftedSubproblemsLSF _find_lifted_edges SolveLiftedSubproblemsLocal embedding_distances EmbeddingDistancesBase EmbeddingDistancesSlurm EmbeddingDistancesLocal EmbeddingDistancesLSF _embedding_distances_block _compute_all_gradients gradients GradientsSlurm GradientsBase GradientsLSF _compute_average_gradients GradientsLocal _gradients_block InsertAffinitiesSlurm InsertAffinitiesBase _insert_affinities insert_affinities InsertAffinitiesLocal _insert_affinities_block cast dilate InsertAffinitiesLSF InsertAffinitiesWorkflow ToBoundariesBase ToBoundariesSlurm to_boundaries _to_boundaries_block ToBoundariesLocal ToBoundariesLSF AgglomerativeClusteringLSF AgglomerativeClusteringBase AgglomerativeClusteringSlurm AgglomerativeClusteringLocal agglomerative_clustering BigcatMetadata BigcatWorkflow BigcatLabelAssignment ConnectedComponentsAndWatershedWorkflow ConnectedComponentsWorkflow ConnectedComponentBlocksBase connected_components_block ConnectedComponentBlocksSlurm ConnectedComponentBlocksLocal ConnectedComponentBlocksLSF _load_input _cc_block _cc_block_with_mask _threshold_impl MergeAssignmentsLSF MergeAssignmentsLocal MergeAssignmentsSlurm merge_assignments MergeAssignmentsBase MergeFacesSlurm merge_faces MergeFacesLocal MergeFacesLSF MergeFacesBase _process_face _process_faces write_source CopySourcesLSF CopySourcesBase CopySourcesLocal CopySourcesSlurm copy_source load_source copy_sources get_copy_task CopyVolumeBase cast_type CopyVolumeLocal copy_volume CopyVolumeLSF _copy_blocks CopyVolumeSlurm EdgeCostsWorkflow PredictLSF PredictSlurm PredictBase predict PredictLocal ProbsToCostsSlurm ProbsToCostsLSF ProbsToCostsLocal ProbsToCostsBase _apply_node_labels probs_to_costs _check_components_impl check_components CheckComponentsLSF CheckComponentsLocal CheckComponentsBase CheckComponentsSlurm check_sub_graphs CheckSubGraphsLSF check_block CheckSubGraphsLocal CheckSubGraphsSlurm CheckSubGraphsBase CheckSubGraphsWorkflow CheckWsWorkflow MergePairwiseDistances PairwiseDistanceWorkflow _object_distances _distances_id_chunks _get_faces _enlarge_bb ObjectDistancesLSF ObjectDistancesSlurm ObjectDistancesLocal ObjectDistancesBase object_distances _labels_and_distances _compute_face_distances _submit_blocks DownscalingBase _ds_vigra _ds_vol DownscalingLSF DownscalingLocal _ds_skimage downscaling DownscalingSlurm _ds_block WriteDownscalingMetadata PainteraToBdvWorkflow DownscalingWorkflow ScaleToBoundariesSlurm _scale_block scale_to_boundaries ScaleToBoundariesLocal compute_halo ScaleToBoundariesBase ScaleToBoundariesLSF UpscalingLSF _submit_blocks upscaling UpscalingSlurm UpscalingBase UpscalingLocal _upsample_block MeanAPWorkflow EvaluationWorkflow MeanAPTask ObjectIouWorkflow ObjectViWorkflow load_overlaps measures MeasuresSlurm overlaps_to_sizes contigency_table_from_overlaps MeasuresBase MeasuresLSF MeasuresLocal ObjectIouSlurm ObjectIouLSF compute_ious object_iou ObjectIouLocal ObjectIouBase ObjectViLSF ObjectViLocal ObjectViSlurm ObjectViBase object_vi _accumulate_filter _accumulate_with_filters block_edge_features BlockEdgeFeaturesLocal BlockEdgeFeaturesBase _accumulate_block _accumulate BlockEdgeFeaturesSlurm BlockEdgeFeaturesLSF RegionFeaturesWorkflow EdgeFeaturesWorkflow image_filter ImageFilterBase ImageFilterLSF _apply_filter ImageFilterSlurm ImageFilterLocal MergeEdgeFeaturesSlurm MergeEdgeFeaturesLocal MergeEdgeFeaturesLSF MergeEdgeFeaturesBase merge_edge_features MergeRegionFeaturesSlurm MergeRegionFeaturesLSF MergeRegionFeaturesLocal MergeRegionFeaturesBase merge_region_features _extract_and_merge_region_features merge_feats region_features relabel_sequential RegionFeaturesLSF RegionFeaturesLocal _block_features RegionFeaturesSlurm RegionFeaturesBase GraphWorkflow _graph_block InitialSubGraphsLocal InitialSubGraphsBase initial_sub_graphs InitialSubGraphsSlurm InitialSubGraphsLSF MapEdgeIdsSlurm MapEdgeIdsLocal MapEdgeIdsLSF MapEdgeIdsBase map_edge_ids merge_sub_graphs _merge_graph _merge_subblocks MergeSubGraphsLSF MergeSubGraphsBase MergeSubGraphsSlurm MergeSubGraphsLocal WriteCarving IlastikPredictionWorkflow IlastikCarvingWorkflow MergePredictionsLSF MergePredictionsLocal MergePredictionsSlurm merge_predictions MergePredictionsBase _merge_block _predict_block PredictionLocal PredictionSlurm prediction PredictionBase _to_dtype _predict_and_serialize_block PredictionLSF _predict_block_impl stack_block StackPredictionsLocal StackPredictionsLSF stack_predictions StackPredictionsBase StackPredictionsSlurm cast InfernoPredicter PredicterBase normalize01 preprocess_torch get_predictor get_preprocessor PytorchPredicter cast TensorflowPredicter normalize preprocess_tf BioimageioPredicter _to_uint8 InferenceBase InferenceLocal InferenceSlurm _load_input inference InferenceLSF _run_inference InferenceEmbl MultiscaleInferenceLSF MultiscaleInferenceBase _load_inputs MultiscaleInferenceSlurm _load_input _show_inputs MultiscaleInferenceLocal align_out_bb multiscale_inference _center_align_offset _run_inference view_multiscale_inputs get_prep_model extract_unet add_sigmoid CreateMultisetLocal CreateMultisetBase CreateMultisetSlurm create_multiset _create_multiset_block CreateMultisetLSF write_metadata DownscaleMultisetSlurm DownscaleMultisetLSF background_multiset normalize_chunks _downscale_multiset_block write_metadata downscale_multiset DownscaleMultisetBase DownscaleMultisetLocal LabelMultisetWorkflow EdgeLabelsLSF edge_labels EdgeLabelsLocal EdgeLabelsBase EdgeLabelsSlurm LearningWorkflow LearnRFLocal LearnRFBase learn_rf LearnRFLSF LearnRFSlurm ClearLiftedEdgesFromLabelsLocal clear_lifted_edges_from_labels ClearLiftedEdgesFromLabelsSlurm ClearLiftedEdgesFromLabelsLSF ClearLiftedEdgesFromLabelsBase CostsFromNodeLabelsLSF costs_from_node_labels CostsFromNodeLabelsSlurm CostsFromNodeLabelsLocal _costs_for_edge_block CostsFromNodeLabelsBase NodeLabelsWithThreshold LiftedFeaturesFromNodeLabelsWorkflow MergeLiftedProblemsSlurm merge_lifted_problems MergeLiftedProblemsBase MergeLiftedProblemsLocal MergeLiftedProblemsLSF sparse_lifted_neighborhood SparseLiftedNeighborhoodSlurm SparseLiftedNeighborhoodLocal SparseLiftedNeighborhoodBase SparseLiftedNeighborhoodLSF BlocksFromMaskLocal BlocksFromMaskLSF blocks_from_mask BlocksFromMaskSlurm BlocksFromMaskBase _get_blocks_in_mask BlocksFromMaskWorkflow _minfilter_block MinfilterBase MinfilterLSF MinfilterLocal MinfilterSlurm minfilter compute_meshes ComputeMeshesLocal _compute_meshes_id_block ComputeMeshesBase ComputeMeshesLSF ComputeMeshesSlurm MeshWorkflow BlockMorphologyLSF block_morphology _morphology_for_block BlockMorphologySlurm BlockMorphologyBase BlockMorphologyLocal MergeMorphologyBase MergeMorphologySlurm MergeMorphologyLSF merge_morphology MergeMorphologyLocal RegionCentersWorkflow MorphologyWorkflow region_centers region_centers_for_label_range RegionCentersBase RegionCentersLSF RegionCentersLocal RegionCentersSlurm MulticutWorkflow SubSolutionsWorkflow MulticutWorkflowBase ReducedSolutionWorkflow reduce_problem ReduceProblemLSF ReduceProblemLocal ReduceProblemSlurm ReduceProblemBase _get_new_edges _serialize_new_problem _load_cut_edges _merge_nodes SolveGlobalLocal SolveGlobalLSF SolveGlobalBase SolveGlobalSlurm solve_global SolveSubproblemsLocal SolveSubproblemsLSF _solve_block_problem SolveSubproblemsBase solve_subproblems SolveSubproblemsSlurm SubSolutionsLocal SubSolutionsSlurm SubSolutionsLSF _write_block_res sub_solutions _read_subresults SubSolutionsBase MwsBlocksLSF MwsBlocksSlurm MwsBlocksLocal mws_blocks _mws_block MwsBlocksBase _get_bbs MwsWorkflow TwoPassMwsWorkflow TwoPassAssignmentsLocal TwoPassAssignmentsBase two_pass_assignments TwoPassAssignmentsLSF TwoPassAssignmentsSlurm TwoPassMwsLSF two_pass_mws _mws_block_pass1 _mws_block_pass2 TwoPassMwsBase TwoPassMwsLocal _write_nlabels TwoPassMwsSlurm BlockNodeLabelsSlurm BlockNodeLabelsBase block_node_labels BlockNodeLabelsLSF BlockNodeLabelsLocal _labels_for_block MergeNodeLabelsSlurm MergeNodeLabelsBase MergeNodeLabelsLocal merge_node_labels MergeNodeLabelsLSF NodeLabelWorkflow WritePainteraMetadata ConversionWorkflow LabelBlockMappingLocal LabelBlockMappingBase LabelBlockMappingLSF label_block_mapping LabelBlockMappingSlurm UniqueBlockLabelsLocal UniqueBlockLabelsLSF unique_block_labels _uniques UniqueBlockLabelsBase UniqueBlockLabelsSlurm BackgroundSizeFilterBase apply_block background_size_filter BackgroundSizeFilterLocal BackgroundSizeFilterLSF BackgroundSizeFilterSlurm filling_size_filter FillingSizeFilterSlurm FillingSizeFilterLocal FillingSizeFilterLSF apply_block FillingSizeFilterBase filter_blocks _filter_block_inplace _filter_block FilterBlocksSlurm FilterBlocksLSF FilterBlocksBase FilterBlocksLocal GraphConnectedComponentsBase graph_connected_components GraphConnectedComponentsLSF GraphConnectedComponentsSlurm GraphConnectedComponentsLocal GraphWatershedAssignmentsLSF graph_watershed_assignments GraphWatershedAssignmentsLocal GraphWatershedAssignmentsBase GraphWatershedAssignmentsSlurm IdFilterLocal IdFilterLSF id_filter IdFilterBase IdFilterSlurm OrphanAssignmentsLSF OrphanAssignmentsLocal orphan_assignments OrphanAssignmentsBase OrphanAssignmentsSlurm FilterOrphansWorkflow SizeFilterAndGraphWatershedWorkflow FilterByThresholdWorkflow ConnectedComponentsWorkflow SizeFilterWorkflow ApplyThreshold FilterLabelsWorkflow SizeFilterBlocksLocal SizeFilterBlocksBase SizeFilterBlocksLSF size_filter_blocks SizeFilterBlocksSlurm FindLabelingSlurm FindLabelingBase find_labeling FindLabelingLSF FindLabelingLocal FindUniquesSlurm uniques_in_block find_uniques FindUniquesBase FindUniquesLocal FindUniquesLSF MergeUniquesBase MergeUniquesLocal merge_uniques MergeUniquesLSF MergeUniquesSlurm RelabelWorkflow UniqueWorkflow SkeletonizeLocal SkeletonizeSlurm SkeletonizeLSF skeletonize SkeletonizeBase _skeletonize_id_block SkeletonEvaluationLocal SkeletonEvaluationBase SkeletonEvaluationLSF SkeletonEvaluationSlurm skeleton_evaluation SkeletonWorkflow UpsampleSkeletonsBase upsample_skeletons UpsampleSkeletonsLSF _upsample_skeleton _upsample_block UpsampleSkeletonsSlurm UpsampleSkeletonsLocal BlockStatisticsLocal BlockStatisticsSlurm block_statistics BlockStatisticsLSF BlockStatisticsBase _compute_block_stats merge_stats MergeStatisticsSlurm MergeStatisticsLSF MergeStatisticsLocal merge_statistics MergeStatisticsBase DataStatisticsWorkflow SimpleStitchAssignmentsLSF SimpleStitchAssignmentsLocal simple_stitch_assignments SimpleStitchAssignmentsSlurm SimpleStitchAssignmentsBase SimpleStitchEdgesLSF SimpleStitchEdgesBase SimpleStitchEdgesLocal simple_stitch_edges SimpleStitchEdgesSlurm StitchingMulticutLocal StitchingMulticutBase stitching_multicut StitchingMulticutLSF StitchingMulticutSlurm StitchingAssignmentsWorkflow StitchFacesLSF _stitch_face _filter_ignore_label stitch_faces StitchFacesBase _stitch_faces StitchFacesSlurm StitchFacesLocal threshold _threshold_block ThresholdBase ThresholdSlurm ThresholdLSF ThresholdLocal AffineBase affine AffineLocal AffineLSF AffineSlurm _copy_blocks LinearBase _transform_data linear _transform_linear LinearLSF LinearLocal _transform_block LinearSlurm _load_transformation AffineTransformationWorkflow TransformixTransformationWorkflow LinearTransformationWorkflow TransformixCoordinateTransformationWorkflow TransformixLSF transformix apply_for_file TransformixLocal TransformixBase TransformixSlurm _write_coords TransformixCoordinateBase TransformixCoordinateLocal transformix_coordinate process_block TransformixCoordinateLSF TransformixCoordinateSlurm tail log_job_success log_block_success log parse_runtime_task parse_runtime_segmentation_workflow parse_blocks_task parse_job parse_job_lsf parse_runtime parse_blocks DummyTask DummyTarget _make_checkerboard _bdv_metadata force_dataset get_face file_reader get_shape get_format_key preserving_erosion load_mask fit_to_hmap iterate_faces blocks_in_volume fit_to_hmap_3d _make_checkerboard_with_roi normalize make_checkerboard_block_lists fit_to_hmap_2d _ome_zarr_metadata write_format_metadata _paintera_metadata create_ngff_metadata mask_corners block_to_bb _copy_max_id fit_seeds get_formats apply_filter faces_to_ovlp_axis _bdv_ome_zarr_metadata AgglomerateLSF AgglomerateSlurm AgglomerateBase AgglomerateLocal agglomerate _agglomerate_block _slice_agglomeration SliceAgglomerationLSF slice_agglomeration agglomerate_slice SliceAgglomerationLocal SliceAgglomerationBase SliceAgglomerationSlurm TwoPassWatershedLocal TwoPassWatershedLSF _apply_watershed_with_seeds _ws_pass2 two_pass_watershed TwoPassWatershedSlurm TwoPassWatershedBase WatershedLSF _apply_dt _read_data WatershedLocal _make_seeds _apply_watershed _points_to_vol _ws_block _make_hmap watershed WatershedSlurm WatershedBase _get_bbs _ws_block_masked _read_data WatershedFromSeedsLocal WatershedFromSeedsSlurm WatershedFromSeedsLSF _ws_block watershed_from_seeds WatershedFromSeedsBase WatershedWorkflow WriteSlurm _write _write_with_offsets WriteLocal _write_maxlabel write WriteLSF _apply_node_labels _load_assignments _write_block_with_offsets WriteBase _write_block downscale_raw run_mc graph_watershed_size_filter filter_orphans check_results view_skeletons skeletons make_input carving_wf copy_input run_multicut make_lifted_problem solve_lifted_problem region_feats write_result compute_lifted_nh map_to_lifted_costs run_lifted_multicut view_result predict filter_size mc_fib25 check_scale skeletonize skeletons_to_volume oracle make_lifted_problem get_max_costs precompute_skeleton_problems copy_and_crop_seg parse_runtimes lifted_problem roc roc_point solve_separately solve_jointly mixed_plot plot_fixed_recall load_results make_fixed_plots plot_fixed_precision resolve_jointly resolve_separately CopyAndCropLSF copy_and_crop CopyAndCropLocal CopyAndCropBase CopyAndCropSlurm evaluate_fib edges_from_skeletons to_coords skeleton_to_edges edges_to_problem combine_edges_and_costs oracle write_edge_result read_edge_result LiftedEdgesBase LiftedEdgesLSF full_lifted_problem LiftedEdgesLocal lifted_edges get_max_costs LiftedEdgesSlurm _solve_objects ResolveIndividualObjectsSlurm ResolveIndividualObjectsLSF ResolveIndividualObjectsLocal ResolveIndividualObjectsBase resolve_inidividual_objects ResolvingWorkflow run_pred extract_boundaries run_mc run_lmc workflow validate_block evaluate_seg view_block get_bb validate_all get_scores _points_to_vol run_ws dt_watershed run_mc run_lmc evaluate_seg view_gt_crop load_seg_and_gt compute_time eval_solver compute_energy eval_hierarchical extract_middle_cutout initial_mc prepare_data run_lmc probs_to_costs set_up_problem prepare_test BaseTest TestConnectedComponents TestDownscaling TestEvaluation TestEdgeFeatures TestRegionFeatures TestGraph TestLabelMultisets TestLiftedFeatureWorkflow TestNHWorkflow TestMorphology TestMws TestMwsWithMask TestNodeLabels TestPostprocess TestRelabel FailingTaskBase failing_task FailingTaskLocal _failing_block TestRetry TestSkeletons TestNodeLabels TestLinear TestFunctionUtils TestVolumeUtils TestWatershedWithoutMask TestWatershedWithMask TestLiftedMulticutWorkflow TestMulticutWorkflow TestWrite Parameter IntParameter cpu_count Parameter TaskParameter IntParameter Parameter DictParameter IntParameter BoolParameter Parameter DictParameter IntParameter BoolParameter IntParameter Parameter IntParameter FloatParameter IntParameter Parameter Parameter IntParameter Parameter Parameter ListParameter Parameter Parameter TaskParameter IntParameter abspath boost_ufd int ones merge inflateLabeling find relabelConsecutive _load_cut_edges max log len edgeMapping newUvIds mapEdgeValues EdgeMapping serializeMergedGraph require_group blocks_in_volume File _serialize len get log_job_success _get_new_edges _serialize_new_problem blocking _merge_nodes log Parameter TaskParameter IntParameter abspath get undirectedGraph int solver log_job_success len get_lifted_multicut_solver relabelConsecutive max log insertEdges Parameter TaskParameter IntParameter abspath arange in1d len undirectedGraph read_chunk lifted_solver relabelConsecutive solver concatenate tuple getBlock log blockGridPosition write_chunk takeDict _find_lifted_edges log_block_success extractSubgraphFromNodes insertEdges get list Graph log_job_success get_lifted_multicut_solver get_multicut_solver blocking N5File log uvIds len Parameter FloatParameter TaskParameter abspath ListParameter getBlockWithHalo log_block_success outerBlock block_to_bb innerBlockLocal tuple tolist innerBlock compute_embedding_distances zeros abs max log enumerate len append log_job_success sorted log Parameter TaskParameter BoolParameter abspath gradient mean zeros array enumerate gradient mean zeros array enumerate getBlockWithHalo log_block_success outerBlock block_to_bb _compute_all_gradients tuple innerBlock _compute_average_gradients innerBlockLocal log len append log_job_success sorted log Parameter ListParameter abspath TaskParameter astype range zeros_like dtype clip compute_affinities cast dilate normalize range getBlockWithHalo sum log_block_success outerBlock block_to_bb fit_to_hmap _insert_affinities size tolist astype innerBlock copy unique innerBlockLocal log binary_erosion get str log_job_success log Parameter ListParameter Parameter TaskParameter IntParameter abspath dtype log_block_success block_to_bb getBlock astype log log_job_success getattr log Parameter FloatParameter TaskParameter abspath mala_clustering int undirectedGraph log_job_success len max log insertEdges Parameter TaskParameter Parameter ListParameter TaskParameter Parameter ListParameter Parameter FloatParameter Parameter FloatParameter IntParameter Parameter FloatParameter TaskParameter abspath tuple squeeze mean zeros enumerate normalize apply_filter int join log_block_success block_to_bb getBlock _load_input blockShape prod unique save label _threshold_impl log int join block_to_bb getBlock astype log _load_input blockShape prod unique save label _threshold_impl log_block_success get log_job_success log Parameter ListParameter abspath TaskParameter boost_ufd concatenate log_job_success astype copy merge unique find relabelConsecutive log len Parameter TaskParameter abspath squeeze logical_and unique concatenate log_block_success log unique concatenate join concatenate log_job_success save unique log Parameter DictParameter TaskParameter abspath ListParameter imread get_downsampler update write_source load_source write_format_metadata copy_source log_job_success log Parameter TaskParameter abspath ListParameter BoolParameter normalize dtype get list log_job_success getattr log Parameter DictParameter Parameter TaskParameter abspath log_job_success astype blocking log diff Parameter TaskParameter DictParameter abspath int reshape shape any isn sum max log len get items list log_job_success min transform_probabilities_to_costs _apply_node_labels max log Parameter TaskParameter abspath ListParameter IntParameter ChainMap list concatenate keys dict array values get _check_components_impl log_job_success prod log Parameter TaskParameter abspath read_chunk block_to_bb getBlock chunks unique allclose join log_job_success log Parameter Parameter Parameter TaskParameter IntParameter Parameter FloatParameter ListParameter IntParameter Parameter FloatParameter abspath ListParameter IntParameter astype distanceTransform append min _get_faces int slice min append max enumerate tuple _enlarge_bb min shape unique _labels_and_distances _compute_face_distances update _object_distances getBlock range max log get log_job_success log Parameter ListParameter abspath TaskParameter max round sampler clip getBlockWithHalo dtype log_block_success outerBlock block_to_bb isinstance tuple _ds_vol getBlock innerBlock ndim astype range shape zeros innerBlockLocal log zeros range resize get partial ndim shape getattr blocking _ds_block get log_job_success list log Parameter DictParameter TaskParameter ListParameter IntParameter Parameter DictParameter get_formats BoolParameter ListParameter IntParameter Parameter DictParameter BoolParameter Parameter TaskParameter IntParameter abspath max isinstance values getBlockWithHalo log_block_success outerBlock block_to_bb fit_to_hmap squeeze compute_halo innerBlock innerBlockLocal log get log_job_success list log Parameter ListParameter abspath TaskParameter dtype clip log_block_success isinstance block_to_bb tuple getBlock astype sampler shape round zeros range max log _upsample_block resize get log_job_success list log Parameter BoolParameter Parameter BoolParameter Parameter Parameter TaskParameter Parameter ListParameter Parameter TaskParameter abspath argsort tolist array unique list overlaps_to_sizes dict unique zip sum array update get compute_vi_scores load_overlaps file_reader log_job_success number_of_chunks compute_rand_scores contigency_table_from_overlaps log Parameter TaskParameter abspath update list min astype linear_sum_assignment tolist set dict shape intersection_over_union zip zeros range get load_overlaps file_reader log_job_success compute_ious number_of_chunks contigency_table_from_overlaps log Parameter TaskParameter abspath get load_overlaps compute_object_vi_scores file_reader log_job_success number_of_chunks contigency_table_from_overlaps log Parameter TaskParameter abspath affinity_function str log boundary_function read_chunk tuple getBlock flatten log str shape write_chunk normalize log_block_success outerBlock concatenate Graph slice innerBlock blockGridPosition innerBlockLocal getBlockWithHalo block_to_bb reshape ndim log get _accumulate_with_filters log_job_success _accumulate log Parameter IntParameter Parameter IntParameter Parameter ListParameter abspath getBlockWithHalo log_block_success outerBlock block_to_bb innerBlock apply_filter normalize innerBlockLocal log get log_job_success log Parameter TaskParameter abspath log_job_success mergeFeatureBlocks blocking log diff Parameter TaskParameter IntParameter abspath read_chunk tuple getBlock astype chunks logical_and merge_feats zeros numberOfBlocks range log enumerate len log_job_success log Parameter TaskParameter IntParameter abspath relabel_sequential block_to_bb zeros tuple getBlock astype log blockShape write_chunk unique extractRegionFeatures normalize log_block_success enumerate len log_job_success log Parameter IntParameter Parameter TaskParameter abspath begin log_block_success end computeMergeableRegionGraph getBlock log get get_shape _graph_block log_job_success blocking log Parameter TaskParameter IntParameter abspath mapEdgeIds log_job_success log Parameter BoolParameter TaskParameter abspath IntParameter mergeSubgraphs mergeSubgraphs getBlockIdsInBoundingBox getBlock log log_block_success _merge_graph log_job_success _merge_subblocks blocking log Parameter TaskParameter BoolParameter Parameter ListParameter IntParameter Parameter BoolParameter Parameter TaskParameter abspath ListParameter IntParameter getBlockWithHalo remove log_block_success block_to_bb transpose innerBlock innerBlockLocal log get_shape log_job_success log blocking Parameter ListParameter IntParameter abspath join log check_call _predict_block_impl log_block_success outerBlock log dtype getBlockWithHalo dtype log_block_success outerBlock block_to_bb innerBlock _to_dtype _predict_block_impl innerBlockLocal log get str get_shape join _predict_block log_job_success _predict_and_serialize_block blocking log Parameter abspath astype log_block_success block_to_bb concatenate getBlock cast log log_job_success log min max cast ndarray normalizer isinstance Parameter DictParameter TaskParameter abspath ListParameter shape pad any ceil floor compute pipe blockShape append sum log get str get_shape log_job_success get_preprocessor getattr get_prep_model blocking log Parameter DictParameter TaskParameter abspath ListParameter BoolParameter _center_align_offset shape zip view tuple astype shape resize append to_source enumerate zip slice tuple warn start any stop append print _show_inputs get str log_job_success get_preprocessor getattr get_prep_model log compute open_file arange pipe print blocks_in_volume shuffle _show_inputs shape append blocking array numberOfBlocks Sigmoid Sequential getattr Parameter TaskParameter abspath block_to_bb tuple getBlock log write_chunk serialize_multiset log_block_success create_multiset_from_labels attrs get_shape list log_job_success blocking log Parameter TaskParameter abspath ListParameter IntParameter zeros list prod array sum argmin array blocksPerAxis list log_block_success ravel_multi_index all tuple getBlock ndim downsample_multiset blockShape normalize_chunks write_chunk chunks_overlapping_roi serialize_multiset array log merge_multisets get_shape list int log_job_success blocking prod log Parameter ListParameter Parameter TaskParameter abspath get log_job_success astype logical_or log len Parameter DictParameter Parameter DictParameter abspath TaskParameter get items list concatenate RandomForestClassifier tuple log_job_success shape append sum log fit Parameter TaskParameter abspath get file_reader log_job_success chunks create_dataset log len Parameter TaskParameter abspath ones getBlock log log_block_success len log_job_success log Parameter FloatParameter TaskParameter Parameter FloatParameter IntParameter Parameter TaskParameter IntParameter abspath get concatenate log_job_success File append require_dataset log Parameter TaskParameter IntParameter abspath get log_job_success computeLiftedNeighborhoodFromNodeLabels log Parameter ListParameter abspath TaskParameter get list tuple log_job_success ResizedVolume blocking _get_blocks_in_mask log Parameter ListParameter Parameter abspath getBlockWithHalo log_block_success outerBlock block_to_bb innerBlock minimum_filter innerBlockLocal log log_job_success log Parameter TaskParameter abspath ListParameter IntParameter join marching_cubes log_block_success tuple getBlock write_obj range write_numpy log get blocking log_job_success log Parameter ListParameter IntParameter Parameter TaskParameter abspath begin block_to_bb tuple getBlock computeAndSerializeMorphology log log_block_success log_job_success blocking list log Parameter TaskParameter IntParameter abspath log_job_success getBlock mergeAndSerializeMorphology blocking log Parameter IntParameter Parameter ListParameter Parameter ListParameter abspath TaskParameter tuple distance_transform_edt shape unravel_index zeros argmax range int log_job_success astype blocking log Parameter IntParameter Parameter Parameter ListParameter Parameter Parameter TaskParameter IntParameter abspath get log_job_success _get_new_edges _serialize_new_problem blocking _merge_nodes log Parameter TaskParameter IntParameter abspath get undirectedGraph update int relabelConsecutive solver log_job_success len get_multicut_solver max log insertEdges Parameter TaskParameter IntParameter abspath get update list Graph log_job_success get_multicut_solver blocking N5File log uvIds Parameter TaskParameter abspath ListParameter IntParameter cumsum array log roll block_to_bb getBlock log log_block_success takeDict get join list log_job_success _read_subresults blocking N5File log Parameter ListParameter abspath TaskParameter getBlockWithHalo outerBlock block_to_bb getBlock innerBlock innerBlockLocal int astype log blockShape prod apply_size_filter mutex_watershed relabelConsecutive normalize max log_block_success _get_bbs get log_job_success log Parameter ListParameter BoolParameter Parameter ListParameter Parameter TaskParameter abspath log_job_success relabelConsecutive max exists log takeDict str list shape append blocking range astype zip merge load join ufd elementLabeling dict numberOfBlocks len Parameter ListParameter abspath TaskParameter int max getBlockWithHalo join log_block_success outerBlock block_to_bb compute_grid_graph astype innerBlock blockShape shape compute_state_for_segmentation mutex_watershed _write_nlabels relabelConsecutive normalize innerBlockLocal prod log logical_not flatten save relabelConsecutive log in1d _write_nlabels normalize getNeighborId prod log_block_success range outerBlock concatenate astype innerBlock get_seed_assignments_from_node_labels blockShape mutex_watershed_with_seeds unique innerBlockLocal getBlockWithHalo join block_to_bb get log_job_success log Parameter TaskParameter IntParameter abspath block_to_bb tuple getBlock astype log computeAndSerializeLabelOverlaps log_block_success list file_reader log_job_success close shape any ResizedVolume blocking log Parameter BoolParameter TaskParameter abspath IntParameter get list log_job_success getBlock mergeAndSerializeOverlaps blocking log Parameter BoolParameter IntParameter Parameter ListParameter IntParameter TaskParameter Parameter ListParameter IntParameter BoolParameter Parameter TaskParameter abspath ListParameter IntParameter get log_job_success log serializeBlockMapping Parameter ListParameter TaskParameter abspath read_chunk log_block_success tuple getBlock ids write_chunk unique log log_job_success log Parameter TaskParameter abspath tuple reshape getBlock log shape log_block_success load log_job_success blocking log len Parameter TaskParameter BoolParameter abspath dtype squeeze astype watershed load log_job_success set blocking range max log Parameter TaskParameter abspath block_to_bb reshape getBlock log shape log_block_success block_to_bb reshape getBlock log shape log_block_success list log_job_success blocking array log Parameter TaskParameter abspath get int undirectedGraph all log_job_success connectedComponentsFromNodeLabels relabelConsecutive max log insertEdges Parameter TaskParameter BoolParameter abspath get int load undirectedGraph log_job_success min edgeWeightedWatershedsSegmentation in1d sum max log insertEdges Parameter ListParameter abspath TaskParameter log_job_success tolist in1d array log len Parameter TaskParameter BoolParameter abspath get int undirectedGraph log_job_success astype EdgeMapping array unique insertEdges relabelConsecutive newUvIds max log len Parameter IntParameter BoolParameter Parameter ListParameter Parameter FloatParameter TaskParameter Parameter FloatParameter BoolParameter Parameter BoolParameter Parameter IntParameter BoolParameter Parameter TaskParameter IntParameter abspath int join concatenate log_job_success unique zip save zeros log Parameter TaskParameter abspath arange concatenate log_job_success unique log len Parameter TaskParameter BoolParameter abspath block_to_bb tuple getBlock log unique log_block_success int join concatenate log_job_success unique save zeros log Parameter TaskParameter abspath log_job_success log unique len Parameter Parameter Parameter TaskParameter abspath ListParameter IntParameter get_method_names log_block_success tuple getBlock write_n5 shape skel_impl range log blocking log_job_success log Parameter TaskParameter abspath get join computeGoogleScore sort log_job_success SkeletonMetrics listdir log Parameter ListParameter IntParameter Parameter TaskParameter abspath zeros_like tuple imageToEdgeMap distance_transform_edt where shape max undirectedGridGraph getBlockWithHalo outerBlock zeros_like innerBlock _upsample_skeleton innerBlockLocal get tuple log_job_success blocking log Parameter abspath max sum array min var float getBlock block_to_bb join list log_job_success blocking log merge_stats Parameter ListParameter abspath TaskParameter join log_job_success log append range merge_stats Parameter Parameter BoolParameter TaskParameter abspath IntParameter int file_reader log_job_success squeeze ufd astype logical_and log elementLabeling logical_or max range merge Parameter TaskParameter abspath log_job_success log findBlockBoundaryEdges Parameter TaskParameter abspath get undirectedGraph relabelConsecutive numberOfEdges solver log_job_success transform_probabilities_to_costs get_multicut_solver zeros log insertEdges Parameter IntParameter BoolParameter Parameter FloatParameter TaskParameter abspath ListParameter load tuple _filter_ignore_label faces_to_ovlp_axis unique append overlap overlapArraysNormalized log_block_success log concatenate get join concatenate log_job_success unique save blocking log Parameter FloatParameter TaskParameter DummyTask abspath normalize log_block_success block_to_bb tuple squeeze getBlock astype mean apply_filter zeros log enumerate get log_job_success log Parameter TaskParameter abspath ListParameter IntParameter getBlock log block_to_bb log_job_success list log parameters_to_matrix Parameter TaskParameter abspath log block_to_bb _transform_data getBlock astype log range log_block_success _transform_block log_job_success list log load_mask Parameter Parameter ListParameter IntParameter Parameter interpolation_modes FloatParameter result_types formats Parameter FloatParameter Parameter ListParameter abspath TaskParameter join chdir getcwd check_output log split get join log_block_success apply_for_file log_job_success log makedirs Parameter TaskParameter abspath _write_coords join begin end tuple getBlock rmtree coordinateTransformationZ5 run makedirs join log_job_success log print print print int list rstrip total_seconds map datetime split append range parse_runtime join parse_blocks extend range list tolist getBlockIdsOverlappingBoundingBox blocking len filters getattr isinstance astype recurse numberOfBlocks range set getBlockIdsOverlappingBoundingBox recurse coordinatesToBlockId set ResizedVolume slice tuple getBlock shape len get_face blockShape getNeighborId range len shape product ndim tuple sum binary_erosion preserving_erosion astype binary_erosion zeros_like fit_seeds astype distanceTransform normalize range fit_seeds normalize astype distanceTransform fit_to_hmap_2d isinstance fit_to_hmap_3d unique max values require_dataset join get_key attrs get list write_h5_metadata write_xml_metadata write_n5_metadata get list ndim append values get get replace endswith _ome_zarr_metadata _write_xml_metadata _bdv_metadata _bdv_ome_zarr_metadata _ome_zarr_metadata _paintera_metadata Parameter TaskParameter BoolParameter abspath mala_clustering getBlock gridRag relabelConsecutive log undirectedGraph accumulateAffinityStandartFeatures accumulateEdgeMeanAndLength agglomerative_clustering normalize log_block_success get projectScalarNodeDataToPixels concatenate astype unique numberOfNodes uvIds int block_to_bb min ndim any insertEdges get_shape list log_job_success blocking log Parameter TaskParameter BoolParameter abspath get list zeros_like compute_edge_costs astype compute_rag multicut_kernighan_lin prod any shape relabelConsecutive get_stitch_edges uvIds get log_block_success block_to_bb concatenate getBlock ndim normalize max log get_shape list log_job_success blocking log Parameter abspath get zeros_like _make_seeds astype logical_not _make_hmap unique relabelConsecutive run_watershed range takeDict _apply_dt _read_data astype log logical_not blockShape _apply_watershed_with_seeds prod log_block_success _get_bbs get_shape list log_job_success blocking log Parameter abspath get zeros_like astype distanceTransform range normalize apply_filter zeros tuple get nonMaximumDistanceSuppression max_fu view transpose isnan shape _points_to_vol apply_filter labelMultiArrayWithBackground log get zeros_like _make_seeds _make_hmap run_watershed range get list get normalize int _apply_dt _read_data ones tuple _apply_watershed astype log blockShape prod log_block_success labelVolumeWithBackground _get_bbs get_shape list log_job_success blocking log Parameter TaskParameter abspath get block_to_bb getBlock max watershed get int log_block_success block_to_bb _read_data getBlock astype logical_not max log watershed get_shape list log_job_success blocking log Parameter BoolParameter IntParameter Parameter TaskParameter abspath take isinstance unique takeDict block_to_bb getBlock log _apply_node_labels log_block_success log block_to_bb getBlock log _apply_node_labels log_block_success int list ndarray isinstance max values get log_job_success _write_maxlabel _load_assignments log update get_config build DownscalingWorkflow makedirs update get_config build makedirs FilterOrphansWorkflow build task_name build File view update SkeletonWorkflow get_config build makedirs read_n5 zeros_like view tuple File unique max gaussianSmoothing astype watershedsNew update task get_config build makedirs update join task get_config build makedirs task build task build zeros computeLiftedNeighborhoodFromNodeLabels len zeros transform_probabilities_to_costs len compute_lifted_nh map_to_lifted_costs region_feats task write_result build update join make_lifted_problem solve_lifted_problem get_config makedirs update task print default_task_config default_global_config build mkdir len update get_config MulticutSegmentationWorkflow build mkdir update task build print shape File update SkeletonWorkflow get_config build makedirs File chunks shape require_dataset zeros edges_from_skeletons task build File max items list join require_group attrs File combine_edges_and_costs path symlink exists join make_lifted_problem print get_max_costs edges_to_problem update copy_task default_task_config build update task default_task_config build update task build update join task get_config copyfile build makedirs join copy_and_crop_seg lifted_problem print rmtree evaluate_fib append solve_separately range solve_jointly makedirs get str print extend linspace roc_point exists show items list subplots plot suptitle load_results set_xlabel set_ylabel append isclose split show items list subplots plot suptitle load_results set_xlabel set_ylabel append isclose split show items list std subplots errorbar plot print load_results len set_xlabel set mean set_ylabel legend append isclose split plot_fixed_precision plot_fixed_recall update join task get_config copyfile build makedirs task build makedirs Parameter ListParameter abspath get int tuple log_job_success File require_dataset max log update join task build int undirectedGraph read_n5 all product print tuple sort min astype logical_and write_edge_result array unique max to_coords insertEdges Graph join File require_dataset rand logical_not copy flatten write_chunk read_edge_result zeros sum len require_dataset File concatenate print File lexsort min path require_dataset rot90 len write_chunk tolist array len int read_chunk astype reshape Parameter FloatParameter abspath items list join require_group attrs File combine_edges_and_costs path symlink exists get log_job_success full_lifted_problem get_max_costs log edges_to_problem Parameter TaskParameter abspath int max uvIds get join file_reader _solve_objects Graph log_job_success chunks require_dataset key_to_lifted_agglomerator log Parameter update get_config mkdir build MulticutSegmentationWorkflow update get_config LiftedMulticutSegmentationWorkflow build update run_mc makedirs tuple voi adapted_rand get_scores list items tuple File astype evaluate_seg where get_bb labelVolume labelVolumeWithBackground print range validate_block view print tuple File astype where get_bb labelVolume nonMaximumDistanceSuppression transpose localMaxima3D astype distanceTransform gaussianSmoothing _points_to_vol shape normalize labelVolumeWithBackground watershed dt_watershed view print File astype create_dataset join join makedirs join cremi_score print load_seg_and_gt view File astype load_seg_and_gt labelVolumeWithBackground evalNodeLabels join undirectedGraph setCosts Graph File setGraphEdgesCosts liftedMulticutObjective numberOfNodes uvIds insertEdges strptime print compute_time compute_energy print compute_time compute_energy view tuple File chunks shape create_dataset update get_config MulticutSegmentationWorkflow build mkdir join symlink copyfile makedirs task prepare_data join Graph print File len UndirectedGraph create_dataset liftedMulticutObjective insertLiftedEdgesBfs liftedUvIds probs_to_costs numberOfNodes uvIds insertEdges join remove print download exists makedirs abspath Parameter ListParameter abspath getBlock log_block_success block_to_bb get_shape log_job_success blocking | [](https://anaconda.org/conda-forge/cluster_tools) # Cluster Tools Workflows for distributed Bio Image Analysis and Segmentation. Supports Slurm, LSF and local execution, easy to extend to more scheduling systems. ## Workflows - [Hierarchical Multicut](http:/openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w1/Pape_Solving_Large_Multicut_ICCV_2017_paper.pdf) / [Hierarchical lifted Multicut](https://arxiv.org/abs/1905.10535) - Distance Transform Watersheds - Region Adjacency Graph - Edge Feature Extraction from Boundary-or-Affinity Maps - Agglomeration via (lifted) Multicut | 1,770 |
cooliotonyio/dime | ['cross modal retrieval'] | ['DIME: An Online Tool for the Visual Comparison of Cross-Modal Retrieval Models'] | dime/model.py feature_extractors/yli_med_video_processor.py nuswide_processing_scripts/filter_concepts.py dime/engine.py server.py train_model.py nuswide_processing_scripts/make_nuswide_folder_labels.py trainer.py evaluate_model.py feature_extractors/resnet152_nuswide_processor.py utils.py feature_extractors/spatial_feature_generator.py nuswide_processing_scripts/make_concepts.py nuswide_processing_scripts/make_tag_matrix.py networks.py dime/index.py losses.py nuswide_processing_scripts/make_relevancy_matrix.py feature_extractors/resnet18_nuswide_processor.py dime/dataset.py dime/utils.py web/app.py setup.py datasets.py BaseCMRetrievalDataset label_matrices_maker idx_maker NUS_WIDE NUS_WIDE_KNN make_loaders_text make_db_images MiAP f1_precision_recall faiss_similarity display_metrics make_tag_ranking make_db_text make_image_ranked_relevancy_matrix InterTripletLoss OnlineContrastiveLoss TripletLoss OnlineTripletLoss ContrastiveLoss TextEmbeddingNet IntermodalTripletNet Resnet18EmbeddingNet TwoStreamVideoEmbeddingNet ModalityDiscriminator SiameseNet RevGrad TripletNet FeatureExtractor ClassificationNet Resnet152EmbeddingNet EmbeddingNet EmbeddingNetL2 handle_info get_upload handle_query get_data handle_upload handle_search load_vectors pass_epoch fit HardNegativePairSelector FunctionNegativeTripletSelector HardestNegativeTripletSelector SemihardNegativeTripletSelector embedding_indexing_system extract_embeddings semihard_negative TripletSelector RandomNegativeTripletSelector AllPositivePairSelector AllTripletSelector random_hard_negative plot_embeddings pdist k_nearest_neighbors PairSelector hardest_negative ImageDataset TextDataset Dataset load_dataset SearchEngine load_engine load_index Index Model load_model BatchKeySampler in_and_true sanitize_dict allowed_file save_batch load_batch get_image_feature get_image_feature query index enumerate append enumerate len divide zeros sum enumerate multiply seterr divide zeros sum range load int list seed SubsetRandomSampler shuffle is_available DataLoader floor open range len sort faiss_similarity zeros flip range full max zip enumerate IndexFlatL2 make_db_images search add make_db_text tuple sampler eval numpy empty range enumerate len eval empty enumerate len make_loaders_text print MiAP f1_precision_recall faiss_similarity range len int idx_to_target print name search target_to_tensor modality join print secure_filename save filename list in_and_true params valid_index_names keys values sanitize_dict print handle_search list FloatTensor map open split format print pass_epoch step range format batch_sampler model backward print dataset zero_grad metric mean eval reset item loss_fn train step enumerate append len t mm view argmax zeros close ylim scatter title figure legend savefig xlim range eval NearestNeighbors embedding_indexing_system fit isfile vprint time SearchEngine post_processing Index load_embeddings add load unpackbits reshape astype warn normpath array len packbits astype normpath save str list keys remove fill_ register_forward_hook convert resnet152 cuda resnet18 json | # DIME (Dataset, Index, Model, Embedding) An Online Tool for the Visual Comparison of Cross-Modal Retrieval Models *Paper link*: https://doi.org/10.1007/978-3-030-37734-2_61 ## Abstract Cross-modal retrieval relies on accurate models to retrieve relevant results for queries across modalities such as image, text, and video. In this paper, we build upon previous work by tackling the difficulty of evaluating models both quantitatively and qualitatively quickly. We present DIME (Dataset, Index, Model, Embedding), a modality-agnostic tool that handles multimodal datasets, trained models, and data preprocessors to support straightforward model comparison with a web browser graphical user interface. DIME inherently supports building modality-agnostic queryable indexes and extraction of relevant feature embeddings, and thus effectively doubles as an efficient cross-modal tool to explore and search through datasets. | 1,771 |
cosmic-cortex/modAL | ['active learning'] | ['modAL: A modular active learning framework for Python'] | examples/runtime_comparison.py examples/ensemble.py examples/shape_learning.py examples/sklearn_workflow.py examples/ranked_batch_mode.py modAL/uncertainty.py modAL/models/learners.py tests/example_tests/bayesian_optimization.py modAL/acquisition.py tests/example_tests/active_regression.py tests/example_tests/bagging.py examples/active_regression.py modAL/models/base.py examples/bagging.py examples/stream-based_sampling.py examples/query_by_committee.py modAL/utils/selection.py modAL/utils/validation.py modAL/__init__.py modAL/cluster.py modAL/density.py tests/example_tests/query_by_committee.py examples/multilabel_svm.py tests/core_tests.py modAL/multilabel.py examples/deep_bayesian_active_learning.py examples/keras_integration.py modAL/models/__init__.py tests/example_tests/ensemble.py examples/ensemble_regression.py examples/bayesian_optimization.py modAL/expected_error.py tests/mock.py examples/custom_query_strategies.py modAL/disagreement.py docs/source/conf.py examples/pool-based_sampling.py tests/example_tests/multidimensional_data.py docs/source/_themes/sphinx_rtd_theme/__init__.py examples/information_density.py examples/bayesian_optimization_multidim.py tests/example_tests/ensemble_regression.py modAL/batch.py tests/example_tests/custom_query_strategies.py tests/example_tests/pool_based_sampling.py modAL/utils/combination.py examples/pytorch_integration.py modAL/utils/__init__.py tests/example_tests/stream_based_sampling.py setup.py modAL/utils/data.py tests/example_tests/information_density.py tests/example_tests/multilabel_svm.py tests/example_tests/shape_learning.py tests/example_tests/ranked_batch_mode.py setup get_html_theme_path GP_regression_std custom_query_strategy max_entropy uniform create_keras_model create_keras_model Torch_Model comparisons modAL_EER acton_QBC libact_EER timeit libact_QBC alp_QBC acton_uncertainty libact_uncertainty modAL_uncertainty modAL_QBC alp_uncertainty random_sampling optimizer_PI EI max_PI PI max_EI optimizer_EI UCB optimizer_UCB max_UCB uncertainty_batch_sampling select_cold_start_instance select_instance ranked_batch HierarchicalClustering information_density similarize_distance max_std_sampling vote_entropy_sampling KL_max_disagreement consensus_entropy max_disagreement_sampling vote_entropy consensus_entropy_sampling expected_error_reduction min_confidence max_loss avg_confidence _SVM_loss avg_score mean_max_loss SVM_binary_minimum max_score _proba_entropy classifier_entropy entropy_sampling _proba_uncertainty _proba_margin uncertainty_sampling classifier_margin margin_sampling classifier_uncertainty BaseLearner BaseCommittee Committee ActiveLearner CommitteeRegressor BayesianOptimizer make_product make_linear_combination make_query_strategy drop_rows data_hstack data_vstack data_shape enumerate_data retrieve_rows add_row shuffled_argmax weighted_random multi_argmax check_class_proba check_class_labels MockCommittee MockFunction MockActiveLearner MockEstimator custom_query_strategy MockClassifier dirname abspath add_html_theme abspath dirname predict linear_combination compile Sequential add Dense MaxPooling2D Conv2D Flatten Dropout mean array function choice update IdealLabeler make_query array LogisticRegressionLibact label train Dataset range UncertaintySampling update IdealLabeler make_query EER array LogisticRegressionLibact label train Dataset range update IdealLabeler make_query array QueryByCommittee label train Dataset range ActiveLearner teach LogisticRegression query range Committee query range teach ActiveLearner teach LogisticRegression query range acton_main acton_main concatenate LogisticRegression rank ActiveLearnerALP range fit concatenate rank ActiveLearnerALP range fit modAL_EER acton_QBC libact_EER load_iris libact_QBC alp_QBC acton_uncertainty libact_uncertainty modAL_uncertainty modAL_QBC alp_uncertainty predict predict predict optimizer_PI optimizer_EI optimizer_UCB mean argmin pairwise_distances pairwise_distances_argmin_min reshape min shape argmax minimum on_transformed data_vstack ones select_instance select_cold_start_instance append bool range transform_without_estimating len classifier_uncertainty pairwise_distances entropy len Counter classes_ vote zeros enumerate transpose predict_proba entropy entropy transpose mean vote_proba zeros enumerate vote_entropy consensus_entropy KL_max_disagreement reshape predict _proba_entropy inf drop_rows data_vstack clone y_training estimator X_training predict_proba enumerate_data unique zeros add_row _proba_uncertainty enumerate fit len maximum dot classes_ eye sum predict enumerate abs T min argmax _SVM_loss _SVM_loss predict_proba min mean predict_proba predict_proba max predict mean predict_proba predict partition predict_proba max predict_proba partition predict_proba classifier_uncertainty classifier_margin classifier_entropy ones ones DataFrame any ndarray isinstance ndarray TypeError isinstance concat type any DataFrame ndarray isinstance issparse DataFrame ndarray isinstance issparse ndarray isinstance ones DataFrame issparse DataFrame isinstance isinstance permutation len list choice sum range len range len array_equal hstack enumerate | <img src="https://modal-python.readthedocs.io/en/latest/_static/modAL_b.png" alt="modAL" style="width: 400px;"> Modular Active Learning framework for Python3 [](https://travis-ci.org/modAL-python/modAL) [](https://codecov.io/gh/modAL-python/modAL) [](http://modal-python.readthedocs.io/en/latest/?badge=latest) ## Page contents - [Introduction](#introduction) - [Active learning from bird's-eye view](#active-learning) - [modAL in action](#modAL-in-action) - [From zero to one in a few lines of code](#initialization) - [Replacing parts quickly](#replacing-parts) - [Replacing parts with your own solutions](#replacing-parts-with-your-own-solutions) | 1,772 |
cosmozhang/Modular_Neural_CRF | ['sentiment analysis'] | ['Sentiment Tagging with Partial Labels using Modular Architectures'] | train.py model/vlstm.py model/simple_lstm.py model/advtr.py model/hrn.py model/evaluator.py model/lstm_crf.py model/tensor_utils.py model/highway.py model/utils.py cal_adv _l2_normalize iob_seg eval_batch iobes_iob hw flatten main HRNN HRNNCell LSTM_CRF LSTM_TH init_linear log_sum_exp to_scalar init_embedding adjust_learning_rate init_lstm argmax VLSTMCell main VLSTM clone sqrt masked_select unsqueeze expand_as sum grad _l2_normalize print replace append enumerate append enumerate list sorted LongTensor forward sort transpose pack_padded_sequence pad_packed_sequence map print set flatten zip numpy cuda embed enumerate max max param_groups uniform_ sqrt size zero_ data orthogonal_ bias bidirectional zero_ from_iterable | # Modular Neural CRF This repository is the source code for the paper: **Sentiment Tagging with Partial Labels using Modular Architectures** In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL) , 2019 *Xiao Zhang, Dan Goldwasser* * Please download the corresponding word embeddings and the dataset before running the program. * Then run the *train.py* file to start. Run `train.py -h` to check the usage of the program. For data, please refer to the references in our paper and download from the original sources of the datasets. The code is under BSD-3 license. | 1,773 |
coursekevin/weakformghnn | ['time series'] | ['Weak Form Generalized Hamiltonian Learning', 'Weak Form Generalized Hamiltonian Learning'] | weakformghnn/__init__.py weakformghnn/_src/_weak_form.py weakformghnn/_src/__init__.py setup.py weakformghnn/_src/_vector_calc.py weakformghnn/_src/_models.py tests/ghnn_test.py test_ScalarFuncPos test_ZeroDivMat test_ConcaveFunc GHNN_model test_ConvexFunc test_ScalarFuncZero test_ConcaveZero test_hessian test_gauss_rbf test_poly_bf vector_calc_data ivp_integration_data test_divergence_calc test_curl_calc test_PosLinear test_GHNN convex_concave_data test_ScalarFunc ScalarFuncZero GHNNwHPrior ZeroDivMat PosLinear ScalarFuncZeroPos ReHU ConvexFunc ScalarFuncPosUnbnd GHNN ODEFCN ConcaveFunc ScalarFunc ConvexFuncZero ConcaveFuncZero HNN curl divergence gauss_rbf poly_bf weak_form_loss pow linspace MSELoss tensor join linspace curl_f f div_f randn PosLinear randn assert_array_less pow zeros numpy ScalarFuncZero assert_almost_equal numpy zeros ScalarFuncZeroPos randn assert_array_less assert_array_almost_equal zeros numpy randn ScalarFunc assert_array_equal scalar_func numpy ZeroDivMat randn shape J assert_array_equal numpy model randn shape stack assert_array_almost_equal assert_array_equal numpy range append h assert_array_almost_equal numpy randn backward ConvexFunc zero_grad Adam range parameters conv_func2 conv_func1 step loss conc_func2 backward zero_grad Adam range parameters conc_func1 ConcaveFunc step loss conv_zero backward zero_grad Adam range parameters numpy assert_array_almost_equal ConcaveFuncZero zeros step loss reshape gauss_rbf assert_array_almost_equal append tensor numpy range cat reshape poly_bf append tensor assert_almost_equal numpy range cat assert_array_equal numpy divergence assert_array_equal curl numpy append range append range mul pow exp view cat view trapz einsum | # Weak Form Generalized Hamiltonian Learning This library provides a PyTorch implementation for performing Weak Form Generalized Hamiltonian Learning. This code accompanies this [Neurips2020 paper](https://proceedings.neurips.cc/paper/2020/file/d93c96e6a23fff65b91b900aaa541998-Paper.pdf) [1] by Kevin L. Course, Trefor W. Evans, and Prasanth B. Nair. As everything is written in PyTorch, all algorithms provide full GPU support. --- <p align="center"> <img align="middle" src="./assets/lorenz_trajectories.png" alt="Lorenz63 gen hamiltonian neural net" width="240"/> </p> Please cite our paper if you find this code useful in your research. The bibliographic information for the paper is, ```bash @inproceedings{course_wfghnn_2020, | 1,774 |
cplusx/QATM | ['template matching'] | ['QATM: Quality-Aware Template Matching For Deep Learning'] | utils.py models.py QATM MyNormLayer all_sample_iou locate_bbox score2curve plot_success_curve evaluate_iou compute_score IoU min max ones convolve argmax max int linspace append sum array len locate_bbox append IoU range len show format plot score2curve grid mean ylim title figure linspace xticks yticks | # [**QATM**: Quality-Aware Template Matching for Deep Learning](http://openaccess.thecvf.com/content_CVPR_2019/papers/Cheng_QATM_Quality-Aware_Template_Matching_for_Deep_Learning_CVPR_2019_paper.pdf) <img src="https://www.isi.edu/images/isi-logo.jpg" width="300"/> <img src="http://cvpr2019.thecvf.com/images/CVPRLogo.png" width="300"/> *** This is the official repo for the QATM DNN layer (CVPR2019). For method details, please refer to ``` @InProceedings{Cheng_2019_CVPR, author = {Cheng, Jiaxin and Wu, Yue and AbdAlmageed, Wael and Natarajan, Premkumar}, title = {QATM: Quality-Aware Template Matching for Deep Learning}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, | 1,775 |
crazydemo/Progressive-Multi-stage-Feature-Mix-for-Person-Re-Identification | ['person re identification'] | ['Progressive Multi-stage Feature Mix for Person Re-Identification'] | models/resnet.py utils/validation_metrics.py datasets/data_manager.py main_reid.py utils/transforms.py utils/random_erasing.py trainers/evaluator.py utils/serialization.py models/progressive_networks.py utils/DistWeightDevianceLoss.py utils/meters.py trainers/re_ranking.py trainers/trainer.py models/networks.py config.py utils/loss.py datasets/samplers.py datasets/data_loader.py utils/LiftedStructure.py DefaultConfig train test read_image ImageData Market1501 init_dataset RandomIdentitySampler BatchDrop Resnet BFE IDE ResNetBuilder weights_init_classifier SELayer BatchCrop weights_init_kaiming gradCAM IDE Resnet GetGrad BFE ResNetBuilder weights_init_classifier SELayer CutMixBatchDrop weights_init_kaiming CBAM_Module ResNet cbam_resnet50 Bottleneck CBAMBottleneck ResNetEvaluator k_reciprocal_neigh re_ranking_new re_ranking cls_tripletTrainer main GaussDistribution DistWeightBinDevianceLoss similarity main LiftedStructureLoss pdist similarity Margin hard_example_mining euclidean_dist pdist CrossEntropyLabelSmooth TripletLoss normalize topk_mask AverageMeter RandomErasing Cutout save_checkpoint Logger mkdir_if_missing TrainTransform pad_shorter Random2DTranslation TestTransform accuracy init_dataset ImageData IDE SGD query DataLoader ResNetBuilder save_checkpoint Logger _state_dict ResNetEvaluator dataset save_dir cuda LiftedStructureLoss seed get_optim_policy Margin max_epoch Resnet Adam pprint load_state_dict TrainTransform sum range state_dict manual_seed_all SummaryWriter format test start_epoch CrossEntropyLabelSmooth manual_seed is_available join gallery evaluate print datatype cls_tripletTrainer BFE best_rank _parse TestTransform margin num_train_pids pretrained_model TripletLoss adjust_lr makedirs format print eval dataset len convert affine bias normal_ kaiming_normal_ weight __name__ constant_ bias normal_ weight __name__ constant_ tensor max cuda mm append range detach relu size mean stack is_available uint8 backward reshape min GetGrad numpy array getGrad minimum exp zeros_like concatenate transpose astype float32 mean int32 unique append zeros sum max range len zeros_like around max list exp transpose append sum range concatenate astype mean unique minimum int float32 argpartition k_reciprocal_neigh zeros len t matmul mean sqrt pow list Variable print rand IntTensor mm range t clamp mm expand_as expand_as t sqrt addmm_ expand data ne view size min squeeze set_printoptions t expand eq gather max makedirs join copy save mkdir_if_missing new max paste topk size t eq mul_ expand_as append sum max | # Progressive-Multi-stage-Feature-Mix-for-Person-Re-Identification  pytorch code for paper Progressive Multi-stage Feature Mix for Person Re-Identification: https://arxiv.org/abs/2007.08779 This project is based on batch-drop-block: https://github.com/daizuozhuo/batch-dropblock-network The proposed PMM(Progressive-Multi-stage-Feature-Mix) model can be found in models/progressive_networks.py ## Setup running environment This project requires python3, cython, torch, torchvision, scikit-learn, tensorboardX, fire. ## Prepare dataset Create a directory to store reid datasets under this repo via | 1,776 |
crim-ca/wiki-bias | ['bias detection'] | ['Multilingual sentence-level bias detection in Wikipedia'] | pov.py nlp.py sents.py diff.py dataset.py filter.py url_extractor.py normalize.py utils.py split_dataset label main outlier punct_diff unchanged process cross_check insertions_only preprocess redirected filter_length filter_distance main min_distance deletions_only diff main download_file sbd clean_punct clean_wiki clean_tag POVProcessor get_classes balance_classes get_sentences main remove_duplicates main to_text_file check_lang check_date to_pickle unpickle seed int sort shuffle to_text_file upper len lang split_dataset add_argument inputfile ArgumentParser label parse_args unpickle dict split dict cross_check clean_tag dict filter_length cross_check append dict min_distance dict print outputfile print to_pickle get extract decode to_string rstrip separate_output verbose exists str basename write_tags download_file append readlines close POVProcessor time remove StringBuilder BZ2File nlp sub sub sub extend set add set choice len list print extend set balance_classes get_sentences remove_duplicates get_classes dict read urlopen findall date endswith endswith int | # wiki-bias This repository contains code for the paper [Multilingual Sentence-Level Bias Detection in Wikipedia](https://www.researchgate.net/profile/Desislava_Aleksandrova/publication/334612399_Multilingual_Sentence-Level_Bias_Detection_in_Wikipedia/links/5d5bd0c392851c37636bfdf2/Multilingual-Sentence-Level-Bias-Detection-in-Wikipedia.pdf), as well as three datasets for the task of bias detection. ## Prerequisites - Python 3.6 or later - All dependencies `pip install -r /path/to/requirements.txt` ## Usage #### 1. URLs Extract the urls of all parts of the complete page edit history dump. While dumps of small Wikipedias (like the one in Bulgarian) come in a single file, the large ones (English, French, etc.) are split into multiple smaller files. | 1,777 |
crispchris/IALE | ['imitation learning', 'active learning'] | ['IALE: Imitating Active Learner Ensembles'] | train_helper.py data/kmnist.py data/fmnist.py train_policy.py alil_mnist/AL-baselines.py data/cifar10.py active/mc_dropout.py active/kmeans.py alil_mnist/utils.py active/acq_metrics.py data/data_helpers.py active/badge_sampling.py alil_mnist/model.py active/strategy.py properties.py visualization/plot_cnn.py models/Policy.py helpers/policy_training_helpers.py active_learn.py models/model_helpers.py active/coreset_robust.py active/core_set_alt.py active/learn_to_learn.py active/entropy_sampling.py models/CNN.py active/policy_helpers.py alil_mnist/ALIL-simulation.py alil_mnist/queryStrategy.py alil_mnist/train-classifier.py alil_mnist/ALIL-transfer.py active/ensemble.py data/mnist.py active/policy_learner.py active/least_confidence.py models/MLP.py active/random.py results_reader.py models/resnet.py active_learn get_mean_std set_results read_results reinit_seed train_policy_model train_validate_model test_model train_policy_one_epoch train_ensemble_models train_one_epoch validate_model StateActionActionDataset BALD variation_ratios init_centers BadgeSampling RobustCoresetSampling solve_fac_loc CoreSet EnsembleSampling get_model_pool_preds EntropySampling KMeansSampling get_prototypes get_embeddings LearnedSampling LearnerMLP monte_carlo_selection LeastConfidence get_mc_pool_preds get_model_pool_preds MCDropoutSampling get_state get_one_hot get_model_predictions get_state_action get_model_gradient_embeddings get_model_embeddings PolicyLearner RandomSampling Strategy getConv2DClassifier getAState get_intermediatelayer getState getbottleFeature getPolicy jaccard_similarity getEntropy sampleOneDatapoint get_top_certainty get_top_uncertainty uncertaintySample getdiversityRank sample_from_top_n_uncertainty square_rooted sample_from_top_n_certainty randomSample getuncertaintyRank cosine_similarity diversitySample diversityallSample randomKSamples tensorflow_shutup partition_data get_args partition_test_data construct_embedding_table load_embeddings load_data init_logger shuffle_test_data get_data_splits get_policy_training_splits transform_data split_dataset stratified_split_dataset make_tensordataset concat_datasets get_data_splits transform_data get_data_splits transform_data get_data_splits get_policy_training_splits transform_data run_episode expert CNN padding_same mlpMod cal_final_Lout padding_same_deconv weights_init conv_size padding_same_conv deconv_size padding_same_kernel Policy ResNet ResNet18 ResNet34 Bottleneck ResNet101 test ResNet50 BasicBlock ResNet152 plot arange train_validate_model make_tensordataset query concat_datasets list stratified_split_dataset name load_state_dict ACQ_SIZE append to state_dict StrategyClass reinit_seed format NUM_ACQS choice set info trange deepcopy get_data_splits RANDOM_SEED len mean array std seed manual_seed format print train_validate_model mean append array range len test_model Adam NUM_EPOCHS_CLASSIFIER parameters validate_model train_one_epoch range CrossEntropyLoss format debug Adam train_policy_one_epoch NUM_EPOCHS_POLICY parameters range model zero_grad DataLoader unsqueeze SINGLE_HEAD CLUSTERING_AUX_LOSS_HEAD TensorDataset bincount to sum format debug float BCELoss view_as enumerate int criterion backward train step CLUSTER_EXPERT_HEAD StateActionActionDataset format criterion model to backward debug step zero_grad DataLoader train sum view_as enumerate format debug eval DataLoader accuracy_score format debug classification_report confusion_matrix eval DataLoader f1_score accuracy_score mean softmax float sum mode str sum T print abs rv_discrete eig astype matmul append argmax range len update addVar LinExpr addConstr Model append range len eval DataLoader sorted list concatenate CurrentStrategy query ACQ_SIZE unique zip array range stack list numpy map NUM_MC_SAMPLES eval append train range get_model_pool_preds get_state CLUSTERING_AUX_LOSS_HEAD device tensor SINGLE_HEAD CLUSTER_EXPERT_HEAD get_model_predictions flatten append sum range cat get_model_embeddings ADD_POOL_MEAN_EMB mean unique float enumerate ADD_GRADIENT_EMBEDDING print NUM_CLASSES sort_together Tensor get_model_gradient_embeddings len NUM_CLASSES zeros zeros eval get_embedding_dim DataLoader cpu eval train DataLoader cpu eval train DataLoader str state EXPERTS format load_model zip set_weights compile Sequential add Dense summary MaxPooling2D info Conv2D get_weights Flatten Dropout Reshape Sequential Adam add Dense TimeDistributed Activation compile function get_activations concatenate get_intermediatelayer expand_dims sum predict append getState input Model output predict arange shuffle append range len list shuffle zip sample range len list shuffle randomSample zip append range len list sorted append array range predict len list sorted append array range predict len get_top_uncertainty append randomKSamples range len get_top_certainty append randomKSamples range len getEntropy sorted list append array range predict len getEntropy sorted append expand_dims array range predict len jaccard_similarity sorted list print exit array append union range len jaccard_similarity sorted union append array range len jaccard_similarity sorted list append array range len float union intersection len sum square_rooted setFormatter getLogger addHandler StreamHandler Formatter setLevel INFO FileHandler add_argument ArgumentParser ERROR set_verbosity asarray info len close split open fit_on_texts texts_to_sequences to_categorical open str sorted pad_sequences shape append Tokenizer asarray format replace debug close lower info listdir join collect isdir word_index len get items list info zeros max len list zip shuffle info sample range append len seed arange shuffle info len int format reshape delete vstack info len Tensor split_dataset VAL_SIZE split_dataset VAL_SIZE POLICY_TEST_SIZE cat arange shuffle len list arange sort hstack set len div deepcopy max train_validate_model make_tensordataset index query ACQ_SIZE load_state_dict append concat_datasets state_dict arange train_validate_model make_tensordataset INIT_SIZE SINGLE_HEAD get_state_action concat_datasets list stratified_split_dataset CLUSTERING_AUX_LOSS_HEAD append to expert state_dict StrategyClass reinit_seed get_policy_training_splits NUM_ACQS concatenate choice set trange deepcopy NUM_CLASSES RANDOM_SEED CLUSTER_EXPERT_HEAD len isinstance bias zeros_ Conv2d weight kaiming_normal_ Linear padding_same_conv get_Lout range len randn Variable ResNet18 print size net endswith show str list read_results ones ylabel ylim legend append range update format xlim keys NUM_EPISODES convolve xlabel rc set_style figure color_palette fill_between array len | # IALE: Imitating Active Learner Ensembles You can find the paper here: https://www.jmlr.org/papers/v23/21-0387.html ``` @article{JMLR:v23:21-0387, author = {Christoffer Löffler and Christopher Mutschler}, title = {IALE: Imitating Active Learner Ensembles}, journal = {Journal of Machine Learning Research}, year = {2022}, volume = {23}, number = {107}, | 1,778 |
cruvadom/Convolutional-RNN | ['audio classification'] | ['Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data'] | CRNN.py crnn | TensorFlow (v1.0) code for the paper "Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data" (https://arxiv.org/abs/1602.05875) by Gil Keren and Björn Schuller. For any questions feel free to contact: [email protected] | 1,779 |
cs-chan/Total-Text-Dataset | ['scene text detection', 'curved text detection', 'text spotting', 'scene text recognition'] | ['Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition', 'ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT)'] | Evaluation_Protocol/Python_scripts/polygon_fast.py Evaluation_Protocol/Python_scripts/Pascal_VOC.py Evaluation_Protocol/Python_scripts/Deteval.py Evaluation_Protocol/Python_scripts/polygon_wrapper.py gt_reading_mod many_to_one input_reading_mod detection_filtering sigma_calculation tau_calculation one_to_one one_to_many input_reading_mod detection_filtering gt_reading_mod approx_area_of_intersection area_of_union iou area area_of_intersection iod approx_area_of_intersection iou area area_of_intersection iod loadmat list squeeze map iod enumerate split range where range where range where stack Polygon minimum min max maximum buffer buffer polygon zeros sum max zeros_like maximum where polygon zeros sum max zeros_like maximum where polygon zeros sum max sum zeros_like maximum where polygon zeros round max | # Total-Text-Dataset (Official site) Updated on April 06, 2022 (Detection leaderboard is updated with FCE, ABPNet, PCR, CentripetalText & HierText) Updated on April 29, 2020 (Detection leaderboard is updated - highlighted E2E methods. Thank you [shine-lcy](https://github.com/shine-lcy).) Updated on March 19, 2020 (Query on the new groundtruth of test set) Updated on Sept. 08, 2019 (New [training groundtruth](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Groundtruth/Text) of Total-Text is now available) Updated on Sept. 07, 2019 (Updated [Guided Annotation toolbox](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Annotation_tools) for scene text image annotation) Updated on Sept. 07, 2019 (Updated [baseline](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Baseline) as to our IJDAR) Updated on August 01, 2019 (Extended version with new baseline + annotation tool is accepted at [IJDAR](https://link.springer.com/article/10.1007/s10032-019-00334-z)) Updated on May 30, 2019 (Important announcement on Total-Text vs. ArT dataset) Updated on April 02, 2019 (Updated table ranking with default vs. our proposed DetEval) | 1,780 |
cschoeller/constant_velocity_pedestrian_motion | ['motion prediction'] | ['What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction'] | evaluate.py metrics.py ped_dataset.py dataset_utils.py DetectedObject Sample Trajectory Detection evaluate_testset load_datasets constant_velocity_model rel_to_abs main parse_commandline RunConfig final_disp avg_disp PedDataset cumsum unsqueeze permute tensor unsqueeze pi repeat DataLoader format replace print PedDataset append dataset_paths parse_args add_argument ArgumentParser evaluate_testset format load_datasets print name len sample parse_commandline append enumerate sqrt sum len LongTensor sqrt type sum len | ## Constant Velocity Pedestrian Motion Prediction This repository contains an implementation of the Constant Velocity Model from paper: [What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction](https://arxiv.org/abs/1903.07933)<br> Christoph Schöller, Vincent Aravantinos, Florian Lay, Alois Knoll<br> Robotics and Automation Letters (RA-L), 2020 In particular, it allows to reproduce the results for **OUR** and **OUR-S** from Table 1 in the paper. The dataset in this repository is the same as the one provided [here](https://github.com/agrimgupta92/sgan), but converted to json format. <br/> Prediction examples of **OUR** (left) and **OUR-S** (right): <img src="images/pred_our.png" width="40%" height="40%" align="left"> <img src="images/pred_our-s.png" width="40%" height="40%" align="center"> | 1,781 |
csehong/VM-NET | ['cross modal retrieval'] | ['Content-Based Video-Music Retrieval Using Soft Intra-Modal Structure Constraint'] | train.py embed_network_music.py data/audio_feature_extractor/librosa_feature.py data/mp4-to-mp3/mp4tomp3.py Logger.py network.py eval.py test.py embed_structure_loss.py flip_gradient.py embed_loss.py data/audio_feature_extractor/youtube-8M_audio.py embed_network_video.py network_structure.py OPTS.py Triplet Music_Model Video_Model Triplet_Structure Recall FlipGradientBuilder Logger Model Model_structure OPTS feature_extraction main check_file_exists main check_file_exists logam vstack chroma_stft open spectral_bandwidth spectral_centroid poly_features delta mfcc melspectrogram range dump logamplitude hstack close mean zero_crossing_rate chroma_cens load int var print rmse spectral_rolloff amax hpss basename print exit feature_extraction append remove call | VM-NET in Tensorflow ==== Tensorflow implementation of [SSPP-DAN: Deep Domain Adaptation Network for Face Recognition with Single Sample Per Person](https://arxiv.org/abs/1704.06761)  Demo ------------- [](https://www.youtube.com/watch?v=ZyINqDMo3Fg) Prerequisites ------------- * [Python 2.7](https://www.python.org/downloads/) | 1,782 |
csjtx1021/DGBO | ['gaussian processes'] | ['Deep Bayesian Optimization on Attributed Graphs'] | base_prior.py load_data.py rdkit_preprocessing/genConvMolFeatures.py parse_arg.py DeepSurrogateModel.py rdkit_preprocessing/neuralFingerprintUtils.py gcn/metrics.py gcn/inits.py ChooseNext.py gcn/layers.py DGBO.py gcn/utils.py AdaptiveBasis.py AdaptiveBasis simpleBasis rseed345_basis HorseshoePrior LognormalPrior BasePrior TophatPrior NormalPrior ChooseNext EI sampler lnprior UCB lnprob lnlik predict preprocess_features Pooling_sum_normal_params2 RelationGraphConvolution_noBasisRegularization RelationGraphConvolution_withBasisRegularization CombGCNwithDNN construct_feed_dict CombDGCNWithDNN chebyshev_polynomials Pooling_sum_paramsfree preprocess_adj CombModel Pooling_sum_normal_params data evaluate_a_vector run_loop load_y evaluate evaluate_a_graph BO_process params run_one store initialization load_candidates load_data_y load_data parse_arg ones zeros uniform glorot get_layer_uid sparse_dropout dot GraphConvolution Dense Layer masked_softmax_cross_entropy masked_accuracy preprocess_features normalize_adj sparse_to_tuple sample_mask construct_feed_dict chebyshev_polynomials parse_index_file load_data preprocess_adj MolGraph graph_from_smiles num_atom_features num_bond_features Node graph_from_smiles_tuple reprocess_smiles_to_graph array_rep_from_smiles gen_descriptor_data memoize one_of_k_encoding_unk bond_features atom_features num_atom_features num_bond_features one_of_k_encoding append array range len y observedy reshape train get_basis_one get_basis All_cand_node_num dataset rng len T matshow loadtxt colorbar rseed dot title savefig figure w_m0 observedy linspace candidates max list AdaptiveBasis sampler phi_matrix hyp_samples shape append range EI shuffle set mean time T print reshape inv dot observedx array len sqrt predict pdf sqrt cdf max predict dot shape T diag T exp inv dot shape log lnlik lnprior any pos exp run_mcmc EnsembleSampler zeros log dict update diags flatten dot sum array normalize_adj eye list normalize_adj format chebyshev_recurrence print eye append range eigsh add_edges_from list number_of_nodes average_clustering print reshape Graph mean coo_matrix number_of_edges load_data preprocess_adj append dataset array range values len load_data_y dataset evaluate_a_vector time print evaluate_a_graph scatter figure evaluate print AdaptiveBasis refresh_phi add linspace append candidates array range len ChooseNext time evaluate print refresh_phi AdaptiveBasis eval_jump add forcejump randint candidates len observedx range run_one observedy run_loop maxiter initialization initn load list print len array append integer keys range open load list len append keys range open parse_args add_argument ArgumentParser random_uniform sqrt random_uniform sparse_retain floor cast sparse_tensor_dense_matmul matmul softmax_cross_entropy_with_logits cast float32 argmax cast equal append int strip open zeros format lil_matrix from_dict_of_lists tolil tuple sort min tolist sample_mask adjacency_matrix shape parse_index_file vstack zeros max to_tuple range isinstance len diags flatten coo_matrix sum array add_subgraph MolGraph sort_nodes_by_degree MolFromSmiles MolGraph new_node GetAtoms add_neighbors GetBonds array neighbor_list MolFromSmiles GetAtoms MolFromSmiles SanitizeMol MolFromSmiles print graph_from_smiles sort_nodes_by_degree array_rep_from_smiles enumerate load list rstrip dump append print len close array open float keys range gen_descriptor_data split GetBondType | This code is implemented according to paper "[Deep Bayesian Optimization on Attributed Graphs](https://www.aaai.org/ojs/index.php/AAAI/article/view/3938/3816)", published on AAAI2019. Deep Graph Bayesian Optimization (DGBO) method can deal with attributed graphs. It prevents the cubical complexity of the GPs by adopting a deep graph neural network to surrogate black-box functions, and can scale linearly with the number of observations. Applications include molecular discovery and urban road network design. If you want to run this code, you should ensure that the following packages have been installed successfully: tensorFlow spicy pickle numpy | 1,783 |
csong27/auditing-text-generation | ['text generation'] | ['Auditing Data Provenance in Text-Generation Models'] | reddit_lm.py dialogue.py dialogue_ranks.py helper.py sated_nmt.py sated_nmt_ranks.py data_loader/load_cornell_movie.py data_loader/load_reddit.py auditing.py reddit_lm_ranks.py data_loader/load_sated.py data_loader/load_wiki.py user_mi_attack sample_with_ratio get_indices_by_labels ranks_to_feats histogram_feats load_ranks get_perp build_dialogue_model group_texts_by_len build_inference_decoder train_cornell_movie pad_texts words_to_indices get_target_ranks get_ranks load_shadow_user_data load_train_users_heldout_data load_cross_domain_shadow_user_data get_shadow_ranks iterate_minibatches DenseTransposeTied CuDNNLSTM _CuDNNRNN flatten_data Attention words_to_indices process_test_data train_reddit_lm build_lm_model group_texts_by_maxlen get_translated_rank group_texts_by_len get_target_ranks rank_lists read_translated_comments get_ranks_labels_by_batch save_users_rank_results load_shadow_user_data get_bigram_probs load_cross_domain_shadow_user_data get_shadow_ranks get_perp group_texts_by_len build_inference_decoder pad_texts train_sated_nmt build_nmt_model words_to_indices user_mi_attack get_target_ranks get_ranks rank_lists ranks_to_feats save_users_rank_results load_shadow_user_data load_train_users_heldout_data histogram_feats test_vocab load_cross_domain_shadow_user_data get_shadow_ranks load_cornell_movie_by_user count_character_lines load_ubuntu_lines load_raw_ubuntu load_lines load_conversations load_cornell_movie save_extracted_cornell_movie preprocess load_ubuntu_by_user load_extracted_ubuntu save_extracted_ubuntu load_extracted_cornell_movie read_top_user_comments read_test_comments_by_user read_random_users read_test_comments translate remove_puncs preprocess read_comments_by_user read_top_users read_ptb_file read_ptb_test_data read_ptb_data_by_user write_processed_comments build_vocab load_texts load_europarl_by_user load_users load_sated_data process_texts read_europarl_file load_sated_data_by_user process_vocabs load_wiki_lines load_wiki_by_users load_wiki_test_data histogram int float tolist len load format exists append range seed int arange get_indices_by_labels sample_with_ratio isinstance concatenate print shuffle histogram_feats append range clip enumerate len decision_function vstack accuracy_score roc_auc_score ranks_to_feats append precision_recall_fscore_support fit_transform range predict format concatenate classification_report load_ranks load Normalizer savez print LinearSVC transform StandardScaler fit int list defaultdict items print shuffle ceil zip append range len encoder_emb_layer encoder_rnn Embedding decoder_rnn Model decoder_emb_layer Input rnn Embedding decoder_rnn Model decoder_emb_layer Input rnn append max insert function sparse_categorical_crossentropy trainable_weights save sorted exp group_texts_by_len Adam placeholder load_ubuntu_by_user pad_texts append sum range format get_perp build_dialogue_model inputs shuffle mean keys words_to_indices enumerate deepcopy load_cornell_movie_by_user get_updates savez print output len int asarray arange reshape shuffle len defaultdict setdiff1d arange load_extracted_ubuntu process_texts process_vocabs seed format asarray setdiff1d defaultdict print process_vocabs shuffle Counter process_texts zip append load_extracted_cornell_movie len int asarray arange reshape flatten zip append len list format sorted function build_dialogue_model print inputs output placeholder load_weights softmax load_shadow_user_data mkdir save_users_rank_results keys load_cross_domain_shadow_user_data int defaultdict process_texts zip append load_extracted_cornell_movie len sorted load_cornell_movie_by_user format function build_dialogue_model inputs output placeholder load_weights softmax mkdir load_train_users_heldout_data save_users_rank_results keys arange slice shuffle range len range len Embedding emb_layer Model Input range iterate_minibatches function model build_lm_model sparse_categorical_crossentropy flatten_data trainable_weights save argmax read_top_user_comments sorted exp load_wiki_by_users read_test_comments Adam placeholder process_test_data shape sum range format mean pred_fn keys words_to_indices enumerate get_updates savez load_wiki_test_data print reshape write len format print load_wiki_lines range build_vocab len join read_top_users range build_vocab reshape astype int32 append range enumerate len shape range group_texts_by_maxlen concatenate reshape rank_lists flatten shape zip append argsort arange empty_like format savez write get_ranks_labels_by_batch enumerate words_to_indices model build_lm_model len read_top_user_comments model build_lm_model read_top_users defaultdict range format len read_top_user_comments format function model placeholder build_lm_model read_translated_comments load_weights softmax read_top_users mkdir save_users_rank_results encoder_emb_layer encoder_rnn Embedding decoder_rnn Model decoder_emb_layer Input rnn clear_session function sparse_categorical_crossentropy trainable_weights save sorted exp group_texts_by_len Adam placeholder pad_texts append sum range format get_perp inputs load_europarl_by_user shuffle mean load_sated_data_by_user keys words_to_indices enumerate get_updates savez print output build_nmt_model len read_europarl_file load_texts load_users load_texts load_users rankdata enumerate rank_lists get_ranks build_nmt_model load_sated_data_by_user build_nmt_model SVC print words_to_indices flatten_data load_sated_data_by_user float sum len print Counter most_common load_lines len join word_tokenize print load_conversations append range load_lines len seed asarray arange setdiff1d choice len list process_texts chain load_extracted_cornell_movie process_vocabs seed int format asarray defaultdict print process_vocabs shuffle Counter set choice process_texts zip append load_extracted_cornell_movie len join load_ubuntu_lines endswith append listdir len range split join word_tokenize load_raw_ubuntu print preprocess append range len seed defaultdict arange choice load_extracted_ubuntu process_texts process_vocabs replace append isalnum print join listdir seed asarray arange format print shuffle sum len asarray choice items list sorted format arange print Counter dict zip len join range len seed join defaultdict print Counter choice read_top_users range build_vocab len read_random_users join defaultdict print format len seed join format arange defaultdict print choice read_ptb_file range build_vocab len join read_ptb_file enumerate items list sorted format arange print Counter dict zip len list load_texts process_texts chain process_vocabs seed int defaultdict print process_vocabs load_texts shuffle Counter set load_users choice process_texts zip append len seed defaultdict arange process_vocabs choice process_texts read_europarl_file print load_wiki_lines seed format arange defaultdict print len choice load_wiki_lines range build_vocab | # Auditing Data Provenance in Text-Generation Models This repository contains example of experiments for the paper Auditing Data Provenance in Text-Generation Models (https://arxiv.org/pdf/1811.00513.pdf/). ### Train text-generation models The first step is to train target and shadow text-generation models. To train language model, run the function train_reddit_lm in reddit_lm.py To train NMT model, run the function train_sated_nmt in sated_nmt.py To train dialog model, run the function train_cornell_movie in dialogue.py To train multiple shadow models, set field exp_id=1,2,...n in above function, where n is the number of shadow models. Set cross_domain=True to use cross domain datasets for shadow models. An example script for training target model | 1,784 |
cswaynecool/DRT | ['visual tracking'] | ['Correlation Tracking via Joint Discrimination and Reliability Learning'] | caffe/python/caffe/classifier.py caffe/python/caffe/test/test_net.py caffe/examples/coco_caption/retrieval_experiment.py external_libs/matconvnet/matconvnet/utils/proto/caffe_6e3916_pb2.py caffe/tools/extra/resize_and_crop_images.py libsvm/python/svm.py caffe/examples/pycaffe/caffenet.py external_libs/matconvnet/doc/matdocparser.py caffe/src/caffe/test/test_data/generate_sample_data.py caffe/data/coco/make_trainval.py external_libs/matconvnet/utils/proto/caffe_0115_pb2.py external_libs/matconvnet/matconvnet/utils/proto/caffe_0115_pb2.py external_libs/matconvnet/utils/proto/caffe_old_pb2.py external_libs/matconvnet/utils/proto/vgg_caffe_pb2.py caffe/examples/coco_caption/captioner.py caffe/python/detect.py caffe/data/coco/make_test.py libsvm/tools/checkdata.py caffe/python/caffe/detector.py caffe/python/draw_net.py caffe/examples/finetune_flickr_style/assemble_data.py caffe/tools/extra/extract_seconds.py external_libs/matconvnet/matconvnet/utils/proto/caffe_pb2.py external_libs/matconvnet/utils/layers.py external_libs/matconvnet/matconvnet/utils/layers.py caffe/python/caffe/io.py caffe/python/caffe/test/test_layer_type_list.py libsvm/python/svmutil.py external_libs/matconvnet/matconvnet/utils/proto/caffe_old_pb2.py caffe/python/caffe/__init__.py caffe/examples/pycaffe/layers/pyloss.py caffe/examples/web_demo/app.py caffe/python/classify.py external_libs/matconvnet/doc/matdoc.py external_libs/matconvnet/utils/proto/caffe_6e3916_pb2.py libsvm/tools/grid.py caffe/python/caffe/draw.py external_libs/matconvnet/utils/proto/caffe_b590f1d_pb2.py caffe/examples/coco_caption/hdf5_sequence_generator.py external_libs/matconvnet/matconvnet/utils/proto/vgg_caffe_pb2.py caffe/scripts/download_model_binary.py libsvm/tools/easy.py caffe/python/caffe/test/test_python_layer_with_param_str.py caffe/tools/extra/parse_log.py external_libs/matconvnet/utils/proto/caffe_fastrcnn_pb2.py caffe/python/caffe/net_spec.py external_libs/matconvnet/matconvnet/doc/matdoc.py caffe/examples/web_demo/exifutil.py external_libs/matconvnet/matconvnet/utils/proto/caffe_b590f1d_pb2.py caffe/python/caffe/test/test_python_layer.py caffe/python/caffe/test/test_solver.py external_libs/matconvnet/matconvnet/utils/import-caffe.py caffe/scripts/cpp_lint.py external_libs/matconvnet/matconvnet/utils/proto/caffe_fastrcnn_pb2.py caffe/scripts/copy_notebook.py external_libs/matconvnet/matconvnet/doc/matdocparser.py external_libs/matconvnet/utils/proto/caffe_pb2.py caffe/examples/coco_caption/coco_to_hdf5_data.py libsvm/tools/subset.py caffe/python/caffe/pycaffe.py external_libs/matconvnet/utils/import-caffe.py caffe/python/caffe/test/test_net_spec.py gen_stats random_choice_from_probs Captioner softmax process_coco process_dataset CocoSequenceGenerator split_sentence HDF5SequenceWriter SequenceGenerator main gen_stats CaptionExperiment download_image make_net max_pool caffenet conv_relu fc_relu EuclideanLossLayer start_tornado start_from_terminal embed_image_html classify_upload index allowed_file ImagenetClassifier classify_url open_oriented_im apply_orientation main main main parse_args Classifier Detector get_edge_label draw_net get_layer_label get_pydot_graph choose_color_by_layertype get_pooling_types_dict draw_net_to_file Transformer blobproto_to_array datum_to_array array_to_blobproto arraylist_to_blobprotovecor_str array_to_datum resize_image blobprotovector_str_to_arraylist load_image oversample Layers Function Parameters Top NetSpec assign_proto param_name_dict to_proto _Net_blobs _Net_forward_all _Net_set_input_arrays _Net_backward _Net_params _Net_forward _Net_outputs _Net_forward_backward_all _Net_blob_loss_weights _Net_batch _Net_inputs TestLayerTypeList simple_net_file TestNet lenet TestNetSpec silent_net anon_lenet exception_net_file parameter_net_file SimpleLayer TestPythonLayer ParameterLayer python_net_file ExceptionLayer SimpleParamLayer TestLayerWithParam python_param_net_file TestSolver ParseNolintSuppressions CheckVlogArguments CheckSectionSpacing FindNextMultiLineCommentEnd ReplaceAll CheckForFunctionLengths _SetOutputFormat _IsTestFilename _VerboseLevel CheckBraces RemoveMultiLineComments ResetNolintSuppressions CheckForNonStandardConstructs _SetVerboseLevel PrintUsage _NestingState CheckIncludeLine CheckAccess _CppLintState Search CheckInvalidIncrement RemoveMultiLineCommentsFromRange CleansedLines CheckForBadCharacters UpdateIncludeState FindPreviousMatchingAngleBracket CheckEmptyBlockBody FindNextMultiLineCommentStart Match _NamespaceInfo CheckMakePairUsesDeduction CheckCheck IsBlankLine _SetFilters ProcessLine _FunctionState CheckPosixThreading GetLineWidth GetHeaderGuardCPPVariable IsCppString _IncludeState CheckSpacing _ClassInfo CheckForCopyright IsErrorSuppressedByNolint ProcessFileData CheckForMultilineCommentsAndStrings CloseExpression _PreprocessorInfo _OutputFormat CheckForIncludeWhatYouUse CheckSpacingForFunctionCall FindEndOfExpressionInLine FindNextMatchingAngleBracket _SetCountingStyle ProcessFile _IncludeError CleanseRawStrings CheckAltTokens CheckForNewlineAtEOF ParseArguments CheckForNonConstReference PrintCategories _Filters main FilesBelongToSameModule CheckCStyleCast FileInfo _BlockInfo CheckForHeaderGuard CheckCaffeDataLayerSetUp ReverseCloseExpression CleanseComments _DropCommonSuffixes _ClassifyInclude CheckStyle CheckCaffeAlternatives FindStartOfExpressionInLine _ShouldPrintError CheckComment Error _GetTextInside CheckLanguage CheckCaffeRandom GetPreviousNonBlankLine reporthook parse_readme_frontmatter model_checks_out valid_dirname get_start_time extract_seconds extract_datetime_from_line get_log_created_year write_csv parse_log fix_initial_nan_learning_rate save_csv_files main parse_args parse_line_for_net_output ResizeCropImagesMapper PILResizeCrop OpenCVResizeCrop extract render_L render_V render_P render_DIVL render_DH render_BL render_L_from_indent render_SL render_S render Context render_DI Frame render_B findNextFunction getFunctionDoc readText MatlabFunction render_DL clean Lexer P PL Parser BH EOF DI L DL SL BL DH DIVL Terminal S DIV NonTerminal B V Symbol extract render_L render_V render_P render_DIVL render_DH render_BL render_L_from_indent render_SL render_S render Context render_DI Frame render_B findNextFunction getFunctionDoc readText MatlabFunction render_DL clean Lexer P PL Parser BH EOF DI L DL SL BL DH DIVL Terminal S DIV NonTerminal B V Symbol blobproto_to_array versiontuple dict_to_struct_array keyboard tolist escape bilinear_interpolate getopts find rowcell CaffeInnerProduct ConversionError CaffeScale CaffeBatchNorm CaffeLayer CaffeCrop CaffeConcat CaffeConv CaffePooling CaffeData reorder CaffeElementWise getFilterOutputSize CaffeROIPooling CaffeSoftMaxLoss CaffeReLU getFilterTransform CaffeModel CaffeTransform CaffeDeconvolution row dictToMatlabStruct rowarray CaffeBlob transposeTransform CaffeDropout CaffeLRN CaffeEltWise composeTransforms CaffeSoftMax HingeLossParameter BlobProto BlobProtoVector NetStateRule LayerParameter PowerParameter FillerParameter ArgMaxParameter V0LayerParameter InnerProductParameter ConvolutionParameter SolverState EltwiseParameter SliceParameter WindowDataParameter DummyDataParameter HDF5OutputParameter TanHParameter TransformationParameter SoftmaxParameter ConcatParameter DataParameter SolverParameter MVNParameter ContrastiveLossParameter NetState NetParameter PoolingParameter DropoutParameter Datum SigmoidParameter AccuracyParameter MemoryDataParameter LRNParameter ReLUParameter ImageDataParameter InfogainLossParameter HDF5DataParameter ThresholdParameter ReductionParameter HingeLossParameter BlobProto BlobProtoVector NetStateRule LayerParameter PowerParameter FillerParameter ArgMaxParameter V0LayerParameter InnerProductParameter ConvolutionParameter SolverState EltwiseParameter LossParameter SliceParameter WindowDataParameter DummyDataParameter HDF5OutputParameter TanHParameter TransformationParameter SoftmaxParameter ConcatParameter DataParameter SPPParameter ParamSpec EmbedParameter SolverParameter MVNParameter ContrastiveLossParameter NetState NetParameter PoolingParameter DropoutParameter Datum SigmoidParameter BlobShape ExpParameter AccuracyParameter LogParameter ThresholdParameter TileParameter MemoryDataParameter LRNParameter ReLUParameter ImageDataParameter ReshapeParameter InfogainLossParameter V1LayerParameter HDF5DataParameter PReLUParameter FlattenParameter PythonParameter ReductionParameter HingeLossParameter BlobProto BlobProtoVector NetStateRule LayerParameter PowerParameter FillerParameter ArgMaxParameter V0LayerParameter InnerProductParameter ConvolutionParameter SolverState EltwiseParameter LossParameter SliceParameter BatchNormParameter WindowDataParameter DummyDataParameter HDF5OutputParameter TanHParameter TransformationParameter SoftmaxParameter ConcatParameter DataParameter SPPParameter ParamSpec EmbedParameter SolverParameter MVNParameter ContrastiveLossParameter NetState NetParameter BiasParameter PoolingParameter DropoutParameter Datum SigmoidParameter BlobShape ExpParameter AccuracyParameter LogParameter ThresholdParameter TileParameter MemoryDataParameter LRNParameter ReLUParameter ImageDataParameter ELUParameter ReshapeParameter InfogainLossParameter ScaleParameter V1LayerParameter HDF5DataParameter PReLUParameter FlattenParameter PythonParameter ROIPoolingParameter HingeLossParameter BlobProto BlobProtoVector NetStateRule LayerParameter PowerParameter FillerParameter ArgMaxParameter V0LayerParameter InnerProductParameter ConvolutionParameter SolverState EltwiseParameter LossParameter SliceParameter WindowDataParameter DummyDataParameter HDF5OutputParameter TanHParameter TransformationParameter SoftmaxParameter ConcatParameter DataParameter ParamSpec SolverParameter MVNParameter ContrastiveLossParameter NetState NetParameter PoolingParameter DropoutParameter Datum SigmoidParameter BlobShape ExpParameter AccuracyParameter ThresholdParameter MemoryDataParameter LRNParameter ReLUParameter ImageDataParameter InfogainLossParameter V1LayerParameter HDF5DataParameter PReLUParameter PythonParameter NetParameter LayerConnection BlobProto BlobProtoVector LayerParameter FillerParameter Datum SolverParameter SolverState BlobProto BlobProtoVector PowerParameter LayerParameter FillerParameter V0LayerParameter InnerProductParameter ConvolutionParameter SolverState WindowDataParameter HDF5OutputParameter ConcatParameter DataParameter SolverParameter NetParameter PoolingParameter DropoutParameter Datum MemoryDataParameter LRNParameter ImageDataParameter InfogainLossParameter HDF5DataParameter NetParameter EvalHistoryIter LayerConnection BlobProto BlobProtoVector LayerParameter FillerParameter Datum EvalHistory SolverParameter SolverState blobproto_to_array versiontuple dict_to_struct_array keyboard tolist escape bilinear_interpolate getopts find rowcell CaffeInnerProduct ConversionError CaffeScale CaffeBatchNorm CaffeLayer CaffeCrop CaffeConcat CaffeConv CaffePooling CaffeData reorder CaffeElementWise getFilterOutputSize CaffeROIPooling CaffeSoftMaxLoss CaffeReLU getFilterTransform CaffeModel CaffeTransform CaffeDeconvolution row dictToMatlabStruct rowarray CaffeBlob transposeTransform CaffeDropout CaffeLRN CaffeEltWise composeTransforms CaffeSoftMax HingeLossParameter BlobProto BlobProtoVector NetStateRule LayerParameter PowerParameter FillerParameter ArgMaxParameter V0LayerParameter InnerProductParameter ConvolutionParameter SolverState EltwiseParameter SliceParameter WindowDataParameter DummyDataParameter HDF5OutputParameter TanHParameter TransformationParameter SoftmaxParameter ConcatParameter DataParameter SolverParameter MVNParameter ContrastiveLossParameter NetState NetParameter PoolingParameter DropoutParameter Datum SigmoidParameter AccuracyParameter MemoryDataParameter LRNParameter ReLUParameter ImageDataParameter InfogainLossParameter HDF5DataParameter ThresholdParameter ReductionParameter HingeLossParameter BlobProto BlobProtoVector NetStateRule LayerParameter PowerParameter FillerParameter ArgMaxParameter V0LayerParameter InnerProductParameter ConvolutionParameter SolverState EltwiseParameter LossParameter SliceParameter WindowDataParameter DummyDataParameter HDF5OutputParameter TanHParameter TransformationParameter SoftmaxParameter ConcatParameter DataParameter SPPParameter ParamSpec EmbedParameter SolverParameter MVNParameter ContrastiveLossParameter NetState NetParameter PoolingParameter DropoutParameter Datum SigmoidParameter BlobShape ExpParameter AccuracyParameter LogParameter ThresholdParameter TileParameter MemoryDataParameter LRNParameter ReLUParameter ImageDataParameter ReshapeParameter InfogainLossParameter V1LayerParameter HDF5DataParameter PReLUParameter FlattenParameter PythonParameter ReductionParameter HingeLossParameter BlobProto BlobProtoVector NetStateRule LayerParameter PowerParameter FillerParameter ArgMaxParameter V0LayerParameter InnerProductParameter ConvolutionParameter SolverState EltwiseParameter LossParameter SliceParameter BatchNormParameter WindowDataParameter DummyDataParameter HDF5OutputParameter TanHParameter TransformationParameter SoftmaxParameter ConcatParameter DataParameter SPPParameter ParamSpec EmbedParameter SolverParameter MVNParameter ContrastiveLossParameter NetState NetParameter BiasParameter PoolingParameter DropoutParameter Datum SigmoidParameter BlobShape ExpParameter AccuracyParameter LogParameter ThresholdParameter TileParameter MemoryDataParameter LRNParameter ReLUParameter ImageDataParameter ELUParameter ReshapeParameter InfogainLossParameter ScaleParameter V1LayerParameter HDF5DataParameter PReLUParameter FlattenParameter PythonParameter ROIPoolingParameter HingeLossParameter BlobProto BlobProtoVector NetStateRule LayerParameter PowerParameter FillerParameter ArgMaxParameter V0LayerParameter InnerProductParameter ConvolutionParameter SolverState EltwiseParameter LossParameter SliceParameter WindowDataParameter DummyDataParameter HDF5OutputParameter TanHParameter TransformationParameter SoftmaxParameter ConcatParameter DataParameter ParamSpec SolverParameter MVNParameter ContrastiveLossParameter NetState NetParameter PoolingParameter DropoutParameter Datum SigmoidParameter BlobShape ExpParameter AccuracyParameter ThresholdParameter MemoryDataParameter LRNParameter ReLUParameter ImageDataParameter InfogainLossParameter V1LayerParameter HDF5DataParameter PReLUParameter PythonParameter NetParameter LayerConnection BlobProto BlobProtoVector LayerParameter FillerParameter Datum SolverParameter SolverState BlobProto BlobProtoVector PowerParameter LayerParameter FillerParameter V0LayerParameter InnerProductParameter ConvolutionParameter SolverState WindowDataParameter HDF5OutputParameter ConcatParameter DataParameter SolverParameter NetParameter PoolingParameter DropoutParameter Datum MemoryDataParameter LRNParameter ImageDataParameter InfogainLossParameter HDF5DataParameter NetParameter EvalHistoryIter LayerConnection BlobProto BlobProtoVector LayerParameter FillerParameter Datum EvalHistory SolverParameter SolverState fillprototype print_null svm_parameter svm_problem svm_node svm_model gen_svm_nodearray genFields toPyModel svm_train svm_load_model svm_read_problem svm_save_model svm_predict evaluations main my_float err process_options LocalWorker redraw calculate_jobs permute_sequence TelnetWorker SSHWorker main WorkerStopToken range_f Worker exit_with_help main process_options exp isnan isinf sum max random softmax enumerate exp len print HDF5SequenceWriter write_to_exhaustion num_outs num_pads COCO CocoSequenceGenerator num_truncates dump_vocabulary dump_image_file write_filelists process_dataset set_image_batch_size list CaptionExperiment print min set COCO CocoSequenceGenerator image_sentence_pairs retrieval_experiment set_caption_batch_size append generation_experiment Captioner keys len imread urlretrieve Convolution InnerProduct Data SoftmaxWithLoss LRN Accuracy max_pool InnerProduct conv_relu fc_relu Dropout get read info load_image classify_image StringIO join replace info secure_filename save filename open_oriented_im classify_image fromarray replace astype save resize StringIO items list listen HTTPServer format print start WSGIContainer update start_tornado add_option OptionParser debug port parse_args ImagenetClassifier forward run hasattr _getexif astype float32 tile apply_orientation open transpose model_def endswith ArgumentParser save mean_file channel_swap output_file dirname expanduser parse_args input_file predict Classifier set_mode_cpu load time isdir add_argument set_mode_gpu pretrained_model gpu DataFrame Detector format to_hdf detect_selective_search mean set_index to_csv detect_windows read_csv add_argument ArgumentParser read NetParameter output_image_file rankdir Merge draw_net_to_file items list DESCRIPTOR batch_size str num_output get_pooling_types_dict add_edge get_edge_label list Dot get_layer_label values name choose_color_by_layertype Edge Node bottom append type layer add_node top shape BlobProto extend flat extend BlobProtoVector ParseFromString BlobProtoVector extend tostring shape Datum flat data len astype float32 tile zoom tuple resize fill empty array concatenate shape tile empty array LayerParameter list NetParameter _to_proto extend Counter OrderedDict values iteritems hasattr isinstance extend add getattr setattr items list layers index set outputs _forward len items list _backward layers inputs index set len items list asarray extend copy next _batch iter forward values len items list asarray backward extend next _batch zip_longest zip iter forward values len ascontiguousarray list concatenate iter num zeros next range values len NamedTemporaryFile str close write data Pooling pool1 conv2 pool2 ip1 relu1 SoftmaxWithLoss Convolution NetSpec DummyData ip2 ReLU InnerProduct label conv1 Pooling SoftmaxWithLoss Convolution DummyData ReLU InnerProduct data NetSpec DummyData Silence data2 error search add group clear compile compile compile SetOutputFormat SetCountingStyle SetFilters _Filters startswith IsErrorSuppressedByNolint _ShouldPrintError write IncrementErrorCount replace append Match group find startswith endswith range error FindNextMultiLineCommentEnd RemoveMultiLineCommentsFromRange FindNextMultiLineCommentStart rstrip find range len FindEndOfExpressionInLine range len FindStartOfExpressionInLine error min search I range len FileInfo RepositoryName sep sub ParseNolintSuppressions error startswith split GetHeaderGuardCPPVariable enumerate error enumerate error len error replace count error find error find error find error find error Search error match InnermostClass replace error escape Match Search error group Search Check error lines Count End group Begin NumLines Match raw_lines range Search error match group error Match group pop group append Search pop group append Search elided replace CheckSpacingForFunctionCall rfind error len group min CloseExpression NumLines sub find CheckComment Match range Search lines_without_raw_strings error group starting_linenum Match range Search error rfind len group ReverseCloseExpression Search Match CloseExpression find error Match CloseExpression find elided error strip group FindEndOfExpressionInLine find Match range CloseExpression len error Match finditer normalize isinstance GetLineWidth int InnermostClass CheckCheck error CheckAltTokens CheckBraces CheckSpacing CheckSectionSpacing CheckEmptyBlockBody CheckAccess GetHeaderGuardCPPVariable lines_without_raw_strings _DropCommonSuffixes RepositoryName match split CheckNextIncludeOrder CanonicalizeAlphabeticalOrder FileInfo error search group SetLastHeader match _ClassifyInclude Match pop end search set append values M rstrip replace CheckCStyleCast error _GetTextInside CheckIncludeLine search group lstrip startswith Match ResetSection Search split rfind error group ReverseCloseExpression lstrip findall Match range Search ReplaceAll error Match Search endswith replace setdefault group search CleanseComments open list FilesBelongToSameModule error search copy sub NumLines FullName keys range error search CheckPosixThreading ParseNolintSuppressions CheckVlogArguments CheckMakePairUsesDeduction CheckCaffeDataLayerSetUp CheckLanguage CheckInvalidIncrement CheckCaffeRandom CheckForNonConstReference check_fn Update CheckForNonStandardConstructs CheckStyle raw_lines CheckForMultilineCommentsAndStrings CheckCaffeAlternatives CheckForFunctionLengths CleansedLines _NestingState CheckForBadCharacters CheckForNewlineAtEOF _IncludeState RemoveMultiLineComments CheckForCopyright ResetNolintSuppressions CheckForHeaderGuard NumLines CheckCompletedBlocks CheckForIncludeWhatYouUse range ProcessLine _FunctionState Error rstrip endswith len write ProcessFileData _SetVerboseLevel range split write exit join write exit _VerboseLevel int getopt _SetOutputFormat set _SetVerboseLevel PrintCategories _SetFilters _OutputFormat PrintUsage _SetCountingStyle split getreader ParseArguments ResetErrorCounts stderr exit verbose_level PrintErrorCounts StreamReaderWriter ProcessFile getwriter int time write flush load join index int rfind datetime split getctime year strip extract_datetime_from_line get_start_time total_seconds strip write get_log_created_year close extract_datetime_from_line open float get_log_created_year compile fix_initial_nan_learning_rate search group OrderedDict append float join basename write_csv print excel parse_log save_csv_files output_dir logfile_path search clean match strip group append getFunctionDoc findNextFunction print print print children render_SL print pop render_DH print Frame push render_DIVL children render_DI print children render_L print pop children render_L_from_indent isa Frame render_B push indent pop children Frame push render_DIVL children render_BL render_V render_S render_DL isa render_P print Context render_DIVL dim tolist hasattr list empty keys RepeatedScalarFieldContainer isinstance update print f_locals interact copy reshape asarray astype clip hasattr list ndarray isinstance append empty keys CaffeTransform CaffeTransform genFields list sorted isinstance keys range enumerate len genFields genFields contents open float split print toPyModel len zip svm_set_print_string_function svm_cross_validation isinstance print svm_parameter svm_problem print_func svm_check_parameter cross_validation x_space toPyModel evaluations n int get_svr_probability print get_svm_type len svm_predict_probability get_nr_class is_probability_model svm_predict_values gen_svm_nodearray evaluations split print pop int err open my_float range split stdin join list print exit map append split append pop int append len sort write encode round max flush permute_sequence append float range_f range len appendleft get process_options getuser write calculate_jobs redraw put getpass start Queue flush print exit exit_with_help int len Label sort close label float | ### Usage * Supported OS: the source code was tested on 64-bit Arch and Ubuntu 14.04 Linux OS, and it should also be executable in other linux distributions. * Dependencies: * A modified version of [caffe](http://caffe.berkeleyvision.org/) framework and all its dependencies. * Cuda enabled GPUs * Installation: 1. Install caffe: we use a modified version of the original caffe framework. Compile the source code in the ./caffe directory and the matlab interface following the [installation instruction of caffe](http://caffe.berkeleyvision.org/installation.html). 2. Download the 16-layer VGG network from https://gist.github.com/ksimonyan/211839e770f7b538e2d8, and put the caffemodel file under the ./model directory. 3. Download imagenet-vgg-m-2048 from http://www.vlfeat.org/matconvnet/pretrained/, and put the file into ./networks 4. Compile matconvnet in the sub-folders. | 1,785 |
ctr4si/A-Hierarchical-Latent-Structure-for-Variational-Conversation-Modeling | ['response generation'] | ['A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues'] | ubuntu_preprocess.py model/eval.py model/utils/vocab.py model/utils/bow.py model/utils/tensorboard.py model/train.py model/layers/decoder.py cornell_preprocess.py model/utils/mask.py model/utils/embedding_metric.py model/utils/tokenizer.py model/layers/encoder.py model/layers/loss.py model/layers/feedforward.py model/utils/convert.py model/layers/rnncells.py model/solver.py model/layers/beam_search.py model/utils/time_track.py model/configs.py model/layers/__init__.py model/utils/__init__.py model/models.py model/data_loader.py model/utils/pad.py model/utils/probability.py model/eval_embed.py prepare_cornell_data tokenize_conversation loadConversations pad_sentences loadLines _tokenize_conversation train_valid_test_split_by_conversation to_pickle get_dialog_path_list pad_sentences prepare_ubuntu_data _tokenize_conversation to_pickle read_and_tokenize Config get_config str2bool DialogDataset get_loader load_pickle load_pickle VHCR HRED VHRED VariationalSolver Solver load_pickle Beam DecoderRNN BaseRNNDecoder BaseRNNEncoder EncoderRNN ContextRNN FeedForward masked_cross_entropy StackedGRUCell StackedLSTMCell to_bow bag_of_words_loss cosine_similarity embedding_metric sequence_mask pad pad_and_pack normal_kl_div normal_logpdf TensorboardWriter time_desc_decorator base_time_desc_decorator time_test no_arg_method Tokenizer clean_str Vocab prepare_cornell_data tokenize_conversation loadConversations pad_sentences loadLines _tokenize_conversation train_valid_test_split_by_conversation to_pickle get_dialog_path_list prepare_ubuntu_data read_and_tokenize Config get_config str2bool DialogDataset get_loader load_pickle VHCR HRED VHRED VariationalSolver Solver load_pickle Beam DecoderRNN BaseRNNDecoder BaseRNNEncoder EncoderRNN ContextRNN FeedForward masked_cross_entropy StackedGRUCell StackedLSTMCell to_bow bag_of_words_loss cosine_similarity embedding_metric sequence_mask pad pad_and_pack normal_kl_div normal_logpdf TensorboardWriter time_desc_decorator base_time_desc_decorator time_test no_arg_method Tokenizer clean_str Vocab prepare_cornell_data tokenize_conversation loadConversations pad_sentences loadLines _tokenize_conversation train_valid_test_split_by_conversation to_pickle get_dialog_path_list prepare_ubuntu_data read_and_tokenize Config get_config str2bool DialogDataset get_loader load_pickle VHCR HRED VHRED VariationalSolver Solver load_pickle Beam DecoderRNN BaseRNNDecoder BaseRNNEncoder EncoderRNN ContextRNN FeedForward masked_cross_entropy StackedGRUCell StackedLSTMCell to_bow bag_of_words_loss cosine_similarity embedding_metric sequence_mask pad pad_and_pack normal_kl_div normal_logpdf TensorboardWriter time_desc_decorator base_time_desc_decorator time_test no_arg_method Tokenizer clean_str Vocab urlretrieve print extractall close joinpath rename mkdir ZipFile seed int print shuffle len append pad_conversation print joinpath urlretrieve mkdir update add_argument ArgumentParser vars parse_args DataLoader DialogDataset sequence_mask view log_softmax size float sum zeros list Counter values log_softmax view sqrt sum max T min matmul absolute mean sqrt logical_not zip append sum array max to_var size expand expand_as long isinstance stack max to_var FloatTensor to_var FloatTensor print sleep print sub | ctr4si/A-Hierarchical-Latent-Structure-for-Variational-Conversation-Modeling | 1,786 |
ctyeong/IO-GEN | ['time series analysis', 'time series'] | ['Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change'] | train.py metrics.py test.py models.py synthesize.py utils.py score smooth_accuracy feat_matching_loss euclidean_distance_square_loss build_classifier build_DCAE build_IO_GEN load_of_data round cast equal mean reshape len Model Input dsvdd layers print Adam gen Model dsc clone_model dsvdd summary encoder Input l compile enumerate Model Input join sorted format asarray glob transpose convert len append range array read_csv split | # Inner Outlier Generator *Inner Outlier Generator (IO-GEN)* is designed to better solve One-class Classification (OC) problems by "replacing" the central vector `c` in Deep Support Vector Data Description (DSVDD) with *synthetic* data from a GAN as shown below — i.e., (b) vs (d). As a result, classifiers deployed on the space can utilize the *multi-dimensional* features for anomaly detection in complex data, whereas DSVDD only relies on a *simplistic* distance metric. <img src=Imgs/feature_space.png width="85%"> This repository provides the official Tensorflow implementations of IO-GEN, which was first proposed in the paper: *"Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change", Taeyeong Choi, Benjamin Pyenson, Juergen Liebig, and Theodore P. Pavlic,* published in the [Proceedings of AAAI-21](https://ojs.aaai.org/index.php/AAAI/article/view/17794). <img src=Imgs/scenario.jpg width="85%"> Although theoretically, IO-GEN is applicable to any type of OC problems, here we focus on the exemplar scenario discussed in the above paper, where the classifier is trained only with observational samples from *stable* colony but has to distinguish *unstable* samples. Instructions below start with a quick introduction to the pipeline of involved networks during training and test, followed by technical manuals to reproduce similar results to the paper. Ant motional data are also available at https://github.com/ctyeong/OpticalFlows_HsAnts. # Contents 1. [Model Pipeline](https://github.com/ctyeong/IO-GEN#model-pipeline) 2. [Installation](https://github.com/ctyeong/IO-GEN#installation) | 1,787 |
cucapra/approx-vision | ['demosaicking'] | ['Reconfiguring the Imaging Pipeline for Computer Vision'] | CVbenchmarks/Stereo/sched.py pipelines/coco-2014/coco-convert.py pipelines/strecha-mvs/sched.py test/util_compare.py powersim/sensor_powersim.py powersim/powersim.py pipelines/lfw/lfw-converter.py pipelines/lfw/thread-sched.py pipelines/middlebury-stereo/middlebury-stereo-converter.py pipelines/middlebury-opt-flow/middlebury-opt-flow-converter.py powersim/gen_lin_levels.py test/test_pipeline.py pipelines/lfw/sched.py pipelines/middlebury-opt-flow/sched.py pipelines/cifar-10/hi-low-converter.py pipelines/CLI/pipeline.py pipelines/voc-2007/sched.py pipelines/cifar-10/sched-hi-res.py CVbenchmarks/OpticalFlow/opt_flow.py pipelines/middlebury-stereo/sched.py powersim/adc_powersim.py pipelines/CLI/legacy/sched-convert.py pipelines/common/sched-convert.py pipelines/cifar-10/cifar-10-hi-res-converter.py CVbenchmarks/Stereo/stereo.py powersim/pipe_stages/isp_powersim.py CVbenchmarks/OpticalFlow/sched.py pipelines/strecha-mvs/strecha-mvs-converter.py pipelines/cifar-10/sched-low-res.py pipelines/common/run-pipe.py pipelines/casia/sched.py pipelines/cifar-10/cifar-convert.py makecolorwheel viz_flow flow_read convert_img Pipeline convert_img quantize TestPipeline config_parser_list compare_images mse reshape open uint8 arctan2 makecolorwheel print astype pi sqrt shape zeros range max log zeros arange call str getpid save open range sum astype imread mse | # The Approximate Vision Project This is the public release of code developed for the paper ["Reconfiguring the Imaging Pipeline for Computer Vision"](https://capra.cs.cornell.edu/research/visionmode/) by Mark Buckler, Suren Jayasuriya, and Adrian Sampson. It contains the Configurable & Reversible Imaging Pipeline (CRIP) described in the paper, documentation on how to run and edit the CRIP for your own use, and both Dockerfiles and instructions for how to run our supported computer vision benchmarks. # License and citation All code in this git repository is released under the MIT license. If you use this code in your research, please our ICCV 2017 paper: ``` @inproceedings{buckler-iccv2017, author = {Mark Buckler and Suren Jayasuriya and Adrian Sampson}, title = {Reconfiguring the Imaging Pipeline for Computer Vision}, booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, year = {2017}, | 1,788 |
curbmap/curbmap-ml | ['scene text detection', 'curved text detection'] | ['Detecting Oriented Text in Natural Images by Linking Segments'] | processing/cm-image-processor.py processing/cm-image-preprocessor.py | # curbmap-ml ML aspects of curbmap ## Image Pipeline ---- Problem: A user uploads a photo of multiple street signs in one image. 1. Preprocess 1. Verify that upload location indicated is related to EXIF data 2. Contrast Limited Histogram Equalization of image (CLAHE) of LAB image 3. Reduce image size to max dimension < 2600 using Lanczos4 interpolation sinc approx 4. Grayscale image (possibly not right to do since some images have Red nos and such) | 1,789 |
cvikasreddy/skt | ['morphological analysis'] | ['Building a Word Segmenter for Sanskrit Overnight'] | train.py test.py utils/data_loader.py SKTDataLoader | # Sanskrit compound segmentation using seq2seq model Code for the paper titled ['Building a Word Segmenter for Sanskrit Overnight'](https://arxiv.org/abs/1802.06185) Instructions ============ Pre-requisites -------------- The following python packages are required to be installed: * Tensorflow: https://www.tensorflow.org/, tensorflow version 0.12.1 was used for this project. File organization ------------------------------------------- | 1,790 |
cvjena/ITAL | ['content based image retrieval', 'active learning', 'image retrieval'] | ['Information-Theoretic Active Learning for Content-Based Image Retrieval'] | run_experiment.py ital/retrieval_base.py ital/adapt_al.py ital/ital_regression.py ital/ital.py ital/__init__.py viz_utils.py ital/gp.py optimize_parameters.py ital/regression_base.py ital/mcmi.py datasets.py utils.py ital/external/kernel_kmeans.py ital/baseline_methods.py ToyRegressionDataset LeafDataset NaturalScenesDataset ConcreteDataset WineDataset MultitaskRetrievalDataset MIRFLICKRDataset OxfordDataset ToyDataset load_dataset RetrievalDataset ImageNetDataset WinequalityDataset RegressionDataset IrisDataset YachtDataset StoredDataset USPSDataset MultilabelRetrievalDataset Dataset optimize_gp_params cross_validate_fewshot cross_validate_gp simulate_regression_feedback simulate_retrieval_feedback run_retrieval_experiment run_regression_experiment ndcg ConversionInterpolation area_under_curve read_config_file load_dataset_from_config load_config plot_regression_step plot_learning_step plot_dist_and_topk canonicalize_img_name canonicalize_image plot_distribution plot_data _parallel_density AdaptAL reduced_inv _init_pool EntropySampling_Regression TCAL RandomRetrieval_Regression VarianceSampling_Regression EMOC SUD TopscoringSampling USDM UncertaintySampling EMOC_Regression EntropySampling RandomRetrieval BorderlineSampling RBMAL BorderlineDiversitySampling VarianceSampling extend_inv GaussianProcess invh ITAL AppendedMutualInformation group_cov _init_pool _parallel_mi MutualInformation ITAL_Regression AppendedMutualInformation _init_pool _parallel_mi MutualInformation _parallel_ce AppendedConditionalEntropy _init_pool ConditionalEntropy MCMI_min ActiveRegressionBase ActiveRetrievalBase KernelKMeans predict_stored asarray X_train arange ndarray fit GaussianProcess split X_train_norm len predict_stored asarray X_train arange fit GaussianProcess split append X_train_norm len max list format cross_validate_fewshot join evaluate print any cross_validate_gp sum keys X_train_norm len append choice append rvs subplots ndcg simulate_retrieval_feedback fetch_unlabelled seed show list set_title plot_learning_step OrderedDict append range predict update get format tight_layout choice getint mean datasets zip trange keys X_train_norm enumerate int isinstance print fit tqdm average_precision_score dict reset set_ylabel hist split median X_test_norm std len median subplots simulate_regression_feedback fetch_unlabelled seed show list all std set_title append range predict update format plot_regression_step tight_layout getint sqrt mean zip trange print y_test tqdm dict reset hist y_train mean_squared_error X_test_norm array len get items read list ConfigParser dirname update isinstance read_config_file load_dataset X_train_norm read_config_file load_dataset log2 sum sum asarray show asarray setdiff1d arange where scatter figure len show scatter figure asarray show subplots plot_distribution plot_data show subplots set_title isinstance X_train axis tight_layout canonicalize_img_name imshow rel_mean canonicalize_image set_color zip tick_params ravel max plot_distribution plot_data show subplots set_title isinstance X_train tight_layout mean y_train zeros plot_distribution plot_data len isinstance transpose astype float32 mean tile imread isinstance concatenate reduced_inv concatenate potri float64 astype dpotrf warn triu_indices_from spotri spotrf dpotri potrf concatenate hstack vstack zeros range setdiff1d arange concatenate intersect1d append array | # Information-Theoretic Active Learning (ITAL) ![ITAL Toy Example][teaser] This repository contains the reference implementation of ITAL and the configuration files of the experiments described in the following paper: > [**Information-Theoretic Active Learning for Content-Based Image Retrieval.**][paper] > Björn Barz, Christoph Käding, and Joachim Denzler. > German Conference on Pattern Recognition (GCPR), 2018. ## Dependencies For ITAL itself: - **Python 3** (tested with 3.5) - **numpy** (tested with 1.12) | 1,791 |
cvjena/aid | ['content based image retrieval', 'image retrieval'] | ['Automatic Query Image Disambiguation for Content-Based Image Retrieval'] | common.py aid.py clue.py evaluate_query_disambiguation.py utils.py eval_metrics.py extract_features.py adjust_distances determine_num_clusters_spectral _init_pool _aid_worker hard_cluster_selection _hs_worker automatic_image_disambiguation RecursiveNormalizedCuts clue baseline_retrieval _retrieval_worker _init_pool select_best_cluster select_clusters_by_precision query_metrics ndcg mean_average_precision precision_at_k query_metrics_dict average_precision avg_query_metrics ap_from_ranks extract_cnn_features ptqdm get_dataset_queries print_metrics get_dataset_images baseline_retrieval seed KMeans float64 adjust_distances astype argsort _select_clusters fit_predict sqrt eigh rbf_kernel graph_laplacian norm max maximum baseline_retrieval KMeans float64 astype _select_clusters fit_predict sort_items_by_centroid_distance sort clusters select_clusters baseline_retrieval RecursiveNormalizedCuts enumerate fit argsort sum float sum enumerate len items list dict float len append set float enumerate append sort float sort set log2 append range len Transformer set_channel_swap isinstance set_mean index preprocess set_transpose _forward set_raw_scale append join format exists glob join exists join list format items print sum max | Automatic Query Image Disambiguation (AID) ========================================== This repository contains the reference implementation of AID and code that can be used to reproduce the results from the corresponding [paper][4]: > Björn Barz and Joachim Denzler. > "Automatic Query Image Disambiguation for Content-based Image Retrieval." > International Conference on Computer Vision Theory and Applications (VISAPP), 2018. If you use AID, please cite that paper. What is AID? ------------ | 1,792 |
cvlab-dresden/DSAC | ['camera localization', 'visual localization'] | ['DSAC - Differentiable RANSAC for Camera Localization'] | link_7scenes.py main link_file link_data unlink symlink islink join remove format exists print dry_run readlines enumerate abspath link_file flush len print data_dir exit dest_dir link_data | # DSAC Code associated with the paper: E. Brachmann, A. Krull, S. Nowozin, J. Shotton, F. Michel, S. Gumhold, C. Rother, "[DSAC – Differentiable RANSAC for Camera Localization](https://arxiv.org/abs/1611.05705)", CVPR 2017 Please see the documentation.pdf for additional information. Also see the [project page](https://hci.iwr.uni-heidelberg.de/vislearn/research/scene-understanding/pose-estimation/#DSAC). You can download pre-trained models for 7Scenes [here](https://heidata.uni-heidelberg.de/api/access/datafile/:persistentId?persistentId=doi:10.11588/data/3JVZSH/TSMZA8) **Note:** Beginning of August 2017, we updated the public version of the code to contain a fix in the pose evaluation metric, and to utilize a more stable variant of the PnP algorithm. These changes result in improved numbers compared to the original version of the paper. The improved numbers and a more detailed explanation can be found in the current version of the paper on arXiv. The current version of the code contains both fixes, and also the pre-trained models have been updated, accordingly. **Update:** An improved version of this system is available [here](https://github.com/vislearn/LessMore). Higher accuracy, stable end-to-end training, training without a 3D model or depth maps. | 1,793 |
cvpaperchallenge/FashionCultureDataBase_DLoader | ['time series', 'human detection'] | ['Weakly Supervised Dataset Collection for Robust Person Detection', 'Changing Fashion Cultures'] | VocFormat.py ImageFolder.py CocoFormat.py | # Fashion Culture DataBase (FCDB) ## Issues * Mar. 4, 2020: YFCC100M, the source dataset of Fashion Culture DataBase currently may have an issue on downloading. Please check updates of [this page][3]. ## Updates * Mar. 26, 2020: Pre-train weights are published * Mar. 4, 2020: Repository is published * Nov. 8, 2019: Repository creation ## Summary FCDB has been constructed based on the following papers. | 1,794 |
cvqluu/Factorized-TDNN | ['speech recognition'] | ['Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks'] | models.py TDNN FTDNN DenseReLU FTDNNLayer StatsPool SharedDimScaleDropout SOrthConv | # Factorized-TDNN PyTorch implementation of the Factorized TDNN (TDNN-F) from ["Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks"](http://danielpovey.com/files/2018_interspeech_tdnnf.pdf)[1]. This is also known as TDNN-F in nnet3 of [Kaldi](https://github.com/kaldi-asr/kaldi).  Taken from [1] A TDNN-F layer is implemented in the class `FTDNNLayer` of `models.py`. To be specific to the description in [1], it is an implementation of the **"3-stage splicing"** implementation, in which three convolutions are used in sequence, with the first two being constrained to be semi-orthogonal. These convolutions are followed by a ReLU and then BatchNorm layer. The semi-orthogonal constraint is the **"floating case"** in [1]. (TODO: implement the scaled case like in Kaldi) # Usage ## `FTDNNLayer` This `FTDNNLayer` of `models.py` is used as follows: ```python import torch from models import FTDNNLayer, SOrthConv | 1,795 |
cvxgrp/strat_models | ['weather forecasting'] | ['Fitting Laplacian Regularized Stratified Gaussian Models'] | examples/crime.py examples/mesothelioma.py strat_models/losses.py strat_models/regularizers.py examples/eigen_stratified_models/weather.py test.py examples/house.py strat_models/__init__.py setup.py strat_models/utils.py examples/eigen_stratified_models/cardio.py strat_models/models.py strat_models/fit.py examples/elections.py examples/utils.py test_lasso test_nonparametric_discrete test_poisson test_bernoulli test_eigen test_ridge_regression test_log_reg df_to_data extract_data get_data rms prediction_error latexify create_age_graph create_sex_graph train_strat_model fit_stratified_model fit_eigen_stratified_model bernoulli_loss nonparametric_discrete_prox joint_cov_prox poisson_loss log_reg_prox Loss find_solution logistic_loss turn_into_iterable sum_squares_loss nonparametric_discrete_loss covariance_max_likelihood_loss StratifiedModel G_to_data_eigen G_to_data turn_into_iterable BaseModel transfer_result_to_G transfer_result_to_G_eigen mtx_scaled_plus_sum_squares_reg trace_reg project_onto_simplex zero_reg simplex_reg trace_offdiagL1Norm mtx_scaled_sum_squares_reg scaled_plus_sum_squares_reg nonnegative_reg Regularizer L2_reg L1_reg sum_squares_reg min_threshold_reg_one_elem elastic_net_reg clip_reg neg_log_reg cartesian_product flatten set_edge_weight StratifiedModel format randn print BaseModel dict anll randint cycle_graph predict fit StratifiedModel format set_edge_weight print BaseModel dict eye randint cycle_graph anll fit StratifiedModel format print BaseModel dict sample randint cycle_graph anll fit StratifiedModel format randn print BaseModel dict anll randint cycle_graph predict fit StratifiedModel format randn print BaseModel dict anll randint cycle_graph predict fit StratifiedModel format randn print BaseModel dict anll randint cycle_graph predict fit StratifiedModel format print BaseModel dict sample randint cycle_graph anll fit int lat_bin lon_bin iterrows Week Dayofweek nodes Hour append append unique nodes extend query array drop update sqrt Graph add_nodes_from add_edge edges path_graph relabel_nodes edges cartesian_product StratifiedModel set_edge_weight print scaled_plus_sum_squares_reg BaseModel nonparametric_discrete_loss cycle_graph anll fit ndindex l_prox max Pool clip shape append diags prod r_prox range close perf_counter copy sqrt diagonal enumerate join T norm print eye zeros max T norm join print close perf_counter copy range shape prod sqrt append zeros l_prox Pool clip r_prox iter roots shape T sqrt eigh from_numpy requires_grad_ LBFGS step CrossEntropyLoss float64 step from_numpy requires_grad_ LBFGS type zeros nodes enumerate nodes enumerate zeros nodes enumerate nodes enumerate ones maximum argsort bisectsearch sum len edges tuple nodes flatten range len isinstance | # strat_models `strat_models` is a Python package for fitting Laplacian regularized stratified models. The implementation is based on our paper [A distributed method for fitting Laplacian regularized stratified models](http://web.stanford.edu/~boyd/papers/strat_models.html). ## Installation To install pytorch, follow instructions from [pytorch.org](https://pytorch.org/), e.g. (python 3.7 pip installation): ``` pip install https://download.pytorch.org/whl/cpu/torch-1.0.1.post2-cp37-cp37m-linux_x86_64.whl ``` To install the latest version, run: | 1,796 |
cy93lin/SBM_node_embedding | ['graph generation'] | ['Node Embedding via Word Embedding for Network Community Discovery'] | examples/demo_PoliticalBlog.py examples/demo_simulatedSBM.py VEClib.py all_scripts/deepclustering_exp4.py all_scripts/deepclustering_exp8.py all_scripts/deepclustering_exp2.py all_scripts/deepclustering_exp5.py all_scripts/deepclustering_exp9.py SBMlib.py all_scripts/deepclustering_exp7.py tsne.py all_scripts/deepclustering_exp1.py all_scripts/deepclustering_exp6.py all_scripts/deepclustering_exp3.py utils.py alias_draw SBM_savemat SBM_param_init3 SBM_param_init1 SBM_param_init_n SBM_param_init_ln alias_setup SBM_SNR SBM_simulate SBM_param_init2 SBM_simulate_fast x2p pca Hbeta tsne SBM_savemat lda_to_mat_deepwalkdata SBM_param_init_n_badQ create_rand_works SBM_learn_deepwalk_lda update_a_res SBM_SNR SBM_simulate SBM_learn_deepwalk_2 SBM_learn_deepwalk_3 cal_metrics_3 check_cycle alias_setup get_label_list get_true_labels build_node_alias lda_normalize_embs maxfinding_embs_noramlized cal_modularity SBM_learn_fromcorpus_2 summary_res SBM_param_init_n_unequalcomm SBM_param_init_n plot_res SBM_param_init_ln clustering_embs SBM_param_init2 SBM_ABP lda_to_mat SBM_simulate_fast multi_abp SBM_learn_fromcorpus_1 alias_draw SBM_learn_deepwalk_1 SBM_learn_writecorpus1 SBM_param_init3 SBM_visual_tsne save_clusters_in_parallel cal_metrics create_rand_works_inmem abp_params SBM_learn_deepwalk_lda_another clustering_embs_noramlized SBM_savemat create_rand_works update_a_res SBM_SNR SBM_learn_deepwalk_2 SBM_learn_deepwalk_3 cal_metrics_3 check_cycle parse_txt_data alias_setup get_label_list get_true_labels build_node_alias cal_modularity SBM_learn_fromcorpus_2 summary_res plot_res_3 SBM_ABP multi_abp SBM_learn_fromcorpus_1 alias_draw SBM_learn_deepwalk_1 SBM_learn_writecorpus1 SBM_visual_tsne save_clusters_in_parallel cal_metrics create_rand_works_inmem abp_params cal_metrics_3 summary_res update_a_res plot_res_3 get_label_list cal_metrics_3 summary_res update_a_res plot_res_3 get_label_list cal_metrics_3 summary_res update_a_res randguess plot_res_3 get_label_list cal_metrics_3 summary_res update_a_res randguess plot_res_1 get_label_list cal_metrics_3 summary_res update_a_res randguess plot_res_1 get_label_list cal_metrics_3 summary_res update_a_res randguess plot_res_1 get_label_list cal_metrics_3 summary_res update_a_res randguess plot_res_1 get_label_list plot_res_2 cal_metrics_3 summary_res update_a_res plot_res_3 get_label_list multi_abp plot_res_2 cal_metrics_3 summary_res update_a_res plot_res_3 get_label_list multi_abp pop len append zeros enumerate int rand floor len ones float log eye ones float log eye ones float eye ones linspace eye float sum log ones eye float sum log add_edge alias_draw Graph print alias_setup binomial range add_node add_edge alias_draw Graph random_graph print len alias_setup edges append fast_gnp_random_graph range add_node nodes write_edgelist sorted print eig dot float diag sum exp log T Hbeta inf print ones square copy add range shape zeros sum log T print eig mean shape dot tile x2p T isinstance randn print ones transpose maximum square add mean shape log real tile zeros sum range print float ones eye ones float eye nodes sum nodes alias_setup join alias_draw list write close append keys range open alias_draw list append keys range print build_node_alias create_rand_works print LineSentence Word2Vec print str load_word2vec_format system print create_rand_works LineSentence Word2Vec build_node_alias str print save_word2vec_format system create_rand_works load_word2vec_format build_node_alias print create_rand_works_inmem build_node_alias Word2Vec tsne show scatter figure shortest_path has_path add_edge remove_edge normal str list diag nodes check_cycle dot edges eye append zeros float sum keys range len print KMeans fit nodes labels_ zeros SBM_ABP range len str write close open range len normalized_mutual_info_score linear_sum_assignment confusion_matrix float sum len size len append float range has_edge nodes int SBM_SNR ceil float log str get_feature_names topic_word_ print strip fit len create_rand_works CountVectorizer append range fit_transform build_node_alias LDA open print strip create_rand_works append build_node_alias open print zeros range len print zeros range str norm print copy shape zeros sum range len KMeans labels_ fit norm print KMeans fit labels_ sum range len argmax norm print append sum range len normalized_mutual_info_score adjusted_rand_score linear_sum_assignment confusion_matrix float sum len str print cal_metrics_3 update_a_res mean list std values add_edge Graph len close nodes set open split show sorted list errorbar xlabel figure legend keys append randint range xscale xlim show sorted list errorbar xlabel ylim figure legend keys xscale xlim plot show sorted list errorbar plot xlabel ylim figure legend keys ylim | ## Word embedding for Community Detection in Graph
This repository contains all the functions and scripts for getting word2vec embeddings for community detection on graphs. For details please refer to [the following paper](https://arxiv.org/abs/1611.03028):
W Ding, C Lin, P Ishwar, "Node Embedding via Word Embedding for Network Community Discovery", in arXiv preprint arXiv:1611.03028, 2016.
A shorter version of this is in IEEE ICASSP conference (2017).
### Dependencies:
| 1,797 |
cyang03/CHECKED | ['misinformation'] | ['CHECKED: Chinese COVID-19 Fake News Dataset'] | baseline/code/utils_fasttext.py code/spider_fake.py baseline/code/models/Att_TextRNN.py baseline/code/models/Transformer.py baseline/code/models/TextCNN.py baseline/code/train_eval.py code/jsonToCSV.py baseline/code/run.py baseline/code/models/FastText.py baseline/code/models/TextRNN.py baseline/code/utils.py code/spider_real.py train evaluate init_network test build_iterator DatasetIterater get_time_dif build_dataset build_vocab build_iterator DatasetIterater get_time_dif build_dataset build_vocab Config Model Config Model Config Model Config Model Config Position_wise_Feed_Forward Multi_Head_Attention Encoder Model Positional_Encoding Scaled_Dot_Product_Attention get_first_page_comments get_reports get_weibo_info check_num get_next_page_comments get_reposts get_first_page_comments get_next_part_weibos get_first_part_weibos parse_weibo get_next_page_weibos check_num get_next_page_comments get_reposts named_parameters normal_ xavier_normal_ kaiming_normal_ constant_ model zero_grad save accuracy_score Adam log_path range cross_entropy state_dict SummaryWriter format save_path close test get_time_dif item float num_epochs enumerate time evaluate backward print parameters cpu step add_scalar load time format evaluate save_path print eval load_state_dict get_time_dif classification_report confusion_matrix eval f1_score accuracy_score array load vocab_path train_path dump dev_path print pad_size test_path load_dataset open exists build_vocab device DatasetIterater batch_size time get str replace print text apparent_encoding select get_weibo_info BeautifulSoup loads select_one range get_first_page_comments strip BeautifulSoup loads string get_text get_reposts str select sleep append get replace unquote check_num print text dumps select_one children mktime strip localtime BeautifulSoup loads string str strftime select sleep append get replace strptime startswith int text get_next_page_comments select_one findall str int children replace mktime select_one strptime strip strftime select localtime string startswith sleep append mktime localtime BeautifulSoup loads string get_text Session str strftime select sleep append range get replace strptime int print text select_one get get_next_part_weibos replace print parse_weibo get_next_page_weibos urlencode BeautifulSoup loads select_one sleep get replace print parse_weibo urlencode BeautifulSoup loads sleep get text parse_weibo urlencode BeautifulSoup loads sleep get get_first_page_comments sleep unquote print text strip dumps urlencode select BeautifulSoup raise_for_status loads check_num string select_one append get_reposts | # CHECKED The first Chinese COVID-19 fake news dataset based on the Weibo platform. Please check out our paper [here](https://arxiv.org/pdf/2010.09029.pdf). ## Notice We care about users' privacy and made (will keep making) efforts to protecting it. * For microblogs: We released the **hashed** `id` instead of the original `id` of microblogs. * For users: We did not make the `user_name` public, which enables to identify Weibo users. In addition, we released the **hashed** `user_id` instead of the original `user_id`. * Please use the CHECKED data only for academic research. **Update:** We corrected a small amount of dates displayed in the comment sections of two microblogs, which occured due to unknown errors during the automatic information extraction process. We accomplished this by checking the dataset manually. We appreciate the user for pointing this issue out. If similar problems are to be found by future users, please contact us as we will fix them immediately. (August 22, 2021) We add `analysis` as a new keyword for each microblog labeled as *fake*. `analysis` contains the expert analysis and justification, which details the news falseness. Please check out the [*dataset*](https://github.com/cyang03/CHECKED/tree/master/dataset) folder for details. We also provide benchmark results of [FastText](https://arxiv.org/pdf/1607.01759.pdf), [TextCNN](https://arxiv.org/pdf/1408.5882.pdf), [TextRNN](https://arxiv.org/pdf/1605.05101.pdf), [Att-TextRNN](https://www.aclweb.org/anthology/P16-2034.pdf), and [Transformer](https://arxiv.org/pdf/1706.03762.pdf) using the CHECKED data in predicting fake news. Please check out the [*baseline*](https://github.com/cyang03/CHECKED/tree/master/baseline) folder for details. (June 9, 2021) | 1,798 |
cycentum/bert-based-text-generation | ['denoising'] | ['Generalized Denoising Auto-Encoders as Generative Models'] | word_replacement/word_replacement.py word_replacement/run.py run read_examples get_masked_lm_output gather_indexes InputFeatures input_fn_builder saveGenerated InputExample _truncate_seq_pair convert_examples_to_features main singleIter model_fn_builder ModelSpecificConfig str join items load_model print extend dict unlink Path zip append argmax array predict masked_lm_positions input_type_ids input_mask append unique_id input_ids join tokens_a tokens_b InputFeatures convert_tokens_to_ids _truncate_seq_pair tokenization info append unique_id enumerate len pop len tokenization iter_size batch_size TPUEstimator singleIter model_fn_builder read_examples str RunConfig tokenizer saveGenerated output_file input_file range ModelSpecificConfig tokenization vocab_file loadBertConfig print load_vocab output_epoch_dir PER_HOST_V2 len tokens replaceWord max list sorted from_iterable convert_examples_to_features append ceil shapeTokens text_a predict joinTokens tokensMask input_fn_builder choice maskIndex max_predictions_per_seq tokenize enumerate int sort min index argsort reset makeTokens0 randint bool mask_prob len gather_indexes reshape get_shape_list range gather set | # bert-based-text-generation Text generation by iterative word replacement. ## How does it work? BERT based Text Generation applies one of the two pretraining steps of BERT, masked word prediction, for text generation. Masked word prediction in BERT pretraining looks like: ``` Masked input: the man went to the [MASK] . Prediction: [MASK] = store (Modified from README.md in bert) ``` | 1,799 |
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